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513 articles for “deep network”
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Image Super-resolution Using Super-resolution Convolutional Neural Network
Abstract: Several studies have been conducted to improve low-resolution photos to a higher resolution version utilizing classic super-resolution methods such as pixel-wise interpolation. While the resolution of images has been rapidly increasing in recent years with the development of high-performance cameras, advanced image compression, and display panels, the demand to generate a high-resolution image from pre-existing low-resolution images is also increasing for rendering on high-resolution displays. Some of these methods involve …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 2, 2022 · pp. 33–39 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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An Examination of Energy-Saving Techniques for UAV Communication
Abstract: Unmanned aerial vehicles (UAVs), or drones, have become essential in various industries due to their low deployment costs and exceptional versatility. As a result, they are widely used in industries like mining, agriculture, logistics, and search and rescue. The effectiveness of UAV applications is largely dependent on robust communication technology, which is crucial for control, data transmission, and coordination. However, the limited capacity of the low-power batteries used in UAVs …
Published in International Journal on Drones · Vol. 1, Issue 1, 2025 · pp. 1–7 Read article
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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
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article
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Deep Learning Approaches for Nanoplasmonics: Expanding Horizons
Abstract: Deep learning, a subfield of machine learning, has revolutionized various domains by providing powerful tools to process and analyze complex data. In the realm of nanoplasmonics, deep learning techniques have emerged as promising approaches to tackle the challenges associated with large-scaledata analysis, modeling, and optimization of plasmonic systems. This article presents an in-depth exploration of deep learning approaches applied to nanoplasmonics research, focusing on the advances, opportunities, and future directions …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 13, Issue 1, 2023 · pp. 30–38 Read article
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Analysis and Identification of Malicious Mobile Applications Using Machines Learning
Abstract: Over the past few years, malware attacks on the Android platform have surged, posing significant risks to users' financial security, personal information, and device integrity. In the first half of 2019 alone, approximately 25 million smartphones were infected, highlighting the severity of these threats. The model ranks manifest features based on their frequency in normal and malicious apps, identifying key components that distinguish benign from malicious applications. To improve detection …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 17–24 Read article
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Recent Advances in Content-based Image Retrieval: Techniques and Applications
Abstract: Content-based image retrieval (CBIR) plays a vital role in computer vision, driven by the increasing need for fast and accurate image retrieval across fields like healthcare, e-commerce, and digital libraries. This study offers a detailed review of CBIR methodologies, charting their progression from traditional feature extraction techniques, such as Local Binary Patterns (LBP), to contemporary deep learning-driven methods. The transformative impact of convolution neural networks (CNNs) is highlighted, emphasizing their …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 67–71 Read article
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Survey on Progress in Agricultural Yield Prediction-from Machine Learning to Deep Learning
Abstract: Machine learning has offered new prospects for data-intensive study in the multidisciplinary field of Agri-technology, especially with the rise of big data systems and advanced computers. Machine learning is a powerful decision-making tool for predicting crop yields, as well as determining which crops to plant and what activities to engage in during the crop's growing season. To facilitate the study of the prediction of the agricultural yield many techniques of …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 12, Issue 1, 2022 · pp. 6–16 Read article
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Visual Recognition with Convolutional Neural Networks for Object Detection
Abstract: Various research and development have taken place over the years on computer vision which is a branch of AI. AI disciplines like a vision system is applied in various fields like self-driving cars, face detection by social media apps and law enforcement software’s google lens and so on. The proposed system deals with design and implementation of an efficient way of training a GPU using python libraries to process and …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 2, 2024 · pp. 07–13 Read article
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Big Data Dimensionality Reduction Technique with Deep Autoencoder
Abstract: Dimensionality reduction refers to the act of narrowing the focus from an abundance of potential variables to a more manageable set. Google created the open-source Flutter framework for creating mobile apps. The issue of high-dimensional data hindered the system's effectiveness in terms of providing correct findings (the quantity of false-positive, negative, true-positive, and negative outcomes). The process of solving a classification problem, carrying out a precise visualization, and transmitting vast …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 1–13 Read article
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Deep Learning Based Plant Disease Detection
Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of images of diseased and healthy plant leaves collected under controlled …
Published in Journal Of Network security · Vol. 8, Issue 2, 2020 · pp. 33–42 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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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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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article
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Performance Evaluation of Topology Based Routing Protocols for VANET in City Scenario
Abstract: Vehicular Ad-hoc Networks (VANET) is an emerging technology that will help in increasing road safety of commuters and comfort of passengers. In this paper, we compare various topology based routing protocols Sequence Distance Vector (DSDV), Ad-hoc on Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) that are Mobile Ad-Hoc Network (MANET) protocols for their behavior in VANET networks, that is a subclass of MANET based on few parameters. The …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 3, Issue 1, 2016 · pp. 47–54 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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Speech Emotion Recognition using Convolutional Neural Networks
Abstract: This research work presents an assortment of techniques in Speech Emotion Recognition with the help of spectrograms and handcrafted Deep learning architecture: Convolutional Neural Networks (CNN). Emotional kingdom detection is a crucial part of human-device interplay studies. To make interaction between man and machine more natural, many milestones are conquered in speech emotion recognition, but still this process requires more up-to-the-mark results. To make an attempt for the same, this …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 3, 2021 · pp. 26–32 Read article