In this paper, we demonstrate a practical system for automatic weather-oriented clothing suggestion, given the weather information, the system can automatically recommend the most suitable clothing from the user s personal clothing album, or intelligently suggest the most pairing one with the userspecified reference clothing.
This is an extremely...
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Today, forecasting the stock market has been one of the most challenging issues for the ‘‘artificial intelligence’’ AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market pred...
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Most companies nowadays are using digital platforms for the recruitment of new employees to make the hiring process easier. The rapid increase in the use of online platforms for job posting has resulted in fraudulent advertising. The scammers are making money through fraudulent job postings.
Online recruitment fraud has emerged as an important is...
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Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions.
However, most credit card fraud models in the literatu...
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The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a challenge lies in detecting student engagement during online learning due to limited interactio...
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Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness.
If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vess...
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Description: The world today has bought on a need to pay increased attention to safety and security issues, for example, search and rescue operations, surveillance, and protection of critical infrastructure. These tasks are often labour intensive and potentially dangerous. This provides an incentive to create systems that aid operators to gain situ...
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Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods.
Previous research has shown that the use of the logistic regression, svm and random forest algorithms in breast can...
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Cyberbullying is when someone is bullied using technology as an intermediary. Despite the fact that it has been a problem for many years, the impact on young people has just recently become more widely recognized.
Bullies thrive on social media platforms, and teens and children who use them are vulnerable to attacks. A copious amount of usergener...
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Background: Crop diseases can devastate yields, leading to significant financial losses for farmers. Early detection and timely intervention are crucial for effective management. Description: Develop an AI-driven system that analyzes crop images and environmental data to predict potential disease outbreaks.
This system will provide farmers with a...
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Background: Use of encrypted messaging/social media apps like Telegram, WhatsApp and Instagram for drug trafficking are on the rise. Channels operating on Telegram and WhatsApp and Instagram handles are blatantly being misused by drug traffickers for offering various narcotic drugs and Psychotropic substances for sale. Description: WhatsApp and Tel...
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Background: There are large number of cryptographic algorithms available for ensuring data confidentiality and integrity in secure communication. Identification of the algorithm is a research activity that leads to better understanding of the weakness in its implementation, in order to make the algorithm more robust and secure.
Description: The a...
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Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”.
It analyzes the data and labels the ‘better’ and ‘worse’ sentiment as...
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Nowadays, detecting credit card fraud is a major social issue. Credit card usage on e-commerce and banking websites has quickly expanded in recent years. The usage of credit cards in online transactions has made it simple, but it has also increased the frequency of fraud transactions.
Modernization will have both beneficial and negative effects. ...
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Three phase transmission line are soul of power system. In this paper different types of fault are classified in the three phase transmission line. In the present scenario the both end ratios are considered for the data acquisition of voltage and currents. This states are measured and fed to the control panel for the fault analysis and detection. T...
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Real estate investments have become more popular last few decades. People who are investing in a new house are more conservative with their budget and market strategies. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase.
The proposed system has two modes of oper...
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As artificial intelligence (AI) develops quickly, Python has become the de facto fully object-oriented programming language. Python'ssimplicity, language variety, and vast library ecosystem make it a valuable tool for image processing .
This research study examines Python's role in image processing in detail, outlining its benefits, drawbacks, a...
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Nowadays, detecting credit card fraud is a major social issue. Credit card usage on e-commerce and banking websites has quickly expanded in recent years. The usage of credit cards in online transactions has made it simple, but it has also increased the frequency of fraud transactions.
Modernization will have both beneficial and negative effects. ...
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Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis.
The application of Convolutional Neural Networks (CNNs) in deep learning has revolutioniz...
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The purpose of this project is to detect the fraudulent transactions made by credit cards by the use of machine learning techniques, to stop fraudsters from the unauthorized usage of customers’ accounts.
The increase of credit card fraud is growing rapidly worldwide, which is the reason actions should be taken to stop fraudsters.
Putting a ...
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Online transactions have become a significant and crucial aspect of our lives in recent years. It's critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren't charged for things they didn't buy.
The number of fraudulent transactions is rapidly increasing as the frequency of transactions increa...
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Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss.
Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters f...
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Diagnosing a brain tumor takes a long time and relies heavily on the radiologist’s abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifyin...
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Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health.
Images from these devices are a source of...
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The main purpose of this project is to build a face recognition-based attendance monitoring system for educational institution to enhance and upgrade the current attendance system into more efficient and effective as compared to before.
The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Ma...
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Brain tumor detection is a critical application in the field of medical imaging, aimed at aiding healthcare professionals in the early and accurate diagnosis of brain tumors. This project leverages machine learning and deep learning techniques in Python to developa robust and reliable brain tumor detection system.
The system undergoes sensitivi...
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In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world.
With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects...
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The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability.
In general, accurate deep-learning classifiers provide little model interpretability, whi...
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Fake news, deceptive information, and conspiracy theories are part of our everyday life. It is really hard to distinguish between false and valid information.
As contemporary people receive the majority of information from electronic publications, in many cases fake information can seriously harm people’s health or economic status.
This article...
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To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to au...
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In India, there are continuously more criminal cases filed, which results in an increase in the number of cases still outstanding. These ongoing increases in criminal cases make them challenging to categorise and resolve.
