Search
268 articles for “Adaptive Learning Systems”
-
Pneumonia Detection and Classification Using Deep Learning
Abstract: Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 9–19 Read article
-
Fraud Detection in Government Procurement Using Machine Learning
Abstract: Fraud represents a significant challenge in the realm of procurement, with estimates indicating that between 12 and 30% of global procurement budgets are lost to fraudulent activities (OECD, 2023). The pervasive nature of procurement fraud, which may encompass a range of deceptive practices such as bid rigging, invoice fraud, and procurement kickbacks, not only undermines the integrity of financial operations but also results in substantial losses for organizations. These losses …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 19–34 Read article
-
Automated Healthcare Support System with AI
Abstract: The Automated Healthcare Support System with Artificial Intelligence (AI) presents a smart and scalable digital solution aimed at improving the accessibility and efficiency of healthcare services. The system is designed to provide preliminary medical guidance, perform symptom-based analysis, and deliver health-related insights through an intuitive user interface. By enabling early identification of potential health conditions, it assists users in determining the necessity of professional medical consultation. The proposed platform utilizes …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
-
Autonomous Agentic AI for Adaptive Cure Optimization and Defect Prevention in Thermoset Polymer Composite Manufacturing
Abstract: Thermoset polymer composites occupy a central position in modern structural manufacturing, from aircraft fuselages to wind-turbine blades. Despite progress in resin chemistry and fiber architecture, the “cure process” that transforms compliant preforms into load-bearing structures remains difficult to manage. Manufacturers encounter ‘voids’, “interlaminar delaminations”, and “spring-back distortion” when curing complex or thick-section parts. The cause is not ignorance of the relevant physics, but rather that ‘temperature’, ‘chemistry’, ‘rheology’, and ‘mechanics’ …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 301–320 Read article
-
Adaptive Machine Learning Framework for Navigation Control of Autonomous Drones
Abstract: The rise of autonomous drones has expanded UAV applications across sectors like surveillance, delivery, agriculture, and rescue operations. However, traditional navigation systems face limitations in adapting to dynamic environments. This study proposes an AI-driven adaptive navigation framework that leverages real-time sensor data, reinforcement learning, and adaptive control strategies to enhance drone autonomy, scalability, and security. The system processes mission inputs, environmental data (from LiDAR, cameras, GPS, and weather sensors), and …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 3, 2025 · pp. 1–7 Read article
-
Designing Intelligent Agents for Effective Collaboration with Human in Complex Environment
Abstract: An intelligent agent is a self-governing system that can take action to accomplish its goals based on how it perceives its surroundings. This paper introduces a newly designed humanoid robot that demonstrates significant advancements in performance, reliability, durability, energy efficiency, and environmental sustainability. The design emphasizes a bio-inspired musculoskeletal system, enabling natural and flexible motion while improving operational lifespan. A self-repair system is integrated to allow the robot to address …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 3, 2025 · pp. 1–7 Read article
-
Lyapunov-Stable Adaptive Fractional-Order Interval Type-2 Fuzzy Control for Robust Anti-Lock Braking Under Uncertain Road Adhesion Conditions
Abstract: This paper proposes a Lyapunov-stable Adaptive Fractional-Order Interval Type-2 Fuzzy Logic Controller (FO-IT2FLC) for robust anti-lock braking system (ABS) control under nonlinear vehicle dynamics and uncertain road adhesion conditions. The proposed framework integrates fractional-order error dynamics to capture memory-dependent tire–road interaction, interval Type-2 fuzzy inference to model uncertainty via footprint-of-uncertainty representation, and a Lyapunov-based adaptive learning mechanism for real-time parameter tuning. A rigorous stability proof guarantees boundedness of all closed-loop …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 Read article
-
AIducate: Your Smart Tutor–A Survey
Abstract: The ongoing “datafication” of our social reality has led directly to the emergence of new data-driven business models across various sectors, including education. As a result, the market for educational services is rapidly expanding. Educational technology (EdTech) companies are at the forefront, offering data-driven pedagogical solutions that are reshaping the landscape of learning. Despite the market's growth, few companies are harnessing the full potential of artificial intelligence (AI) and machine …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 2, 2024 · pp. 25–30 Read article
-
The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments
Abstract: The novel approach that this work discusses is a DCD-based iterative learning filter approach improved with deep learning methodologies, designed to improve the efficiency of acoustic echo cancellation. The proposed system can really manage both linear and nonlinear echo scenarios, dynamically adapting to fluctuating acoustic environments. The above comparative evaluations with standard filter, the standard RLS filter, indicate that the mean square error, and the standard deviation of the correlation …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 2, 2024 · pp. 9–24 Read article
-
Investigative Study of Adaptive Fault Tolerance in Optical Networks
