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

作者： Aswathy, R.H. Suresh, P. Mani, C. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai Erode India

ISBN: (纸本)9798331523923

Today, all countries have a socio-economic concern about the healthcare services. Advanced healthcare systems leverage technology, such as the Internet of Things (IoT), to enhance quality and reduce costs. IoT integrates smart devices, medical professionals, and patients into a connected network. The advanced healthcare trend extends the possibilities for doctors to monitor the patient remotely and give medication during emergencies. Since it is a critical application security plays a vital role as the data transferred through the wireless environment and it is maintained digitally as a digital patient record. A major challenge in IoT healthcare includes security, privacy and power consumption. In this proposed work the secure cryptographic based MIAS server is applied for establishing the data transmission and tested over the known security attacks such as DDoS, replay attack, password guessing, sensor node spoofing etc. In contrast to alternative approaches, the proposed scheme is better suited to ensuring security and minimizing power consumption within the limited confines of IoT environments. © 2025 IEEE.

关键词： Electronic health record

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Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions

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computers, Materials & Continua 2025年第5期83卷 3553-3583页

作者： Faisal Ghazi Beshaw Thamir Hassan Atyia Mohd Fadzli Mohd Salleh Mohamad Khairi Ishak Abdul Sattar Din School of Electrical and Electronic Engineering Universiti Sains MalaysiaSeberang Perai14300Malaysia Electrical Engineering Department Tikrit UniversityTikrit34001Iraq Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjmanP.O.Box 346United Arab Emirates

The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of *** order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)*** study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest *** models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the *** findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to *** detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 *** outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’*** addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy *** outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.

关键词： Renewable energy consumption machine learning explainable AI random forest support vector machine decision trees forecasting energy modeling

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Perception-Aware Attack Against Music Copyright Detection: Impacts and Defenses

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2959-2977页

作者： Duan, Rui Qu, Zhe Zhao, Shangqing Ding, Leah Liu, Yao Lu, Zhuo University of Missouri-Kansas City School of Science and Engineering Kansas CityMO64110 United States Central South University School of Computer Science and Engineering Changsha410017 China University of Oklahoma School of Computer Science TulsaOK74135 United States American University Department of Computer Science WashingtonDC20016 United States University of South Florida Department of Computer Science and Engineering TampaFL33620 United States University of South Florida Department of Electrical Engineering TampaFL33620 United States

Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker recognition, music copyright detection. Most existing studies have mainly fo cused on ensuring the effectiveness of attacking an audio signal classifier via creating a noise-like perturbation on the original signal, which remains a gap in preserving the human perception of adversarial audios. This paper presents a novel perspective to cre ate adversarial audios by integrating the human perception model into the attack formulation to generate well-perceived adversarial *** from conventional approaches which primarily focused on using Lp norm to preserve the audio quality, we adopt a human study to understand how human participants react to different types of music perturbations, build a Siamese Neural Network (SNN) based model to characterize the human percep tion. The new findings of the human perception study guide us to formulate a new computationally efficient, multiple-feature-based perception-aware (CEMF-PA)attack, which manipulates different audio signal features to find an optimal perturbed music signal against music copyright detection. This novel attack vector opens a new door to generating highly effective, well-perceived adversarial audio signals via manipulating the auditory features. Experimental results show that the proposed attack is effective against YouTube’s copyright detection. Finally, we propose the defense strategy design to make the copyright detection more robust to adversarial music signals generated by the CEMF-PA attack. © 2004-2012 IEEE.

