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348 articles for “A algorithm.”
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A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction
Abstract: The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 · pp. 8–14 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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An Overview on Metal Processing using Machine Learning
Abstract: Metalworking, which is an important part of modern industry, includes a wide range of steps, such as shaping and casting the metal, machining it, and riveting it. The industry has always relied on trial and error and other empirical methods, but machine learning (ML) is now causing a big change. It is expected that this change will lead to higher production, better product quality, and better use of resources. The …
Published in International Journal of Manufacturing and Materials Processing · Vol. 12, Issue 1, 2026 · pp. 43–53 Read article
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Adaptive Production Scheduling in Multi-Machine Manufacturing Environments
Abstract: For manufacturing processes to meet evolving market demands, they must be executed in the most efficient manner while maintaining flexibility against unanticipated disruptions. Often, traditional scheduling methods overlook dynamic variability and real-time interruptions. An inverse scheduling framework for a multi-machine flowshop system is proposed in this research with the primary objective of makespan minimisation. Several issue situations are analysed to validate the algorithmic efficacy, and metaheuristic techniques are used to …
Published in International Journal of Manufacturing and Materials Processing · Vol. 12, Issue 1, 2026 · pp. 32–42 Read article
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Design and Implementation of 32-Bit High Speed Vedic Multiplier
Abstract: To accomplish quicker and more effective calculation, this project combines a Kogge-Stone Adder with the Vedic Urdhva Tiryagbhyam (vertical and crosswise) multiplication method to create a high-speed 32-bit binary multiplier. The KoggeStone Adder minimizes total latency by reducing carry delay at the final addition stage, while the Vedic algorithm allows partial products to be formed simultaneously, increasing speed. This architecture divides 32-bit values into smaller components, multiplies them in parallel, …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 12, Issue 1, 2026 · pp. 18–26 Read article
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A study AI-Driven IoT in Self-Healing Grid Power Systems
Abstract: Modern electricity infrastructure is undergoing a transformation thanks to the combination of artificial intelligence (AI) and the Internet of Things (IoT), especially in the development of self- healing grid systems. In the context of smart grid automation and resilience, this article provides a thorough review of AI-driven IoT infrastructures. By leveraging distributed sensor networks, real-time data acquisition, and advanced KSK Approach, and machine learning algorithms—includes ANN, DT and K-NN—the proposed …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 15–24 Read article
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Solar-Powered Electric Vehicle Charging Station with Smart Load Scheduling
Abstract: The rapid rise in EV usage has increased demand for environmentally friendly and cutting-edge charging infrastructure. This paper outlines the creation and deployment of a solar-powered EV charging station that incorporates a smart load scheduling system aimed at optimizing energy consumption, minimizing reliance on the grid, and improving charging efficiency. To guarantee practical feasibility and scalability, the proposed system encompasses thorough design work, modelling, and performance assessment. A bidirectional converter …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 7–14 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A Hybrid Metaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 35–46 Read article
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An Advanced Framework for Energy Harvesting in Device-to-Device Communication within 5G-Enabled IoT Healthcare Networks
Abstract: The healthcare sector is forever advancing, and one of the key technologies that have started playing a part in better patient care and management is Internet of Things (IoT) devices. The Device-to-Device (D2D) communication in 5G-enabled IoT healthcare networks provide better data transmission rate and very low latency. But despite all that, IoT devices still have over-the-top energy consumptions requiting attention. In this work, an innovative energy harvesting framework is …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 12, Issue 1, 2026 · pp. 22–30 Read article
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AI-Based Noise Reduction Techniques for Digital and Analog Communication Systems
Abstract: Noise is one of the major challenges in modern communication systems because it significantly affects signal quality, transmission efficiency, and overall system reliability. Both digital and analog communication systems are influenced by different types of noise such as thermal noise, impulse noise, quantization noise, atmospheric noise, and channel interference. Conventional noise reduction techniques including filtering, adaptive equalization, and statistical signal processing have been extensively used to improve communication performance. However, …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 1, 2026 · pp. 40–48 Read article
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Microwave Frequency Optimization Techniques for High-Speed Wireless Communication
Abstract: The rapid growth of mobile devices, cloud computing, Internet of Things (IoT), smart applications, and high-definition multimedia services has significantly increased the demand for high-speed wireless communication systems. Microwave communication technologies play a vital role in modern wireless networks because they provide large bandwidth, high data transmission rates, low latency, and efficient spectrum utilization. Microwave and millimeter- wave frequencies are widely used in satellite communication, radar systems, wireless broadband, and …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 1, 2026 · pp. 14–21 Read article
