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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
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Deep Learning models for real time detection of crop diseases in the Maharashtra/Mumbai district
Abstract: This research project addresses the critical agricultural challenge of crop disease management in the Maharashtra region of India by leveraging modern deep learning techniques. The primary objective is to identify, implement, and compare the efficacy of various deep learning architectures—including Convolutional Neural Networks (CNNs), MobileNet, and EfficientNet—for the real-time classification of diseases in key crops such as cotton, soybean, and sugarcane. A custom dataset of agricultural images specific to Maharashtra's …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 36–48 Read article
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Modern Heritage Restoration Using GANs: Insights into Polymer–Composite Aging
Abstract: In the Era of modernization, accomplish a Net Zero goes beyond installing more solar panels and wind turbines but we also need to make sure their lifespan possibility. With the help of Polymer compound for protective coatings to find the applications in solar cells, turbine blades, batteries that degrade over the course of time causes by sun exposure, moisture, and pollution. However, due to these same types of degradation have …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 508–516 Read article
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CNN-Based Diagnosis of Skin Cancer from Dermoscopic Images
Abstract: Skin cancer has become one of the diseases widely spread over the globe, with melanoma becoming a severe threat to one’s health. Detection of such diseases at the initial stage saves an individual from drastic damage. Using a Convolutional Neural Network (CNN) for detecting skin cancer through image classification as benign or malignant provides significant support to dermatological practice and reduces dependence solely on subjective visual examination. Dermatologists often face …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 1, 2026 · pp. 37–42 Read article
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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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SkinSight: Design and Implementation of an Intelligent Skin Type Detection System
Abstract: Identifying an individual’s skin type accurately is essential for creating personalized dermatological treatments and formulating skincare products that genuinely meet user needs. In this project, a real- time skin type classification system is developed using a combination of convolutional neural networks (CNNs) and modern computer vision techniques. The system processes live video streams, isolates the facial region through Haar cascade–based detection, and applies a series of preprocessing steps to enhance …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 35–45 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Intelligent Brain Tumor Diagnosis with AI-Based Classification* * Harnessing Deep and Machine Learning for Tumor Identification
Abstract: Brain tumors have become a leading cause of cancer- related deaths, posing significant health risks to many patients. This urgent medical challenge calls for rapid, automated, and reliable techniques to detect brain tumors accurately. Timely and precise tumor identification is crucial for devising effective medical plans that have the potential to save lives and improve patient outcomes. By leveraging advanced image processing methods, healthcare professionals can enhance their diagnostic capabilities …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 1, 2026 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 Read article
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection
Abstract: The security and operational dependability of Internet of Things (IoT) networks are seriously threatened by the growing susceptibility to Distributed Denial of Service (DDoS) assaults brought about by their rapid expansion. The intricacy and dynamic character of these advanced attacks can provide a challenge to conventional intrusion detection systems. This study presents a novel method for strengthening IoT network security by combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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A Machine Learning-Based Non-Invasive System for Blood Group Prediction Using Fingerprint Biometrics
Abstract: The research is targeted at the creation of innovative solution "Fingerprint Based Blood Group Prediction" for instant, non-invasive blood group determination from analysis of finger impressions, a breakthrough possibility in emergency health care. Sophisticated machine learning can be employed to map fingerprint patterns to corresponding blood group information and overcome the current lack of a direct connection between the two. Integration of various technologies: employed React for frontend development, Flask …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 9–18 Read article
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An Intelligent Neural Networks Approach for Monitoring of Soilless Urban Farms
Abstract: Urban agriculture is increasingly recognized as a sustainable approach to addressing food security challenges in rapidly growing and densely populated cities. Conventional soil-based farming often faces limitations such as space scarcity, excessive water consumption, and environmental degradation. To overcome these challenges, soilless farming techniques such as hydroponics and aeroponics have gained significant attention due to their efficient utilization of space, reduced water requirements, and potential for year-round crop production. However, …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 31–37 Read article
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AGRISMART: Crop and Soil Management System
Abstract: Agriculture has played a crucial role in developing countries where the majority of the rural population relies on it for their livelihoods. A finer-grade crop classification has become crucial in the context of precision agriculture. In recent years, the volume of open image data has grown significantly. This can be used in combination with machine learning techniques to classify crop types in the agricultural industry. The proposed crop species recognition …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 50–55 Read article
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Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System
Abstract: Convolutional Neural Networks exhibit remarkable capabilities in flow pattern recognition, pressure drop prediction, leak detection, and system optimization through their ability to process complex spatial and temporal data patterns. The study examines CNN architectures specifically adapted for fluid dynamics applications, including data preprocessing techniques, feature extraction methods, and performance optimization strategies. Key applications include real-time flow monitoring, predictive maintenance, design parameter optimization, and anomaly detection in pipe networks. Comparative analysis …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 1–9 Read article
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Implementing Machine Learning in Data Classification
Abstract: Data classification forms an essential aspect of artificial intelligence (AI) and soft computing, helping a great deal in the transformation of raw data into knowledge that forms the basis of numerous applications, such as fraud detection, medical diagnostics, and natural language processing. This study discusses the challenges and the state of the art in data classification, as far as scalability, noise handling, and feature selection optimization are concerned. It gives …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 15–22 Read article