48 results for “Risk Score”

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Research ArticleOpen accessPhrase in the titleDesign and Study of Online Fuzzy Risk Score Analyzer for Diabetes MellitusSunithaKarnam Anantha, Senthil Kumar Natarajan, Subransu Sekar DashThe aim of this study is to determine the risks of various subjects to type 2 Diabetes and its dependence on the different subject records. A Fuzzy based system was designed to find the risk scores for diabetes based on risk score derived from Chennai Urban Rural Epidemiology Study (CURES). The risk score that has been adapted into the system … …American Journal of Applied Sciences, Vol. 10, Issue 9 · 24 Aug 2013 · 6,379 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsOptimization of Coaching Using Six SigmaDumitru Colibaba-Evulet, Andrei EvuletProblem statement: In today’s sports world, coaching and training methods are the subject of intensive competition amongst rivals. Innovative technologies, methodologies and preparation methods are employed to achieve a sustainable competitive advantage. Effective performance measurement, statistical analysis and data based decision taking have been identified as key elements towards efficient performance management. The statistical approach has been emphasized in order … …Journal of Social Sciences, Vol. 7, Issue 3 · 16 Jul 2011 · 8,054 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Dynamically Zero Trust and Risk Aware Cryptographic Orchestration Framework of Secure Multi Cloud Electronic Health RecordsD. Veerabhadra Rao, G. Appa Rao, S. AnuradhaThe adoption of multi-cloud infrastructures for Electronic Health Records (EHRs) improves scalability and accessibility but introduces security challenges such as fragmented trust management, high cryptographic overhead, and inefficient integrity verification. Existing frameworks rely on static authorization and uniform encryption mechanisms, which fail to dynamically evaluate behavioural risks associated with user access requests. Consequently, Distributed Trust Inconsistency, Cryptographic Scalability Saturation, and … …Journal of Computer Science, Vol. 22, Issue 10 · 7 Oct 2026 · 453 accessesView PDF Cite

Research ArticleOpen accessIn the abstractReal-Time Monitoring and Injury Risk Warning Algorithm Based on Data Fusion of Motor Muscle ContractionGuomin Wang, Jingying HuangThe existing sports injury warning algorithms are difficult to achieve dynamic injury risk modeling based on individual muscle characteristics while ensuring real-time fusion efficiency of multimodal signals. This article proposes a real-time muscle contraction state perception and injury risk warning algorithm based on adaptive weighted feature fusion and lightweight temporal modeling. The system synchronously collected surface electromyography (sEMG), Inertial Measurement … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 3 · 10 Aug 2026 · 1,089 accessesView PDF Cite

Research ArticleOpen accessIn the abstractForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDF Cite

Research ArticleOpen accessIn the abstractHybrid Soft Voting Ensemble of XGBoost and DNN for At-Risk Student Performance PredictionEugene Wan, Po Chan Chiu, Mohammad bin Hossin et al.Early identification of at-risk students in higher education is important for timely academic intervention, yet conventional prediction methods often struggle with data imbalance and limited model precision. This study proposes a hybrid soft voting ensemble model that integrates Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) to enhance multi-class student grade prediction (A-F classification) and at-risk student identification. This … …Journal of Computer Science, Vol. 22, Issue 5 · 28 May 2026 · 1,390 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Context-Aware Temporal Convolutional Network for Water Replacement Prediction in Catfish Biofloc Ponds With Imbalanced Event HandlingYaddarabullah, Inanpi Hidayati Sumiasih, Mutiara Dewi PuspitawatiWater replacement is a biologically critical yet under-automated decision in biofloc-based aquaculture systems. Mistimed actions can destabilize microbial ecosystems, elevate fish mortality, and compromise sustainability through excessive water usage. Traditional rule-based heuristics often fail to account for the nonlinear and multiscale dynamics of pond environments. To address this, we propose a Context-Aware Temporal Convolutional Network (CA-TCN), a rare-event classification framework … …Journal of Computer Science, Vol. 22, Issue 3 · 5 Mar 2026 · 1,664 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDeep Sense: Deep Learning for Early Staging the Onset of Diabetes Emanating Recognition of Activity PatternsMohammed Shamsul Alam, Erfanul Hoque Bahadur, Md Fokrul Islam Khan et al.This study presents a comprehensive framework for Human Activity Recognition (HAR) using smartphone sensor data, with a specific focus on identifying activities associated with diabetic risk factors. We investigate the efficacy of two deep learning architectures (Long Short-Term Memory (LSTM) networks and Graph Convolutional Networks (GCNs)) for activity recognition under conditions of limited training data. To address data scarcity, we … …Journal of Computer Science, Vol. 21, Issue 10 · 15 Dec 2025 · 2,268 accessesView PDF Cite

Research ArticleOpen accessIn the abstractPredicting Financial Statement Failure of Listed Firms in Market for Alternative Investment (mai) using Machine LearningPattanapong Kumsin, Salakjit Ninlaphay, Anupong SukprasertThe purpose of this research is to investigate the financial ratios of companies listed on the Market for Alternative Investment (mai) in Thailand, as well as the likelihood of these companies' financial statement failure. It also intends to create a financial statement failure predicting model for companies listed on the mai and assess the performance of various forecasting models using … …Journal of Computer Science, Vol. 21, Issue 9 · 24 Nov 2025 · 2,240 accessesView PDF Cite

Research ArticleOpen accessIn the abstractSuicide Ideation and Risk Detection from Social Media Using GPT ModelsSara Lasri, El Habib Nfaoui, Karima MrizikAs a reason for the sensitiveness of suicide ideation and its considerable impact on people's lives, the demand to treat and prevent the suicide ideation issue has become an obligation. Suicide ideation is a result of a combination of psychological pain and hopelessness. According to the World Health Organization, the task of reducing the global suicide mortality rate is a … …Journal of Computer Science, Vol. 20, Issue 10 · 26 Aug 2024 · 5,704 accessesView PDF Cite