UNDERSTANDING DEPTH OF REFLECTIVE WRITING IN WORKPLACE LEARNINGASSESSMENTS USING MACHINE LEARNING CLASSIFICATION

 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...

MACHINE LEARNING UNDERSTANDING DEPTH

UNDERSTANDING DEPTH OF REFLECTIVE WRITING IN WORKPLACE LEARNINGASSESSMENTS USING MACHINE LEARNING CLASSIFICATION

 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...

MACHINE LEARNING UNDERSTANDING DEPTH

3-D DECONVOLUTIONAL NETWORKS FOR THE UNSUPERVISED REPRESENTATION LEARNING OF HUMAN MOTIONS

 The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of anno...

MACHINE LEARNING 3-D DECONVOLUTIONAL NETWORKS

MACHINE LEARNING FOR INTER-TURN SHORT-CIRCUIT FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS

 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...

MACHINE LEARNING INTER-TURN SHORT-CIRCUIT

I LET DEPRESSION AND ANXIETY DROWN ME…” IDENTIFYING FACTORS ASSOCIATEDWITH RESILIENCE BASED ON JOURNALING USING MACHINE LEARNING AND THEMATIC

 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 ...

MACHINE LEARNING BASED ON JOURNALING

FUSING SELL-SIDE ANALYST BIDIRECTIONAL FORECASTS USING MACHINE LEARNING

 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...

MACHINE LEARNING FUSING SELL-SIDE ANALYST

FAIRNESS IN SEMI-SUPERVISED LEARNING: UNLABELED DATA HELP TO REDUCE DISCRIMINATION

 Machine learning is widely deployed in society, unleashing its power in a wide rangeof applications owing to the advent of big data.One emerging problem faced by machine learning is the discrimination from data, and such discrimination is reflected in the eventual decisions made by the algorithms. Recent study has proved that increasing the size of...

MACHINE LEARNING FAIRNESS IN SEMI-SUPERVISED LEARNING

ENRICHING THE TRANSFER LEARNING WITH PRE-TRAINED LEXICON EMBEDDINGFOR LOW-RESOURCE NEURAL MACHINE TRANSLATION

 Most State-Of-The-Art (SOTA) Neural Machine Translation (NMT) systems today achieve outstanding results based only on large parallel corpora. The large-scale parallel corpora for high-resource languages is easily obtainable. However, the translation quality of NMT for morphologically rich languages is still unsatisfactory, mainly because of the dat...

MACHINE LEARNING ENRICHING THE TRANSFER LEARNING

AUTOMATED SCREENING SYSTEM FOR ACUTE MYELOGENOUS LEUKEMIA DETECTION

 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...

MACHINE LEARNING AUTOMATED SCREENING SYSTEM

AN ONLINE TRANSFER LEARNING FRAMEWORK WITH EXTREME LEARNING MACHINFOR AUTOMATED CREDIT SCORINGE

 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 ...

MACHINE LEARNING AN ONLINE TRANSFER LEARNING

THE HIDDEN SEXUAL MINORITIES: MACHINE LEARNING APPROACHES TO ESTIMATE THE SEXUAL MINORITY ORIENTATION AMONG BEIJING COLLEGE STUDENTS

 The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...

MACHINE LEARNING THE HIDDEN SEXUAL MINORITIES

ON THE SYNERGIES BETWEEN MACHINE LEARNING AND BINOCULAR STEREO FO DEPTH ESTIMATION FROM IMAGES A SURVEY

 Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods.Recently, the rise of machine learning and the rapid...

MACHINE LEARNING DEPTH ESTIMATION

MACHINE LEARNING AND MARKETING A SYSTEMATIC LITERATURE REVIEW

 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...

MACHINE LEARNING A SYSTEMATIC LITERATURE

ICS: TOTAL FREEDOM IN MANUAL TEXT CLASSIFICATION SUPPORTED BY UNOBTRUSIVE MACHINE LEARNING

 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...

MACHINE LEARNING FREEDOM IN MANUAL TEXT

MACHINE LEARNING FOR STRUCTURE DETERMINATION IN SINGLE-PARTICLE CRYO ELECTRON MICROSCOPY: A SYSTEMATIC REVIEW

 Traditionally, X-ray crystallography and NMR spectroscopy represent major workhorses of structural biologists, with the lion share of protein structures reported in protein data bank (PDB) being generated by these powerful techniques.Despite their wide utilization in protein structure determination, these two techniques have logical limitations, wi...

MACHINE LEARNING A SYSTEMATIC REVIEW

Data Privacy and key based security using SH256

 Information security means protecting data, such as a database, from destructive forces and from the unwanted actions of unauthorized users. Information Security can be achieved by using cryptographic techniques. It is now very much demanding to develop a system to ensure better long lasting security services for message transaction over the Intern...

