An Interpretable and Accurate Deep-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...

DNNs CNN DP Machine Learning

Predicting Market Performance Using Machine and Deep Learning Techniques

 Today, forecasting the stock market has been one of the most challenging issues for the ‘‘artificial intelligence’’ AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market pred...

Predicting Market Performance Deep Learning Stock Market Prediction Predicting Economic Trends Forecasting Stock Market Prices

Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

 Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literatu...

Credit Card Fraud Detection Deep Learning for Credit Card Fraud Detection Deep Learning Artificial intelligent machine learning

Automatic detection of students’ engagement during online learning: A bagging ensemble deep learning approach

 The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a challenge lies in detecting student engagement during online learning due to limited interactio...

students engagement online learning deep learning Student online learning system online class training

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for FineGrained Heterogeneity Activity Recognition

 This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight int...

Healthcare Elderly Activity Recognition Deep Learning Heterogeneity Activity Recognition machine learning

DeepDiabetic An Identification System of Diabetic Eye Diseases Using Deep Neural Networks

 Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vess...

diabetic retinopathy using python resnet50 diabetic retinopathy using resnet50 diabetic retinopathy using CNN Artificial intelligent machine learning

Breast cancer detection using Resnet50

 Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the logistic regression, svm and random forest algorithms in breast can...

Breast cancer Breast cancer detection Resnet50 InceptionresnetV2 ResNet50-Based Effective Model for Breast Cancer

Development and Optimization of Al model for Feature identification/ Extraction from drone orthophotos

 Background 1. The hon’ble prime minister launched the svamitva scheme on the national panchayati raj day, 24th april 2020 with a resolve to enable the economic progress of rural india by providing “record of rights” to every rural household owner. The scheme aims to demarcate inhabited (abadi) land in rural areas through the latest surveying dron...

AI Model for Feature Identification/Extraction from Drone object based approach for image feature extraction OBJECT BASED APPROACH FOR IMAGE FEATURE Aerial orthoimage generation for UAV remote sensing AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED

Enhancing body detection in CSSR Operations Using Advanced Technology

 Background: During CSSR (Collapsed Structure Search and Rescue) operations, NDRF teams encounter challenges in identifying buried deceased bodies amidst rubble and debris. Traditional search methods are often time-consuming and labor-intensive, hampering the timely recovery of victims and increasing the risk of further casualties. Description: THe ...

A device to detect human presence amidst rubble Human Body Detection in Search and Rescue Operation Enhancing Body Detection in CSSR Operations enhancing body detection in cssr operation Programming Language

Development of AI-ML based models for predicting prices of agri-horticultural commodities such as pulses and vegetable (onion, potato, onion)

 The Department of Consumer Affairs monitors the daily prices of 22 essential food commodities through 550 price reporting centres across the country. The Department also maintains buffer stock of pulses, viz., gram, tur, urad, moon and masur, and onion for strategic market interventions to stabilize the volatility in prices. Decisions for market ...

AI-ML based models for predicting prices of agri-horticultural commodities Machine learning techniques for forecasting agricultural Machine Learning for Price Prediction for Agricultural AI & ML revolutionizing crop price prediction in agriculture Unlocking the Power of AI-ML for Agri-Commodity Prices

AI-Driven Crop Disease Prediction and Management System

 Background: Crop diseases can devastate yields, leading to significant financial losses for farmers. Early detection and timely intervention are crucial for effective management. Description: Develop an AI-driven system that analyzes crop images and environmental data to predict potential disease outbreaks. This system will provide farmers with a...

AI-Driven Crop Disease Prediction Crop Disease Detection with AI Revolutionizing agriculture with artificial intelligence AI-Powered Predictive Analysis for Pest and Disease plant disease detection methods

Analysis of Stock Market Prediction

 Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very ...

Stock Market Stock Market Prediction Long Short Term Memory Convolution Neural Networks Mean Absolute Error

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

Credit Card Fraud Detection Using Machine Learning with Python

 Online transactions have become a significant and crucial aspect of our lives in recent years. It's critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren't charged for things they didn't buy. The number of fraudulent transactions is rapidly increasing as the frequency of transactions increa...

Credit Card Fraud Detection Credit Card Fraud Python Credit Card Fraud CNN Atm Fraud Detection Disease Detection

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

Brain Tumor Detection and Classification Using Artificial Intelligence

 Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of...

Brain Tumor Detection Classification Using Artificial Intelligence Tumor Detection Using CNN Brain Disease Analysis Disease Detection

Optical Character Recognition System (OCR) using machine learning

 OCR is used to identify the character from human written text. To recognize the text segmentation of character is important stage. So here, we addressed different techniques to recognize the character. This document also presents comparison of different languages for character and numeral recognition with its accuracy achieved by different writer...