关键词： Speech recognition

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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A Machine Learning Based Framework For Bankruptcy Prediction In Corporate Finances Using Explainable AI Techniques

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 15-26页

作者： Maktum, Tabassum Pulgam, Namita Chandgadkar, Vaishnavi Pathak, Prachi Solanki, Aditya Department of Computer Engineering School of Engineering & Technology Anjuman I Islam’s Kalsekar Technical Campus Maharashtra New Panvel India D J Sanghvi College of Engineering Maharashtra Mumbai India Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Maharashtra Navi Mumbai India

Forecasting bankruptcy within corporate finances is an indispensable endeavor crucial for sustaining business growth and fostering stability. The paper presents a methodology to redefine the conventional approach to bankruptcy prediction within corporate finance. Through the adept utilization of advanced machine learning techniques, notably classification models, a dynamic and adaptable framework is established, enabling the systematic categorization of companies based on their bankruptcy risk profiles. Moreover, the methodology addresses the inherent challenge of data bias by integrating oversampling techniques like the Synthetic Minority Over-sampling Technique (SMOTE), thereby ensuring a more equitable representation of minority class samples and bolstering the model’s predictive accuracy. The resulting model delivers timely and precise forecasts of bankruptcy risk, fortified by crucial recommendations such as the Altman Z-Score for vulnerability assessment, Debt-to-Equity Ratio for insights into leverage, Quick Ratio for assessing liquidity, and Explainable AI Techniques like SHapley Additive exPlanations (SHAP) analysis for transparent interpretations. This comprehensive approach equips stakeholders with tailored recommendations, empowering them to proactively safeguard their organizations’ financial well-being and avert the perils of bankruptcy. The comparative analysis presented in paper demonstrates that the proposed method assesses the bankruptcy risk more accurately. The integration of Explainable AI techniques and key financial metrics helps the stakeholders to take vital decisions about corporate finances. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Risk assessment

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Smart Pressure E-Mat for Human Sleeping Posture and Dynamic Activity Recognition

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 9-20页

作者： Yuan, Liangqi Wei, Yuan Li, Jia Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This article introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and 13 dynamic activities inspired by Nintendo Switch Ring Fit Adventure are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies in both applications, demonstrating the high accuracy and generalizability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties. © 2024 The Authors.

关键词： Deep neural networks

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Event Management System Using Spatial and Event Attribute Information

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SN computer Science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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Graph Representation Learning for Recommender Systems

Graph Representation Learning for Recommender Systems

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Manikrao, Kulkarni Manjusha Hiremath, Savitha Manjula, S.H. Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India University of Visvesvaraya College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331529833

Graph data represents information efficiently and can be used to learn subsequent tasks easily. In the domain of biological science, recommender systems, social network analysis graph representation learning has become ubiquitous with tremendous success. A graph structured data can make a system to learn, produce new information and can be analyzed for set of tasks. Deep learning-based recommender systems are using deep graph embeddings for improved accuracy by exploring user item relations. This relation exploration can be utilized for other recommendation task such as link prediction. In this study we are providing overview of graph representation learning with complete analysis of data with graph representation. This study provides importance of graph representation in recommender system to learn node representations. The results and discussion section denotes how simple graph neural network technique successfully applied on graph data for node representation learning and challenges. © 2025 IEEE.

关键词： Contrastive Learning

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Synthesis of Material Samples Based on the Texture's Structure

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Journal of Harbin Institute of Technology(New Series) 2025年第1期32卷 49-57页

作者： Adib Akl Rita Harb Department of Electrical Telecommunications and Computer EngineeringSchool of EngineeringHoly Spirit University of KaslikJounieh 1200Lebanon Department of Chemical and Petroleum Engineering School of EngineeringHoly Spirit University of KaslikJounieh 1200Lebanon

In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is *** presented approach uses an arbitrary image to construct a structure layer of the *** resulting structure layer is then used to constrain the material texture *** field of second-moment matrices is used to represent the structure *** tests with various constraint images are conducted to ensure that the proposed approach accurately reproduces the visual aspects of the input material *** results demonstrate that the proposed algorithm is able to accurately synthesize arbitrary-shaped material textures while respecting the local characteristics of the *** paves the way toward the synthesis of 3D material textures of arbitrary shapes from 2D material samples,which has a wide application range in virtual material design and materials characterization.

关键词： local orientation material sample second⁃moment matrix structure synthesis

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