MACHINE LEARNING key based security

Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16

  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...

MACHINE LEARNING Deep Learning

EMOJI TEXT BASED CHATBOT MUSIC RECOMMENDATION SYSTEM USING MACHINE LEARNING

 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 ...

MACHINE LEARNING EMOJI TEXT

A MULTIPLE GRADIENT DESCENT DESIGN FOR MULTI-TASK LEARNING ON EDGE COMPUTING MULTI-OBJECTIVE MACHINE LEARNING APPROACH

 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...

MACHINE LEARNING EDGE COMPUTING MULTI-OBJECTIVE

INTEGRATING MACHINE LEARNING ALGORITHMS WITH QUANTUM ANNEALING SOLVERS FOR ONLINE FRAUD DETECTION

 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...

MACHINE LEARNING INTEGRATING MACHINE LEARNING

LEARNING WITH SELECTED FEATURES

 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...

MACHINE LEARNING SELECTED FEATURES

MACHINE LEARNING TO IDENTIFY PSYCHOMOTOR BEHAVIORS OF DELIRIUM FOR PATIENTS IN LONG-TERM CARE FACILITY

 This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients.A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 cl...

MACHINE LEARNING LONG-TERM CARE FACILITY

PREDICTING BRAIN AGE USING MACHINE LEARNING ALGORITHMS A COMPREHENSIVE EVALUATION

 The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning mod...

MACHINE LEARNING PREDICTING BRAIN AGE

QUANTUM-INSPIRED MACHINE LEARNING FOR 6G FUNDAMENTALS, SECURITY, RESOURCE ALLOCATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

 Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search.Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machin...

MACHINE LEARNING 6G FUNDAMENTALS

PREDICTION OF DIABETES EMPOWERED WITH FUSED MACHINE LEARNING

 In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes To predict the disease, it is extremely important to understand its symptoms. C...

MACHINE LEARNING DIABETES EMPOWERED

META-TRANSFER LEARNING THROUGH HARD TASKS

 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...

MACHINE LEARNING META-TRANSFER

MACHINE LEARNING BASED HEALTHCARE SYSTEM FOR INVESTIGATING THE.ASSOCIATION BETWEEN DEPRESSION AND QUALITY OF LIFE

 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...

MACHINE LEARNING HEALTHCARE SYSTEM

CREDIT CARD FRAUD DETECTION USING STATE-OF-THE-ART MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

 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...

MACHINE LEARNING DEEP LEARNING

COGNITIVE WORKLOAD RECOGNITION USING EEG SIGNALS AND MACHINE LEARNING A REVIEW

 Machine learning and its subfield deep learning techniques provide opportunities for the development of operator mental state monitoring, especially for cognitive workload recognition using electroencephalogram (EEG) signals. Although a variety of machine learning methods have been proposed for recognizing cognitive workload via EEG recently, there...

MACHINE LEARNING EEG SIGNALS

ANOMALY DETECTION IN SELF-ORGANIZING NETWORKS CONVENTIONAL VERSUS.CONTEMPORARY MACHINE LEARNING

 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...

MACHINE LEARNING SELF-ORGANIZING

A REVIEW ON MACHINE LEARNING STYLES IN COMPUTER VISION—TECHNIQUES AND FUTURE DIRECTIONS

 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...

MACHINE LEARNING FUTURE DIRECTIONS

A MINI-REVIEW OF MACHINE LEARNING IN BIG DATA ANALYTICS APPLICATIONS, CHALLENGES, AND PROSPECTS

 The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...

MACHINE LEARNING BIG DATA ANALYTICS APPLICATIONS

ADVERSARIAL EVOLVING NEURAL NETWORK FOR LONGITUDINAL KNEE OSTEOARTHRITIS PREDICTION

 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...

DEEP LEARNING KNEE

UNSUPERVISED ALGORITHMS TO DETECT ZERO-DAYATTACKS STRATEGY AND APPLICATION

 ? 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...

DEEP LEARNING APPLICATION

TRAFFIC PREDICTION FOR INTELLIGENT TRANSPORTATION SYSTEM USING MACHINE LEARNING

 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 ...

DEEP LEARNING TRAFFIC PREDICTION

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 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 ...

DEEP LEARNING SASDL AND RBATQ

PREDICTION OF 12 PHOTONIC CRYSTAL FIBER OPTICAL PROPERTIES USING MLP IN DEEP LEARNING

 Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and conf...

DEEP LEARNING DEEP LEARNING

MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR CYBERSECURITY A

 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...

