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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Upadhyay, Yash Gahlawat, Muskan Nivetha, S. Rana, Saurabh Natarajan, B. Gupta, Pratik Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Mathematics Chandigarh University India Department of Computer Science and Engineering RVS College of Engineering & Technology Coimbatore641402 India Amrita Vishwa Vidyapeetham Chennai601103 India Department of Applied Mathematics Gautam Buddha University Noida India

ISBN: (纸本)9798350305258

The demand for wireless connections is rising, and the advent of mobile internet has sparked a hunt for fresh, creative ways to expand the capacity and reach of cellular networks. One strategy to provide ground mobile users with long-term communication coverage is to employ Unmanned Aerial vehicles (UAV) as mobile base stations. The proposed method incorporates a Deep Reinforcement Learning (DRL) method to address the challenges that persist with traditional approaches. We introduced Deep Q-Learning with an attention mechanism-based multi-agent model to develop an energy-efficient control system for UAVs in cellular networks. Due to the proposed strategy's dynamic nature, it efficiently handles the crowded environment by resolving scalability and mobility issues. The researchers evaluated the proposed model using the benchmark dataset, producing promising results. Further, the researchers tested the proposed model using the sum rate metric to analyze the capability of the proposed algorithm to provide a reasonable throughput rate. © 2023 IEEE.

关键词： Cellular Communication Convolutional Neural Network Deep Q-Networks Deep Reinforcement Learning Deployment Optimization Energy Consumption Optimum Transport Target Network Unmanned Aerial Vehicle

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Resonant stroboscopic Rydberg dressing: Electron-motion coupling and multibody interactions

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Physical Review A 2025年第4期111卷 L041104-L041104页

作者： Chris Nill Sylvain de Léséleuc Christian Groß Igor Lesanovsky Institut für Theoretische Physik and Center for Integrated Quantum Science and Technology Universität Tübingen Auf der Morgenstelle 14 72076 Tübingen Germany Institute for Applied Physics University of Bonn Wegelerstraße 8 53115 Bonn Germany Institute for Molecular Science National Institutes of Natural Sciences 444-8585 Okazaki Japan RIKEN Center for Quantum Computing (RQC) 351-0198 Wako Japan Physikalisches Institut and Center for Integrated Quantum Science and Technology Universität Tübingen Auf der Morgenstelle 14 72076 Tübingen Germany School of Physics and Astronomy and Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems The University of Nottingham Nottingham NG7 2RD United Kingdom

Rydberg dressing traditionally refers to a technique where interactions between cold atoms are imprinted through the far off-resonant continuous-wave excitation of high-lying Rydberg states. Dipolar interactions between these electronic states are then translated into effective interactions among ground-state atoms. Motivated by recent experiments, we investigate two dressing protocols, in which Rydberg atoms are resonantly excited in a stroboscopic fashion. The first one is nonadiabatic, meaning Rydberg states are excited by short pulses. In this case, mechanical forces among Rydberg atoms result in electron-motion coupling, which generates effective multibody interactions. In the second, adiabatic protocol, Rydberg states are excited by smoothly varying laser pulses. We show that also, in this protocol, substantial multibody interactions emerge.

关键词： Rydberg states

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Thenmozhi K K.V. Shiny T Sujatha Wilfred Blessing N.R Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Department of Compute Science Kristu Jayanti College (Autonomous) Bengaluru India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Information Technology Department College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis.

关键词： Support vector machines Accuracy Sociology Machine learning Feature extraction Breast cancer Convolutional neural networks

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Game Theory-Based IoT Efficient Power Control in Cognitive UAV

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Computers, Materials & Continua 2022年第7期72卷 1561-1578页

作者： Fadhil Mukhlif Norafida Ithnin Omar B.Abdulghafoor Faiz Alotaibi Nourah Saad Alotaibi Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Electronic and Telecommunication Department College of EngineeringThe American University of KurdistanIraq Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia Computer Department Faculty of Applied CollegeImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread *** this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground ***,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource *** quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility ***,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless ***,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through *** results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’***,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

关键词： UAV drones WSN IoT game theory energy efficiency 5G&B5G networks

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Dataset Evaluation Framework for Secure Electric Vehicle Systems