Therefore, it's crucial to identify a location's patterns of criminal activity in order to stop it from happening in order to ...
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Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem.
In this article, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analy...
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The present study aims to elucidate the main variables that increase the level of depression at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model.
Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the tr...
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IoT-enabled smart healthcare systems has the characteristics of heterogeneous fusion, cross domain, collaborative autonomy, dynamic change and open interconnection, but they bring huge challenges in privacy issues. We proposed a scheme of forward privacy preserving for IoT-enabled healthcare systems, which mainly includes a searchable encryptio...
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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these a...
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Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries.
This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detec...
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Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate.
But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection a...
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In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays.
The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification.
The heading model construction phase utilizes four modified d...
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The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socioenvironmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of...
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The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socioenvironmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of...
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Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faul...
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Over the years, there has been a global increase in the use of technology to deliver interventions for health and wellness, such as improving people’s mental health and resilience. An example of such technology is the Q-Life app which aims to improve people’s resilience to stress and adverse life events through various coping mechanisms, including ...
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Sell-side analysts’ recommendations are primarily targeted at institutional investors mandated to invest across many companies within client-mandated equity benchmarks, such as the FTSE/JSE All-Share index. Given the numerous sell-side recommendations for a single stock, making unbiased investment decisions is not often straightforward for portfoli...
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Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults.The average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming, and its accuracy depen...
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Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we ...
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Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing.This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This gro...
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We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification.The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions. The key requirement we have establishe...
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In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID...
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Emojis are used in Computer Mediated Communication (CMC) as a way to express paralinguistics otherwise missing from text, such as facial expressions or gestures. However, finding an emoji on the ever expanding emoji list is a linear search problem and most users end up using a small subset of emojis that are near the top of the emoji list. Current ...
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In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of pertask losses. However, thi...
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Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time. Since spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection very challenging and...
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Feature selection is the task of choosing a small subset of features that is sufficient to predict the target labels well. Here, instead of trying to directly determine which features are better, we attempt to learn the properties of good features. For this purpose we assume that each feature is represented by a set of properties, referred to as me...
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Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples o...
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Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms co...
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The usage of credit cards for online and regular purchases is exponentially increasing and so is the fraud related with it. A large number of fraud transactions are made every day. Various modern techniques like artificial neural network Different machine learning algorithms are compared, including Logistic Regression, Decision Trees, Random Forest...
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This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks. While deep learning has gained significant traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong stat...
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Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human.The scenario of manual feeding of the machine is changed in the modern era, it will lear...
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large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization we propo...
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Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has bee...
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? In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characte...
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The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...
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The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...
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We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, ...
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Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in...
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By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...
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The detection and prevention of a network intrusion is a major concern. Machine Learning and Deep Learning methods detect network intrusions by predicting the risk with the help of training the data. Various machine learning and deep learning methods have been proposed over the years which are shown to be more accurate when compared to other networ...
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Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspa...
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Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast canc...
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Monitoringthe depth of unconsciousnessduring anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram(EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anestheti...
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In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between...
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Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantage of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the mos...
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In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
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? In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characte...
|
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, ...
|
The detection and prevention of a network intrusion is a major concern. Machine Learning and Deep Learning methods detect network intrusions by predicting the risk with the help of training the data. Various machine learning and deep learning methods have been proposed over the years which are shown to be more accurate when compared to other networ...
|
large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization we propo...
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Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for a...
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Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for a...
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The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable...
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In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
|
In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
|
In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between...
|
Monitoringthe depth of unconsciousnessduring anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram(EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anestheti...
|
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspa...
|
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...
|
Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in...
|
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...
|
The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable...
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Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidne...
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In modern world, the IoT is at its peak. The world is becoming smarter, the home automation is emerging. Smart Door control system is a latest technology in home automation. The main purpose of smart Door technology is to provide a complete security to the door, ease and comfort for users. The aim of this paper is to enlarge the door automation tec...
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The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training a...
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Drowsiness of drivers is one of the significant cause of road accidents. Every year, there is an increase in the amount of deaths and fatal injuries globally. By detecting the driver’s drowsiness, road accidents can be reduced. This paper describes a machine learning approach for drowsiness detection. Face detection is employed to locate the region...
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Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sonography (LU...
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Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. First, it is...
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Based on environmental, legal, social, and economic factors, reverse logistics and closed-loop supply chain issues have attracted attention among both academia and practitioners. This attention is evident by the vast number of publications in scientific journals which have been published in recent years. Hence, a comprehensive literature review of ...
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Road lane detection systems play a crucial role in the context of Advanced Driver Assistance Systems (ADASs) and autonomous driving. Such systems can lessen road accidents and increase driving safety by alerting the driver in risky traffic situations. Additionally, the detection of ego lanes with their left and right boundaries along with the recog...
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This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments...
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Knee osteoarthritis (KOA) is a leading cause of disability among elderly adults, and it causes pain and discomfort and limits the functional independence of such adults. The aim of this study was the development of an automated classification model for KOA, based on the Kellgren–Lawrence (KL) grading system, using radiographic imaging and gait anal...
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Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite the...
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Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continui...
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Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. How...
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This paper investigates the applicability of machine-driven Speech Emotion Recognition (SER) towards the augmentation of theatrical performances and interactions (e.g. controlling stage color /light, stimulating active audience engagement, actors’ interactive training, etc.). For the needs of the classification experiments, the Acted Emotional Spee...
