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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Naga Venkata Ramakrishna Guduri S. Sethu R. Shalinirajan R. Reena Karthikeyan M G.S. Uthayakumar Electrical and Electronics Section Engineering Department University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamilnadu India

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%.

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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Computing (ICOCO), IEEE International Conference on

作者： Nik Azlina Nik Ahmad Anitawati Mohd Lokman Munaisyah Abdullah Ahmad Iqbal Hakim Suhaimi Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Malaysia

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs.

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Developing a practical model for noise in entangled photon detection

arXiv

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arXiv 2025年

作者： Truong, Taman Arenz, Christian Lukens, Joseph M. School of Electrical Computer and Energy Engineering and Research Technology Office Arizona State University TempeAZ85287 United States Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We develop a comprehensive model for the effective two-photon density matrix produced by a parametric source of entangled photon pairs under a variety of detector configurations commonly seen in a laboratory setting: two and four photon number-resolving (PNR) and threshold detectors. We derive the probability of obtaining a single coincidence assuming Poisson-distributed photon pairs, non-unit detection efficiency, and dark counts;obtain the effective density matrix;and use this quantity to compute the fidelity of the generated quantum state. The 4 PNR case admits an analytic result valid for any combination of parameters, while all other cases leverage low-efficiency approximations to arrive at closed-form expressions. Interestingly, our model reveals appreciable fidelity improvements from four detectors as opposed to two yet minimal advantages for PNR over threshold detectors in the regimes explored. Overall, our work provides a valuable tool for the quantitative design of two-photon experiments under realistic nonidealities. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

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Micromachines 2023年第9期14卷 1677-1677页

作者： Zameer, Aneela Naz, Sidra Raja, Muhammad Asif Zahoor Hafeez, Jehanzaib Ali, Nasir Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50–60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques. © 2023 by the authors.

关键词： Genetic algorithms

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A new model based on Dynamic Naive Bayes classifier for retweet behavior prediction

A new model based on Dynamic Naive Bayes classifier for retw...

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Conference on Artificial Intelligence and Robotics and Asia-Pacific International Symposium

作者： Rahebeh Mojtahedi Safari Amir Masoud Rahmani Sasan H. Alizaeh Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin Taiwan Faculty of Information Technology ICT Research Institute (Iran Telecommunication Research Center) Tehran Iran

At present, the prediction of user retweeting behavior plays a significant role in perceiving the underlying mechanism of information diffusion in the microblogging network. The problem of retweeting behavior prediction is usually considered as a classification task hence, the effort for the classification of the time-series data in the microblogging network has gained spacious attention. It is challenging to improve the classification accuracy of retweeting behavior prediction models, because the applied classifications still lack a tailored method. To address this, we present a retweeting behavior prediction model based on Dynamic Naïve Bayes classifier that is an appropriate classifier dealing with time-series data classification. In fact, we apply a time discretization approach and incorporate user individual, social and temporal features into our proposed prediction model to estimate accurately user retweeting behavior in the microblogging network over time. The real Twitter(X) microblogging dataset is used to evaluate the performance of the proposed model in the user retweeting prediction. The experimental results show that our proposed classifier due to its simple structure can avoid overfitting problem, and outperforms in compared to other baseline methods.

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Measuring the social influence for retweet behavior prediction in microblogging network

Measuring the social influence for retweet behavior predicti...

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International Symposium on Telecommunications, IST

作者： Rahebeh Mojtahedi Safari Amir Masoud Rahmani Sasan H. Alizaeh Department of Computer Engineering Lahijan Branch Islamic Azad University Lahijan Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section3 Douliou Yunlin Taiwan Faculty of Information Technology ICT Research Institute (ITRC) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Nowadays, retweeting behavior is considered a substantial behavior to analyze information diffusion in the microblogging network. To understand the underlying mechanism of information diffusion and analyze the evolution of the diffusion corresponding to a given original microblog, user retweeting behavior prediction has recently gained considerable attention. In this paper firstly, we introduce the most influential factors which affect user retweeting behavior and propose an effective measurement based on the random walk model to measure the social influence. Secondly, by incorporating our defined influential factors into a logistic regression classifier model, we predict the future activated user set concerning the diffusion of a given original microblog at the next time point. Unlike other comparative models, our prediction model can illustrate the evolution of diffusion and predict the retweeting behavior of all potential users through the diffusion of any original microblog in the microblogging network. Finally, based on the real Twitter microblogging dataset, the experimental results show by the contribution of our defined influential factors and presenting an effective measure of the social influence factor, we can obtain more accurate predictions in comparison to other baseline methods.

