Search
412 articles for “Neural Network Techniques”
-
Intelligent Big Data Processing: Optimizing Analytics Pipelines Through Ensemble Machine Learning, Distributed Computing, and Adaptive AI Architectures
Abstract: Traditional data analytics methods are facing significant challenges due to the explosive rise of digital data. Global data generation is expected to reach nearly 175 zettabytes by 2025. This puts significant pressure on existing computing infrastructure. Conventional analytics methods often struggle with scalability, processing speed, and smart decision-making. Rule-based systems and standalone machine learning models frequently cannot handle the complexity and scale of modern big data environments effectively. To tackle …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 1–11 Read article
-
Research and Industrial Insight: Neuroscience
Abstract: Neuroscience is the scientific learning of the nervous system. Conventionally, neuroscience has been seen as a subdivision of biology. However, it is presently an interdisciplinary science that in partnership with other fields such as chemistry, cognitive science, computer science, engineering, linguistics, mathematics, medicine (including neurology), genetics, and allied disciplines accounting with philosophy, physics, and psychology. It influence on other fields, such as neuroeducation, neuroethics, and neurolaw. The term neurobiology is …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 6, Issue 1, 2016 · pp. 31–33 Read article
-
Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
-
Stock Price Movement Prediction using Machine Learning Algorithms and Time Series Models: A Review
Abstract: The stock market is a very important place for investment in a country. It also serves as the index of the growth for an economy. To determine the behavior of the stock market trends has been the focal point of researchers for very long. The nonlinear structure of the stock market makes it challenging to predict how the stock price will change. But it has been demonstrated that stock market …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 2, 2022 · pp. 7–13 Read article
-
AI, Robotics, and the Future of Waste Management: A Systematic Review of Advanced Collection and Sorting Systems
Abstract: The rapid growth of cities and rise in population have made waste management a major concern that calls for innovative and efficient solutions. Conventional waste collecting techniques are dangerous, time-consuming, and frequently ineffective. The development of automated waste management systems powered by cutting-edge technology like robotics, deep learning, artificial intelligence (AI), and the Internet of Things (IoT) is examined in this study. Vision-based systems, convolutional neural networks (CNN) for garbage …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 1, 2025 · pp. 1–6 Read article
-
Machine Learning Optimization for VARTM Carbon Polymer Laminates
Abstract: Vacuum-assisted resin transfer moulding (VARTM) is a key low-cost, out-of-autoclave process for manufacturing large-scale carbon-fibre reinforced polymer (CFRP) laminates crucial to aerospace wings, wind-turbine blades, marine hulls, and automotive structures. Unpredictable resin flow often leads to voids, dry spots, and race-tracking defects, resulting in 27.9% scrap rates and lengthy, costly trial-and-error design cycles. Although surrogate models provide rapid impregnation predictions for simple flat-plate geometries, vision-based monitoring is limited to idealized …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 229–245 Read article
-
Alzheimer Detection Using MRI Imaging Modality
Abstract: Alzheimer’s disease (AD) is a neurological disease that affects memory and livelihood of the people that are diagnosed with it. Many different imaging modalities have been used to help diagnose the disease. Each of these modalities offers something different towards the detection and possible treatments for AD. In this project, we developed a new approach based on mathematical and image processing techniques for better classification of AD. We proposed to …
Published in Current Trends in Signal Processing · Vol. 6, Issue 1, 2016 · pp. 11–17 Read article
-
Alzheimer’s Disease Classification Based on Transfer Learning of New-CNN Model
Abstract: The long-term, irreversible brain disorder “Alzheimer’s disease (AD)” currently has no known cure. Nonetheless, current medications may impede their advancement. Globally, those over 65 are the primary population affected by Alzheimer’s disease. Accurate detection of this condition requires early diagnosis. Because there are so many people who come with an ailment, manual diagnosis by health specialists is laborious and prone to error. Early detection of AD is a difficult undertaking …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 16–23 Read article
-
Drug Screening Using Stem Cell Technology
Abstract: Stem cell technology in drug screening has transformed the research and development sector of pharmaceuticals. Although stem cells imitate their human counterparts in terms of both safety and potential, it is a new paradigm for mimicking diseases of humans and scale measuring for toxicity. These tissues will, therefore, be more predictive of what will happen in humans during clinical situations, compared to conventional cell lines or animal models, since they …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 3, 2025 · pp. 42–51 Read article
-
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
-
Recipe-Fusion: Multimodal Food Recipe Recommendation System
Abstract: The food recipe recommendation system using data science is a software solution designed to help users discover new and delicious food options based on their food history and other relevant data. This system recommends various recipes based on the input given by the user and it helps to filter out the recipes on course type, diet type, and nature of the food (including non-veg, and veg) using a recommendation technique. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 82–91 Read article
-
An Abnormal Expression Detection System (AEDS) Using Deep Learning Algorithms