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This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting an...
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Networking, which is one of the most significant aspects of information technology revolution, is developing increasingly day after day. This is because it offers a huge amount of knowledge, resources and human experiences. On the one hand, it contains a considerable amount of harmful content, because of misusing. On the other hand, sitting for a l...
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Design of fishing boat for Pelabuhanratu fisherman as one of effort to increase production of capture fisheries. The fishing boat should be proper for the characteristic of its service area, as ;capacity of fishing boat up to 60 GT, the fishing boat has minimum 6 fish holds and location of fish hold in the middle body, the fishing boat hull has the...
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There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven cla...
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Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by r...
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Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures.Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a...
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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these appro...
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The Hilbert Huang Transform (HHT) has been used extensively in the time-frequency analysis of electroencephalography (EEG) signals and Brain-Computer Interfaces. Most studies utilizing the HHT for extracting features in seizure prediction have used intracranial EEG recordings. Invasive implants in the cortex have unknown long term consequences and ...
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Field-programmable gate arrays (FPGAs) are increasingly used as the computing platform for fast and energyefficient execution of recognition, mining, and search applications. Approximate computing is one promising method for achieving energy efficiency. Compared with most prior works on approximate computing, which target approximate processors and...
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In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energ...
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This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial point...
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A ternary content addressable memory (TCAM) speeds up the search process in the memory by searching through prestored contents rather than addresses. The additional don’t care (X) state makes the TCAM suitable for many network applications but the large amount of cell requirement for storage consumes high power and takes a large design area. This p...
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Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consist...
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A scalable approximate multiplier, called truncation- and rounding-based scalable approximate multiplier (TOSAM) is presented, which reduces the number of partial products by truncating each of the input operands based on their leading one-bit position. In the proposed design, multiplication is performed by shift, add, and small fixed-width multipl...
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This brief presents a low-complexity I/Q (in-phase and quadrature components) imbalance calibration method for the transmitter using quadrature modulation. Impairments in analog quadrature modulator have a deleterious effect on the signal fidelity. Among the critical impairments, I/Q imbalance (gain and phase mismatches) deteriorates the residual s...
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Multiply–accumulate (MAC) computations account for a large part of machine learning accelerator operations. The pipelined structure is usually adopted to improve the performance by reducing the length of critical paths. An increase in the number of flip-flops due to pipelining, however, generally results in significant area and power increase. A la...
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In recent times the concept of smart cities have gained grate popularity. The ever increasing population has led to chaotic city traffic. As a result of the process of searching a parking lot becomes tedious. It is time consuming task leading to discomfort. The fuel consumption is on an increasing side due to such scenarios. The increase in vehicul...
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India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examinat...
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India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in exam...
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This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about a...
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Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring. This study evaluates the seizure detection performance of custom-...
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In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robo...
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Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or ...
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Some of the modern smart grid infrastructures, phasor measurement units (PMUs) for instance, are vulnerable to cyber attacks due to their ever-increasing dependence on information and communications technologies. In general, existing solutions to cyber attacks focus on creating redundancy and/or enhancing security levels of sensing and communicatio...
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Hemoglobin can be measured normally after the analysis of the blood sample taken from the body and this measurement is named as invasive. Hemoglobin must continuously be measured to control the disease and its progression in people who go through hemodialysis and have diseases such as oligocythemia and anemia. This gives a perpetual feeling of pain...
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Wireless sensor networks (WSN) are integral part of Industrial Internet of Things (IIOT), the said networks comprise of elements possessing low power processors. WSNs are used for gathering data in the monitoring region, using which vital information about the sensor and the monitoring region can be attained (placement of the sensor node is critica...
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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these appro...
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Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become ...
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With the increasing popularity of blockchain technology, it has also become a hotbed of various cybercrimes. As a traditional way of scam, the phishing scam has new means of scam in the blockchain scenario and swindles a lot of money from users. In order to create a safe environment for investors, an ecient method for phishing detection is urge...
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Network embedding assigns nodes in a network to low dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods,...
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In this paper, we design a home outlet and a LED array lamp controlled by hand gesture recognition with a smart phone that has a system composed of two parts: a smart phone's application and a wireless remote control unit (WRCU). The application can read the accelerometer and gyroscope in a smart phone by means of hand gesture recognition and s...
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This project gives the idea about Detection of various disease on grape field. Also provide the information about how to control this disease. India exported so many tons of grape every year. So, Grape played vital role in economic condition of country. But, because of disease the Grape quality is decrease so that we cannot exported this grape in f...
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Biosensors integrated into the vehicle controller area network are used for detecting symptoms such as anxiety, pain, and fatigue that may affect driving safety. The proposed system provides a flexible option for implementation in a diverse range of mass-produced automotive accessories without affecting the driver’s movement.
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In this study, we present our implementation of using a motion controller to control the motion of a drone via simple human gestures. We have used the Leap as the motion controller and the Parrot AR DRONE 2.0 for this implementation. The Parrot AR DRONE is an off the shelf quad rotor having an on board Wi-Fi system. The AR DRONE is connected to the...
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To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systemat...