Context-Aware Recognition Noise Handling OCR Optical Character Recognition System Deep learning

Tumor Detection using classification – Machine Learning and Python

 Brain tumor detection is a critical application in the field of medical imaging, aimed at aiding healthcare professionals in the early and accurate diagnosis of brain tumors. This project leverages machine learning and deep learning techniques in Python to developa robust and reliable brain tumor detection system. The system undergoes sensitivi...

Tumor detection brain tumor detection magnetic resonance imaging (MRI) Support Vector Machines central nervous system

Enhancing Digital Image Forgery Detection Using Transfer Learning

 Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detec...

Digital Image Forgery Transfer Learning MobileNetV2 Convolutional neural network (CNN) ResNet VGG

Disease Detection in coffee plants using convolutional Neural Network

  Rust is a severe disease affecting many productive coffee regions. It is caused by pathogenic fungi that attack the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to th...

Disease Detection coffee plants Convolutional Neural Network

Breast cancer detection using Adaboost classifier

 Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate. But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection a...

Regression Neural Network (GRNN) Probabilistic Neural Network (PNN) Ada Booster Breast cancer convolutional neural network (CNN)

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

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

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

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

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 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 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...

DEEP LEARNING UNSUPERVISED DEEP

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

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

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...

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...

DEEP LEARNING METRIC LEARNING

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

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

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

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...

METRIC LEARNING DEEP JOINT LABEL DISTRIBUTION

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

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

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...

DEEP BACKGROUND MATTING DEEP MATTE PRIOR

IRIS TUMOR DETECTION USING CNN

 Iris tumors, so called intraocular tumors are kind of tumors that start in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris tumor detection system since the available techniques used currently are still not efficient. The combination of the image processing different techniques ...

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...

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...

STRESS DETECTION USING GALVANIC SKIN RESPONSE: AN ANDROID APPLICATION

 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...

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...

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...

Fabric defects detection using convolution neural network and multispectral imaging

 Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite the...

EAD-NET A NOVEL LESION SEGMENTATION METHOD IN DIABETIC RETINOPATHY USING NEURAL NETWORKS

 Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensiv...

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...

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...

Automated Identification of Fish Species (AutoFiS)

 There is a need for automatic systems that can reliably detect, track and classify fish and other marine species in underwater videos without human intervention. Conventional computer vision techniques do not perform well in underwater conditions where the background is complex and the shape and textural features of fish are subtle. Data-driven cla...

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...

VEGETABLE DISEASES DETECTION USING CNN SEQUENTIAL NEURAL NETWORKS

 India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examinat...

Towards End-to-End Lane Detection: an Instance Segmentation approach

 Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a ...

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...

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...

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...

EEG based Epileptic Seizures Detection using Intrinsic Time-Scale Decomposition

 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 ...

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...

Review of Automatic Detection and control of Disease for Grape Field

 This project gives the idea about Detection of various disease on grape field. Also provide the information about how to control this disease. India exported so many tons of grape every year. So, Grape played vital role in economic condition of country. But, because of disease the Grape quality is decrease so that we cannot exported this grape in f...

A Wearable Device for Continuous Detection and Screening of Epilepsy during Daily Life

 Imbalance in the nervous system causes Epilepsy disease which may lead to death. The most common symptoms of epilepsy disease are sudden fluctuations in heart beat rate and involuntary muscular movements (seizures).The wireless electronic diagnosing system used here is meant for epilepsy patients only. Occurrence of seizures predicated by this syst...

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...

AI-Assisted Energy-Efficient and Intelligent Routing for Reconfigurable Wireless Networks

 Intelligent network management for reconfigurable wireless networks such as 5G and beyond applications is crucial for many industrial applications, and has been the subject of ongoing research. This paper proposes an Artificial Intelligence(AI)-Assisted energy-efficient and intelligent routing, based on both energy efficiency prioritization and AI ...

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...

Dynamic Age Minimization with Real-Time Information Preprocessing for Edge-Assisted IoT Devices with Energy Harvesting

 Age of information (AoI) is a newly proposed metric to quantify the freshness of system status. However, in many cases, the original raw data collected by IoT devices needs to be preprocessed in real-time to extract the hidden effective information, which is usually time consuming. To this end, we promote an edge computing assisted approach and aim...

Online-offline Interactive Urban Crowd Flow Prediction toward IoT-based Smart City

 Urban crowd flow prediction is very challenging for public management and planning in smart city applications. Existing work mostly focuses on spatial and temporal dependence based flow prediction that are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents. In this paper, we propose ...

Intelligent Security Performance Prediction for IoT-Enabled Healthcare Networks Using Improved CNN

 The global healthcare industry and artificial intelligence has promoted the development of the diversified intelligent healthcare applications. IoT will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the healthcare big data transmission is vulnerable to a potential att...