DEEP LEARNING MACHINE LEARNING

INVESTIGATING DEEP LEARNING BASED BREAST CANCER SUBTYPING USING PAN-CANCER AND MULTI-OMIC DATA

 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...

DEEP LEARNING INVESTIGATING DEEP LEARNING

INFERENCE OF BRAIN STATES UNDER ANESTHESIA WITH META LEARNING BASED DEEP LEARNING MODELS

 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...

HUMAN-IN-THE-LOOP EXTRACTION OF INTERPRETABLE CONCEPTS IN DEEP LEARNING MODELS

 The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decis...

DEEP LEARNING HUMAN-IN-THE-LOOP

FEDERATED DEEP LEARNING FOR THE DIAGNOSIS OF CEREBELLAR ATAXIA: PRIVACY PRESERVATION AND AUTO-CRAFTED FEATURE EXTRACTOR

 Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA . Although these approaches achieved ...

DEEP LEARNING AUTO-CRAFTED FEATURE EXTRACTOR

DEPTH SELECTION FOR DEEP RELU NETS IN FEATURE EXTRACTION AND GENERALIZATION

 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...

DEEP LEARNING FEATURE EXTRACTION AND GENERALIZATION

UNSUPERVISED ALGORITHMS TO DETECT ZERO-DAY.ATTACKS STRATEGY AND APPLICATION

 ? 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...

DEEP LEARNING ATTACKS STRATEGY AND APPLICATION

A SURVEY ON MATHEMATICAL, MACHINE LEARNING AND DEEP LEARNING MODELS FOR COVID-19 TRANSMISSION AND DIAGNOSIS

 COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initia...

DEEP LEARNING FOR COVID-19 TRANSMISSION

HUMAN-IN-THE-LOOP EXTRACTION OF INTERPRETABLE CONCEPTS IN DEEP LEARNING MODELS

 The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decis...

DEEP LEARNING HUMAN-IN-THE-LOOP

MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR CYBERSECURITY

 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...

DEEP LEARNING MACHINE LEARNING

A SYSTEMATIC REVIEW ON RECENT ADVANCEMENTS IN DEEP AND MACHINE LEARNING BASED DETECTION AND CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA

 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...

DEEP AND MACHINE LEARNING ACUTE LYMPHOBLASTIC LEUKEMIA

A SYSTEMATIC REVIEW ON RECENT ADVANCEMENTS IN DEEP AND MACHINE LEARNING BASED DETECTION AND CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA

 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...

DEEP AND MACHINE LEARNING ACUTE LYMPHOBLASTIC LEUKEMIA

A SYSTEMATIC REVIEW TOWARDS BIG DATA ANALYTICS IN SOCIAL MEDIA

 The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the governmentPeople are open to sharing opinions, views, and ideas on any topic in different formats out loud. This cr...

ANALYTICS IN SOCIAL MEDIA REVIEW TOWARDS BIG DATA

A SYSTEMATIC REVIEW TOWARDS BIG DATA ANALYTICS IN SOCIAL MEDIA

 The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the governmentPeople are open to sharing opinions, views, and ideas on any topic in different formats out loud. This cr...

BIG DATA ANALYTICS IN SOCIAL MEDIA SYSTEMATIC REVIEW TOWARDS

A SYSTEMATIC REVIEW TOWARDS BIG DATA ANALYTICS IN SOCIAL MEDIA

 The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the governmentPeople are open to sharing opinions, views, and ideas on any topic in different formats out loud. This cr...

BIG DATA ANALYTICS IN SOCIAL MEDIA SYSTEMATIC REVIEW TOWARDS

AI EMPOWERED RIS-ASSISTED NOMA NETWORKS: DEEP LEARNING OR REINFORCEMENT LEARNING

 A novel reconfigurable intelligent surfaces (RISs)- based transmission framework is proposed for downlink nonorthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and t...

DEEP LEARNING REINFORCEMENT LEARNING

INFERENCE OF BRAIN STATES UNDER ANESTHESIA WITH META LEARNING BASED DEEP LEARNING MODELS

 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...

DEEP LEARNING META LEARNING BASED

PREDICTION OF 12 PHOTONIC CRYSTAL FIBER OPTICAL PROPERTIES USING MLP IN DEEP LEARNING

 Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and conf...

DEEP LEARNING PHOTONIC CRYSTAL FIBER OPTICAL

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 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 ...

DEEP LEARNING REINFORCEMENT BASED Q-LEARNING

SEMANTICS OF DATA MINING SERVICES IN CLOUD COMPUTING

 The recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers is empowering users with extremely comprehensive data analysis tools including all the advantages of this type of environment. Providers of Cloud Computing services for Data Mining publish the descriptions and definitions in many formats and o...

PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK

 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...