Dataset Evaluation Framework for Secure Electric Vehicle Sys...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Ghadeer Albesani Ashraf Mashaleh Ammar Almomani Amjad Aldweesh Department of Information Security University of Petra Jordan King Abdullah II School of Information Technology Jordan University Jordan Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Science Higher Colleges of Technology Al-Balqa UAE Applied University Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Intrusion Detection Systems (IDS) play a critical role in safeguarding connected vehicular systems and Electric Vehicle Supply Equipment (EVSE) infrastructures against evolving cyber threats. The effectiveness of these IDS solutions largely depends on the quality and relevance of the datasets used for their training and evaluation. In this paper, we present a systematic framework for assessing datasets intended for IDS development in the automotive and EV charging domains. Our analysis covers essential criteria—such as data completeness, realism, labeling accuracy, and statistical distribution—and provides best practices for evaluating both traditional and novel datasets (e.g., CICIoV2024, CICEVSE2024). We further detail methodologies for intrinsic and extrinsic evaluations, including statistical analysis, time-based assessment, and benchmark testing using machine learning algorithms. Our approach is illustrated through a case study that demonstrates how these criteria and methods can be applied to real-world datasets, highlighting potential pitfalls and improvements. By offering concrete guidelines and metrics for dataset quality, this work aims to advance the development of robust IDS solutions in connected automotive and EV charging environments.

关键词： Training Systematics Statistical analysis Intrusion detection Electric vehicle charging Labeling Computer security Standards Automotive engineering Internet of Vehicles

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Z-scheme systems of ASi_(2)N_4(A=Mo or W)for photocatalytic water splitting and nanogenerators

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Tungsten 2022年第1期4卷 52-59页

作者： Jian Zeng Liang Xu Xin Luo Tong Chen Shuai-Hao Tang Xin Huang Ling-Ling Wang Energy Materials Computing Center School of Energy and Mechanical EngineeringJiangxi University of Science and TechnologyNanchang 330013China Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Applied Physics School of ScienceEast China Jiaotong UniversityNanchang 330013China

Z-scheme heterojunction catalysts have received great attention due to their efficient ability to separate electrons and ***,using the first-principles calculations,we designed a series of promising two-dimensional(2D)/2D Z-scheme systems with interlayer inequivalent,including MoSi_(2)N_(4)/MoSi_(2)N_(4),WSi_(2)N_(4)/WSi_(2)N_(4) and MoSi_(2)N_(4)/WSi_(2)N_(4).Molecular dynamics simulation and phonon dispersion show that they have sufficient environmental *** inequivalent structure between the layers caused the directional formation of built-in potentials,driving the transfer of net charge between layers,which greatly enhanced their catalytic *** smaller band gap and enhanced light absorption performance further revealed their perfect catalytic ***,all they met the redox potential requirements of water splitting in a range of pH 0-7,demonstrate they are very remarkable photocatalysts for H_(2)*** interestingly,they also have good sliding ferroelectricity,and the opposite built-in potential can be obtained by sliding between layers,which is very promising for future *** works may provide new insights into energy conversion devices.

关键词： Tungsten MoSi_(2)N_4 WSi_(2)N_4 Z-scheme systems Water splitting Nanogenerators First-principles calculations

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RedirectedStepper: Exploring Walking-In-Place Locomotion in VR Using a Mini Stepper for Ascents 25

RedirectedStepper: Exploring Walking-In-Place Locomotion in ...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Le, Quang-Tri Huynh, Duc-Nham Tran, Tanh Quang Fjeld, Morten Stuerzlinger, Wolfgang Zank, Markus Tran, Minh-Triet Le, Khanh-Duy University of Science VNU-HCM Ho Chi Minh City Viet Nam School of Computing University of Otago Dunedin New Zealand University of Bergen Bergen Norway Simon Fraser University VancouverBC Canada Lucerne School of Information Technology Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