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An Overview on Harmonic Intelligence: BridgingMicrowave Sensing and AI-Driven IoT for Autonomous Decision-Making
Abstract: As the Internet of Things (IoT) ecosystem expands into complex industrial and urban environments, the demand for high-fidelity data acquisition and near-instantaneous edge processing has reached a critical juncture. Traditional optical and infrared sensors often falter under adverse conditions, such as dense smoke, occlusions, or low-light environments. This paper explores the transformative potential of microwave signals—leveraging their unique ability to penetrate non-metallic materials and operate independently of ambient lighting—as the …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 1, 2026 · pp. 1–8 Read article
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A Study of Delta Modulation Techniques for Bandwidth- Efficient Signal Transmission
Abstract: Delta modulation is an important signal processing technique widely used in digital communication systems for efficient analog-to-digital signal conversion and bandwidth reduction. With the increasing demand for high-speed and low-complexity communication systems, bandwidth-efficient transmission methods have become essential in modern telecommunication networks. This study presents a comprehensive analysis of delta modulation techniques and their role in improving signal transmission efficiency in digital communication environments. The paper examines the operating principles …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 1, 2026 · pp. 31–43 Read article
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A Comprehensive Review of Transportation Network Design and Congestion Modeling: Methods, Challenges, and Emerging Trends
Abstract: Abstract As urbanization and travel demand continue to grow rapidly, traffic congestion has emerged as a widespread issue worldwide. The design of transportation networks and the modeling of congestion are essential disciplines that provide foundational tools for analyzing, predicting, and mitigating congestion impacts. This review complies key theories, modeling frameworks, optimization techniques, and algorithms used in network design and congestion modeling. We discuss static and dynamic models, equilibrium concepts, congestion …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 1, 2026 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A HybridMetaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 Read article
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AI-Assisted Conversion and Optimization of Analog-to-Digital Signal Transmission forLow-Power IoT and Edge Devices
Abstract: The booming implementation of Internet of Things (IoT) and edge computing devices is requiring theimplementation of efficient signal transmission mechanisms to balance between performance and energyefficiency. The current paper introduces an AI-assisted conversion and optimization system of the analog-to-digital (A/D) signal transmission, aimed at low-power IoT and edge devices. The given strategy combines both machine learning algorithms and adaptive sampling and quantization methods to optimize the A/D conversion process in …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 2, 2025 Read article
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SYNTHETIC MEDICAL DATA GENERATIONUSING GAN MODEL
Abstract: The healthcare industry has witnessed growing interest in synthetic data generation, driven by increasing demands for large and diverse datasets. Real medical data is often inaccessible due to stringent privacy laws, data scarcity, and ethical considerations. Sophisticated models, such as Generative Adversarial Networks (GANs), have emerged as powerful tools to generate synthetic data that closely resembles real-world data. This approach is particularly effective for addressing the challenges of researching rare …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 2, 2025 Read article
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Predictive Maintenance of 6G Infrastructure Using Artificial Intelligence
Abstract: The transition to Sixth Generation (6G) wireless infrastructure introduces unprecedented complexity, characterized by massive densification, reliance on the Terahertz (THz) spectrum, dynamic cell topologies driven by Reconfigurable Intelligent Surfaces (RIS), and stringent requirements for Ultra-Reliable Low-Latency Communication (URLLC). Traditional reactive or fixed-schedule maintenance protocols are fundamentally inadequate for sustaining the necessary service continuity and energy efficiency in this hyper-connected, heterogeneous environment. This paper explores the critical role of Artificial Intelligence …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 2, 2025 Read article
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Hybrid Signal Processing Framework for Seamless Integration of Analog and Digital Communication Systems in 6G Networks
Abstract: The next 6G phase of evolution of the communication system is expected to deliver data speedexceeding terabits per second, very low latency, and connectivity everywhere among heterogeneous devices. These performance goals can only be met through signal processing architecture paradigm shift. The paper will describe a Hybrid Signal Processing Framework (HSPF) which combines the analog and digital communication methods to leverage the complementary benefits of the two spheres. The framework …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 2, 2025 · pp. 28–42 Read article
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Artificial Intelligence for the Art and Science of Separation: Optimizing Chemical Systems
Abstract: Chemical separation processes play a critical role across chemical manufacturing, energy production, pharmaceuticals, and environmental remediation, yet they remain among the most energy-intensive operations in modern industry. Recent advancements in artificial intelligence (AI) offer transformative opportunities to significantly improve the efficiency, selectivity, and sustainability of these systems by leveraging computational power and data-driven insight. This article reviews the emerging integration of machine learning, data-driven modeling, and advanced optimization algorithms with …
Published in International Journal of Chemical Separation Technology · Vol. 11, Issue 2, 2025 · pp. 1–7 Read article