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260 articles for “AI-driven learning”
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The Future of Mathematics Education in the Era of Artificial Intelligence
Abstract: The rapid integration of Artificial Intelligence (AI) into education is fundamentally reshaping the landscape of mathematics teaching and learning. This study examines how AI-powered tools are converting conventional math training into inclusive, individualized, and data-driven learning environments. Through an in-depth examination of global case studies—including Squirrel AI, Carnegie Learning’s MATHia, Khan Academy, Microsoft Math Solver, DIKSHA, Photomath, ALEKS, and BYJU’S—the study highlights AI’s ability to tailor content, deliver real-time feedback, …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 2, 2025 · pp. 37–44 Read article
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The Integration of AI Technologies in Automating Cyber Defense Mechanisms for Cloud Services
Abstract: The swift growth of cloud computing has transformed how organizations handle and store data, providing greater scalability and adaptability. However, the transition to cloud-based environments has heightened the complexity of cybersecurity challenges, especially in detecting and responding to security incidents. Conventional methods of incident response, which heavily depend on manual efforts, are no longer adequate to address the rapidly evolving and complex nature of modern cyber threats. This study explores …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 1–14 Read article
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A Study on AI-Driven Multi-Layered Defense in 6G Ecosystems
Abstract: The 6G networks bring about new degrees of possible functions related to connectivity, latency, data throughput, and integration with artificial intelligence (AI). This enables advances within healthcare, autonomous systems, and smart cities. The positive impact of rapid advancements must also be balanced with heightened risks due to the sheer volume of gaps that can be exploited, and the complex nature of the alignments and breaches. This results in the breaches …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis
Abstract: The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation. In this research, a Generative AI (GenAI)-based hybrid architecture of early fault detection and predictive maintenance is proposed to improve the decision-making process of the maintenance team. The data-driven analytic approach combines methods of data-driven analytics, Generative AI …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 25–33 Read article
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Oceanmind Systems: AI-Driven Marine Life Intelligence for Climate Prediction and Ocean Ecosystem Stability
Abstract: Oceans regulate global climate systems, support biodiversity, and serve as critical carbon sinks, yet they remain under-monitored relative to their ecological importance. Traditional oceanographic methods rely heavily on satellite sensing, buoy networks, and periodic marine surveys, which often fail to capture real-time biological dynamics at micro-ecosystem levels. This paper introduces OceanMind Systems, an artificial intelligence (AI)-driven marine intelligence framework that integrates marine life behavior, oceanographic data, and computational modeling to …
Published in International Journal of Marine Life · Vol. 3, Issue 2, 2026 Read article
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A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework
Abstract: Traditional environmental toxicology relies heavily on labor-intensive, often retrospective, sampling and analysis, limiting our understanding of dynamic pollutant behaviors and their real-time impact on ecosystems and human health. This study presents a novel, integrated framework leveraging advanced sensor networks and artificial intelligence (AI) to revolutionize the monitoring, assessment, and predictive modeling of environmental contaminants. We deployed a sophisticated array of multi-parameter sensors (e.g., electrochemical, optical, biosensors for heavy metals, organic …
Published in Research and Reviews: A Journal of Toxicology · Vol. 15, Issue 3, 2025 Read article
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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Enhancing ABET Summative Direct Assessment with AI and Blockchain: A Framework for Personalized Learning and Secure Evaluation
Abstract: Accreditation Board for Engineering and Technology (ABET) emphasizes the achievement of specific measurable learning outcomes. However, conventional assessment methods often find it challenging to accurately capture the complexities of student learning and program effectiveness within the ABET framework. This study proposes a novel framework that enhances ABET summative direct assessment by integrating a carefully structured, weighted assessment system with the transformative potential of artificial intelligence (AI) and blockchain technologies. The …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 28–42 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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Remote Sensing and Atmospheric Modelling: Data, Processes, Integration and Future Directions
Abstract: Atmospheric modelling plays a central role in weather forecasting, climate projection, and air quality assessment; however, the availability, accuracy, and representativeness of atmospheric observations fundamentally constrain its reliability. Over the past two decades, rapid advances in remote sensing (RS) have transformed atmospheric observation by providing spatially continuous, multiscale measurements of key atmospheric variables, including aerosols, trace gases, clouds, precipitation, and atmospheric thermodynamic profiles. This review synthesises recent progress in integrating …
Published in International Journal of Atmosphere · Vol. 3, Issue 1, 2026 · pp. 54–67 Read article
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The Impact of Artificial Intelligence on the Sales and Marketing of Pharmaceutical Products