Abstract: Optical networks have become the backbone of modern telecommunications infrastructure, enabling high-speed data transmission across global networks. However, these networks face significant reliability challenges due to component failures, signal degradation, and environmental factors. This investigative study examines adaptive fault tolerance mechanisms in optical networks, focusing on emerging technologies and methodologies that enhance network resilience. The research analyzes various fault detection techniques, including machine learning-based approaches, self-healing protocols, and dynamic reconfiguration …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 2, 2025 · pp. 24–30 Read article
-
The Impact of AI-Driven Real-Time Feedback Systems on Students’ Self-Regulated Learning and Academic Persistence in Secondary Schools, Nigeria
Abstract: Self-regulated learning (SRL) is essential for secondary school students to achieve academic success and lifelong learning competencies, particularly in contexts requiring greater learner autonomy. This expository article examines the potential of Artificial Intelligence (AI)-driven real-time feedback systems to support SRL processes—planning (forethought), monitoring (performance/control), and reflection—within Nigerian secondary education. Grounded in Zimmerman’s cyclical SRL model and Bandura’s Social Cognitive Theory, the paper conceptualises AI tools (e.g., intelligent tutoring systems, learning …
Published in Journal of Advancements in Library Sciences · Vol. 13, Issue 2, 2026 Read article
-
Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems
Abstract: This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
-
Revolutionizing Wireless Communication: AI & ; ML in the Era of 6G
Abstract: With rapid technological advancement, sophisticated techniques are significantly enhancing the performance of wireless networks. In parallel, the growth of artificial intelligence (AI) has empowered systems to perform intelligent decision-making, automate processes, analyze data, generate insights, and predict future outcomes. AI systems are now capable of learning and adapting to dynamic environments. Particularly, machine learning and deep learning techniques have achieved remarkable success across a wide range of applications in recent …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 29–36 Read article
-
A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket
Abstract: This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 09–19 Read article
-
A Survey on Ensemble Technique for Enhanced Cyberattack Detection
Abstract: It is now more difficult than ever to safeguard enterprises against cyberattacks due to their fast growth and growing sophistication. Stronger cyberattack detection systems are becoming more and more necessary as hostile strategies continue to evolve in order to safeguard information, preserve corporate trust, and protect sensitive data. An overview of contemporary detection techniques is given in this study, with a focus on integrating machine learning (ML) to increase efficacy. …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 50–54 Read article
-
Machine Learning-Driven Force Analysis for Tool Wear Prediction Systems
Abstract: A system designed to forecast tool wear by utilizing a force sensor to monitor the wear of the tool's flank and applying a Convolutional Neural Network (CNN) for forecasting purposes. The methodology is demonstrated through experiments in milling, utilizing dry machining with a ball endmill on a stainless-steel component. The flank wear of the tool is directly assessed using a digital microscope throughout the operation. The forecasts produced by the …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 3, 2024 · pp. 16–25 Read article
-
Enhancing LAN Security Using Machine Learning
Abstract: The modern Local Area Network (LAN) is a critical component of any organization's infrastructure, facilitating communication, resource sharing, and access to the wider internet. However, this connectivity also brings inherent security risks. Traditional security measures, relying on signature-based detection and rule-based systems, are increasingly struggling to keep pace with the evolving sophistication of cyberattacks. This is where Machine Learning (ML) offers a powerful alternative, enabling proactive threat detection and enhanced …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 07–16 Read article
-
Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
-
Advancements in Multiple-Input DC–DC Converters for Hybrid Renewable Energy Systems: Topologies, Control Strategies, and Applications
Abstract: Multiple-input DC to DC converters (MICs) are essential components in hybrid energy systems, enabling efficient management of diverse energy inputs from renewable sources such as solar photovoltaic (PV) panels and wind turbines. These converters facilitate the seamless integration of variable power outputs, addressing the intermittent nature of renewable energy through advanced power electronics. This paper provides a comprehensive review of the topologies, control strategies, and applications of MICs within renewable …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 10–17 Read article
-
Optimization of Pesticide Requirement Calculations for IoT-Operated Hexacopter Delivery Systems
Abstract: The integration of Internet of Things (IoT) technology into precision agriculture has transformed pesticide application strategies, enabling resource-efficient and environmentally sustainable practices. This study presents a computational methodology for optimizing pesticide requirements in an IoT-operated hexacopter system, designed for dynamic, data-driven pesticide delivery. Leveraging a fusion of real-time telemetric data from onboard LiDAR, multispectral imaging sensors, and environmental monitoring modules, the system employs predictive analytics and edge computing to calculate …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 08–14 Read article