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...
|
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...
|
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...
|
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...
|
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 ...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
The present study aims to elucidate the main variables that increase the level of depression at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model.
Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the tr...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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, ...
|
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...
|
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...
|
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...
|
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 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...
|
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, ...
|
In mental health assessment, it is validated that nonverbal cues like facial expressions can be indicative of depressive disorders. Recently, the multimodal fusion of facial appearance and dynamics based on convolutional neural networks has demonstrated encouraging performance in depression analysis. However, correlation and complementarity between...
|
Monitoringthe depth of unconsciousnessduring anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram(EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anestheti...
|
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...
|
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...
|
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 ...
|
The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training a...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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 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...
|
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...
|
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 ...
|
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...
|
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...
|
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....
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
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...
|
Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensors networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broad...
|
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...
|
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...
|
Most recent semi-supervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, the online fine-tuning process is usually time-consuming, limiting the practical use of such methods. We propose a directional dee...
|
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...
|
In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS [1] observation protocol that is widely used in educational research. ACORN uses convolutional neural networks to analyze spectral audio feat...
|
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 <...
|
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...
|
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...
|
Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization based ...
|
? 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...
|
? Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies.
? In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield.
? However, the wafer test itself may induce defects to otherwise good dies.
? Test-induced defects not only hurt overall manufact...
|
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...
|
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...
|
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 (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...
|
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...
|
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 ...
|
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...
|