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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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Taking the temperature of quantum many-body scars

arXiv

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arXiv 2025年

作者： Burke, Phillip C. Dooley, Shane School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland Dublin Institute for Advanced Studies School of Theoretical Physics 10 Burlington Rd Dublin Ireland

A quantum many-body scar is an eigenstate of a chaotic many-body Hamiltonian that exhibits two seemingly incongruous properties: its energy eigenvalue corresponds to a high temperature, yet its entanglement structure resembles that of low-temperature eigenstates, such as ground states. Traditionally, a temperature is assigned to an energy eigenvalue through the textbook canonical temperature-energy relationship. However, in this work, we use the eigenstate subsystem temperature – a recently developed quantity that assigns a temperature to an energy eigenstate, based on the structure of its reduced density matrix. For a thermal state, the eigenstate subsystem temperature is approximately equal to its canonical temperature. Given that quantum many-body scars have a ground-state-like entanglement structure, it is not immediately clear that their eigenstate subsystem temperature would be close to their canonical temperature. Surprisingly, we find that this is the case: the quantum many-body scars have approximate "knowledge" of their position in the spectrum encoded within their state structure. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis

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Defence technology（防务技术） 2019年第4期15卷 545-556页

作者： S.Ajith Arul Daniel R.Pugazhenthi R.Kumar S.Vijayananth Department of Mechanical Engineering Vels Institute of ScienceTechnology and Advanced Studies

This study aims to optimize the input parameters such as mass fraction and particle size of SiC along with depth of cut,feed and cutting speed in the milling of Al5059/SiC/*** hybrid metal matrix composites are generally fabricated by reinforcing of different sizes(10,20,40 μm)of SiC with aluminium at a different levels(5%,10%& 15%)whereas the MoS2 addition is fixed as 2%.The effect of each control factor on response variables are analyzed through Taguchi S/N ratio ***,the most significant method for prediction of response parameters is satisfied by ANN model than the regression *** of variance(ANOVA)results envisage that mass fraction of SiC,feed rate is the most domineering factor on response variable.

关键词： Silicon carbide Temperature Surface roughness Cutting forces Artificial neural network Grey relational analysis

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wasim Haidar SK Wilfred Blessing N. R Sudhakar Kumar Chaubey Zahid Mehmood Kavitha S. J Sutherlin Subitha G College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Wireless sensor networks Data analysis Smart cities Decision making Throughput Routing protocols Real-time systems Energy efficiency Delays Predictive maintenance

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CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data

arXiv

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arXiv 2025年

作者： Sumon, Md. Shaheenur Islam Islam, Md. Sakib Bin Rahman, Md. Sohanur Hossain, Md. Sakib Abrar Khandakar, Amith Hasan, Anwarul Murugappan, M. Chowdhury, Muhammad E.H. Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh Department of Industrial and Mechanical Engineering Qatar University Doha2713 Qatar Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India

The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-ofthe-art models to evaluate our transformer-driven framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Nomograms

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How Do Hackathons Foster Creativity? Towards Automated Evaluation of Creativity at Scale 25

How Do Hackathons Foster Creativity? Towards Automated Evalu...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Jeanette Falk Yiyi Chen Janet Rafner Mike Zhang Johannes Bjerva Alexander Nolte Computer Science Aalborg University Copenhagen Denmark Department of Computer Science Aalborg University Copenhagen Denmark Center for Hybrid Intelligence Department of Management Aarhus University Aarhus Denmark and Aarhus Institute for Advanced Studies Aarhus University Aarhus Denmark Software Engineering and Technology Eindhoven University of Technology Eindhoven Netherlands

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Heart Disease Prediction using Logistic Regression with PCA-MFSA Feature Extraction Technique and Multidimensional Scaling (MDS) Pre-processing Approach 3rd

Heart Disease Prediction using Logistic Regression with PCA-...

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3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Priscila, S. Silvia Sakthivanitha, M. Sadhana, C. Lavanya, M. Poornima, V. Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of MCA Prathyusha Engineering College Tamil Nadu Chennai India Department of Computer Applications S.D.N.B Vaishnav College for Women Tamil Nadu Chennai India Department of Computer Science Vels Isntitute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9783031862922

Heart disease prediction is critical because it has the potential to save lives, increase patient outcomes, and save the cost of healthcare. An important task in medical diagnostics is the forecasting of heart disease. In this article, we investigate the efficacy of integrating Multidimensional Scaling (MDS) preprocessing with various feature extraction methods for precise LR (Logistic Regression) heart disease prediction. The feature extraction methods under investigation include Independent Component Analysis (ICA), Principal Component Analysis (PCA) and Mean Fisher Score-based Algorithm (PCA-MFSA). PCA with MFSA emerges as the most effective method, consistently producing superior outcomes in the forms of accuracy rate, precision, and recall value compared to ICA and PCA. This underscores the significance of combining PCA with the specialized feature selection process offered by PCA-MFSA to enhance the LR model’s discriminatory power. Moreover, The incorporation of MDS preprocessing with PCA-MFSA results in a significant improvement in accuracy rate, precision value, and recall. From the result obtained PCA-MFSA produces accuracy of 91%, precision of 0.90 and recall of 0.86 respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Creating Sodium Ion Channels via De Novo Encapsulation of Ionophores for Enhanced Water Energy Harvesting

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advanced Materials 2025年第20期37卷 e2420636页

作者： Guo, Qing Xing, Zhiwei Guo, Huaxi Lai, Zhuozhi Yi, Jiaming Wu, Di Dai, Zhifeng Zhang, Li Wang, Sai Ma, Shengqian Sun, Qi Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province School of Chemistry and Chemical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Hangzhou Institute of Advanced Studies Zhejiang Normal University Hangzhou311231 China Department of Chemistry University of North Texas 1508 W Mulberry St DentonTX76201 United States

Biological ion channels achieve remarkable permselectivity and cation discrimination through the synergy of their intricate architectures and specialized ionophores within confined nanospaces, enabling efficient energy conversion. Emulating such selectivity in synthetic nanochannels, however, remains a persistent challenge. To address this, a novel host-guest assembly membrane is developed by incorporating sodium-selective ionophores into a β-ketoenamine-linked covalent organic framework (COF). This design confers exceptional permselectivity and Na+ selectivity, achieving Na+/K+ and Na+/Li+ selectivity ratios of 3.6 and 103, respectively, along with near-perfect Na+/Cl− selectivity under a 0.5 M || 0.01 M salinity gradient. Notably, the membrane dynamically switches its permselectivity to favor anion transport in the presence of high-valent cations (e.g., Ca2+), overcoming limitations such as uphill cation diffusion and back currents observed in conventional cation-selective membranes. This adaptive behavior yields a 4.6-fold increase in output power density in Ca2+-rich environments. These findings advance the design of biomimetic nanochannels with unparalleled ion selectivity and enhanced energy conversion efficiency. © 2025 Wiley-VCH GmbH.

关键词： Ionophores

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive Sensor Networks

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Karthik Jakranpally Nagabathula Ramesh Babu Doddavarapu Sreenu Babu K. Rakesh Amit Barve Atmaram Shelke Valiant IT Services4000 Stanwick ave Sacramento CA Department of Electronics and Communication Engineering J.B. Institute of Engineering & Technology Hyderabad India Department of Electronics and Communication Engineering Bharatratna Indira Gandhi College of Engineering Solapur India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O.Limda Gujarat India Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks.

关键词： Cross layer design Graph convolutional networks Reinforcement learning Throughput Routing Topology Synchronization Reliability Optimization Radio spectrum management

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