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491 articles for “Deep learning models”
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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Hybrid DL-ML Approach for Android Malware Detection
Abstract: The widespread growth of Android malware has become a significant mobile security threat during the past few years thus requiring the development of strong detection solutions. The primary tool applied in this research for Android malware detection consists of app permissions. The main indicator in the dataset for identifying malicious and benign applications functions through displaying application permission information. The evaluation of particular permission relationships with malware behavior leads to …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 18–25 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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NLP Revolution in Education Feedback Analysis: Trends and Challenges
Abstract: Artificial Intelligence (AI) is a rapidly growing area of study in many domains like research and business. Various subsets of AI, such as Machine Learning, Deep Learning, and Natural Language Processing (NLP), are employed to address diverse aspects of data processing and modeling. This review is about the impact of AI on the education system and students feedback for the analysis is required for the enhancement of the technology used …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 1, 2023 · pp. 21–25 Read article
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Ligand Based Drug Virtual Screening in Computer Aided Drug Design: A Review
Abstract: Computer-Aided Drug Design (CADD) has revolutionized the drug discovery process by providing efficient and cost-effective methods for identifying potential drug candidates. Within CADD, ligandbased virtual screening techniques play a pivotal role in the early stages of drug discovery. This review provides a comprehensive overview of ligand-based drug virtual screening, focusing on its principles, applications, challenges, and future directions. Fundamentals of ligand-based virtual screening, including pharmacophore-based methods, similarity searching, and machine …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article
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Advances in Lung Cancer Detection and Diagnosis: An Integrative Approach Using Computational Chemistry, Statistics, Bioinformatics, Artificial Intelligence, and Machine Learning
Abstract: Lung cancer is still one of the most common and lethal cancers globally, accounting for more than a million deaths each year. Prompt detection is important, and imaging techniques like chest X-rays, MRIs, PETs, CTs, and molecular imaging have become important tools. But still, even though all these techniques do not provide an accurate classification of the lesion, they have led to the development of computer-based high-resolution image analysis. Computer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Review of Deep Learning in Medical Imaging
Abstract: In the past few years, computer-aided analysis has fallen quickly behind. Organ categorization and segmentation is a key stage in computer-aided diagnosis. In recent years, there has been a lot of interest in the division of abdominal organs such as the liver, stomach, kidney, pancreas, and bladder from various picture modalities. Radiologists or medical specialists analyze abdominal pictures the majority of the time. Due to subjectivity, intricacy of the picture, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 3, 2022 · pp. 16–24 Read article
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Enhancing Wildlife Tourism Management Using Deep Learning and Particle Swarm Optimization (PSO) for Animal Detection in Wildlife Sanctuaries
Abstract: Wildlife tourism is one of the most thriving sectors, faced with huge challenges in terms of safeguarding protected areas. As demand for wildlife experiences accelerates, it becomes necessary to find efficient measures that are friendly to conservation. The use of these advanced techniques in this field such as YOLO and PSO algorithm presents a new dimension on managing wildlife tourism. To harness the abilities of these techniques, this research centers …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 41–50 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
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Face Recognition Systems: An Anti-spoofing and Liveliness Verification Toolkit
Abstract: The safety of facial recognition technology is vital in the current digital era. In this research, a deep learning approach to face spoofing detection is presented, covering the complete process from data collection to real-time application. To guarantee model robustness, a 7,000-image bespoke dataset that includes both “real” and “fake” facial scenarios is curated. The YOLO architecture, a state-of-the-art object identification model, is used after necessary preprocessing. The model, which …
Published in Journal of Operating Systems Development & Trends · Vol. 10, Issue 2, 2023 · pp. 36–4`1 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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Birdwatcher’s Assistant: AI- Based Bird Species Classification
Abstract: Many bird species have gone extinct because of human activities and changes in the climate. The loss of habitats is a significant danger to global biodiversity. Therefore, it is important to monitor species distribution and identify the components of biodiversity in an area to develop conservation strategies. The ultimate objective is to build a machine learning model that can accurately differentiate between various bird species based solely on visual cues. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 17–27 Read article
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Innovations in Forensic Imaging: Leveraging Deep Learning for Authenticity Verification
Abstract: The advent of digital media has necessitated advancements in forensic imaging, especially for the detection and verification of image authenticity. In this context, digital image forensics plays a critical role in identifying manipulated or counterfeit images. This paper presents a new method that uses deep learning techniques to enhance image forgery detection. The approach utilizes a convolutional neural network (CNN) to automatically learn and recognize the intricate features present in …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 2, 2024 · pp. 28–33 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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A Study on Various Approaches in Remote Sensing
Abstract: AbstractThis paper studies the various approaches used in the remote sensing. The importance of Remote Sensing is that to learn to extract valuable information from it. The traditional approach has been to analyse RS images and then construct information-extraction models, these approaches are able to construct spectral, textural and geometric attributes of images. The various approaches using machine language, deep learning, Zigbee platform and microwave sensing is discussed here.Keywords: RS …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 2, 2020 · pp. 32–37 Read article
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Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms
Abstract: The style of handwriting varies from person to person. Handwritten numbers are not always the same size, orientation and width. To develop a system to understand this, the machine recognizes handwritten digit images and classifies them into 10 digits (from 0 to 9). The recognition of handwritten digits is a technology which is used for the automatic recognizing and detecting handwritten digital data through various deep lerning and machine learningmodels. …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 2, 2022 · pp. 20–28 Read article
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Computational Approaches to Understanding Cellular Signaling Pathways
Abstract: Cellular signaling pathways are fundamental in regulating vital processes, such as cell growth, differentiation, and apoptosis. The intricate and interconnected nature of these signaling networks requires sophisticated methods for their analysis. Computational approaches, including mathematical modeling, network analysis, and machine learning, have revolutionized the way researchers analyze and simulate cellular signaling. This article provides a comprehensive overview of computational strategies employed to model signaling pathways, with a focus on integrating …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 8–13 Read article