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In this paper, the integration of face feature detection and extraction, and facial expression recognition are discussed. In this paper, we propose an algorithm that utilizes multi-stage integral projection to extract facial features. Furthermore, in this project, we propose a statistical approach to process the optical flow data to obtain the over...
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With the increased popularity of online social networks, spammers find these platforms easily accessible to trap users in malicious activities by posting spam messages. In this work, we have taken Twitter platform and performed spam tweets detection. To stop spammers, Google SafeBrowsing and Twitter’s BotMaker tools detect and block spam tweets. Th...
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In recent years, the prospect of the Vehicle market is worrisome. The development of automotive aftermarket business has become the core of 4s stores. And the spare parts in stock are the key part of the aftermarket. Therefore, it is very important for the development of the aftermarket to make a good classification of spare parts. Aiming at the pr...
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Data mining is an important technique used in many fields with the purpose of acquiring valuable information from big data. This study aims to reveal the relations between the attributes of independent criminal records. The NIBRS database, which includes criminal records in USA that are recorded in 2013, is used in this study. The association rules...
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With the rapid development of Internet, e-commerce had been widely using by more and more people, so had shopping online . The J2EE architecture was adopted in this paper. An florist’s shop online system was designed and implemented based on the analysis of the actual needs of users. Through running test for a period of time, the system could be op...
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This history column article provides a tour of the main software development life cycle (SDLC) models. (A lifecycle covers all the stages of software from its inception with requirements definition through to fielding and maintenance.) System development lifecycle models have drawn heavily on software and so the two terms can be used interchangeabl...
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Locating files is one of the biggest problems in universities nowadays. Time is wasted in searching files, energy is wasted chasing misspelled files, deadlines are missed. Due to this , I decided to make a system for file tracking that will solve all problems in the best way.File Tracking System is a internet application that manages all the file m...
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Our project is a website in which the clients can view the future market status of the shares and also they can view the track records of various companies’ shares. According to the market status the share values are updated frequently. After login into the system they can view and access the details of the shares. In this system we are getting the...
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Online assessments are a significant technique for assessing the achievement capability of understudies. This examination exertion the people viable were understudies who might be joining up with PC courses or Technologies Registrations. A model of an online arrangement assessment framework is depicted from the point of view of the examination exer...
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The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. In this paper the general description of the developed f...
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Herbal database management is one of the emerging trends nowadays. There are many agencies working on herbal database management. This system helps to overcome many adverse reactions of drugs and their interactions. The safety profile of an herbal is maintained in this database which is very useful for appropriate use of herbal medicine. Here, an a...
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Using a novel approach with video-recordings of sales interactions, this study focuses on a dynamic analysis of salesperson effectiveness in handling customer queries. We conceptualize salesperson behaviors, namely, resolving, relating, and emoting, as separate elements of customer query handling and empirically identify the distinct verbal and non...
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Television group of onlookers rating is a vital pointer as to prevalence of projects and it is likewise a factor to impact the income of communicate stations through promotions. Albeit higher evaluations for a given program are gainful for the two supporters and promoters, little is thought about the components that make programs increasingly allur...
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This paper introduces a video copy detection system which efficiently matches individual frames and then verifies their spatio-temporal consistency. The approach for matching frames relies on a recent local feature indexing method, which is at the same time robust to significant video transformations and efficient in terms of memory usage and compu...
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Large amount of Twitter accounts are suspended. Over five year period, about 14% accounts are terminated for reasons not specified explicitly by the service provider. We collected about 120,000 suspended users, along with their tweets and social relations. This thesis studies these suspended users, and compares them with normal users in terms of th...
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Now a days Attacker’s launch attack campaigns targeting the zero day vulnerability, compromising internet users on a large scale. The first response to such campaigns is to detect them and collect sufficient information regarding tools, techniques used to exploit the vulnerability. Hence effective capturing of the attack data and its timely dissemi...
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The evolution of WWW leads to continuous growth of demands that are placed on web applications that results in creating sophisticated web architectures. To minimize the complexity behind their design, software frameworks were introduced. There are hundreds of web frameworks, hence the choice of the right framework can be seen as searching for the h...
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Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks...
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Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based bio...
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A novel carrier sense multiple access strategy with collision avoidance (CSMA/CA) balancing contention probability and channel access time is proposed. The approach can be applied to any context where the computational simplicity of the MAC must be preferred to the complexity of the channel access strategy. Our MAC, called Delay-Collision CSMA (DC-...
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In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achie...
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Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establis...
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This paper proposes a communication strategy for decentralized learning in wireless systems that employs adaptive modulation and coding capability. The main objective of this work is to address a critical issue in decentralized learning based on the cooperative stochastic gradient descent (C-SGD) over wireless systems: the relationship between the ...
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The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, ...
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Urban crowd flow prediction is very challenging for public management and planning in smart city applications. Existing work mostly focuses on spatial and temporal dependence based flow prediction that are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents. In this paper, we propose ...
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The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people's lives in all directions. The rapid development of the IoT technology accelerates the process of the era of ``Internet of everything'' but also changes the role of terminal equipment at the edge of...
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Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as cloudlets,...
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Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hi...
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Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper invest...
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In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challen...
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Recent advances in smart connected vehicles and Intelligent Transportation Systems (ITS) are based upon the capture and processing of large amounts of sensor data. Modern vehicles contain many internal sensors to monitor a wide range of mechanical and electrical systems and the move to semi-autonomous vehicles adds outward looking sensors such as c...
