Hand gesture recognition is one of the most effective modes interaction between humans and computers due to being highly flexible user-friendly. A real-time hand system should aim develop a user-independent interface with high performance. Nowadays, convolutional neural networks (CNNs) show rates in image classification problems. Due unavailability large labeled samples static images, it challenging task train deep CNN such as AlexNet, VGG-16 ResNet from scratch. Therefore, inspired by...

Kidney stone disease is a serious public health concern that getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney also called renal calculus, hard buildup of urine minerals form the kidneys. Computed tomography (CT) one imaging models used to identify stones by clinical experts. Due low resolution these images, sometimes detecting tedious naked eye, which may lead false alarms. In this work, computer-based diagnosis system deep learning technique...

An electrocardiogram (ECG) is a unique representation of person’s identity, similar to fingerprints, and its rhythm shape are completely different from person person. Cloning tampering with ECG-based biometric systems very difficult. So, ECG signals have been used successfully in number recognition applications where security top priority. The major challenges the existing literature (i) noise components signals, (ii) inability automatically extract feature set, (iii) performance system....

In machine learning, an efficient classifier model design is mostly based on effective feature extraction and appropriate selection. This work mainly focused different optimized selection algorithms for automatic biometric recognition system with the use of electrocardiogram (ECG) signals. Initially, features are extracted from P-QRS-T segments ECG signal position normalization. The processed through optimization quality assessment prior to classification stage. this work, two methods...

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is bioelectrical signal that helps monitor the heart's electrical activity. It can provide health information about normal and abnormal physiology heart. Early diagnosis critical patients to avoid stroke or sudden death. main aim this detect crucial beats damage functioning Initially, modified Pan–Tompkins algorithm identifies characteristic points,...

Electrocardiogram (ECG) signals carried important clinical information in the form of intervals and amplitude or morphology. Therefore, it is very to identify these fiducial points effectively. The major limitations existing semi- fully automatic ECG beat classification systems are extensive data requirements low performance. This letter proposes a modified 1-D U-Net architecture find exact locations waves signal. proposed network utilized convolutions instead 2-D, which different from...

The increasing prevalence of mental disorders among youth worldwide is one society's most pressing issues. proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the neurological disorders. This work draws upon analysis Cities Health Initiative dataset. It employs advanced machine learning deep techniques, integrated with data science, statistics, optimization, mathematical modeling, to correlate various lifestyle environmental factors...

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. automated diagnostic tool required to detect significant episodes long-term ECG records. It a very challenging task for cardiologists analyze records short time. Therefore, computer-based diagnosis identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are diseases occur when coronary artery...

The rising risk of diabetes, particularly in emerging countries, highlights the importance early detection. Manual prediction can be a challenging task, leading to need for automatic approaches. major challenge with biomedical datasets is data scarcity. Biomedical often difficult obtain large quantities, which limit ability train deep learning models effectively. noisy and inconsistent, make it accurate models. To overcome above-mentioned challenges, this work presents new framework modeling...

Breast cancer is the most prevalent among women and poses a significant global health challenge due to its association with uncontrolled cell proliferation. Artificial intelligence (AI) integration into medical practice has shown promise in boosting diagnosis accuracy treatment protocol optimisation, thus contributing improved survival rates globally. This paper presents comprehensive analysis utilizing Wisconsin Cancer dataset, comprising data from 569 patients 30 attributes. We propose...

At present, people spend most of their time in passive rather than active mode. Sitting with computers for a long may lead to unhealthy conditions like shoulder pain, numbness, headache, etc. To overcome this problem, human posture should be changed particular intervals time. This paper deals using an inertial sensor built the smartphone and can used sitting behaviors (HSBs) office worker. monitor, six volunteers are considered within age band 26 ± 3 years, out which four were male two...

Electrocardiogram (ECG)-based biometric systems are popular due to their uniqueness and tamper-resistance. The author has designed a digital signal-based deep learning technique for person identification using the ECG signal, in this letter. temporal variations very high traditional signal. Hence, processing computational costs nonstationary converted analogue signal into quantiszed representation minimize variations. These quantized representations transformed 128×128 images utilized as...

The internet has become an indispensable tool for organizations, permeating every facet of their operations. Virtually all companies leverage Internet services diverse purposes, including the digital storage data in databases and cloud platforms. Furthermore, rising demand software applications led to a widespread shift toward computer-based activities within corporate landscape. However, this transformation exposed information technology (IT) infrastructures these organizations heightened...

Electroencephalogram (EEG) is used to analyze the state of brain. One critical states brain drowsiness. Physical, mental tiredness, and unconsciousness are some reasons for Drowsiness may lead fatal crashes, severe injury, property damage; sometimes, it can be analyzed detected by using EEG. Analyzing EEG signals complicated tedious, so an automated diagnosis required interpret these effectively. In recent years, finding drowsy feeling while working has become important research area. this...

This paper explored the integration of machine learning into healthcare has revolutionized early disease detection, offering a multidimensional approach to data analysis. Advanced algorithms, rooted in deep learning, process diverse datasets encompassing medical records, genetics, and imaging data, enabling subtle pattern detection. Deep predictive analytics, natural language processing, anomaly personalized medicine have ushered proactive era, leading better patient outcomes, reduced...

Obstructive sleep apnea (OSA) is a long-term disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) the technique for monitoring different signals during patient's cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to high cost inconvenience of polysomnography, usefulness ECG detecting OSA explored this work, which proposes two-dimensional convolutional neural network (2D-CNN)...