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513 articles for “deep network”
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A Comparative Machine Learning Framework for Early Prediction of Liver Cancer Using Clinical Attributes
Abstract: One of the main causes of cancer-related death globally is liver cancer, and improving patient outcomes depends heavily on early detection. However, low contrast, noise, organ similarity, and tumor shape and size variability make it difficult to accurately identify and segment liver tumors from medical imaging. Automated liver cancer diagnosis, segmentation, and prognosis have been greatly improved by recent developments in artificial intelligence (AI), especially deep learning. This work presents …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 · pp. 39–47 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 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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Review Paper on Platform for Big Data Analytics as a Service with Stock Market Prediction and Analysis
Abstract: Petabyte-scale data is referred to as big data. The sum of the data in this quantity, which includes audio files, image files, and many more. Due to the unstructured nature of the data, it is challenging to evaluate the information that is gathered from it. As a result of social networking and cloud computing, data has significantly increased. It is therefore difficult to assess, process, and store. Big data processing …
Published in Journal of Advanced Database Management & Systems · Vol. 10, Issue 1, 2023 · pp. 1–5 Read article
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Advancements in Reinforcement Learning: A Comprehensive Analysis of Algorithms, Applications, and Future Directions in Artificial Intelligence
Abstract: This work provides an overview of Reinforcement Learning (RL), an important field of artificial intelligence (AI) aims to provide the long-term benefits by learning a relating with a given environment. It spells out everything, what agents and environments do, to how rewards, states, and behaviours. It spent lot of time on looking the most usable RL algorithms, like DQN, SARSA, and Q-Learning. These studies provide a clear view of RL. …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 1, 2024 · pp. 17–22 Read article
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Brain Tumor Detection Using RestNet50 Architecture
Abstract: This paper presents a novel deep learning model for brain tumor diagnosis from MRI scans on the basis of ResNet50 with some modifications. Optimizing the modified layers and pre-trained ResNet50 for improved diagnostic accuracy and reliability in real-world clinical settings is one of the key contributions of this paper. The model was trained on an extremely well-balanced data of 2,577 MRI scans, which were split equally among the tumor and …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 1–13 Read article
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Sentiment Analysis Using Emojis
Abstract: Sentiment analysis is a fast-growing research part in NLP (Natural Language Processing). It is fully focused on categorizing customer’s opinion about a particular product, blogs or comments etc. Public opinion has a significant impact on people's desire to contact with businesses, as well as overall brand perception. According to a Podium research, 93% of buyers believe online reviews affect their shopping decisions. Users may not give you another chance after …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 1, 2022 · pp. 43–46 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article
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Elderly Healthcare Using Federated Learning Approach
Abstract: The healthcare system for elderly people faces several challenges, which can be addressed using advanced machine learning models. These models can help monitor chronic diseases, detect falls, and provide personalized health recommendations. The study uses comprehensive datasets like MIMIC-III/IV, WESAD, and UCIHAR to explore human movements, device limitations, and the differences in fall occurrences. A detailed review of existing literature discusses current technologies for activity monitoring and fall detection, focusing …
Published in Current Trends in Information Technology · Vol. 16, Issue 1, 2025 · pp. 13–23 Read article
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A Study of DCGAN-Based Generative Models for Anime Character Face Generation
Abstract: Artificial intelligence, or AI, has in recent years moved from simple rule-based systems to models that are now fully capable of creative content generation and are referred to as generative AI. One such approach for content generation, introduced in the year 2014, is called generative adversarial networks (GANs), which consists of training a generator to create fake content that tries to mimic real content as closely as possible and a …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 31–41 Read article
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State of the Art: A Pandemic Big HealthCare Analytics Solution: Image Data Classification Using Quantum MAML
Abstract: The modern age is facing many pandemic healthcare problems, e.g., covid 19, infections, inflammations, and many more, leading to critical, deadly situations. Survival rate can be increased with proper diagnosis of such data. We have proposed one of the implementations based on a medical image dataset for classification using deep reinforcement learning (RL) with quantum computing. Deep RL is the combination of DL (deep learning), generative adversarial network (GAN), and …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 1–9 Read article
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Empowering Vehicle: The Impact of Deep and Reinforcement Learning in IoV
Abstract: Deep learning and reinforcement learning represent two pivotal pillars within the realm of artificial intelligence and machine learning, bearing transformative potential in the domain of the Internet of Vehicles (IoV). This abstract explores the multifaceted applications of these cutting-edge techniques within the IoV framework. Deep learning, exemplified by convolution neural networks (CNNs) and recurrent neural networks (RNNs), empowers IoV systems with the prowess to discern complex patterns in sensory data. …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 2, 2025 · pp. 1–12 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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Energy-efficient Image Classification on Edge Devices: Implementation and Evaluation
Abstract: Image classification is a computer vision problem where an algorithm determines a class or label for a given image. Various real-time applications like object recognition, medical diagnosis, person recognition, etc. Image classification property on edge devices is useful for autonomous vehicles, surveillance, and healthcare and internet of things deployments. The advancement of deep learning based methods and graphics processing units (GPU) devices allows efficient processing locally. The study utilizes a …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 10–18 Read article
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Image Processing and Deep CNN-based Automatic Liver Cancer Detection
Abstract: Liver cancer ranks among the leading causes of mortality for people worldwide. In the current situation, manually identifying the cancer tissue is a challenging and timeconsuming task. Treatment planning, response monitoring, tumor load assessment, and prediction are all made possible by the segmentation of liver lesions in CT scans. To address the current problem of liver cancer, the Hybridized Fully Convolutional Neural Network (HFCNN), which has been theoretically modeled, has …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 1, 2025 · pp. 39–41 Read article
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Deep-globe Challenge for Road Extraction Using Convolution Neural Network
Abstract: High-resolution lackey pictures contain a riches of information. They're too intense to decipher. For various operations, it's vital to snappily and straightforwardly distinguish streets from fawning pictures. The thought is to create a bracket demonstration to prize street systems from today’s pictures. The technique of the proposed strategy is grounded on the pre-processing of the disciple information to enhance the picture quality, which in turn comes about in superior comes …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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Fake Currency Detection Using Convolutional Neural Networks
Abstract: In today’s world, due to increasing technology like scanning, color printing, and duplicating, the identification of bogus notes by the human eye is almost getting impossible. Knowingly or unknowingly, due to the usage of bogus currency notes, the Indian economy is also being impacted badly. Hence, the identification of bogus currency notes is really important. This paper deals with regard to identifying whether the given sample of the currency note …
Published in Journal of Electronic Design Technology · Vol. 14, Issue 2, 2023 · pp. 9–17 Read article
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A Survey on Deep Learning based Detection of Abnormal Human Behaviour using Computer Vision Human Activity Recognition System
Abstract: Abnormal Human activity recognition (Abnormal HAR) systems are very popular among researchers nowadays, they attempt to identify and analyze human activities using acquired information from sensors. Several papers have already been published in the abnormal HAR topics, the technologies in this field have multidisciplinary nature. They need constant updates. Our literature survey divided the approaches into three categories, the first one is about wearable sensor-based approach, the second one is …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 3, 2021 · pp. 32–41 Read article
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Breast Cancer Detection and Multiple Classification Using CNN
Abstract: Although some efforts have been made in the form of preventative screening programs, breast cancer remains one of the rising causes of death in women. Computer-assisted diagnosis is needed because of the rapidly increasing number of mammograms that can be collected by these programs. Performance metrics are not significantly improved by computer aided detection methods designed to improve diagnosis without a large number of sequential readings. In this context, self-imaging …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 12, Issue 2, 2023 · pp. 28–38 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