Detection and classification of brain tumor using hybrid deep learning models

 Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutioniz...

brain tumor brain tumor detection hybrid deep learning models tumor analysis using CNN classification of brain tumor

Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization

 Diagnosing a brain tumor takes a long time and relies heavily on the radiologist’s abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifyin...

Brain Tumor Detection Tumor Detection Grey Wolf Optimization Brain Disease Detection Disease Detection

An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning

 The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, whi...

Remove noise semi-supervised learning Deep learning machine learning GlobalNet

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...

Deep Learning Covid-19 outbreak

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

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

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...

DEEP LEARNING LARGE-SCALE DEEP LEARNING

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

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, ...

DEEP LEARNING FEATURES IN DEEP LEARNING

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

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...

DEEP LEARNING SYSTEM GPU-TRAINED

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...

DEEP LEARNING MODULARIZING 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

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...

DEEP LEARNING SEISMIC DATA USING MODEL-BASED

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...

DEEP LEARNING IPHOSS(DEEP)-PSEAAC

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

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...

DEEP LEARNING MEDICAL IMAGE ENCRYPTION AND DECRYPTION

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...

DEEP LEARNING A SURVEY

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...

DEEP LEARNING A SURVEY AND OUTLOOK

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...

DEEP LEARNING COMPRESSION TECHNIQUES

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 ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

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 ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

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

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, ...

DEEP LEARNING FEATURES

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

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...

DEEP LEARNING LARGE-SCALE

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 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...

DEEP LEARNING VIDEO PREDICTION

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

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...

DEEP LEARNING COMPRESSION TECHNIQUES

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...

DEEP LEARNING COMPRESSION TECHNIQUES

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...

DEEP LEARNING PERSON RE-IDENTIFICATION

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...

DEEP LEARNING NUCLEAR INDUSTRY

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

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...

DEEP LEARNING GENERAL PSEUDO AMINO ACID COMPOSITIONS

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...

DEEP LEARNING VIA PAIRWISE LEARNING

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...

DEEP LEARNING POLLEN GRAINS

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

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...

Deep Learning Video Prediction

Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

 This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial point...

WEAKLY SUPERVISED DEEP LEARNING FOR CUSTOMER REVIEW SENTIMENT ANALYSIS

 In recent times the concept of smart cities have gained grate popularity. The ever increasing population has led to chaotic city traffic. As a result of the process of searching a parking lot becomes tedious. It is time consuming task leading to discomfort. The fuel consumption is on an increasing side due to such scenarios. The increase in vehicul...

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...

EARLY DETECTION OF MALPOSITIONED CATHETERS AND LINES ON CHEST X-RAYS USING DEEP LEARNING

 Hospital patients can have catheters and lines inserted during the course of their admission and serious complications can arise if they are positioned incorrectly. Early recognition of malpositioned tubes is the key to preventing risky complications (even death), even more so now that millions of COVID-19 patients are in the need of these tubes an...

Quantum Neural Networks for Resource Allocation in Wireless Communications

 This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning-inspired QNN (RL-QNN) is presented to improve the performance. Quantum circuit design of the QNN is presented to ensure the practi...

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...

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...

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...

Boundary Net Extraction and Completion of Road Boundaries With Deep Learning Using Mobile Laser Scanning Point Clouds and Satellite Imagery

 Robust road boundary extraction and completion play an important role in providing guidance to all road users and supporting high-definition (HD) maps. The significant challenges remain in remarkable and accurate road boundary recovery from poor road boundary conditions. This paper presents a novel deep learning framework, named BoundaryNet, to ext...

Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations

 With the increasing demand of users for personalized social services, social recommendation (SR) has been an important concern in academia. However, current research on SR universally faces two main challenges. On the one hand, SR lacks the considerable ability of robust online data management. On the other hand, SR fails to take the ambiguity of p...

BoundaryNet Extraction and Completion of Road Boundaries With Deep Learning Using Mobile Laser Scanning Point Clouds and Satellite Imagery

  Robust road boundary extraction and completion play an important role in providing guidance to all road users and supporting high-definition (HD) maps. The significant challenges remain in remarkable and accurate road boundary recovery from poor road boundary conditions. This paper presents a novel deep learning framework, named BoundaryNet, to...

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...

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...

Effective Similarity Search on Heterogeneous Networks A Meta-path Free Approach

 Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. In contrast, graphs that have a single type of nodes and edges, are often called homogeneous graphs. Measuring similarities among objects is an important task in data mining applications, such as web search, link prediction...

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...

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...

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

 Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sono graphy (L...

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...

A Dynamic Deep-learning-based Virtual Edge Node Placement Scheme for Edge Cloud Systems in Mobile Environment

 Edge node placement is a key topic to edge cloud systems for that it affects their service performances significantly. Previous solutions based on the existing information are not suitable for the mobile environment due to the mobility and random Internet access of end users. In this paper, we propose a dynamic virtual edge node placement scheme, i...