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147 articles for “Neural networks”
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Prediction of Rainfall Using MLP and RBF Networks for Monsoon and Post-Monsoon Seasons of Pune and Mahabaleshwar Regions
Abstract: Prediction of rainfall for a river basin has utmost importance for planning of irrigation and drainage system as also for command area development. For example, analysis of consecutive days of rainfall is more relevant for drainage design of agricultural lands whereas analysis of weekly rainfall data is relevant for planning of cropping pattern. Likewise, analysis of monthly and seasonal data is more useful for water management practices. With the development …
Published in International Journal of Water Resources Engineering · Vol. 7, Issue 1, 2021 · pp. 34–51 Read article
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Blind Assistance System using Tensorflow
Abstract: Because of item recognition's cozy relationship with video investigation and picture understanding, it has drawn in much exploration consideration lately. Customary item discovery techniques are based on carefully assembled highlights and shallow teachable designs. Their exhibition effectively deteriorates by building complex outfits which join numerous low-level picture highlights with undeniable level setting from object finders and scene classifiers. With the fast improvement in profound learning, all the more amazing assets, …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 7, Issue 1, 2021 · pp. 6–12 Read article
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Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel
Abstract: Use of solar panel in generating electricity is increasing globally day by day to meet the increase in demand of power. Many developments have been made in order to achieve higher efficiency of the system. But efficiency of the system may still decrease due to harsh environmental conditions or poor handling of the. These factors highly contribute in the performance degradation of the system. For detection of these irregularities, human …
Published in International Journal of Analog Integrated Circuits · Vol. 7, Issue 1, 2021 · pp. 15–24 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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Stability Analysis of Type-III Wind Turbine Systems Using Radial Basis Function Networks (RBFNs)
Abstract: To accomplish power smoothing, contemporary controllers like adaptive and predictive controllers are used on both the machine and grid sides. Effective machine-side torque control requires robust technologies. Under order to maintain stable performance under nonlinear circumstances, this work employs the sliding mode control technique to control the pitch angle during disturbances. Even in situations where wind conditions fluctuate, the sliding mode control approach optimizes wind power generation by effectively resolving …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 10, Issue 2, 2024 · pp. 25–42 Read article
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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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CyberSentinel: A Proposed Hybrid AI Framework for Real-Time Cyber Threat Intelligence
Abstract: Modern cyberattacks have grown far more sophisticated, frequent, and fast-moving than the signature-based intrusion detection systems many organizations still rely on. This paper proposes CyberSentinel, a hybrid artificial intelligence framework for real-time cyber threat intelligence (CTI) that combines three complementary techniques into a single detection pipeline: a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) module for network traffic anomaly detection, a Random Forest ensemble for structured log classification, and a transformer-based …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation
Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 Read article
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Classification of Fetal ECG Arrhythmia by Using Advance Hybrid Deep Learning Technique CNN-LSTM
Abstract: Fetal heart abnormalities, particularly fetal heart arrhythmias, are becoming an increasing concern in maternal healthcare. Early and accurate detection remains challenging due to limitations in conventional diagnostic techniques and inadequate signal processing methods. Existing studies have applied various machine learning and deep learning approaches; however, capturing the complex characteristics of fetal electrocardiogram (ECG) signals remains a significant gap. This study proposes an advanced deep learning-based approach for detecting fetal heart …
Published in International Journal of Cardiovascular Nursing · Vol. 12, Issue 1, 2026 · pp. 27–36 Read article
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Dynamic Matrix Control Versus Nonlinear ModelPredictive Control for the Van de Vusse ContinuousStirred-Tank Reactor: Diagnosing and Correcting aCost-Function Pathology That Masked NMPC’sTrue Performance
Abstract: The Van de Vusse reaction in a continuous stirred-tank reactor (CSTR) is a benchmark for nonlinear multivariable control, but its narrow operating envelope creates a trap for nonlinear model predictive control (NMPC): when the setpoint step is small relative to the plant’s reachable range, a standard quadratic NMPC cost function can make zero control action mathematically optimal, even with a perfect prediction model. This paper documents that failure mode, traces …
Published in International Journal of Chemical Separation Technology · Vol. 12, Issue 2, 2026 · pp. 55–66 Read article
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Detailed survey on detection of Dirtiness of PV panels using different approaches
Abstract: The accumulation of dust, dirt, bird droppings, pollen, and other environmental contaminants on photovoltaic (PV) panels significantly reduces their power generation efficiency and increases operational and maintenance costs. Therefore, timely and accurate detection of the cleanliness condition of solar panels is essential for maintaining optimal energy production, improving system reliability, and extending the operational lifespan of PV installations. In recent years, considerable research interest has been directed toward automated soiling …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 2, 2026 Read article
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Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation
Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 Read article
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Classification of power quality disturbances using advanced deep learning algorithms for Accurate and efficient electrical signal analysis
Abstract: Power systems today have to deal with more and more power quality issues (PQDs) like voltage drops, rises, harmonics, and transients because more people are using renewable energy sources and electronic devices. These problems can hurt equipment, stop services, and lower the overall reliability of power. In real-time settings, traditional detection methods often don't work as quickly or accurately as they should. This paper looks at how deep learning models, …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 12, Issue 2, 2026 Read article
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Speech Emotion Recognition using Multi- Kernel CNN for Features extraction and Random Forest Classifier
Abstract: Identifying human emotional states from speech signals is the main goal of Speech Emotion Recognition (SER), which has grown to be a significant area of study in affective computing. However, class imbalance and efficient feature extraction from speech data pose problems for many of the current methods. This study suggests a hybrid framework that combines a Random Forest (RF) classifier with a multi-kernel Convolutional Neural Network (CNN) to address these …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 2, 2026 Read article
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Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons
Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article
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A Study on CNN-Based Image Mosaicing
Abstract: The intricate tapestry of image mosaicing, while a cornerstone of computer vision, has long been constrained by the rigidity of classical, geometry-driven pipelines. Hand-crafted feature detectors and homography estimators, while effective in controlled environments, prove brittle when confronted with the challenges of real-world scenes: significant parallax, dynamic moving objects, repetitive textures, and illumination variance. This work introduces a fundamental paradigm shift, proposing a deep convolutional neural network (CNN) architecture designed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 1–7 Read article
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Intelligent Data Analytics on Cloud Infrastructure:Real-Time Insights and Managed Service Models
Abstract: This paper examines the integration of advanced data analytics and machine learning within modern cloud infrastructure, highlighting how organizations can transform large volumes of unstructured data into actionable business insights that drive competitive advantage. Cloud platforms leverage scalability and elasticity to efficiently process and analyze massive datasets in real time, allowing enterprises to identify hidden patterns, detect operational anomalies early, and generate accurate forecasts of future outcomes. A key factor …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 36–46 Read article
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A Study on Machine-Learning-Driven Insight into theThermal Behavior of Polymers and Composite Materials
Abstract: The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database …
Published in International Journal of Polymer Science and Engineering · Vol. 12, Issue 1, 2026 · pp. 1–11 Read article
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Transportable Water and Air Quality Monitoring Equipment Karishma
Abstract: The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of …
Published in International Journal of Environmental Chemistry · Vol. 12, Issue 1, 2026 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