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513 articles for “deep network”
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Spectrum Sensing over Rayleigh Fading in Mutihop Cooperative Networks
Abstract: This paper presents a novel cooperative spectrum sensing for the performance analysis of the multihop system model. In this paper, the performance of cooperative spectrum sensing is investigated through intensive simulation analysis by means of energy detection over Rayleigh fading distribution in multihop scenario. The closed form expressions for end-to-end SNR PDF of multihop networks over Rayleigh fading channels with different hard fusion methods are analyzed. Based on the framework …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 3, Issue 1, 2016 · pp. 1–8 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
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Advanced Lithium-Ion Battery Prognostics: A Comprehensive Review of Machine Learning Approaches for Remaining Useful Life Prediction
Abstract: The lithium-ion battery (LIB), as one of the main sources for portable power systems, has been increasingly popular owing to its widespread applications in electric vehicles, consumer electronics, aerospace and renewable energy. Despite their advantages in high energy density and long cycle life, LIBs suffer from degradation over time of aging and cycling, resulting in loss of performance, safety issues, and economic bottlenecks. Predicting their Remaining Useful Life (RUL) is …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 2, 2025 · pp. 12–27 Read article
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COVID-19 Prediction using X-Ray Images and Deep Learning Model
Abstract: In the year 2019, the novel corona virus was emerged in China. Since, then every individual has put their own self to fight against this pandemic situation. Till April 14, 2022, the total COVID-19 confirmed cases are 500,186,525 and deaths are 6,190,349 worldwide. RT-PCR and Rapid antigen test are the standards for COVID-19 detection by WHO. These both tests are time consuming and costly and also there are higher possibilities …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 2, 2022 · pp. 24–32 Read article
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Lung Cancer Detection and Classification Using Deep Learning
Abstract: Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 3, 2024 · pp. 11–17 Read article
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ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
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Stock Price Prediction Using RNN and LSTM
Abstract: Prediction of stock market has been an attractive topic to the stockbrokers. In stock market the decision on when buying or selling stock is important in order to achieve profit. There are number of techniques that can be used to help investors in order to make a decision for financial gain. In this research work we have proposed a prediction algorithm that will give the relation between the dependent factor …
Published in Journal of Open Source Developments · Vol. 5, Issue 3, 2018 · pp. 26–34 Read article
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Statistical Modeling for Weld Quality Assessment using AI SAW Welding of Mild Steel
Abstract: The main issue to the industries that apply Submerged Arc Welding (SAW) is quality assurance since the structural integrity dictates safety and the performance of the industry. The existing system of checking manuals is not only time consuming but also has human errors that make it mandatory to deploy automated intelligent systems. This study carries out an extensive comparison of the leading approaches based on the use of Artificial Intelligence …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 892–907 Read article
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A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management
Abstract: The Internet of Things has revolutionized data-driven ecosystems and offers advanced services, such as live monitoring and automation in various domains: smart cities, healthcare, and industrial automation. However, with the exponential growth of IoT devices, comes a large amount of data generation, which poses considerable problems like network congestion, latency, and energy inefficiency. Compressive sensing (CS), one of the newest signal processing methodologies, has emerged as an enabler to meet …
Published in Trends in Electrical Engineering · Vol. 14, Issue 3, 2024 Read article
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Advancements in Image Processing Techniques for Computer Vision Applications
Abstract: The fast-developing field of computer vision is transforming how people perceive and comprehend pictures and movies. Autonomous systems, robotics, healthcare, and surveillance are just a few of the many applications that have been made possible by recent significant advances in image and video processing. An overview of current developments in computer vision approaches, algorithms, and techniques for image and video analysis is given in this abstract. In conclusion, the analysis …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 1, 2024 · pp. 27–32 Read article
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Hoax News Detection Using Convolutional Neural Network
Abstract: In this paper our objective is to build a classifier that can predict whether a piece of news is Hoax or not based only its content, thereby approaching the problem from a purely deep learning perspective by technique models like LSTM(GRU), Naive Bayes and CNN. We will show the difference and analysis of results by applying them to the dataset Hoax_news which is available on kaggle.com. We found that the …
Published in Journal of Operating Systems Development & Trends · Vol. 7, Issue 2, 2020 · pp. 19–23 Read article
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Facial Expression Recognition using Convolutional Neural Network
Abstract: Machine learning is a subset of Artificial intelligence. Machine learning enables self-learning from past data and predicts the future. Machine learning is used in many applications such as self-driving cars, Google translate etc. Deep learning is a subset of machine learning. CNN is a convolutional neural network. The CNN approach is very effective in image classification. The target of this exploration is to assist a look acknowledgment framework dependent on …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 1, 2021 · pp. 30–35 Read article
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Generative AI for VR: Creating Physically Realistic Models
Abstract: Virtual Reality has revolutionized the traditional learning system by creating and interactive and engaging environment. However, its ability to show precise real-world experiences is limited due to lack of physical realism. This study investigates the potential of Generative Adversarial Network (GAN) in creating physically realistic 3D models. Proposed system incorporates deep learning techniques along with physics-based constraints to enhance model’s accuracy and usability. To achieve this, experiments were conducted on …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 3, 2025 · pp. 14–22 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 1–9 Read article
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Survey on Techniques to Segment Salt Bodies in Seismic Images
Abstract: The areas in the earth which are rich in oil and natural gas also have huge deposit of salt below the surface. So, it is very important for the hydrocarbon industries to acquire the needed resources. The important challenge of seismic imaging is to detect subsurface salt structure which is required for the identification of hydrocarbon reservoirs and drill path planning. Unfortunately, the accurate identification and classification of large salt …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 8, Issue 1, 2021 · pp. 25–30 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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The Evolution and Impact of Numbers: From Ancient Tallies to Quantum Computing: Review Article on Numbers
Abstract: Numbers are among the most fundamental constructs in human civilization, serving as the backbone of mathematics, science, technology, and virtually every aspect of daily life. They represent not only quantities and measures but also relationships, structures, and patterns that underpin the fabric of human understanding. From the earliest tallies etched on bones by prehistoric humans to the sophisticated numerical systems embedded in today’s artificial intelligence and quantum computing, the evolution …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 3, 2025 · pp. 15–19 Read article
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Using Machine Learning for Key phrase Extraction in Digital Libraries
Abstract: Machine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 8–13 Read article
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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article