Abstract: In the last decade, many deep learning algorithms have achieved remarkable success and gained popularity in various computer vision tasks, including object detection, image recognition, and segmentation. This AEDS (Abnormal Expression Detection System)leverages the power of deep learning algorithms to detect abnormal facial expressions in real-time automatically. AEDS proposed two important models; those are Deep CNN and RNN. CNN is responsible for learning discriminative features from facial images and capturing …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 72–79 Read article
-
Empowering Healthcare Through IoMT: Unveiling the Architecture, Protocols, and Applications
Abstract: The "Internet of Things" (IoT) is the infrastructure of web-connected devices that automatically gather and transport data through a remote network. Due to the rapid growth of IoT-based gadgets and their expansion towards making medical care facilities financially smart, proactive, and customizable, the "Internet of Medical Things (IoMT)" is presumed proactive in all areas of healthcare. Using cutting-edge technology, the IoMT-based healthcare system offers rapid and reliable treatment, increased communication, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 10, Issue 2, 2023 · pp. 10–23 Read article
-
A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture
Abstract: Soil health is a critical factor in ensuring sustainable agricultural practices and food security. Traditional methods for soil health assessment are often time-consuming, localized, and lack scalability. This study explores the integration of cloud-enabled deep learning techniques to monitor and predict soil health efficiently. Leveraging data from IoT sensors, satellite imagery, and lab-based analyses, a cloud-based framework is proposed to process and analyze soil health parameters such as pH, moisture …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 2, 2025 · pp. 8–16 Read article
-
The Adoption of Artificial Intelligence in Different Network Security Concepts
Abstract: The obstacles of each security system combined with the increase of cyber-attacks, negatively affect the effectiveness of network security management and rise the activities to be taken by the security staff and network administrators. So, there is a growing need for automated auditing and intelligent reporting strategies for reliable network security with as less model complexity as possible. Newly, artificial intelligence has been effectively applied to various network security issues, …
Published in Journal Of Network security · Vol. 11, Issue 3, 2023 · pp. 30–43 Read article
-
Fabrication and drilling characterization of Hemp & Grewia-Optiva hybrid composite and comparison with ANN model
Abstract: In recent days the use of natural fibers has increased over synthetic fibers due to various advantages. The alkali treatment for these natural fibers further improves the adhesion between fiber and matrix and greatly enhances the mechanical properties of the composite. The present study involves the fabrication of a hybrid composite using the alkali-treated (5% NaOH concentration) natural fibers – Hemp &Grewia-optiva as reinforcement material and epoxy as a matrix …
Published in Journal of Polymer & Composites · Vol. 12, Issue 3, 2024 · pp. 138–146 Read article
-
Fabrication and Drilling Characterization of Hemp & Grewia-Optiva Hybrid Composite and Comparison with ANN Model
Abstract: In recent days the use of natural fibers has increased over synthetic fibers due to various advantages. The alkali treatment for these natural fibers further improves the adhesion between fiber and matrix and greatly enhances the mechanical properties of the composite. The present study involves the fabrication of a hybrid composite using the alkali-treated (5% NaOH concentration) natural fibers – Hemp &Grewia-optiva as reinforcement material and epoxy as a matrix …
Published in Journal of Polymer & Composites Read article
-
A Study on Machine Learning and Cryptographic based Intrusion Detection System in Blockchain and Data Science Environment
Abstract: The recent speedy development of Internet of Things, vehicular networks and cloud computing has greatly boosted the need of effective and safe intrusion detection systems. The shortcomings of the traditional IDS-based approaches are centralized architecture, issue of scalability, and inability to analyze the encrypted and distributed data. To address these issues, the recent studies have paid attention to the incorporation of the latest technologies such as machine learning, deep learning, …
Published in International Journal of Wireless Network Security · Vol. 4, Issue 2, 2026 Read article
-
A Neuromorphic-Inspired, Low-Power VLSI Architecture for Edge AI in IoT Sensor Nodes
Abstract: As the proliferation of Internet of Things (IoT) devices continues to rise, there is an increasing demand for real-time, energy-efficient artificial intelligence (AI) processing directly at the network edge. Traditional edge AI accelerators, often based on deep learning models like convolutional neural networks (CNNs), struggle to meet the ultra-low-power requirements of battery-constrained IoT sensor nodes. In response to this challenge, this study introduces a neuromorphic-inspired, low-power very- large-scale integration (VLSI) …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 41–47 Read article
-
The Rise of Fractional Calculus: Novel Applications in Engineering and Biological Systems
Abstract: Fractional calculus (FC) is an advanced mathematical framework that generalizes the classical concepts of differentiation and integration to non-integer, or fractional, orders. This extension of traditional calculus allows for the modeling of complex dynamic systems that exhibit behavior not easily captured by integer-order differential equations. Over the last few decades, fractional calculus has seen a rapid rise in popularity, particularly in applied mathematics, engineering, and biological sciences, due to its …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 7–11 Read article