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Increasingly, the task of detecting and recognizing the actions of a human has been delegated to some form of neural network processing camera or wearable sensor data. Due to the degree to which the camera can be affected by lighting and wearable sensors scantiness, neither one modality can capture the required data to perform the task confidently....
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Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element--``diversity.'' The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, ...
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Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new ...
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Cross-manifold clustering is an extreme challenge learning problem. Since the low-density hypothesis is not satisfied in cross-manifold problems, many traditional clustering methods failed to discover the cross-manifold structures. In this article, we propose multiple flat projections clustering (MFPC) for cross-manifold clustering. In our MFPC, th...
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Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that i...
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With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of weakly discriminating marginal representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant met...
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Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its perf...
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Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines. Therefore, on some occasions, fault di...
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For a broad range of applications, hyperspectral image (HSI) classification is a hot topic in remote sensing, and convolutional neural network (CNN)-based methods are drawing increasing attention. However, to train millions of parameters in CNN requires a large number of labeled training samples, which are difficult to collect. A conventional Gabor...
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Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UD...
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Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific ...
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Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on ...
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Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the di...
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In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overvi...
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Recently, the restricted Boltzmann machine (RBM) has aroused considerable interest in the multiview learning field. Although effectiveness is observed, like many existing multiview learning models, multiview RBM ignores the local manifold structure of multiview data. In this article, we first propose a novel graph RBM model, which preserves the dat...
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The cross-lingual sentiment analysis (CLSA) aims to leverage label-rich resources in the source language to improve the models of a resource-scarce domain in the target language, where monolingual approaches based on machine learning usually suffer from the unavailability of sentiment knowledge. Recently, the transfer learning paradigm that can tra...
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Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of ...
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By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual lea...
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Predicting attention-modulated brain responses is a major area of investigation in brain-computer interface (BCI) research that aims to translate neural activities into useful control and communication commands. Such studies involve collecting electroencephalographic (EEG) data from subjects to train classifiers for decoding users' mental states. H...
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This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), expre...
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Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepar...
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Image annotation aims to jointly predict multiple tags for an image. Although significant progress has been achieved, existing approaches usually overlook aligning specific labels and their corresponding regions due to the weak supervised information (i.e., ``bag of labels'' for regions), thus failing to explicitly exploit the discrimination from d...
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Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of ch...
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Complex-valued neural network is a kind of learning model which can deal with problems in complex domain. Fully complex extreme learning machine (CELM) is a much faster training algorithm than the complex backpropagation (CBP) scheme. However, it is at the cost of using more hidden nodes to obtain the comparable performance. An upper-layer-solution...
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The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-In-Time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can pr...
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Andriod malware poses a serious threat to users privacy, money, equipment and file integrity. A series of data-driven malware detection methods were proposed. However, there exist two key challenges for these methods: (1) how to learn effective feature representation from raw data; (2) how to reduce the dependence on the prior knowledge or human la...
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IoT-enabled smart healthcare systems has the characteristics of heterogeneous fusion, cross domain, collaborative autonomy, dynamic change and open interconnection, but they bring huge challenges in privacy issues. We proposed a scheme of forward privacy preserving for IoT-enabled healthcare systems, which mainly includes a searchable encryption sc...
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Data in modern industrial applications and data science presents multidimensional progressively, the dimension and the structural complexity of these data are becoming extremely high, which renders existing data analysis methods and machine learning algorithms inadequate to the extent. In addition, high-dimensional data in actual scenarios often sh...
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It is a challenging task to deploy lightweight security protocols in resource-constrained IoT applications. A hardware-oriented lightweight authentication protocol based on device signature generated during voltage over-scaling (VOS) was recently proposed to address this issue. VOS-based authentication employs the computation unit such as adder...
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One of the major challenges for Internet-of-Things applications is that the existing cellular technologies do not support the uplink IoT traffic in an energy-efficient manner. There are two principal ways for serving the uplink IoT traffic: grant-based (i.e., scheduled) and grant-free (i.e., random access). Grant-based access provides fine-grained ...
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With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst for the digital revolution, contributing to the generation of big amounts of (transient) data, which calls into question the storage and processing performance of the conventional cloud. Moving storage resources at the edge can reduce the data retrieva...
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Current Internet of Things (IoT) infrastructures rely on cloud storage however, relying on a single cloud provider puts limitations on the IoT applications and Service Level Agreement (SLA) requirements. Recently, multiple decentralized storage solutions (e.g., based on blockchains) have entered the market with distinct architecture, Quality of Ser...
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Strongly quantized fixed-point arithmetic is now considered a well-established solution to deploy Convolutional Neural Networks (CNNs) on limited-memory low-power IoT endnodes. Such a trend is challenging due to the lack of support for low bitwidth fixed-point instructions in the Instruction Set Architecture (ISA) of state-of-the-art embedded Micro...
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Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a great challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN mode...
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Barrier coverage scheduling is an energy conservation scheme in which a subset of sensor nodes with overlapped sensing area (also called barrier) is activated to meet the key Quality of Service (QoS) requirements such as energy-efficiency, coverage, and connectivity. However, sudden and unexpected node failures in a barrier due to security attacks ...
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The integration of drones, IoT, and AI domains can produce exceptional solutions to today's complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. In this paper, an intelligent technique is proposed to predict the signa...
