Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays.
The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification.
The heading model construction phase utilizes four modified d...
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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...
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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...
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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...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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ENHANCING REMEMBRANCE OF PASSWORD AS AN IMAGE
This paper centers around changing the process for having 'text' as secret word as individuals will generally frequently fail to remember their passwords and need to recuperate it. Proposing the procedure for having picture as a secret word which is kept encoded in a data set and unscrambled and matched to check for approval of client. It has been ...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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A NOVEL TWO-MODE INTEGRAL APPROACH FOR THERMAL ERROR MODELING IN CNC MILLING-TURNING MACHINING CENTER
Thermal errors have the largest contribution, as much as about 70%, to the machining inaccuracy of computer-numerical-controlled (CNC) machining centers. The error compensation method so far plays the most popular and effective way to minimize the thermal error. How to accurately and quickly build an applicable thermal error model (TEM) is the kern...
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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...
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LAMINAR FLOW WATER TURBINE
The aim of our project is to design and fabricate a pneumatically operated tapping machine is called universal tapping machine. This device is operated by compressed air. It consists of the following main parts.
1. Barrel
2. Shaft
3. bearing
4. Couplings, etc.
A high pressure compressed air is forced on a fan and the fan is made...
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UNIVERSAL TAPPING MACHINE
The aim of our project is to design and fabricate a pneumatically operated tapping machine is called universal tapping machine. This device is operated by compressed air. It consists of the following main parts.
1. Barrel
2. Shaft
3. bearing
4. Couplings, etc.
A high pressure compressed air is forced on a fan and the f...
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WHEEL CHAIR CUM STRETCHER MODEL
The wheel chair cum stretcher model is a mechanism which is used for moving bed up and down. This is used for handicapped person for living like the normal persons do. For making the handicapped person’s job easy (i.e., they can move bed up and down). Two way switches is used to control the flow wheel chair. The Battery used to drive the D.C motor....
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SCALABLE AND PRACTICAL NATURAL GRADIENT FOR LARGE-SCALE DEEP LEARNING
large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization we propo...
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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...
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UNSUPERVISED DEEP BACKGROUND MATTING USING DEEP MATTE PRIOR
Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existi...
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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...
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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 ...
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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 ...
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QUANTIFYING THE ALIGNMENT OF GRAPH AND FEATURES IN DEEP LEARNING
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, ...
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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...
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POLLEN GRAINS CLASSIFICATION WITH A DEEP LEARNING SYSTEM GPU-TRAINED
Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in...
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Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks
With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...
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MODULARIZING DEEP LEARNING VIA PAIRWISE LEARNING WITH KERNELS
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...
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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...
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LEARNING FROM NOISY DATA AN UNSUPERVISED RANDOM DENOISING METHOD FOR SEISMIC DATA USING MODEL-BASED DEEP LEARNING
For the noise removal problem of noisy seismic data, an improved noise reduction technique based on feedforward denoising neural network (DnCNN) is proposed. The previous DnCNN, which was designed to minimise noise in seismic data, had an issue with a large network depth, which hampered training efficiency. The revised DnCNN technique was previousl...
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IPHOSS(DEEP)-PSEAAC: IDENTIFICATION OF PHOSPHOSERINE SITES IN PROTEINS USING DEEP LEARNING ON GENERAL PSEUDO AMINO ACID COMPOSITIONS
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspa...
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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...
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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...
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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...
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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 ...
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FACIAL DEPRESSION RECOGNITION BY DEEP JOINT LABEL DISTRIBUTION AND METRIC LEARNING
In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between...
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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...
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DEEPKEYGEN: A DEEP LEARNING-BASED STREAM CIPHER GENERATOR FOR MEDICAL IMAGE ENCRYPTION AND DECRYPTION
The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients’ medical imaging data. In this paper, a novel deep learningbased key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medi...
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DEEP LEARNING IN NUCLEAR INDUSTRY: A SURVEY
As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy. Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medica...
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DEEP LEARNING FOR PERSON RE-IDENTIFICATION: A SURVEY AND OUTLOOK
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a...
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COMBINING DEEP LEARNING MODEL COMPRESSION TECHNIQUES
In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
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BITCOIN TRANSACTION FORECASTING WITH DEEP.NETWORK REPRESENTATION LEARNING
Bitcoin and its decentralized computing paradigm for digital currency trading are one of the most disruptive technology in the 21st century. This paper presents a novel approach to developing a Bitcoin transaction forecast model, DLForecast, by leveraging deep neural networks for learning Bitcoin transaction network representations. DLForecast make...