ISBN: (纸本)9798400713941

Walking on inclined surfaces is common in some Virtual Reality (VR) scenarios, for instance, when moving between floors of a building, climbing a tower, or ascending a virtual mountain. Existing approaches enabling realistic walking experiences in such settings typically require the user to use bulky walking-in-place hardware or to walk in a physical area. Addressing this challenge, we present RedirectedStepper, a locomotion technique leveraging a novel device based on a mini exercise stepper to provide realistic VR staircase walking experiences by alternating the tilt of the two stepper pedals. RedirectedStepper employs a new exponential mapping function to visually morph the user's real foot motion to a corresponding curved path in the virtual environment (VE). Combining this stepper and the visual mapping function provides an in-place locomotion technique allowing users to virtually ascend an infinite staircase or slope while walking-in-place (WIP). We conducted three within-subject user studies (n=36) comparing RedirectedStepper with a WIP locomotion technique using the Kinect. Our studies indicate that RedirectedStepper improves the users' sense of realism in walking on staircases in VR. Based on a set of design implications derived from the user studies, we developed SnowRun, a VR exergame application, demonstrating the use of the RedirectedStepper concept. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtualization

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements 1

Exploring the Synergy of Artificial and Computational Intell...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Palarimath, Suresh Maran, Pyingkodi Valsalan, Prajoona Wilfred Blessing, N.R. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331532956

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things. © 2024 IEEE.

关键词： Artificial intelligence computational intelligence Internet of things

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IoT Based Smart Fish-Feeder and Monitoring System

IoT Based Smart Fish-Feeder and Monitoring System

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2022 International Conference on Business Analytics for technology and Security, ICBATS 2022

作者： Abu-Khadrah, Ahmed Issa, Ghassan F. Aslam, Shaukat Shahzad, Muhammad Ateeq, Karamath Hussain, Munawar Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia Skyline University College School of Information Technology Sharjah United Arab Emirates NCBAandE School of Computer Science Lahore Pakistan Riphah International University Department of Computing and Applied Sciences Lahore Pakistan Skyline University Bsit Faculty Sharjah United Arab Emirates

ISBN: (纸本)9781665409209

People have always enjoyed having pets around them, but in today's hectic world, it might be difficult to properly care for pets, particularly fish, which require more attention. When fish owners are out of town, their fish may be left with a feed shortfall or over-feed. Another consideration is the water quality (temperature, PH level, and so on). As a result, a solution is provided in this article to address the aforementioned issues. Two components comprise the proposed system. The first is for fish eating, while the second is for water feature and level monitoring. The first module also includes electrical, automatic, and communication parts that are connected to the Internet of Things (IoT) to allow fish to be fed on a schedule or remotely through a web interface. Microcontrollers with PH, Ammonia, Temperature and other sensors are included in the second module, which is used to monitor the aquarium's environment. © 2022 IEEE.

关键词： Internet of things

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Clustering Medical Transcriptions Using K -Means 5

Clustering Medical Transcriptions Using K -Means

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Salloum, Said Tahat, Dina Tahat, Khalaf Alfaisal, Raghad Salloum, Ayham School of Science Engineering and Environment University of Salford Manchester United Kingdom Al Ain University Applied Sociology Department United Arab Emirates United Arab Emirates University and Yarmouk University Media and Creative Industries Jordan Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia College of Medicine University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350354690

The clustering of medical transcriptions is an essential task for the categorization and summarization of large volumes of medical records. This paper explores the efficacy of k-means clustering, a well-known unsupervised machine learning algorithm, to discern patterns and segregate medical transcriptions into distinct clusters. We processed a dataset comprising various medical reports, systematically cleaning and preparing the text for analysis. By employing a Term Frequency-Inverse Document Frequency (TF-IDF) approach, we converted the textual data into a vectorized format amenable to machine learning methods. Subsequent dimensionality reduction through Principal Component Analysis (PCA) facilitated the visualization and interpretation of the high-dimensional data in two-dimensional space. The k-means algorithm was then applied, revealing five distinct clusters. Each cluster was characterized by examining the prevalence of key terms, uncovering thematic consistencies that may correspond to particular medical procedures or specialties. The resulting clusters demonstrate the algorithm's potential to automatically categorize medical documentation in a way that mirrors clinical relevance, thereby providing a foundation for improved information management systems in healthcare settings. © 2024 IEEE.

关键词： Dimensionality reduction

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