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492 articles for “Deep learning models”
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A Review of Various Deep Learning Models for Classification of BrainTumor
Abstract: Brain Tumor is most challenging and life threading decease now these days. Timely accurately detection of brain tumor is play important role for patient health improvement and it increase life survival rate. Traditional of brain tumor detection relay heavily on manual interpretation of medicalimage, it is very time consuming and chance to human error in detection of brain tumor. Now in recent advancement of Artificial intelligence (AI), Machine learning (ML) …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Trust Based Deep Learning Model for Women Information Security Improvement in LBS
Abstract: The pervasiveness of mobile devices equipped with positioning proficiencies has managed to the emergence of frequent location-based applications and services. A huge fraction of the information sought is related to the current women position. This comprises queries for nearby medical services, specialized stores, social activities and groups, and others. In general, location-based service (LBS) operators are expected to be trusted parties that preserve the user’s privacy. Due to the sensitive …
Published in Journal Of Network security · Vol. 7, Issue 1, 2019 · pp. 18–23 Read article
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Revolutionizing Plant Disease Detection: A Comprehensive Review
Abstract: Rise in population demands more food production but the diseases in plants contribute to loss. The advancement in agricultural field has a remarkable effect in detecting plant diseases. These diseases will have a major impact on the quality of plant and yield and hence can destroy the entire plant if they are not controlled on time. To reduce disease-related losses, it is necessary to identify different types of diseases and …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 2, 2023 · pp. 44–55 Read article
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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An Empirical Study of Hyperparameter Impact on Deep Learning Models for Cardamom Leaf Disease Classification
Abstract: Recent advancements in deep learning models like convolutional neural networks and self- attention mechanisms have achieved great success in the field of plant disease classification. This study investigates the efficacy of two pre-trained models, ConvNeXT-Tiny and Swin Transformer-Tiny, for leaf disease classification in cardamom using a publicly available dataset constituting three categories of leaves, namely Healthy, Colletotrichum Blight and Phyllosticta Leaf Spot. The effectiveness of the models highly depends on …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 48–60 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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An Enhanced Application Oriented Predictive Analytic using Optimal Deep Learning Model
Abstract: In order to presents better Predictiveanalytic models of different data using various machine learning algorithms. The data prediction is developed on various applications. These applications are facing many challenges while collecting data and data selection etc. The proposed prediction model involves several phases like (a) Data acquisition, (b) Data cleaning, (c) Data normalization, (d) Optimal Feature selection, and (e) Prediction. The optimal feature selection will be accomplished by a new …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 7, Issue 2, 2020 · pp. 5–10 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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A Comparative Study of Transfer Learning-Based Deep Learning Models for Breast Cancer Detection
Abstract: Breast cancer is a major concern in the world today, and early and accurate diagnosis is most crucial in the case of breast cancer, as it is among the disorders where the total cost of loss of life is high. Traditional screening processes are subjective and vulnerable to inter-observer reliability issues and diagnostic errors, being primarily based on manual interpretation of medical images. To address these limitations, Deep Learning (DL) …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 · pp. 24–34 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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COVID-19 Detection through Deep Learning Models using Chest X-ray
Abstract: Detection of COVID-19 in a minimum amount of time is one of the biggest challenges in front of us because COVID-19 is a rapidly spreading disease. Patients are increasing in a rapid amount of time. Hence, we proposed an AI-technique based deep-convolutional neural network to detect COVID-19 patients using real-world data. In our proposed system, chest X-ray images are used to find out COVID- 19 patients. X-rays are available very …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 2, 2021 · pp. 11–16 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Artificial Intelligence and IoT Integration for Real-Time Violence Monitoring
Abstract: The peace and tranquility of any place can be affected greatly by the insurgence of violence and violent attacks that are perpetrated by individuals with malicious and nefarious intentions. These individuals terrorize the areas and can cause a lot of harm and damage to people and public property. The incidences of violence are undesirable and can be problematic to handle by the law enforcement agencies, as these acts are committed …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 39–45 Read article
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Deep Learning-Based Pneumonia Diagnosis: A Comparative Review of Models and Metrics
Abstract: Pneumonia is a common viral infection that affects a large percentage of people worldwide. It is more common in developing and impoverished areas because of factors like poor sanitation, crowded living quarters, pollution in the environment, and restricted access to medical facilities. In order to improve survival chances and gain access to therapeutic therapies, pneumonia must be diagnosed as soon as possible. A type of artificial intelligence called deep learning …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 Read article
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Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis
Abstract: This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges
Abstract: The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 88–96 Read article
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Optimized Sentiment Analysis Through TextBlob and Hybrid RNN Models
Abstract: In today’s world, analyzing people’s feelings from what they write online has become very important. This is because there is a large amount of content created by users. To make this analysis accurate and fast, we present a method. This method uses a mix of two approaches: one that looks up words in a dictionary and another that uses computer learning. TextBlob is an affordable tool for getting an initial …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 29–28 Read article
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Prostate Cancer Detection Using Deep Learning
Abstract: Prostate Cancer is a cancer occurs in prostate gland which is located in male reproductive system. According to the WHO (World Health Organization) the estimated cancer cases in the year 2020 is about 1.4 Million. Prostate cancer is one of the reason for death in men. In this paper we have presented Secondary verification tool for doctors or for normal users to check patient have a cancer or not. And …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 21–26 Read article
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Sign Language and Face Expression Recognition Using Neural Networks: Deep Learning Approach to Break Communication Barriers
Abstract: Our study proposes a multimodal gesture recognition system specifically designed to aid communication for the deaf community. By employing neural network concepts, we utilize 3D convolutional neural networks (3D CNNs) to extract features from both hand and face images, focusing on relevant regions. Preprocessing techniques are applied to isolate these areas of interest prior to feature extraction. Unique 3D CNN architectures are then trained for each modality to capture the …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article