The dynamic hand skeleton data have become increasingly attractive to widely studied for the recognition of gestures that contain 3D coordinates joints. Many researchers been working develop skeleton-based gesture systems using various discriminative spatial-temporal attention features by calculating dependencies between However, these methods may face difficulties in achieving high performance and generalizability due their inefficient features. To overcome challenges, we proposed a...

Sign language recognition (SLR) is one of the crucial applications hand gesture and computer vision research domain. There are many researchers who have been working to develop a gesture-based SLR application for English, Turkey, Arabic, other sign languages. However, few studies conducted on Korean classification because KSL datasets publicly available. In addition, existing work still faces challenges in being efficiently light illumination background complexity major problems this field....

The definition of human-computer interaction (HCI) has changed in the current year because people are interested their various ergonomic devices ways. Many researchers have been working to develop a hand gesture recognition system with kinetic sensor-based dataset, but performance accuracy is not satisfactory. In our work, we proposed multistage spatial attention-based neural network for overcome challenges. We included three stages model where each stage inherited CNN; first apply feature...

Sign Language Recognition (SLR) represents a revolutionary technology aiming to establish communication between deaf and non-deaf communities, surpassing traditional interpreter-based approaches. Existing efforts in automatic sign recognition predominantly rely on hand skeleton joint information, steering clear of image pixels address challenges like partial occlusion redundant backgrounds. Many researchers have been working develop using information instead overcome background problems....

Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. The absence of universal sign language (SL) leads to diverse nationalities having various cultural SLs, such Korean, American, Japanese language. Existing SLR systems perform well for their SL but may struggle with other or multi-cultural languages (McSL). To address these challenges, this paper introduces novel end-to-end system called GmTC, designed translate...

An intrusion detection system collects and analyzes information from different areas within a computer or network to identify possible security threats that include both outside as well inside of the organization. It deals with large amount data, which contains various ir-relevant redundant features results in increased processing time low rate. Therefore, feature selection should be treated an indispensable pre-processing step improve overall performance significantly while mining on huge...

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid this problem, several types intrusion detection methods have been proposed shown different levels accuracy. This why choice effective robust method for IDS very important topic in information security. In work, we built two models classification purpose. One based on Support Vector Machines (SVM)...

Sign language is designed to assist the deaf and hard of hearing community convey messages connect with society. recognition has been an important domain research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due cost-effectiveness approaches, researchers conducted here also despite drop. The purpose this recognize American sign characters using hand images from web camera. In work, media-pipe hands algorithm was used...

Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, Bengali; however, no significant research has been done on general-shape performance datasets. Most work achieved satisfactory with a small dataset. These may fail to replicate same evaluating larger In this context, paper proposes novel method recognizing...

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach very important for early detection classification children with ADHD. However, diagnosing ADHD using full EEG channels all features may lead to computational complexity overfitting problems. To solve these problems, machine learning (ML)-based was designed by identifying optimal its significant features. In this work,...

Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security or video indexing. Face is an important and very challenging technique automatic recognition. Up date, there no that provides a robust solution all situations face may encounter. In general, we can make sure performance system determined by how extract feature vector exactly classify them into group accurately. It, therefore,...

Abstract. Vegetation includes a significant class of terrestrial ecosystem. Information on tree species categorization is important for environmentalists, foresters, agriculturist, urban managers, landscape architects and biodiversity conservationist. The traditional methods measuring identifying (i.e., through field-based survey) are time taking, laborious costly. Remote sensing data provides an opportunity to identify classify vegetation over large spatial extent. Hyperspectral remote can...

Communication between people with disabilities and who do not understand sign language is a growing social need can be tedious task. One of the main functions to communicate each other through hand gestures. Recognition gestures has become an important challenge for recognition language. There are many existing models that produce good accuracy, but if model test rotated or translated images, they may face some difficulties make performance accuracy. To resolve these challenges gesture...

Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy using their electroencephalography (EEG) signals. The included 61 with 60 aged 7–12 years. Different morphological time-domain features were extracted EEG t-test (p-value < 0.05) least absolute shrinkage selection operator...

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose this study to: (i) extract the prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify as either having ADHD or healthy. We extracted data 45,779 aged 3–17 years from 2018–2019 National Survey Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) were ADHD, rest Since class label highly imbalanced, we adopted...

Sign language recognition is crucial for improving communication accessibility the hearing impaired community and reducing dependence on human interpreters. Notably, while significant research efforts have been devoted to many prevalent languages, Korean Language (KSL) remains relatively underexplored, particularly concerning dynamic signs generalizability. The scarcity of KSL datasets has exacerbated this limitation, hindering progress. Furthermore, most predominantly relies static...

Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies focused on improving detection accuracy while neglecting model complexity the deep domain, making them unsuitable for low-power devices....

Attention deficit hyperactivity disorder (ADHD) for children is one of the behavioral disorders that affect brain's ability to control attention, impulsivity, and its prevalence has increased over time. The cure ADHD still unknown only early detection can improve quality life with ADHD. At same time, often suffer from various comorbidities like autism spectrum (ASD), major depressive (MDD), etc. Various researchers developed computational tools detect depending on handwriting text....

Educational Data Mining (EDM) is the process of extracting useful information and knowledge from educational data. EDM identifies patterns trends data, which can be used to improve academic curriculum, teaching assessment methods, students' performance. Thus, this study uses techniques analyze performance higher secondary students in Bangladesh. Three crucial categories, such as good, average, poorly-performing are considered for analysis. Four significant aspects emphasized evaluation...

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan, approximately 360,000 individuals with hearing speech disabilities rely on Japanese Language (JSL) communication.However, existing JSL systems have faced significant performance limitations due to inherent complexities.In response these challenges, we present a novel system that employs strategic fusion approach, combining joint skeleton-based handcrafted...

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, its mortality rate gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these may missed some important in investigation HCC. To solve problems, we have datasets multiple platforms designed a statistical machine learning-based system to determine...

Lung cancer is one of the most common and leading cause cancer-related death worldwide. Early detection lung can help reduce rate; therefore, identification potential biomarkers crucial. Thus, this study aimed to identify for by integrating bioinformatics analysis machine learning (ML)-based approaches. Data were normalized using robust multiarray average method batch effect corrected ComBat method. Differentially expressed genes identified LIMMA approach carcinoma-associated selected...

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as can lead to severe injuries or even fatalities. Additionally, growing incidence among coupled with urgent need prevent suicide attempts resulting from medication overdose, underscores critical importance accurate and efficient methods detecting a fall. This makes computer-aided fall detection system necessary save elderly people's lives worldwide. Many researchers have been working develop systems....