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492 articles for “Deep learning models”
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Artificial Intelligence Based Plant Disease Detection
Abstract: This paper analyses the advanced Neural Network (NN) techniques for hyperspectral data processing, with a particular focus on detecting plant disease. To initiate, we will discuss the NN mechanism, types, models, and classifiers that are used to process the hyperspectral data using different algorithms. Following that, this project discusses the current state of imaging and non-imaging hyperspectral data for early screening and diagnosis of Disease. The NN-hyperspectral hybridization method has …
Published in Recent Trends in Programming languages · Vol. 8, Issue 3, 2021 · pp. 11–24 Read article
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AI-Driven Prediction of Square-Hole Laser Trepanning Performance in AA7075/15%SiC/15% Glass Fiber Hybrid Composites Using Taguchi–ANOVA and Deep Neural Networks
Abstract: Hybrid AA7075 composites reinforced with 15% silicon carbide (SiC) and 15% glass fiber were fabricated via the stir casting technique to improve machining and structural performance. The addition of dual reinforcements into the aluminum matrix was aimed at enhancing hardness, thermal stability, and surface quality during non-traditional drilling operations. Square-hole drilling was performed using a laser trepanning process, and the key responses—hole size accuracy, surface roughness, and taper angle—were systematically …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1932–1943 Read article
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Oceanmind Systems: AI-Driven Marine Life Intelligence for Climate Prediction and Ocean Ecosystem Stability
Abstract: Oceans regulate global climate systems, support biodiversity, and serve as critical carbon sinks, yet they remain under-monitored relative to their ecological importance. Traditional oceanographic methods rely heavily on satellite sensing, buoy networks, and periodic marine surveys, which often fail to capture real-time biological dynamics at micro-ecosystem levels. This paper introduces OceanMind Systems, an artificial intelligence (AI)-driven marine intelligence framework that integrates marine life behavior, oceanographic data, and computational modeling to …
Published in International Journal of Marine Life · Vol. 3, Issue 2, 2026 Read article
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Text to Image Using Machine Learning
Abstract: In the era of digital transformation, our project addresses the convergence of computer vision and natural language processing to enhance user interaction and visual content creation. This project comprises three distinct modules: user authentication and session management, image colorization from grayscale inputs, and text-to-image generation. The login registration module provides secure access to the system, ensuring user privacy and data integrity. Once authenticated, users can utilize advanced computer vision techniques …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 2, 2024 · pp. 35–41 Read article
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Sign Talk for Blind and Deaf Translating Hand Gestures into Audible and Textual Communication
Abstract: Sign language serves as a vital communication method for individuals who are deaf or have hearing impairments. It relies on hand gestures, facial cues, and body language to convey messages and express meaning. However, many people who do not know sign language find it hard to communicate with sign language users. The latest progress in artificial intelligence and computing has enabled the creation of systems that can automatically recognize sign …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 20–26 Read article
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Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection
Abstract: Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 3, 2024 · pp. 9–15 Read article
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Enhancing Glaucoma Diagnosis with Deep Learning: A Study Using ResNet-50 and DenseNet-121
Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, mainly resulting from progressive optic nerve damage, often related to elevated intraocular pressure. Early detection is essential to prevent vision loss, but traditional diagnostic methods rely on specialized equipment and trained professionals, making large-scale screening difficult. This study uses a publicly available fundus imaging dataset to explore the effectiveness of deep learning models for glaucoma detection. These datasets provide medical images, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 9–18 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 · pp. 41–49 Read article
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Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection
Abstract: Rapid advancement in the Internet of Things (IoT) changed everything, making it possible for seamless interconnectivity of devices and altering data-driven decision processes. This study delves into the intersection of IoT with deep learning approaches and hybrid approaches for managing botnet in IoT systems, especially security, efficiency, and performance optimization. Leveraging deep learning models, for example, CNNs and RNNs, will help the network achieve more intrusion detection and data analysis. …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 18–27 Read article
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Animal Detection in Farms Using Opencv
Abstract: Agriculture plays a fundamental role in sustaining the Indian economy, providing employment and livelihood to a large portion of the population. Despite advancements in farming techniques, one of the persistent challenges faced by farmers is the intrusion of wild animals into agricultural fields. Such intrusions often lead to large-scale crop damage, financial loss, and emotional distress for farmers. Traditional animal deterrent methods, such as manual patrolling, fences, or scarecrows, have …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 Read article
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Real-time Facial Recognition with Convolutional Neural Networks for Personalized Music Therapy