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In the modern world, the connectivity-as-we-go model is gaining popularity. Internet-of-Things (IoT) envisions a future in which human beings communicate with each other and with devices that have identities and virtual personalities, as well as sensing, processing, and networking capabilities, which will allow the developing of smart environments ...
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Data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices demands for edge resources. However, significant challenges and technical barriers exist which complicate resource management of such systems. IoT devices can demonstrate a wide range of behaviors in the devices resource demand that are extremely difficu...
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In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based on fuzzy rules are suitable solutions to stress the interpretability of the global systems. However,...
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The failure of complex artificial intelligence (AI) systems seems ubiquitous. To provide a model to describe these shortcomings, we define complexity in terms of a system's sensors and the number of environments or situations in which it performs. The complexity is not looked at in terms of the difficulty of design, but in the final performance of ...
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Most convolutional neural network (CNN)-based cloud detection methods are built upon the supervised learning framework that requires a large number of pixel-level labels. However, it is expensive and time-consuming to manually annotate pixelwise labels for massive remote sensing images. To reduce the labeling cost, we propose an unsupervised domain...
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Robust road boundary extraction and completion play an important role in providing guidance to all road users and supporting high-definition (HD) maps. The significant challenges remain in remarkable and accurate road boundary recovery from poor road boundary conditions. This paper presents a novel deep learning framework, named BoundaryNet, to ext...
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Point clouds are fundamental in the representation of 3D objects. However, they can also be highly unstructured and irregular. This makes it difficult to directly extend 2D generative models to three-dimensional space. In this paper, we cast the problem of point cloud generation as a topological representation learning problem. To infer the represe...
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Point clouds are the most general data representations of real and abstract objects, and have a wide variety of applications in many science and engineering fields. Point clouds also provide the most scalable multi-resolution composition for geometric structures. Although point cloud learning has shown remarkable results in shape estimation and sem...
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Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer a...
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Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited c...
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The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Ear...
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In recent years, the investigations on Cyber-Physical Systems~(CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea ...
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Online non-intrusive load monitoring methods have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as safety control, anomaly detection, and demand-side management. However, despite the promised energy efficiency by providing appliance specific consumption information feed-backs, the co...
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With increasing of data size and development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine...
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Motivation: Identifying differentially expressed genes (DEGs) in transcriptome data is a very important task. However, performances of existing DEG methods vary significantly for data sets measured in different conditions and no single statistical or machine learning model for DEG detection perform consistently well for data sets of different trait...
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Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are...
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Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrust...
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Next generation wireless systems have witnessed significant R&D attention from academia and industries to enable wide range of applications for connected environment around us. The technical design of next generation wireless systems in terms of relay and transmit power control is very critical due to the ever-reducing size of these sensor enabled ...
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The multi-hop protocols are proved effective in the railway disaster wireless monitoring system. However, farther transmission distance with the larger data will decline the valid lifetime and reliability of the system. Most existing studies focused primarily on the communication protocols optimization, and some works tried to utilize the limited c...
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This article proposes indirect/direct learning control schemes for wireless sensor and mobile robot networks to cover an environment according to the density function, which is the distribution of an important quantity within the environment. When stationary sensors cooperate with mobile robots, the density estimation can be enhanced by using nonst...
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This article proposes indirect/direct learning control schemes for wireless sensor and mobile robot networks to cover an environment according to the density function, which is the distribution of an important quantity within the environment. When stationary sensors cooperate with mobile robots, the density estimation can be enhanced by using nonst...
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Chinese word embedding models capture Chinese semantics based on the character feature of Chinese words and the internal features of Chinese characters such as radical, component, stroke, structure and pinyin. However, some features are overlapping and most methods do not consider their relevance. Meanwhile, they express words as point vectors that...
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Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold lea...
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Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this study. By setting hierarchical prior distributions over the model weights and the parameters o...
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Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies whether a code commit will introduce bugs into the mobile app, aiming to provide prompt feedback to practitioners for priority review. Since collecting sufficient labeled bug data is not always feasible for some mobile apps, one possible approach is to leve...
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Recent years have witnessed the unprecedented growth of online social media, resulting in short texts being the prevalent format of information on the Internet. Given the sparsity of data, however, short-text topic modeling remains a critical yet much-watched challenge in both academia and industry. Research has been devoted to building different t...
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Node embedding aims to encode network nodes as a set of low-dimensional vectors while preserving certain structural properties of the network. In recent years, extensive studies have been conducted to preserve network communities, i.e., structural proximity of network nodes. However, few of them have focused on preserving the structural equivalence...
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In real-world scenarios, a user's interactions with items could be formalized as a behavior sequence, indicating his/her dynamic and evolutionary preferences. To this end, a series of recent efforts in recommender systems aim at improving recommendation performance by considering the sequential information. However, impacts of sequential behavior o...
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Modern enterprises attach much attention to the selection of commercial locations. With the rapid development of urban data and machine learning, we can discover the patterns of human mobility with these data and technology to guide commercial district discovery. In this paper, we propose an unsupervised commercial district discovery framework via ...
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Knowledge graph embedding is an effective way to represent knowledge graph, which greatly enhance the performances on knowledge graph completion tasks, e.g. entity or relation prediction. For knowledge graph embedding models, designing a powerful loss framework is crucial to the discrimination between correct and incorrect triplets. Margin-based ra...