Abstract: The rapid development of computing and technology has permeated all branches of science, with artificial intelligence (AI) emerging as a pivotal field in computer science. AI has significantly influenced disciplines ranging from basic engineering to pharmaceuticals. In healthcare and medicinal chemistry, AI applications have become indispensable. Traditional drug discovery approaches are gradually being overtaken by computer-aided drug design. In recent years, artificial intelligence (AI) and machine learning (ML) have become …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 1, 2025 · pp. 10–18 Read article
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Review on Computer-Aided Drug Design in RAS Inhibitor Discovery
Abstract: This review explores the utilization of computer-aided drug design (CADD) methodologies in the discovery of inhibitors targeting the RAS pathway, a pivotal signaling cascade implicated in various cancers. Through an extensive examination of computational tools, methodologies, challenges, and recent advancements, this review aims to provide insights into the role of CADD in accelerating RAS inhibitor discovery. The discovery of effective inhibitors targeting the RAS pathway is of paramount importance in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 11, Issue 3, 2024 · pp. 7–14 Read article
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Cutting-Edge Developments and Innovations in Amazon Web Services (AWS)
Abstract: In 2025, Amazon Web Services (AWS) continues to dominate the cloud computing industry through groundbreaking innovations in artificial intelligence (AI), strategic partnerships, data center advancements, and expansion into new markets. These initiatives help AWS maintain its position as a top provider of secure, scalable, and efficient cloud services, adapting to the ever-changing demands of businesses across the globe. One of AWS’s most significant advancements is its AI-driven cloud services, which …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 01–08 Read article
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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
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A Comprehensive Survey of Polymer Detection Techniques and Computer-Based Analysis Methods for Advanced Material Characterization
Abstract: Polymers are widely used in aerospace, automotive, biomedical, packaging, electronics, and manufacturing industries because of their lightweight nature, durability, and versatility. Accurate polymer identification and characterization are essential for quality control, recycling, performance assessment, and the development of advanced materials. Characterization helps determine important properties such as chemical composition, molecular structure, thermal stability, mechanical strength, and surface morphology, which influence material performance and application suitability. Traditional polymer detection methods include …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 921–929 Read article
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Novel Strategic Framework for AI-Driven Discovery and Development of Smart and Sustainable Polymers in Healthcare
Abstract: The development of new smart and sustainable polymers is emerging as a priority of new health care innovative development, but event before it may be actualized, the usual culprit is the delay and unproductive execution of the old-fashioned R&D efforts. The current paper proposes a strategic plan which will solve all these shortcomings and speed up the material discovery process by using Artificial Intelligence (AI) and Machine Learning (ML). The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1535–1550 Read article
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AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects
Abstract: The chemical industry, a key growth indicator of the global manufacturing ecosystem, is experiencing a digital transformation driven mainly by advancements in Artificial Intelligence (AI) and Machine Learning (ML) in this sector. These technologies are totally revolutionizing current and traditional methodologies by significantly improving process efficiency, reducing costs of manufacturing, accelerating R&D, and improving safety and sustainability standards. Proper utilization of Artificial intelligence (AI) and machine learning (ML) in chemical …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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Statistical Modeling for Weld Quality Assessment using AI SAW Welding of Mild Steel
Abstract: The main issue to the industries that apply Submerged Arc Welding (SAW) is quality assurance since the structural integrity dictates safety and the performance of the industry. The existing system of checking manuals is not only time consuming but also has human errors that make it mandatory to deploy automated intelligent systems. This study carries out an extensive comparison of the leading approaches based on the use of Artificial Intelligence …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 892–907 Read article
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The Role of AI-Powered Assessment Tools in Improving Educational Feedback in Nigeria
Abstract: Educational systems in Nigeria face a persistent challenge in delivering high-quality, timely, and personalized feedback, due primarily to large class sizes, heavy teacher workloads, and reliance on traditional assessment methods. This study investigates the potential of integrating Artificial Intelligence (AI)-powered assessment tools to overcome these systemic barriers and improve the quality of educational feedback within the Nigerian secondary school context. Employing a quantitative cross-sectional survey design, data was collected from …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 31–41 Read article
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Artificial intelligence’s role in mental health: Innovations, Challenges, and future prospects
Abstract: Among the many ways in which mental health services are gaining from the integration of artificial intelligence (AI) are improvements in diagnosis, tailored treatment programs, and round the- clock patient help. Two AI-driven solutions are virtual therapists and prediction algorithms, which could increase access to mental health therapy and enable early intervention. However, the application of artificial intelligence in this field raises ethical concerns about privacy, discrimination, and the potential …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article