HUMAN STRESS DETECTION AND PREDICTION USING ANN

 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...

ENERGY CONSUMPTION PREDICTION BY USING MACHINE LEARNING FOR SMART BUILDING

 Building Energy Management System (BEMS) has been a substantial topic nowadays due to its importance in reducing energy wastage. However, the performance of one of BEMS applications which is energy consumption prediction has been stagnant due to problems such as low prediction accuracy. Thus, this research aims to address the problems by developing...

DRIVER DROWSINESS DETECTION USING MACHINE LEARNING IMAGE PROCESSING

 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...

COVID-19 prediction using CNN Algorithm

 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...

COMPREHENSIVE ANALYSIS OF TWITTER TRENDING

 Twitter is among the most used microblogging and online social networking services. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a "trending topic" or simply “trend”. Twitter trends has shown their powerful ability in many public events, elections and market changes. Nevertheless, there has been ver...

AUTOMATIC FORENSIC SKETCH TO IMAGE GENERATOR USING GAN

 Image processing has been a crucial tool for refining the image or to boost the image. With the improvement of machine learning tools, the image processing task has been simplified to great range. Generating a semantic and photographic face image, from a sketch image or text description has always been an extremely important issue. Sketch images ba...

AI BASED STROKE DISEASES PREDICTION

 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...

ROAD LANE DETECTION USING MACHINE LEARNING

 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...

SENTIMENTAL ANALYSIS USING A CODE-MIX DATASET OF TAMIL-ENGLISH

 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...

PCOS DETECTION AND PREDICTION USING CNN MACHINE LEARNING ALGORITHMS

 Polycystic Ovary Syndrome (PCOS) is a medical condition which causes hormonal disorder in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS majorly suffer from excessive weight gain, facial hair growth, acne, hair loss, skin darkening and irregular periods leading to inferti...

ONLINE PLANT SALES RECOMMENDER SYSTEM USING MACHINE LEARNING

 In today's technical era, every startup or a company attempt to establish a better sort of communication between their Plants and the users, and for that purpose, they require a type of mechanism which can promote their plant effectively, and here the recommender system serves this motive. It is basically a filtering system that tries to predict an...

MACHINE LEARNING-BASED AUTOMATIC CLASSIFICATION OF KNEE OSTEOARTHRITIS SEVERITY USING GAIT DATA AND RADIOGRAPHIC IMAGES

 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...

Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

 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...

BIKE RIDE SAFETY MANAGEMENT SYSTEM

 Motorcycle accidents have been rapidly growing through the years in many countries. In India more than 37 million people use two wheelers. Therefore, it is necessary to develop a system for automatic detection of helmet and triples wearing for road safety. Therefore, a custom object detection model is created using a Machine learning based algorith...

Speech to text conversion for local ethnic languages of MP like Malavi, Bagheli etc

 In present industry, communication is the key element to progress. Passing on information, to the right person, and in the right manner is very important, not just on a corporate level, but also on a personal level. The world is moving towards digitization, so are the means of communication. Phone calls, emails, text messages etc. have become an in...

Predictive analytic solutions using AI

 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...

Vehicle number recognition by using existing general surveillance cameras

 Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from th...

Big Data Searching

 Due to Increase in the amount of data in the field of genomics, meteorology, biology, environmental research and many others, it has become difficult to find, analyse patterns, associations within such large data. Interesting association is to be found in the large data sets to draw the pattern and for knowledge purpose. The benefit of analysing th...

Network Traffic Analyzer

 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...

Artificial Intelligence enabled Agriculture, Food and Public Robotic stacker for mechanized Distribution Loading/Unloading of food grain

 The agriculture sector faces many challenges such as crop diseases, pest infestation, water shortage, weeds and many more. These problems lead to substantial crop loss, economic loss and also causes severe environmental hazards due to the current agriculture practices. The AI and Robotics technologies have the potential to solve these problems effi...

Fishing boat Activity Clustering and Timeline Studying (FACTS)

 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...

EPILEPTIC SEIZURES PREDICTION USING IOT AND MACHINE LEARNING

 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...

EPILEPTIC SEIZURE DETECTION USING IOT

 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...

Feed forward-Cutset-Free Pipelined Multiply-Accumulate Unit for the Machine Learning Accelerator

 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...

Traffic Prediction for Intelligent Transportation System using Machine Learning

  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...

Stress Detection Using GSR and IOT

 Stress is one of the factors that affect human health in many aspects. It is considered as one of the culprits in increasing the risk of getting sick that could probably lead to critical physical or mental illnesses. Stress can be experienced everywhere and in different circumstances. Hence, stress should be controlled and managed by monitoring its...

Seizure detection using wearable sensors and machine learning: Setting a benchmark

 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-...