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Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedd...
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This work proposes a novel frequency regulation paradigm for multi-area interconnected power systems. The developed approach capitalizes on phasor measurement units (PMUs) advanced monitoring to overcome design limitations imposed by legacy supervisory control and data acquisition (SCADA) systems. For this, a novel measurement-based controller inte...
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Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requirements of many emerging applications. However, in real-world applications, the scarcity of labeled...
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Accurate and fast event identification in power systems is critical for taking timely controls to avoid instability. In this paper, a synchrophasor measurementbased fast and robust event identification method is proposed considering different penetration levels of renewable energy. A difference Teager-Kaiser energy operator (dTKEO)-based algori...
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Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis shall be put on selecting representative bands. In this paper, we propose a robust unsuperv...
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The heterogeneity of today’s Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user’s information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-moda...
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Deep learning methods have played a more and more important role in hyperspectral image classification. However, general deep learning methods mainly take advantage of the sample-wise information to formulate the training loss while ignoring the intrinsic data structure of each class. Due to the high spectral dimension and great redundancy between ...
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Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender, and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disa...
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Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly ...
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The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilitie...
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Robust road boundary extraction and completion play an important role in providing guidance to all road users and supporting high-definition (HD) maps. The significant challenges remain in remarkable and accurate road boundary recovery from poor road boundary conditions. This paper presents a novel deep learning framework, named BoundaryNet, to...
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In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS [1] observation protocol that is widely used in educational research. ACORN uses convolutional neural networks to analyze spectral audio feat...
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Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it faces an over-fitting problem. To overcome it, this work integrates bound optimization theory with Variational Bayesian (VB) inference to derive novel L1 norm-based ELMs. An L1 term is attached to the squared sum cost of prediction errors to formulate an object...
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This paper presents an asset health index (HI) prediction methodology for high voltage transmission overhead lines (OHLs) using supervised machine learning and structured, unambiguous visual inspections. We propose a framework for asset HI predictions to determine the technical condition of individual OHL towers to improve grid reliability in a cos...
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A methodology for automating the identification of single-event transients (SETs) through Ionizing Radiation Effects Spectroscopy (IRES) and machine learning (ML) is provided. IRES enhances the identification of SETs through statistical analysis of waveform behavior, allowing for the capture of subtle circuit dynamics changes.Automated identificati...
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Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it...
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Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the targeted regions, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models. In this study, we investigate feasibi...
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Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved...
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Objectives: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (T...
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Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies.Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions.Considering that future ...
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In exercise gaming (exergaming), reward systems are typically based on rules/templates from joint movement patterns. These rules or templates need broad ranges in definitions of correct movement patterns to accommodate varying body shapes and sizes.This can lead to inaccurate rewards and, thus, inefficient exercise, which can be detrimental to prog...
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Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can ...
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The deficiency of water all throughout the planet compel us to limit the use of water. Over 75% of new water assets were utilizing for water system reason so productive use of water in water system framework with cutting edge strategy is required. This paper presents a cutting-edge innovation based savvy framework to anticipate the water system nec...
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? Proper training is essential to achieve reliable pattern recognition (PR) based myoelectric control.
? The amount of training is commonly determined by experience.
? The purpose of this study is to provide an offline validation method that makes the offline performance transferable to online control and find the proper amount of training tha...
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Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization based ...
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? Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula.
? As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide r...
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? Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candi...
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? This work aims to enhance our fundamental understanding of how the measurement setup used to generate training and testing datasets affects the accuracy of the machine learning algorithms that attempt solving electromagnetic inversion problems solely from data.
? A systematic study is carried out on a one-dimensional semi-inverse electromagneti...
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Phishing is the act of attempting to acquire information such as usernames, passwords, and credit card details by masquerading as a trust worth entity in an electronic communication there are several different techniques to control phishing, including legislation and technology created specifically to protect against phishing. This project provides...
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Phishing is the act of attempting to acquire information such as usernames, passwords, and credit card details by masquerading as a trust worth entity in an electronic communication there are several different techniques to control phishing, including legislation and technology created specifically to protect against phishing. This project provide...
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Social Media has quickly gained prominence as it provides people with the opportunity to communicate and share posts and topics. Tremendous value lies in automated analysing and reasoning about such data in order to derive meaningful insights, which carries potential opportunities for businesses, users, and consumers. Many events in the world are a...
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Digital media archives are increasing to colossal proportions in the world today, which includes audio, video and images An Image refers as a picture produced on an electronic display .A digital image is a numeric representation of a two-dimensional image. Digital image processing refers to processing of digital images by using digital computers. N...
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Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Skin lesions can be generally group into two categories namely primary and secondary skin lesions. Prim...
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Learning with streaming data has received extensiveattention during the past few years. Existing approaches assumethat the feature space is fixed or changes by following explicitregularities, limiting their applicability in real-time applications.For example, in a smart healthcare platform, the feature space ofthe patient data varies when different...
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Engineering changes (ECs) are new product devel-opment activities addressing external or internal challenges, suchas market demand, governmental regulations, and competitivereasons. The corresponding EC processes, although perceived asstandard, can be very complex and inefficient. There seem to besignificant differences between what is the “officia...
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The identification of network attacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices...
