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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Bi Jiahui Zhai Haitao Yuan Ziqi Wang Junfei Qiao Jia Zhang MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

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MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Thanh Trung Nguyen Minh Hai Vu Thi Ha Ly Dinh Phi Le Nguyen Kien Nguyen Faculty of Computer Science Phenikaa University Hanoi Vietnam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Vietnam Institute for Advanced Academic Research Chiba University Japan Graduate School of Engineering Chiba University Japan

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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Dielectrophoretic based Microfluidic for nanoparticles 'viruses' separation 3

Dielectrophoretic based Microfluidic for nanoparticles 'viru...

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Abdelhamid, Hamdy Khalaf, Ibrahim Hussien, Ahmed Abdelbaset, Reda Ajman University Electrical and Computer Engineering Engineering and Information Technology department Ajman United Arab Emirates Cairo University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Biomedical Engineering Department Cairo Egypt Cairo Egypt

ISBN: (纸本)9781665421577

The objective of this paper is to provide a microfluidic platform that may be useful to manipulate and characterize different submicron particles such as latex spheres as well as viruses (E.g. SARS-COV-2) through the concept of dielectrophoresis. The significance of recent research towards microfluidics-based diagnostic chips is apparent in health care, in our homes, and also very prominently in the fight against the COVID 19 pandemic: Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), early diagnosis and management are crucial for containing the outbreak. Here, we report a microfluidic device for separating mixture of different viruses. Using appropriate microfluidic channels, flow velocity, microelectrode arrays and voltage settings, particles can be trapped or moved between regions of high or low electric fields. The magnitude and direction of the dielectrophoretic force on the particle depends on the effective dielectric properties of fluid and particles, so that a heterogeneous mixture of particles can be separated to produce a more homogeneous population. In this paper the controlled separation of nanoparticles is demonstrated. With upper and lower electrode arrays, it is shown that different types of submicron latex beads can be spatially separated. The separation occurs because of differences in magnitude and direction of the dielectrophoretic force and drag force on different populations of particles. © 2021 IEEE.

关键词： Coronavirus

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Selected topics on Wiener index

arXiv

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arXiv 2023年

作者： Knor, Martin Škrekovski, Riste Tepeh, Aleksandra Slovak University of Technology in Bratislava Faculty of Civil Engineering Department of Mathematics Bratislava Slovakia FMF University of Ljubljana Faculty of Information Studies Novo mesto & Institute of Mathematics Physics and Mechanics Ljubljana & University of Primorska FAMNIT Koper Slovenia Faculty of Information Studies Novo mesto & Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

The Wiener index is defined as the sum of distances between all unordered pairs of vertices in a graph. It is one of the most recognized and well-researched topological indices, which is on the other hand still a very active area of research. This work presents a natural continuation of the paper Mathematical aspects of Wiener index (Ars Math. Contemp., 2016) in which several interesting open questions on the topic were outlined. Here we collect answers gathered so far, give further insights on the topic of extremal values of Wiener index in different settings, and present further intriguing problems and conjectures. © 2023, CC BY.

关键词： Graph theory

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

Heart Rate Anomaly Detection in Healthcare Using Elliptic En...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Information Technology Velammal Engineering College Tamilnadu Chennai600062 India Assistant Professor Panimalar Engineering College Tamilnadu Chennai600123 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision - 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement. © 2025 The Author(s).

关键词： Cardiology

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International Conference of computer and Informatics engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Spline Global MPPT with PID Controller Based on Levy Invasive Weed Technique for Renewable Energy Resources of Smart Homes

Spline Global MPPT with PID Controller Based on Levy Invasiv...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Aziza I. Hussein Mady Mohammad Passent Elkafrawy Rabab Hamed M. Aly College of Engineering Energy and Technology Research Center Effat University Jeddah Saudi Arabia College of Architecture Effat University Jeddah Saudi Arabia The Higher Institute for Management and Information Technology Minya Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

Renewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Accuracy Software packages Mathematical models Wind turbines Splines (mathematics) Optimization Tuning

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A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation

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Energy 2023年第1期281卷

作者： Elsisi, Mahmoud Amer, Mohammed Dababat, Alya’ Su, Chun-Lien Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo11629 Egypt Department of Mechanical Engineering Palestine Technical University – Kadoorie Tulkarm Palestine Department of Computer Systems Engineering Faculty of Engineering and Information Technology Arab American University Palestine

The energy consumption of major equipment in residential and industrial facilities can be minimized through a variety of cost-effective energy-saving measures. Most saving strategies are economically viable where several algorithms can be employed to reduce energy consumption to reduce costs to a considerable extent. Machine learning (ML) is one of these techniques. A review of recent research efforts concerning the application of ML strategies to energy conservation and management problems is presented in this study. In addition, ML approaches and strategies for energy-saving problems, management, technologies, and control methods have been discussed. A comprehensive review of all available publications is also used to make observations about past considerations. As a result, it has been concluded that ML is capable of solving a wide range of decision and management problems within a short period of time with minimal energy consumption. In addition, ML perspectives have been viewed from the perspective of emerging communication technologies, instruments, and cyber-physical systems (CPSs), along with the advancement of ultra-durable and energy-efficient Internet-of-Things (IoT) based communication sensors technology. Moreover, a comprehensive review of recent developments in ML algorithms is also included, including safe reinforcement learning (RL), Deep RL, path integral control for RL, and others not previously. Lastly, critical ML considerations such as emergency and remedial measures, integrity protection, fusion with existing robust controls, and combining preventive and emergent measures have been discussed. The implementation of recently applied ML, RL, and IoT strategies for energy management, conservation, and resilient operation is clarified in this paper. The proposed review highlights the advantages and drawbacks of the recent energy conservation strategies. Finally, the perspective solutions have been clarified to cope with the world direction for zero

关键词： Decision making

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APlus: An Intelligent Lecture Delivery System using Artificial Intelligence Learning Analytics 3

APlus: An Intelligent Lecture Delivery System using Artifici...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Kodagoda, Charith Madampe, Vihanga Ekanayake, Ridmi De Silva, Nirma Wijesundara, Malitha Atapattu, Pramadhi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka Ltd Colombo Sri Lanka

ISBN: (纸本)9781665408622

As technology advances, new ways of teaching and learning can be discovered. Online learning platforms are able to provide easy, informative, and effective methods of delivering information since people are getting more accustomed to the use of e-learning. With the help of Artificial Intelligence, these methods can be refined to understand the users on a deeper level and provide services that are helpful and are more tailored to each user. The purpose of this project is to design an e-learning platform equipped for both educators and students. The system will be able to allow users to record/upload lecture sessions and other related study materials to the service which in return, with the use of Artificial Intelligence, try to understand the context of each lesson, segment videos, generate questions and deliver the materials to the Learner. The system will use various inputs from the Learner including video observation time, watched duration, types of videos watched, answers for questions, etc. to build a comprehensive profile of Learners. The system will use the knowledge gathered to provide services such as recommendations to the Learner, track the progress of the Learner as well as provide useful statistics of lecture materials to the Content Providers. © 2021 IEEE.

关键词： Learning systems

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