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From image classification to segmentation: a comprehensive empirical review

Iran Journal of computer science 2025年 1-31页

作者： Ruksana Jim, Jamin Rahman Hossain, Md Jonair Das, Aritra Kabir, Md Mohsin Mridha, M.F. Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka 1229 Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207 Bangladesh Department of Computer Science and Engineering Bangladesh Open University Gazipur 1700 Bangladesh

With the development of the machine learning age, deep learning techniques for computer vision tasks use real-world data to analyze problems across various fields. In particular, it has achieved significant success in image classification, segmentation, and detection on different datasets. As the area of deep learning expands, annotated datasets can be challenging owing to various factors such as the labeling process or difficulty in correctly classifying data, even for experts. Therefore, an extensive survey of machine learning techniques is introduced. Through empirical analysis, several deep learning architectures were applied to benchmark datasets, such as ImageNet, CIFAR-10, COCO, and MNIST. This study includes Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variants in these datasets. Additionally, this work reviews the experimental results from modern studies and explains the methodological nuances and comparative performance. In addition to examining the relevance of model performance to preprocessing techniques, various evaluation metrics and promising challenges with future directions for new frontiers are also discussed. Subsequently, this research investigation addresses challenges such as dataset characteristics, model complexity, and computational efficiency and provides insights into the practical implementation of deep learning solutions for real-world applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： CIFAR-10 Classification COCO Convolutional neural networks Deep learning Detection ImageNET MNIST Segmentation

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Joint Power Control and Multi-Path Routing for Internet of Underwater Things in Varying Environments

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IEEE Internet of Things Journal 2025年第11期12卷 15197-15210页

作者： Xu, Cangzhu Song, Shanshan Liu, Jun Pan, Miao Xu, Gaochao Cui, Jun-Hong Jilin University Department of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Smart Ocean Technology Co. Ltd Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Chengdu China Jilin University College of Computer Science and Technology Changchun130012 China

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput. © 2014 IEEE.

关键词： Routing Reliability Power control Routing protocols Energy efficiency Relays Internet of Things Sensors Reliability engineering computer network reliability

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Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance with EDA and Multi-Channel CNN

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IEEE Access 2025年 13卷 31978-31991页

作者： Wahyu Trisna, Komang Huang, Jinjie Chen, Yuanjian Gede Juliana Eka Putra, I. Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Primakara University Department of Informatic Engineering Denpasar Bali80226 Indonesia Harbin University of Science and Technology Key Laboratory of Advanced Manufacturing and Intelligence Technology Harbin150080 China

The rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. However, the limited availability of labeled datasets and challenges related to data imbalance often hinder the development of robust sentiment analysis models. In this study, we propose a novel framework dynamic Easy Data Augmentation (EDA) technique with a Multi-Channel Convolutional Neural Network (MCNN) to address these issues. The EDA techniques including synonym replacement (SR), random insertion (RI), random deletion (RD), and random swap (RS), enrich the training data by introducing diversity, thereby enhancing model robustness. The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. Furthermore, the integration of Word2Vec embeddings ensures meaningful text representation, leveraging context-independent word relationships for improved sentiment analysis. Experimental evaluations demonstrate the effectiveness of the proposed approach, highlighting significant improvements in F1-score compared to baseline models. The findings underscore the potential of dynamic text augmentation and efficient deep learning architectures to overcome the challenges of limited training data, offering a scalable solution for sentiment analysis in real-world applications. Additionally, the proposed model achieves an average performance improvement of 11.34% over previous models. © 2025 The Authors.

关键词： Convolutional neural networks

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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering National Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Thermal Characterization and Design of AlN/GaN/AlN HEMTs on Foreign Substrates

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 817-820页

作者： Kim, Seokjun Kim, Eungkyun Walwil, Husam Shoemaker, Daniel C. Encomendero, Jimy DeJarld, Matthew T. Tahhan, Maher B. Chumbes, Eduardo M. Laroche, Jeffrey R. Jena, Debdeep Xing, Huili G. Choi, Sukwon The Pennsylvania State University Department of Mechanical Engineering University ParkPA16082 United States Cornell University School of Electrical and Computer Engineering Department of Materials Science and Engineering IthacaNY14850 United States Raytheon Advanced Microelectronics Solutions AndoverMA01810 United States Raytheon Advanced Technologies TewksburyMA01876 United States

AlN/GaN/AlN high electron mobility transistors (HEMTs) offer enhanced carrier confinement and higher breakdown voltage than conventional AlGaN/GaN HEMTs. In this work, Raman thermometry was used to characterize the self-heating behavior of a single-finger AlN/GaN/AlN HEMT on 6H-SiC. A 3D finite element analysis model was created to optimize the thermal design of the device structure. Simulation results reveal that the optimal buffer layer thicknesses to minimize the channel temperature rise of AlN/GaN/AlN HEMTs on 6H-SiC and diamond substrates are ∼2 μm and ∼0.7 μm, respectively. Moreover, diamond substrate integration further enhances the thermal performance, achieving a ∼45% and ∼53% reduction in the device thermal resistance as compared to those of an AlN/GaN/AlN HEMT on 6H-SiC and an AlGaN/GaN HEMT on 4H-SiC, respectively. © 1980-2012 IEEE.

关键词： Gallium nitride

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Social Group Oriented Recommendation Systems in Wireless Edge-Empowered Metaverse

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IEEE Network 2025年第3期39卷 270-276页

作者： Li, Xinping Hong, Yan Zhang, Meng Xie, Xiangpeng Zhang, Jie Guo, Song Soochow University College of Textile and Clothing Engineering Suzhou215021 China The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Metaverse incorporates a variety of information technologies such as advanced wireless communications, edge computing and virtual reality to create a multi-dimensional, intelligent and powerful virtual world. However, the large number of products and user groups can lead to difficulties in matching users with Metaverse products. In this paper, we present a Metaverse-oriented user preference recommendation system that strengthens the connection between Metaverse users and products by matching the vast array of products in the Metaverse with user preferences. In order to solve the problem that the classical collaborative filtering recommendation algorithm is inefficient in dealing with a large amount of multidimensional information in Metaverse, the clustering algorithm is introduced into the recommendation algorithm, which is the key factor to deploy the recommendation system. This idea allows the users to be pre-classified based on their social relationships, improving the efficiency of the recommendation algorithm. Moreover, we enhance the performance of the recommendation system with a Density-Sensitive Distance based Transformer (DSD-Transformer) framework in Metaverse. Extensive experiments demonstrate that our proposed algorithm can significantly improve the Metaverse experience over various state-of-the-art baselines, and can be used to create a variety of product recommendation systems. © 1986-2012 IEEE.

关键词： Virtual environments

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Enhancing WRSN Sustainability through On-demand Directional Wireless Charging with Multiple Green-powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun The School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China The Advanced Networking Laboratory Helen & John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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