关键词： Logistic regression Accuracy Blogs Predictive models Telecommunications Information diffusion

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions

Optimizing Battery Management Systems For Electric Vehicles:...

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Global Conference for Advancement in technology (GCAT)

作者： V.S.Prakash Annamalai Muthu B. Anandan V. Velmurugan T. Alex Stanley Raja Purushothaman V Department of Computer Science Kristu Jayanti College Bengaluru Electrical Section Engineering Department University of Technology and Applied Science Nizwa Oman Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Assistant Professor Level III Bannari Amman Institute of Technology Sathyamangalam Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide.

关键词： Technological innovation Electric potential Battery management systems Estimation Electric vehicles Thermal management Market research Sustainable development Thermal stresses Optimization

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Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

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Complex and Intelligent Systems 2023年第4期9卷 4705-4731页

作者： Alamoodi, A.H. Zaidan, B.B. Albahri, O.S. Garfan, Salem Ahmaro, Ibraheem Y. Y. Mohammed, R.T. Zaidan, A.A. Ismail, Amelia Ritahani Albahri, A.S. Momani, Fayiz Al-Samarraay, Mohammed S. Jasim, Ali Najm R.Q.Malik Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu 64002 Taiwan Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Computer Science Department College of Information Technology Hebron University Hebron Palestine Department of Computing Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq SP Jain School of Global Management Sydney Australia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq E-Business and Commerce Department Faculty of Administrative and Financial Sciences University of Petra Amman 961343 Jordan Foundation of Alshuhda Baghdad Iraq Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis

关键词： COVID-19 Data privacy Federated learning Monoclonal antibodies Multi-criterion decision making Treatment

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Rsr-Ids: A Reliable Score-Based Routing Protocol Using an Intrusion Detection System in Vehicular Ad-Hoc Networks

SSRN

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SSRN 2023年

作者： Banafshehvaragh, Samira Tahajomi Zarei, Mani Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan

One of the most significant challenges of vehicular ad-hoc networks (VANETs) is to establish reliable connections with the network infrastructure despite the participation concern of attacking vehicles in routing. Vehicles need a proper defense mechanism against all types of attacks even if they are accompanied by a reliable path planning strategy. This paper proposes RSR-IDS, a reliable score-based routing protocol using an intrusion detection system in VANETs. RSR-IDS detects abnormal data to calculate the untrust score (US) by deploying an intrusion detection system (IDS) in the data center. An IDS-based scoring mechanism is deployed in the data center. The classifier of the IDS is trained using three machine learning-based algorithms. These algorithms include the decision tree, random forest, and extra trees. Then, RSR-IDS selects a route with the lowest total USs and hop count compared to others for communications between the source and the destination. RSR-IDS is evaluated based on the accuracy, F1-score, false negative rate (FNR), packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2E) delay criteria using OMNeT++ simulator. The most significant improvement of RSR-IDS includes 14.1% in accuracy, 11.4% in F1-score, and 5.4% in PDR regarding various vehicle densities. © 2023, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach

FPGA Implementation of Textural Feature Extractor and Distan...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： S. Hariprasath M. Santhi T.N. Prabakar V. Koushick Aravind. P T. Gunasekaran Department of ECE Saranathan College of Engineering Trichy Department of ECE Sri Sairam College of Engineering Anekal Bangalore Department of ECE Veltech Institute of Science and Technology Chennai Department of Electrical and computer Engineering Mattu University Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Sultanate of Oman

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation.

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