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15 articles for “Hybrid extraction techniques”
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Supercritical Fluid Extraction: Mechanisms, Applications, and Recent Developments
Abstract: Supercritical Fluid Extraction (SFE) is an advanced and versatile separation technique that harnesses the unique properties of supercritical fluids (SCFs) to extract a wide variety of compounds from both solid and liquid matrices. Supercritical fluids, which are substances at a temperature and pressure above their critical points, possess both liquid-like density and gas-like viscosity, making them highly effective solvents for extraction processes. This review delves into the underlying mechanisms of …
Published in International Journal of Chemical Separation Technology · Vol. 11, Issue 1, 2025 Read article
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An Effective use of Renewable-Energy Sources on Charging the Battery of Cell Phone
Abstract: Harnessing and utilizing renewable energy source at micro level is a tedious task, but always has great importance as world is suffering from energy crisis with fast extinction of fossil fuel and minimizing conventional electricity usage pattern. This paper considers application of renewable source at micro level concerning on charging the battery of Cell-Phone. Basically in this paper we showed how two most common renewable source Wind and Solar power …
Published in International Journal of Analog Integrated Circuits · Vol. 2, Issue 2, 2016 · pp. 10–13 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
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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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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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A Hybrid BPS-DCT Based Digital Image Watermarking Technique
Abstract: In this paper a hybrid algorithm using Bit Plane Slicing (BPS) and Discrete Cosine Transform (DCT) for digital image watermarking is proposed. There are two main requirements for digital image watermarking: imperceptibility and robustness. Imperceptibility is measured by Peak Signal to Noise Ratio and robustness is obtained by correlation between original and extracted watermark image. In this algorithm an image and watermark is first sliced into its bit planes using …
Published in International Journal of Digital Communication and Analog Signals · Vol. 7, Issue 2, 2021 · pp. 31–40 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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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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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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Green and Nanotechnology Approaches for the Determination of Tungsten (VI): A Modern Analytical Perspective
Abstract: The determination of tungsten (VI) has gained increasing importance due to its extensive industrial applications and growing environmental relevance. Tungsten is widely used in alloys, catalysts, electronics, and energy systems, leading to its release into environmental matrices such as water, soil, and biological systems. Accurate monitoring of tungsten at trace and ultra-trace levels is therefore essential for environmental assessment and industrial quality control. Conventional analytical techniques, including inductively coupled plasma …
Published in International Journal of Analytical and Applied Chemistry · Vol. 12, Issue 1, 2026 · pp. 26–35 Read article
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Review of Feature Selection Methods and Semi Supervised Feature Selection Algorithms for Classification
Abstract: Current evolution in technology has resulted in tremendous growth of data with respect to dimensionality and sample size. Knowledge discovery from these high dimensional data are becoming cumbersome. Though powerful machine learning algorithms are prevalent, noisy data, imperfect sensing and collection of data leads to poor and defective knowledge discovery. Noise and redundant features cannot be circumvented due to which data collection process is biased. Dimensionality reduction techniques namely feature …
Published in International Journal of Software Computing and Testing · Vol. 6, Issue 1, 2020 · pp. 39–51 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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A Comprehensive Review of AES-Integrated DWT Watermarking Techniques for Image and Video Authentication
Abstract: The exponential growth of digital media production, distribution, and online sharing has intensified the need for reliable mechanisms that ensure image and video authenticity. With the rise of advanced editing tools and deepfake technologies, safeguarding multimedia content from tampering, unauthorized replication, or malicious manipulation has become a critical research challenge. Traditional cryptographic solutions provide confidentiality but fail to maintain post-decryption integrity, whereas watermarking techniques ensure persistent ownership verification but often …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Genre Classification of Music Using Machine Learning
Abstract: One of the most important technological advances of the 21st century is artificial intelligence (AI) and machine learning. They are revolutionizing computers, banking, healthcare, agriculture, music and travel. A strong model has mastered many difficult learning tasks. Speech analysis is one area of artificial intelligence. This includes finding information about music, creating music, and categorizing music. Music data is one of the most complex source data available today. This is …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 2, 2022 · pp. 17–28 Read article