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9 articles for “deep learning (DL)”
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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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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Deep Learning for Smarter Farming: Innovations in Plant Disease Identification
Abstract: Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 27–32 Read article
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Hybrid Machine Learning and Deep Learning approach for Adaptive congestion control in 5G/6G Network
Abstract: The rapid growth of data traffic, fueled by emerging applications such as autonomous systems, augmented reality (AR), virtual reality (VR), and the Internet of Things (IoT), poses major challenges for modern communication networks. With the rollout of 5G and the continuous transition to 6G, sophisticated congestion control techniques are needed to satisfy the demanding requirements of massive machine-type communication (mMTC), ultra-reliable low-latency communication (URLLC), and improved mobile broadband (eMBB). Conventional …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 2, 2025 · pp. 1–8 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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Machine Learning-Based Channel Estimation for 5G NR Systems: A Comprehensive Review of TDL/CDL Channel Models and CNN Architectures
Abstract: Accurate channel estimation is a fundamental requirement for reliable communications in fifth-generation New Radio (5G NR) systems, where highly dynamic propagation environments impose severe challenges on conventional pilot-based methods. This study provides a thorough analysis of channel estimate methods for 5G NR, including everything from contemporary deep learning (DL) techniques based on Convolutional Neural Networks (CNNs) to traditional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators. Our analysis …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 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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Employing ML and DL to Optimize an ElectrochemicalBiosensor
Abstract: A major field of study in science, that have made the understanding of the world and made homo sapiensthe dominant species on the planet is our understanding of medicine. Under this vast umbrella is thedetection and classification of microorganisms. Electrochemical biosensors have emerged aspromising tools in this regard due to their high sensitivity, selectivity, and potential for miniaturization.By integrating a Machine Learning & Deep Learning model for the detection of …
Published in International Journal of Chemical Separation Technology · Vol. 10, Issue 1, 2024 · pp. 33–38 Read article
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Combating Deepfakes and AI-Generated Misinformation: Towards a Digital Likeness and Integrity Framework for India
Abstract: The real struggle is to distinguish what is real and what is fake in the communication of the hyper-digitalized age, in which AI and generative technologies significantly impact the field of communication. The change in the creation and consumption of information has come with the deepfake AI-generated fake media that can imitate a human in voice, image, and video. However, on the one hand, generative models such as Generative Adversarial …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 1, 2026 · pp. 1–20 Read article