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensor flow and Comparison of Performance for Various Hidden Layers

  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...

Privacy Preserving Distributed Data Fusion Based On Attribute Protection

 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 ...

Object Detection System with Voice Output using Python

 As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this paper, we proposed an object detection system for the blind using deep learning t...

Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signals characteristics features

 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...

MEDICAL IMAGE ENHANCEMENT USING DEEP LEARNING, NEUTRAL NETWORKS, AUTO ENCODERS

 One common interest in radiography is producing radiographs with as low as possible radiation exposures to patients. In clinical practices, radiation exposure factors are preset for optimal image qualities to avoid underexposures which will lead to repeating examinations hence increasing radiation exposures to patients. Underexposed radiographs mai...

Hash-Based Conditional Privacy Preserving Authentication and Key Exchange Protocol Suitable for Industrial Internet of Things

 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...

EEG Signal Based Seizures Detection using IOT

 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...

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

 With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the del...

Home Outlet and LED Array Lamp Controlled by a Smartphone with a Hand Gesture Recognition

  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...

Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices

 Robust and reliable detection of falls is crucial especially for elderly activity monitoring systems. In this letter, we present a fall detection system using wearable devices, e.g., smartphones, and tablets, equipped with cameras and accelerometers. Since the portable device is worn by the subject, monitoring is not limited to confined areas, and ...

DIAGONSTIC PREDICTION AFTER COVID

 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...

Agriculture prediction analysis system

 Agriculture is the backbone of Indian economy. Due to global warming and climate change traditional farming in the regular months have been distorted and crops have been ruined is the most common phrase seen today. This not only gives economic losses but also the main reason for farmer sucide. Now agriculture needs support, time has come for techno...

Agriculture prediction analysis system

 Agriculture is the backbone of Indian economy. Due to global warming and climate change traditional farming in the regular months have been distorted and crops have been ruined is the most common phrase seen today. This not only gives economic losses but also the main reason for farmer sucide. Now agriculture needs support, time has come for techno...

Spammer Detection in tweeter dataset

 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...

Smart health prediction system

 Machine learning is a powerful technique which can increase the efficiency and accuracy in disease prediction. In current scenario there is a need for efficient machine learning models that can be used in healthcare system to predict the specific diseases by monitoring the patient’s symptoms over a period. But there exist very few studies pertainin...

Credit card fraud detection system

 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...

Rumors dataset filtering suspended account

 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...

Finger print based fraud detuction cloud encryption system

 Cloud computing is a major blooming technology which has numerous applications in today’s market and is rightly so hyped. Images are a major part of today’s internet data traffic, especially due to widespread social media, and hence, its security is crucial. However, in the present scenario, the images in cloud are a major issue in terms of securit...

Privacy Preserving Distributed Data Fusion Based On Attribute Protection

  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...

An Incentive Mechanism for Federated Learning in Wireless Cellular network An Auction Approach

 Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we...

Data-Driven Beam Management with Angular Domain Information for mmWave UAV Networks

 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...

Rate-Adapted Decentralized Learning Over Wireless Networks

 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 ...

Mega Satellite Constellation System Optimization From Network Control Structure Perspective

 The network control plays a vital role in the mega satellite constellation (MSC) to coordinate massive network nodes to ensure the effectiveness and reliability of operations and services for future space wireless communications networks. One of the critical issues in satellite network control is how to design an optimal network control structure (...

Improved Deep Convolutional Neural Network based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

 Wireless Sensors Networks (WSN) is the self-configured wireless network which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes' (MNs') presence. Thus, to shun security threats, it is...

Energy-Efficient Secure Video Streaming in UAV-Enabled Wireless Networks A Safe-DQN Approach

 Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile nodes, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the qual...

Learning Autonomy in Management of Wireless Random Networks

 This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distri...

PPSF A Privacy-Preserving and Secure Framework using Blockchain-based Machine-Learning for IoT-driven Smart Cities

 With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and in...

Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm

 This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its...

A Two-Stage Feature Transformation-Based Fingerprint Authentication System for Privacy Protection in IoT

 Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., s...

A Transfer Learning Approach for Securing Resource-Constrained IoT Devices

 In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order t...

Towards Blockchain for Suitable Efficiency and Data Integrity of IoT Ecosystem Transactions

 This article analyzes the effectiveness of deploying the Blockchain technology in the implementation of the IoT ecosystem database. To this end, we assess the processing efficiency of transactions originated by smart devices and the storeddata integrity. The processing-efficiency evaluation is carried out through queue-theory-based analytical model...

Energy-Aware Real-time Tasks Processing for FPGA Based Heterogeneous Cloud

 Cloud computing is becoming an popular model of computing. Due to the increasing complexity of the cloud service requests, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, ...