A Security and Privacy-Preserving Approach Based on Data Disturbance for Collaborative Edge Computing in Social IoT Systems

 The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people's lives in all directions. The rapid development of the IoT technology accelerates the process of the era of ``Internet of everything'' but also changes the role of terminal equipment at the edge of...

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....

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...

Hyperspectral Image Classification via Discriminant Gabor Ensemble Filter

 For a broad range of applications, hyperspectral image (HSI) classification is a hot topic in remote sensing, and convolutional neural network (CNN)-based methods are drawing increasing attention. However, to train millions of parameters in CNN requires a large number of labeled training samples, which are difficult to collect. A conventional Gabor...

Small Low-Contrast Target Detection Data-Driven Spatiotemporal Feature Fusion and Implementation

 Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the di...

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification

 By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual lea...

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-LIFT Deep Label-Specific Feature Learning for Image Annotation

 Image annotation aims to jointly predict multiple tags for an image. Although significant progress has been achieved, existing approaches usually overlook aligning specific labels and their corresponding regions due to the weak supervised information (i.e., ``bag of labels'' for regions), thus failing to explicitly exploit the discrimination from d...

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...

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...

A Hybrid Deep Network Framework for Android Malware Detection

 Andriod malware poses a serious threat to users privacy, money, equipment and file integrity. A series of data-driven malware detection methods were proposed. However, there exist two key challenges for these methods: (1) how to learn effective feature representation from raw data; (2) how to reduce the dependence on the prior knowledge or human la...

XpulpNN Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes

 Strongly quantized fixed-point arithmetic is now considered a well-established solution to deploy Convolutional Neural Networks (CNNs) on limited-memory low-power IoT endnodes. Such a trend is challenging due to the lack of support for low bitwidth fixed-point instructions in the Instruction Set Architecture (ISA) of state-of-the-art embedded Micro...

Unsupervised Domain Adaptation for Cloud Detection Based on Grouped Features Alignment and Entropy Minimization

 Most convolutional neural network (CNN)-based cloud detection methods are built upon the supervised learning framework that requires a large number of pixel-level labels. However, it is expensive and time-consuming to manually annotate pixelwise labels for massive remote sensing images. To reduce the labeling cost, we propose an unsupervised domain...

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...

SG-GAN Adversarial Self-Attention GCN for Point Cloud Topological Parts Generation

 Point clouds are fundamental in the representation of 3D objects. However, they can also be highly unstructured and irregular. This makes it difficult to directly extend 2D generative models to three-dimensional space. In this paper, we cast the problem of point cloud generation as a topological representation learning problem. To infer the represe...

HSGAN Hierarchical Graph Learning for Point Cloud Generation

 Point clouds are the most general data representations of real and abstract objects, and have a wide variety of applications in many science and engineering fields. Point clouds also provide the most scalable multi-resolution composition for geometric structures. Although point cloud learning has shown remarkable results in shape estimation and sem...

Ground-Based Remote Sensing Cloud Classification via Context Graph Attention Network

 Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer a...

Cloud versus Edge Deployment Strategies of Real-Time Face Recognition Inference

 In this paper, we present a real-world case study on deploying a face recognition application, using MTCNN detector and FaceNet recognizer. We report the challenges faced to decide on the best deployment strategy. We propose three inference architectures for the deployment, including cloud-based, edge-based, and hybrid. Furthermore, we evaluate the...

3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association

 This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds. The proposed tracker is based on a novel data association scheme guided by prediction confidence, and it consists of two key pa...

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...

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...

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...

Open Named Entity Modeling from Embedding Distribution

  In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule...

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-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 <...

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...

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...

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...

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...

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

 The goal of our work is to discover dominant objects in a very general setting where only a single un labeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), whi...

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...

Malaria detection in blood Sample image using Python

 Malaria is one of the deadliest diseases ever exists in this planet. Automated evaluation process can notably decrease the time needed for diagnosis of the disease. This will result in early onset of treatment saving many lives. As it poses a serious global health problem, we approached to develop a model to detect malaria parasite accurately from ...

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...

Fake News Detection with Generated Comments For News Articles

 Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes makes social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diff...

Fake News Detection with Generated Comments For News Articles

 Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes makes social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diff...

Disease Detection in coffee plants using convolutional Neural Network

 Rust is a severe disease affecting many productive coffee regions. It is caused by pathogenic fungi that attack the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to the ...

Performance analysis of brain Tumor image classification using CNN and SVM

 Tumour is the undesired mass in the body. Brain tumour is the significant growth of brain cells. Manual method of classifying is time consuming and can be done at selective diagnostic centers only. Brain tumour classification is crucial task to do since treatment is based on different location and size of it. Magnetic Resonance Imaging (MRI) is mos...

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...