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53 articles for “Robust metrics”
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Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach
Abstract: In recent years, ensemble learning techniques have become pivotal in advancing predictive analytics within healthcare, particularly for early disease detection. The inherent variability and complexity of medical data, often characterized by high dimensionality, class imbalance, and noise, make it challenging for standalone classifiers to maintain high predictive accuracy. Ensemble learning, by integrating multiple models through bagging, boosting, or stacking, offers a more robust and generalizable approach. This study explores the …
Published in Journal of Communication Engineering & Systems · Vol. 15, Issue 3, 2025 · pp. 26–33 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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A Comprehensive Guide to Application Development Using Flutter
Abstract: Developers are forced to either write the same application many times for other operating systems or use a similar but inferior solution that compromises native performance and accuracy in favor of portability. Flutter is a toolkit developed by Google that helps programmers create apps that look and feel great on different devices like phones, tablets, and computers. What is special about Flutter is that one can write code once and …
Published in International Journal of Electronics Automation · Vol. 2, Issue 1, 2024 · pp. 35–41 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Designing Self-Optimizing Operating Systems: Information-Theoretic Approaches to Thread Scheduler Implementation
Abstract: Thread Level Scheduling (TLS) in multi-core and many-core processor environments represents a critical frontier in next-generation operating system design. As computing systems grow increasingly heterogeneous and concurrent, traditional scheduling strategies often rely on heuristics or localized resource metrics, frequently overlooking the deeper, quantifiable relationships and uncertainties inherent in complex concurrent workloads. This study explores the application of information-theoretic approaches, specifically entropy-based task allocation, mutual information-driven dependency analysis, and channel capacity-inspired …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 31–39 Read article
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A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection
Abstract: Background: Breast cancer remains among the most prevalent malignancies in women worldwide, and effective diagnosis and data integration continue to challenge clinical practice. Diagnostic reports from mammography and ultrasound contain rich clinical information that is often under-utilised due to heterogeneous formats and limited data-sharing infrastructure. In the context of human papillomavirus (HPV) infection, which may influence oncogenic pathways and data complexity, advanced computational methods offer new solutions to this problem. …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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A Novel Direct Weighted Deviation (DWD) Method for Agricultural Enterprise Selection: A Case Study of Namakkal District, Tamil Nadu
Abstract: The study proposes a novel Direct Weighted Deviation (DWD) method for Multi-Criteria Decision Making (MCDM) by eliminating normalization, distance metrics, and pairwise comparisons. DWD is thereafter used to evaluate and select ten rainfed agricultural enterprises against ten generic and context-specific viability criteria in Namakkal District, Tamil Nadu, a water-scarce region in India. Subsequently, other methods (AHP, SAW, WPM, and TOPSIS) are used to obtain a comparative second opinion and validate …
Published in International Journal of Industrial and Product Design Engineering · Vol. 4, Issue 1, 2026 · pp. 1–7 Read article
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AI-Driven Psychological Profiling on Social Media: Mechanisms, Ethical Breaches, and Regulatory Challenges in Data Inference
Abstract: This literature review examines AI-driven psychological profiling on social media, analyzing 21 academic studies that focus on machine learning techniques such as supervised learning, deep neural networks, sentiment analysis, and natural language processing. These methodologies infer mental health indicators—such as depression, anxiety, and stress—from users' digital footprints, encompassing linguistic patterns, engagement metrics, and temporal behaviors. While these tools offer potential for early detection of psychological distress, they also raise significant …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 1–7 Read article
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Nutritional Status, Bioactive Properties & Therapeutic Efficacy of Omega-3 Polyunsaturated Fatty Acids Obtained by Green Synthetic Routes
Abstract: Polyunsaturated fatty acids (PUFAs), particularly omega-3 (n-3) fatty acids like eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are crucial for human health. They support structural brain development, visual acuity, and cardiovascular function, while also exhibiting potent anti-inflammatory, antioxidant, and antimicrobial properties. Historically, marine fish oil has been the primary dietary source of these essential fatty acids. However, escalating concerns regarding ecological environmental impacts, the oceanic bioaccumulation of heavy metal contaminants, …
Published in International Journal of Nutritions · Vol. 3, Issue 2, 2026 Read article
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A Study on Security Requirements of IOT
Abstract: In terms of speed, internet technologies have surpassed traditional technologies. Although the Internet's explosive growth has allowed it to reach its full potential, there are several security dangers associated with it. Many consumer-grade IoT devices are not designed with robust authentication protocols, making them easy targets for hackers. To address this, stronger multi-factor authentication (MFA) methods and certificate-based authentication should be implemented to ensure that only legitimate devices gain access …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 1, 2025 · pp. 24–28 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Performance Forecasting in Solo Sports: Leveraging Psychometrics, Economic Analysis, and Cultural Insights for Predictive Excellence
Abstract: The science of performance forecasting in solo sports has transcended traditional metrics, embracing a multidisciplinary approach. This paper explores the integration of psychometrics, economic analysis, and cultural insights to predict athletic success. By leveraging psychological profiling, economic factors, and cultural dynamics, this study aims to establish a comprehensive model for forecasting athlete performance with heightened accuracy. In the realm of solo sports, where individual prowess dictates success, traditional performance forecasting …
Published in Recent Trends in Sports · Vol. 1, Issue 1, 2024 · pp. 35–44 Read article
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ML-Enhanced Smart Sensing Framework for IoT- Based Structural Health Monitoring Using Conductive Polymer Composites
Abstract: The growing demand for intelligent structural health monitoring (SHM) in dynamic infrastructures necessitates flexible sensing systems that are not only mechanically robust but also capable of real-time interpretation. Conventional SHM frameworks often rely on brittle sensor configurations and cloud-dependent processing pipelines, which suffer from latency, limited durability, and poor adaptability under variable loading conditions. Despite recent advances in composite materials and machine learning, current approaches lack a unified framework that …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 348–369 Read article