Resource Management and Pricing for Cloud Computing based Mobile Blockchain with Pooling

 In a public blockchain system applying Proof of Work (PoW), the participants need to compete with their computing resources for reward, which is challenging for resource-limited devices. Mobile blockchain is proposed to facilitate the application of blockchain for mobile service, in which the lightweight devices can participate mining by renting re...

Optimal Task Allocation and Coding Design for Secure Edge Computing with Heterogeneous Edge Devices

 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...

Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence

 This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial comp...

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin

 Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper pre...

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition

 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....

When Does Diversity Help Generalization in Classification Ensembles

 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, ...

Progressive Self-Supervised Clustering With Novel Category Discovery

 These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel ...

Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints

 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...

A Data-Driven Modeling Method for Stochastic Nonlinear Degradation Process With Application to RUL Estimation

 This article proposes a novel modeling method for the stochastic nonlinear degradation process by using the relevance vector machine (RVM), which can describe the nonlinearity of degradation process more flexibly and accurately. Compared with the existing methods, where degradation processes are modeled as the Wiener process with a nonlinear drift ...

Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data

 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...

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification

 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 ...

Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer

 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...

Type-2 Fuzzy Broad Learning System

 The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a grou...

Federated Continuous Learning With Broad Network Architecture

 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 ...

A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces

 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...

Predictive Analytics for Default of Credit Card Clients

 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...

Neighborhood Preserving and Weighted Subspace Learning Method for Drift Compensation in Gas Sensor

 This article presents a novel discriminative subspace-learning-based unsupervised domain adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning approaches assume that the gas sensor data follow a certain distribution such as Gaussian, which often does not exist in real-world applications. In this article, we addres...

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces

 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...

An Input Weights Dependent Complex-Valued Learning Algorithm Based on Wirtinger Calculus

 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...

Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps

  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...

Orchestration or Automation Authentication Flaw Detection in Android Apps

 Passwords are pervasively used to authenticate users' identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/T...

Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT

 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...

Voltage Over-scaling-based Lightweight Authentication for IoT Security

  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...

A Privacy-Preserving Federated Learning for Multiparty Data Sharing in Social IoTs

 As 5G and mobile computing are growing rapidly, deep learning services in the Social Computing and Social Internet of Things (IoT) have enriched our lives over the past few years. Mobile devices and IoT devices with computing capabilities can join social computing anytime and anywhere. Federated learning allows for the full use of decentralized tra...

The Use of Blockchain to Support Distributed AI Implementation in IoT Systems

 This paper presents a distributed and decentralized architecture for the implementation of Distributed Artificial Intelligence (DAI) using hardware platforms provided by the Internet of Things (IoT). A trained DAI system has been implemented over the IoT, where each IoT device acts as one or more of the neurons within the DAI layers. This is accomp...

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers - Artificial Intelligence to Heuristics

 Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum ar...

IFC-BD An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

 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,...

Applications of Artificial Intelligence on the Modeling and Optimization for Analog and Mixed-Signal Circuits A Review

 Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains...

Computational Cost Analysis and Data-Driven Predictive Modeling of Cloud-based Online NILM Algorithm

 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...

Machine Learning for On-the-Fly Reliability-Aware Cell Library Characterization

  Aging-induced degradation imposes a major challenge to the designer when estimating timing guardbands. This problem increases as traditional worst-case corners bring over-pessimism to designers, exacerbating competitive and close-to-the-edge designs. In this work, we present an accurate machine learning approach for aging-aware cell library cha...

Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning

 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...

MLDEG A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation

 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...

An Adaptive Machine Learning Framework for Behind-the-Meter LoadPV Disaggregation

 A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total loa...

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-shot Fault Diagnosis of Machines

 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...

Friend-as-Learner Socially-Driven Trustworthy and Efficient Wireless Federated Edge Learning

 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...

Adaptive Configuration of Service-Based Smart Sensors in Edge Networks

 Edge computing promises to facilitate the collaboration of smart sensors at the network edge, in order to satisfying the delay constraints of certain requests, and decreasing the transmission of large-volume sensory data from the edge to the cloud. Generally, the functionalities provided by smart sensors are encapsulated as services, and the satisf...

A Damped Decoupled XY Nanopositioning Stage Embedding Graded Local Resonators

 Lightly damping linear dynamic characteristics of a flexure-based stage mainly limit control bandwidth when its piezo-actuating hysteresis is well compensated. In this paper, a novel damped decoupled XY nanopositioning stage embedding graded local resonators (GLRs) is developed and optimized based on the maximization of motion range and first-order...

Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

 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...

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

 In this paper, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems' power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared...