Abstract: In this project, a web-based application has been developed that integrates computer vision-based facial recognition, multiple algorithms, and machine learning approaches. The given system obtains a user’s emotions in the real-time frame by analyzing facial expressions such as eyes, mouth, the forehead, and so on. It detects emotions like happiness, sadness, that is neutrality, or rock. For a given detected emotion, language, and a user’s chosen artist, the system recommends …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 67–76 Read article
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AI-Based Machine Learning Web Application Firewall (ML-WAF)
Abstract: This research investigates the use of deep learning techniques for the real-time detection of malicious activities in web traffic and proposes an intelligent, AI-driven Web Application Firewall (WAF) designed to provide automated and adaptive security. The system analyzes diverse components of HTTP requests, including request methods, URLs, headers, cookies, and payload content, to accurately identify and classify malicious behavior. The proposed model targets a wide range of common and critical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Transformer Health Monitoring System
Abstract: Rising demands for reliable and efficient power distribution in modern electric control grid increasingly call up for robust monitoring systems for critical substructure. Being a vital part of the power conduction system, transformer are subjected to mechanical, electrical, and environmental stresses, which, if not properly controlled, can cause failures. In this project, we propose a Transformer Health Monitoring System (THMS) using machine learning (ML) models and real-time monitoring method to …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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SATELLITE IMAGE CLASSIFICATION USING MACHINE LEARNING
Abstract: Satellite imagery is used in for various applications such as disaster management, defence, surveys, and environmental understanding. Earlier, manual processing was applied to do analysis. These methods are not accurate to detect the target and classify the object. Hence, in this work machine learning is explored. Now a days deep learning has gain prominence to automate target detection and classification. Convolutional Neural Networks (CNN) are playing vital role in deep …
Published in Journal of Remote Sensing & GIS · Vol. 12, Issue 2, 2021 · pp. 10–18 Read article
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Wireless and Wearable Real-time Health Monitoring Systems: A Review of Recent Developments
Abstract: This paper explores the interdependent relationship between mental and physical health, highlighting the impact of stress and anxiety on the body by introducing an innovative Real- Time Health Monitoring System. This system monitors both mental well-being and physical health using advanced physical health sensors and Facial Emotion Recognition (FER) technology. Leveraging high-precision sensors such as Temperature and pulse oximeters, it continuously monitors vital physiological parameters such as Body Temperature and …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 1, 2024 · pp. 1–7 Read article
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A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs)
Abstract: Mobile Ad Hoc Networks (MANETs) comprise wireless networks that lack any conventional infrastructure . Their chief features include highly changing network topologies, lack of centralized administration, and open nature of communication, which collectively result in making MANETs of the wireless kind very susceptible to a diverse range of cyber-attacks like blackhole, greyhole, wormhole, flooding, Sybil and denial-of-service (DoS) among others. Conventionally, Intrusion Detection Systems (IDS) relying on static rule-based methods …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Traffic Density Estimation Using Image Frames in Python
Abstract: Traffic congestion is a critical challenge in modern urban environments, leading to significant delays, environmental pollution, and increased accident rates. With the rapid growth of vehicle populations in metropolitan areas worldwide, the need for intelligent and automated traffic monitoring systems has become increasingly urgent. Traditional monitoring approaches, such as inductive loop detectors and manual surveillance, are limited in scalability, deployment cost, and adaptability to dynamic traffic scenarios. This paper presents …
Published in Trends in Transport Engineering and Applications · Vol. 13, Issue 2, 2026 · pp. 38–45 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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Multinomial classification Identification for Domestic Violence Virtual Posts Based on Improved Convolution Neural Network (ICNN)
Abstract: AbstractDomestic violence isn’t only about the physical violence but further any conduct the purpose of which is to gain power and manage over a spouse, partner, girl/boyfriend or intimate own circle of family member which leads to the violation of human rights. Through the web-based networking media domestic violence crisis support (DVCS) have demonstrated fundamental help directions to abused people and their families. The unrivaled outcomes in online content description …
Published in Journal Of Network security · Vol. 8, Issue 3, 2020 · pp. 1–10 Read article
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Automated Vehicle Entry Monitoring System Using YOLOv5
Abstract: This project showcases an innovative You Only Look Once (YOLO) object detection model-based Automated Vehicle Entry Monitoring System for community gates. By using YOLO, the system transforms conventional access control paradigms by accurately and in real-time detecting vehicles that are seeking to gain entry. Unlike traditional approaches, the project leverages YOLO's effectiveness in vehicle recognition, classification and Number Plate Detection to improve residential security. By providing communities with a cutting-edge …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 2, Issue 2, 2024 · pp. 34–38 Read article