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A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING
A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...
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A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING
A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...
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DRIVER DROWSINESS AND ALCOHOL DETECTION WITH CAR TRACKING SYSTEM USING IOT
Drowsiness in driving causes the major road accidents. Now a day’s drowsiness due to drunken driving is increasing. If driver is found to be drowsiness in eyes more than 5 secs, then the eye blink sensor senses the blink rate. If the eyes are found to be closed, then the speed of the car slows down. In our proposed system, along with drowsiness, al...
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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...
|
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...
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A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING
A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING...
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QUANTIFYING THE ALIGNMENT OF GRAPH AND FEATURES IN DEEP LEARNING
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, ...
|
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...
|
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...
|
SCALABLE AND PRACTICAL NATURAL GRADIENT FOR LARGE-SCALE DEEP LEARNING
large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization we propo...
|
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...
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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...
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A REVIEW ON DEEP LEARNING TECHNIQUES FOR VIDEO PREDICTION
The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable...
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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...
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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...
|
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...
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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...
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COMBINING DEEP LEARNING MODEL COMPRESSION TECHNIQUES
In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
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COMBINING DEEP LEARNING MODEL COMPRESSION TECHNIQUES
In this article, we evaluate the performance ofcombining several model compression techniques. The techni-ques assessed were dark knowledge distillation, pruning, andquantization. We use the classification of chest x-rays as ascenario of experimentation. From this scenario, we found thatthe combination of these three techniques yielded a new modelc...
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DEEP LEARNING FOR PERSON RE-IDENTIFICATION: A SURVEY AND OUTLOOK
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a...
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DEEP LEARNING IN NUCLEAR INDUSTRY: A SURVEY
As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy. Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medica...
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FACIAL DEPRESSION RECOGNITION BY DEEP JOINT LABEL DISTRIBUTION AND METRIC LEARNING
In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between...
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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...
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IPHOSS(DEEP)-PSEAAC: IDENTIFICATION OF PHOSPHOSERINE SITES IN PROTEINS USING DEEP LEARNING ON GENERAL PSEUDO AMINO ACID COMPOSITIONS
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspa...
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MODULARIZING DEEP LEARNING VIA PAIRWISE LEARNING WITH KERNELS
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...
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POLLEN GRAINS CLASSIFICATION WITH A DEEP LEARNING SYSTEM GPU-TRAINED
Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in...
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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...
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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 ...
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UNSUPERVISED DEEP BACKGROUND MATTING USING DEEP MATTE PRIOR
Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existi...
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A Review on Deep Learning Techniques for Video Prediction
The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable...
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TRANSMISSION LINE FAULT DETECTION USING IOT
The fault occurred in transmission line is very much dangerous for the locality. In HV and EHV transmission line there are less fault occurrence but in locality the fault occurrence is more as compared to outer transmission line. In our prototype we design a model which is to be detect the fault in transmission line by comparing the voltage signal ...
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Traffic Light Monitoring System based on NodeMCU using Internet of Things
The increasing numbers of the private car cause the road traffic congestion. That is becoming important problems in the big cities. Another solution to reduce the traffic jam is developing intelligent transportation system such as intelligent traffic light system. However, the solution of such problems is one of the mandatory concern. In recent yea...
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SOLAR POWERED DC HOME WITH MPPT& INTEGRATING VOLTAGE CONTROL
The increase in consumption of low power DC appliances in home, accounts to high AC-DC conversion loss. The solar source is inherently a DC source which mainly provides the supply for DC home with low power DC appliances. This paper presents a DC Home model powered by solar source integrated with conventional backup. The power backup is provided by...
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SMART WEARABLE FOR RESCUERS/VICTIMS
In today’s scenario, there are risks at every moment of our life. People rambling around are found insecure, especially at remote outdoor locations where there are no rescue services provided. One of these scenarios is an aquatic environment such as waterfalls, lakes, trekking, remote locations, water parks, swimming pools, beaches, etc. Many lives...
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SMART LPG CYLINDER MONITORING AND EXPLOSION MANAGEMENT SYSTEM
In India, Liquified Petroleum Gas Cylinder is used as the primary source for cooking. The level of Liquified Petroleum Gas (LPG) Cylinder decreases gradually on daily usage. Upon complete utilization of the LPG Cylinder, a new cylinder can be availed by booking with LPG vendors which may take up to two to three days. Users have no provision to view...
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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...
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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...
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