Embedded Feature Selection Based on Relevance Vector Machines With an Approximated Marginal Likelihood and Its Industrial Application

  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...

Context-aware Service Recommendation based on Knowledge Graph Embedding

 As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and con...

GCN2CDD A Commercial District Discovery Framework via Embedding Space Clustering on Graph Convolution Networks

 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 ...

Searching for Critical Power System Cascading Failures with Graph Convolutional Network

 Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. We propose a more efficient search framework ...

Fast Power System Event Identification using Enhanced LSTM Network with Renewable Energy Integration

  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...

WELFake Word Embedding Over Linguistic Features for Fake News Detection

 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...

PINE Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions

 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 ...

LYRIC Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud

 With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Process...

Toward Automated Classroom Observation Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate

 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...

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

 In this paper, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems' power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared...

Machine Learning-Enabled Joint Antenna Selection and Precoding Design From Offline Complexity to Online Performance

 We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <...

WiDE WiFi Distance based Group Profiling Via Machine Learning

 We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-person distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mo...

Health Index Prediction of Overhead Transmission Lines A Machine Learning Approach

 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...

Analysis of Single Event Transients (SET) using Machine Learning (ML) and Ionizing Radiation Effects Spectroscopy (IRES)

 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...

A Machine Learning-Assisted Model for GaN Ohmic Contacts Regarding the Fabrication Processes

 Gallium nitride (GaN) devices have been successfully commercialized due to their superior performance, especially their high-power transformation efficiency. To further reduce the power consumption of these devices, the optimization for the ohmic contacts is attracting more and more attention. In the light of the mature and powerful machine learnin...

A Cyber-Physical Anomaly Detection for Wide-Area Protection using Machine Learning

 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...

Perspectives on Machine Learning-assisted Plasma Medicine Towards Automated Plasma Treatment

 Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications....

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

  Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The ove...

IPvest Clustering the IP traffic of network entities hidden behind a single IP address using machine learning

 IP Networks serve a variety of connected network entities (NEs) such as personal computers, servers, mobile devices, virtual machines, hosted containers, etc. The growth in the number of NEs and technical considerations has led to a reality where a single IP address is used by multiple NEs. A typical example is a home router using Network Address T...

Applying a random projection algorithm to optimize machine learning model for breast lesion classification

 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...

A Machine Learning Resource Allocation Solution to Improve Video Quality in Remote Education

 The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pres...

A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

 The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations.It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the t...

VeriML Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service

 Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS...

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum with Machine Learning

  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...

Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Arrays

 An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimizat...

A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment

 Dynamic Security Assessment for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real time ope...

Machine Learning for Automating the Design of Millimeter-Wave Baluns

 We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods.From a generalized physical model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-...

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification

 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...

Coordinates-based Resource Allocation Through Supervised Machine Learning

 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 ...

Assessment of Machine Learning Models for Classification of Movement Patterns During a Weight-Shifting Exergame

 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...

An Intrusion Detection Method Based on Machine Learning and State Observer for Train-Ground Communication Systems

 The communication-based train control (CBTC) system is a typical cyber physical system in urban rail transit.The train-ground communication system is a very important subsystem of the CBTC system and uses the wireless communication protocols to transmit control commands.However, it faces some potential information security risks.To ensure informati...

Adaptation Strategies for Automated Machine Learning on Evolving Data

  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 ...

IoT BASED SMART FARMING USING MACHINE LEARNING

 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...

Multi objective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

 Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncert...

Machine Learning Assisted Stochastic Unit Commitment during Hurricanes with Predictable Line Outages

 Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties.An example is an operation during a predicted hurricane with uncertain line out-ages.However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging.This paper evaluates some pos...

Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors

 Automated Machine Learning (AutoML) has achieved remarkable progress on various tasks, which is attributed to its minimal involvement of manual feature and model designs. However, existing AutoML pipelines only touch parts of the full machine learning pipeline, e.g., Neural Architecture Search or optimizer selection.This leaves potentially importan...

An Adaptive Machine Learning Framework for Behind-the-Meter Load PV Disaggregation

 A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total loa...

Machine Learning Exercises on One Dimensional Electromagnetic Inversion

 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 electromagnetic prob...

Evaluating User and Machine Learning in Short-and Long-term Pattern Recognition-based Myoelectric Control

  ? 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...

Dynamic Quaternion Extreme Learning Machine

 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 ...

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

 Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes ...

A Survey on Curriculum Learning

 ? 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...

A Multiple Gradient Descent Design for Multi-task Learning on Edge Computing Multi-objective Machine Learning Approach

 Multi-task learning technique is widely utilized in machine learning modeling where commonalities and differences across multiple tasks are exploited. However, multiple conflicting objectives often occur in multi-task learning. Conventionally, a common compromise is to minimize the weighted sum of multiple objectives which may be invalid if the obj...

