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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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GSINet: Gradual Semantic Interaction Network for Remote Sensing Object Detection Based on Dual Attention Mechanism

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11877-11891页

作者： Xu, Yuntao Qi, Qi He, Wei Zhang, Guoyun Chen, Siyuan Tu, Bing Hunan Institute of Science and Technology College of Mechanical Engineering Yueyang414000 China Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414000 China Nanjing University of Information Science and Technology Institute of Optics and Electronics Nanjing Jiangsu210044 China

Remote sensing object detection (RSOD) is crucial for various monitoring and measurement applications due to its non-contact, large-scale and real-time observation, such as autonomous driving, scene awareness, and environmental surveillance. Despite significant advances driven by deep neural networks, RSOD faces challenges such as directional ambiguity, scale variations, and densely packed tiny objects, limiting its effectiveness in diverse scenarios. To address these challenges, we explore a novel end-to-end deep RSOD method, termed Gradual Semantic Interaction Network (GSINet). Specifically, we first design a Gradual Semantic information Interaction Structure (GSIIS), which integrates contextual information across network layers from shallow to deep, enhancing semantic comprehension and detection precision. Second, we propose a novel Dual Attention Mechanism (DAM) that integrates our proposed Adaptive Context Feature Fusion Module (ACFFM) with Bi-Level Routing Attention (BRA) to optimize feature representation, ACFFM enhances context-aware feature fusion by capturing both local and global information, whereas BRA routes attention across different scales, together, they enhance tiny object detection in remote sensing images by reducing background interference and improving feature discrimination. Finally, we utilize the normalized Wasserstein distance (NWD) in place of the traditional Intersection over Union (IoU) metric to better manage object scale variations and location deviations. The extensive experimental evaluation on three public datasets demonstrates that the proposed method exhibits superior performance compared to state-of-the-art detectors, especially in detecting tiny objects. In particular, GSINet achieves a mean average precision (mAP) of 86.7% on the DIOR dataset, 95.01% on the NWPU VHR-10 dataset, and 96.7% on the RSOD dataset © 2008-2012 IEEE.

关键词： Proximity sensors

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A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4957-4973页

作者： Huang, Jun-Jie Liu, Tianrui Chen, Zihan Liu, Xinwang Wang, Meng Dragotti, Pier Luigi National University of Defense Technology College of Computer Science and Technology Changsha China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Imperial College London Department of Electrical and Electronic Engineering London United Kingdom

Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The core challenge lies in minimizing the commonalities among different sources. Existing deep learning approaches either neglect the significance of feature interactions or rely on heuristically designed architectures. In this paper, we propose a novel Deep Exclusion unfolding Network (DExNet), a lightweight, interpretable, and effective network architecture for SIRR. DExNet is principally constructed by unfolding and parameterizing a simple iterative Sparse and Auxiliary Feature Update (i-SAFU) algorithm, which is specifically designed to solve a new model-based SIRR optimization formulation incorporating a general exclusion prior. This general exclusion prior enables the unfolded SAFU module to inherently identify and penalize commonalities between the transmission and reflection features, ensuring more accurate separation. The principled design of DExNet not only enhances its interpretability but also significantly improves its performance. Comprehensive experiments on four benchmark datasets demonstrate that DExNet achieves state-of-the-art visual and quantitative results while utilizing only approximately 8% of the parameters required by leading methods. © 1979-2012 IEEE.

关键词： Blind source separation

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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Dual-Channel Multiplex Graph Neural Networks for Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3327-3341页

作者： Li, Xiang Fu, Chaofan Zhao, Zhongying Zheng, Guangjie Huang, Chao Yu, Yanwei Dong, Junyu Ocean University of China Faculty of Information Science and Engineering Qingdao266005 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Hong Kong Department of Computer Science Hong Kong

Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10 respectively. The source code of our paper is available at https://***/lx970414/TKDE-DCMGNN. © 1989-2012 IEEE.

关键词： Graph neural networks

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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COVID-19 Pandemic: A Worldwide Critical Review with the Machine Learning Model-Based Prediction

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 339-349页

作者： Misra, Neeraj Kumar Das, Sanchali Satpathy, Sambit Addula, Santosh Reddy Trivedi, Munesh Chandra School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Information Technology University of the Cumberlands Williamsburg United States Computer Science and Engineering PSSCIVE Bhopal Bhopal India

Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. International occurrences of the COVID-19 pandemic have resulted from the rapid global spread of the SARS-CoV-2 virus. In this study, we examine the ML and data analysis techniques for diagnosis, treatment, and impact of COVID-19, which are based on artificial intelligence (AI). Moreover, we provide an overview of the current state of the art in AI-based machine learning and deep learning techniques as well as the datasets, tools, and performance of the tools that can be used to combat COVID-19. In this survey, we have attempted to provide a comprehensive overview of the most recent state-of-the-art approaches available to researchers in Mathematical and Statistical analysis and the larger medical community as a whole. It also includes information on how data and ML can be used to improve COVID-19 status and encourage further research to prevent COVID-19 outbreaks. Future directions and problems are also described in detail. © The Institution of Engineers (India) 2024.

关键词： COVID-19

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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