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The traditional single minimum support data mining algorithm has some problems, such as too much space occupied by data, resulting in insufficient accuracy of the algorithm, which is difficult to meet the needs of the development of the times. Therefore, an intrusion data mining algorithm based on multiple minimum support is proposed. First, the fe...
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Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recognition and recommendation.The majority of existing representation learning approaches often require large amounts of consistent and noise-free labels. However, due to var...
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The goal of our work is to discover dominant objects in a very general setting where only a single un labeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), whi...
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With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio -temporal data has become increasingly available nowadays.Mining valuable knowledge from spatio - temporal data is critically important to many real-world applications including human mobility understanding, sm...
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Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning.
According to different application scenarios, one can impose different constraintson the factorization to find the desired basis, which captures high-level semantics for the given data, and lea...
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Experience mining is considered a substantial extension of opinion mining. Experience mining covers the description of all events that are related to the user's perception in the interaction with the object.
There is information about the user`s experience that cannot be obtained with polarity analysis or sentiment analysis. The information obtai...
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? Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education.
? Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a plethora of dynamic time warping distances. However, the computational costs of using such elastic distances result in unaccepta...
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In this paper, heart disease prediction modeled using partially observable Markova decision process (POMDP) is proposed. In emergency, the patient is alerted through the doctor by fog computing. Ambulance sent to the location of patient at critical situations. The doctor gets the data through fog computing iFogSim. Fog computing in healthcare is a ...
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In WSN network there is Cyber Attack occurs are one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. The purpose of this s...
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Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also produces information about kidney functions.
The goal of this work is to classify the kidney images using US according to relevant features selection. In this work, images of a kidney are classified as abnormal i...
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Nowadays, rice farming is affected by various diseases that are economically significant and worthy of attention. One of these diseases is blast. Rice blast is one of the most important limiting factors in rice yield. The purpose of this study is the timely and rapid diagnosis of rice blast based on the image processing technique in field condition...
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Some of the modern smart grid infrastructures, phasor measurement units (PMUs) for instance, are vulnerable to cyber attacks due to their ever-increasing dependence on information and communications technologies. In general, existing solutions to cyber attacks focus on creating redundancy and/or enhancing security levels of sensing and communicatio...
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The recent scientific advances in understanding the hierarchical nature of the lithosphere and its dynamics based on systematic monitoring and evidence of its space-energy similarity at global, regional, and local scales did result the design of reproducible inter mediate term middle-range earthquake prediction technique. The real-time experimental...
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Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sono graphy (L...
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Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning ...
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Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...
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Cancer is a major threat to the lives of human beings. Around 74% of the people who get affected by cancer lost their lives. But early detection of cancer cells can prevent death rates. CT(Computerized Tomography) is one of the major used for cancer cell identifications by the oncologist. Computer-aided cancer detection plays a major role in the de...
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India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examinat...
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Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in...
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Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...
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Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...
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Air pollution affects human skin in many ways. Skin diseases are common in densely populated regions. These diseases have a devastating impact on people's lives by creating a huge need for the disease diagnosis. The proposed work on skin disease determination system aims for an accurate diagnosis leveraging image processing. The methodology outline...
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Air pollution affects human skin in many ways. Skin diseases are common in densely populated regions. These diseases have a devastating impact on people's lives by creating a huge need for the disease diagnosis. The proposed work on skin disease determination system aims for an accurate diagnosis leveraging image processing. The methodology outline...
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Injuries due to road accidents are one of the most prevalent causes of death apart from health related issues. The World Health Organization states that road traffic injuries caused an estimated 1.35 million deaths worldwide in the year 2016. That is, a person is killed every 25 seconds. This calls for the need to analyse road accidents and the fac...
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Tumour is the undesired mass in the body. Brain tumour is the significant growth of brain cells. Manual method of classifying is time consuming and can be done at selective diagnostic centers only. Brain tumour classification is crucial task to do since treatment is based on different location and size of it. Magnetic Resonance Imaging (MRI) is mos...
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At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disorders, it can lead to classification of certain diseases related to blood. This paper describes a preliminary study of developing a detection of leukemia types using microscopic blood sample images...
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Bananas are favorite tropical fruit since bananas contain source of nutrition. In the industry, the banana ripeness is a mayor issue for harvesting. It is a challenge if we have a good simple instrument for determining ripeness of bananas. In this work, we utilized two optical measurement systems for detecting ripeness of bananas, i.e., reflectance...
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Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objecti...
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With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN....
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This paper presents a technique for detection of kidney stones through different steps of image processing. The first step is the image pre-processing using filters in which image gets smoothed as well as the noise is removed from the image. Image enhancement is a part of preprocessing which is used to enhance the image which is achieved with power...
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The combination of pervasive edge computing and block chain technologies opens up significant possibilities for Industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, we propose a hierarchical...
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Security issues have resulted in severe damage to the cloud computing environment, adversely affecting the healthy and sustainable development of cloud computing. Intrusion detection is one of the technologies for protecting the cloud computing environment from malicious attacks. However, network traffic in the cloud computing environment is charac...
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This paper presents a technique for detection of kidney stones through different steps of image processing. The first step is the image pre-processing using filters in which image gets smoothed as well as the noise is removed from the image. Image enhancement is a part of preprocessing which is used to enhance the image which is achieved with power...
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