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning

 ? 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...

On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images a Survey

 Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapi...

Minimizing Training Time of Distributed Machine Learning by Reducing Data Communication

 ? Due to the additive property of most machine learning objective functions, the training can be distributed to multiple machines. ? Distributed machine learning is an efficient way to deal with the rapid growth of data volume at the cost of extra inter-machine communication. ? One common implementation is the parameter server system which cont...

Machine Learning Exercises on One Dimensional Electromagnetic Inversion

 ? 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...

Machine Learning Based Detection Method for Wafer Test Induced Defects

 ? Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies. ? In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield. ? However, the wafer test itself may induce defects to otherwise good dies. ? Test-induced defects not only hurt overall manufact...

SIMULATION OF AUTONOMOUS FLIGHT CONTROL SYSTEM

 Using various sensors like the accelerometer, GPS module, 3 Axis gyroscope, compass and an ultrasonic range finder, the primary guidance and navigation functions of the UAV can be automated. Additional stabilization controls should be programmed into the Raspberry Pi which is serving as the heart of the System which will provide additional security...

A Study on E-commerce Recommender System Based on Big Data

 Recommender system algorithms are widely used in e-commerce to provide personalized and more accurate recommendations to online users and enhance the sales and user stickiness of e-commerce. This paper discusses several recommendation algorithms and the challenge of tradition recommender system in big data situation, and then proposes a framework o...

Semantics of Data Mining Services in Cloud Computing

 The recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers is empowering users with extremely comprehensive data analysis tools including all the advantages of this type of environment. Providers of Cloud Computing services for Data Mining publish the descriptions and definitions in many formats and o...

Research on Intrusion Data Mining Algorithm Based on Multiple Minimum Support

 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...

Representation Learning from Limited Educational Data with Crowd sourced Labels

 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...

Deep Learning for Spatio-Temporal Data Mining: A Survey

 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...

Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous BigData Recommendations

 The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterog...

Data Representation by Joint Hyper graph Embedding and Sparse Coding

 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...

Data Mining Based Model Simplification and Optimization of An Electrical Power Generation System

 To assess the performance of electrification in an aircraft, multi-physics modeling becomes a good choice for the design of more-electric equipment. The high computational cost and huge design space of this complex model lead to difficulties in the optimal design of the electrical power system, thus, model simplification is mandatory. This paper f...

A NEW IMPULSE ON STROKE PATIENT HEALTH CARE USING DEEP LEARNING (STROKE PREDICTION)

 Many predictive techniques have been widely applied in clinical decision Making such as predicting occurrence of a disease or diagnosis, evaluating Prognosis or outcome of diseases and assisting clinicians to recommend Treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the...

Hash Based Conditional Privacy phishing In WSN

 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...

Classification of Kidney Images Using Cuckoo Search Algorithm and Artificial Neural Network

 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...

Electrical energy consumption prediction using machine learning for industry and smart buildings

 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 ...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 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...

computerized classification of CT lung images using CNN with watershed segmentation

 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...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 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...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 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...

Advanced Skin Diseases Diagnosis leveraging image Processing

 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...

Advanced Skin Diseases Diagnosis leveraging image Processing

 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...

Road accident analysis and prediction of accident severity by using machine learning

 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...

Automatic detection of tuberculosis related abnormalities in Chest X-ray Images using hierarchical feature extraction scheme

 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...

A Proposed Solution for Sentiment Analysis on Tweets to Extract Emotions from Ambiguous Statements

 With the advancementofweb technology and its growth, there is huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideasandsharing opinions. Social networkingsiteslike Twitter, Face book, Google are rapidly gaining popularity as they allow people...

Fault Injection Analytics A Novel Approach to Discover Failure Modes in Cloud Computing Systems

 Cloud computing systems fail in complex and unexpected ways, due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing t...

Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud

 The remarkable advances of Machine Learning (ML) have spurred an increasing demand for ML-as-a-Service on public cloud: developers train and publish ML models as online services to provide low-latency inference for dynamic queries. The primary challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference wo...

Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning

 Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this...

Concurrent Healthcare Data Processing and Storage Framework using Deep Learning in Distributed Cloud Computing Environment

 Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this manuscri...

Architectural Design of Cloud Applications: a Performance-aware Cost Minimization Approach

 Cloud Computing has assumed a relevant role in the ICT, profoundly influencing the life-cycle of modern applications in the manner they are designed, developed, deployed and operated. In this paper, we tackle the problem of supporting the design-time analysis of Cloud applications to identify a cost-optimized strategy for allocating components onto...