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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Sagili, Sreenivas Reddy Shibi, B. Rvs, Praveen Anjana, P. Rochester Institute of Technology Department of Computer Science United States Sona College of Technology Computer Science and Engineering Tamil Nadu Salem India LTI Mindtree Digital Engineering India Awh Engineering College Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

This research is focused on the analysis of how Capsule Networks should be combined with Convolutional Neural Networks (CNNs) for improving the sentiment classification with the target of depression detection based on the data offered by the reviews. Other methodologies of analysing the sentiment of a phrase may encounter disability in the process of analysing texts in natural language following depression since they do not analyse the text deeply enough to consider the context. The proposed method intends to enhance accuracy and resistance when compared to state-of-the-art techniques by combining the attributes of Capsule Networks, that effectively maintain the spatial hierarchy and the connections within the data, with CNNs' feature extraction ability. The proposed hybrid model helps overcome disadvantages of the plain sentiment analysis because it identifies intricate patterns and dependencies in the review data and enhances the depressive sentiments' detection. This paper assesses the propriety of the integrated Capsule-CNN through an evaluation on review datasets in relation to its ability in the successful differentiate between depressive and non-depressive sentiments. The findings reveal a considerable increase in method accuracy and present a leap of progress in the field of automatic depression detection, which in turn helps to develop more accurate and efficient tools for monitoring patients' mental status. © 2025 IEEE.

关键词： Emotion Recognition

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of Information technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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Toward Physically Stable Motion Generation: A New Paradigm of Human Pose Representation

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4158-4171页

作者： Cui, Qiongjie Lou, Zhenyu Song, Zhenbo Shu, Xiangbo Singapore University of Technology and Design Somapah Road 487372 Singapore Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

In machine learning, generating realistic human motion is paramount for a range of applications that require lifelike movements. Traditional methods have often overlooked the adherence to physical principles, leading to motion sequences that exhibit unrealistic behaviors such as foot sliding, penetration, and floating. These issues are particularly pronounced in complex tasks like dance choreography, which demand a higher degree of fidelity and realism. To address these challenges, we introduce RF-Rotation, a novel approach to human pose representation that strategically repositions the root joint of the SMPL model to align with both feet, while representing other joints through recursive bone rotations. It not only aligns more closely with the natural dynamics of human movement but also integrates an advanced contact predictor to ascertain the ground contact status of both feet, thereby preventing physically implausible movements on feet. We note that RF-Rotation is compatible with any motion generation tasks, including dance choreography, text-to-motion synthesis, and motion prediction, and can be seamlessly integrated into existing frameworks without modifications. Extensive experiments across three distinct tasks demonstrate the superior performance of RF-Rotation in enhancing the realism and stability of generated motion sequences. This method can significantly reduce foot sliding, floating, and penetration issues, without affecting computational efficiency, underscores its potential to set new standards in human motion generation. © 1991-2012 IEEE.

关键词： Motion estimation

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

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Movable-Antenna-Assisted Covert Communications With Reconfigurable Intelligent Surfaces

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IEEE Internet of Things Journal 2025年第9期12卷 12369-12382页

作者： Xie, Wenwu Li, Zilong Yu, Chao Xu, Hongbo Wang, Ji Wu, Weimin Li, Xingwang Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate. © 2014 IEEE.

关键词： Markov processes

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance Data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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X-OODM:Leveraging Explainable Object-Oriented Design Methodology for Multi-Domain Sentiment Analysis

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computers, Materials & Continua 2025年第3期82卷 4977-4994页

作者： Abqa Javed Muhammad Shoaib Abdul Jaleel Mohamed Deriche Sharjeel Nawaz Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science(RCET Campus GRW)University of Engineering and TechnologyLahore52250Pakistan Artificial Intelligence Centre(AIRC) Ajman UniversityAjman346United Arab Emirates Smith School of Business University of MarylandCollege ParkMD 20742-5151USA

Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the ***-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user *** design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design ***,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such *** research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment *** proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user *** design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user *** metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the *** study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user *** experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of *** work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.

关键词： Measurable explainable web-based application object-oriented design sentiment analysis multi-domain

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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