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EMC/SI/PI and EMC Europe Symposium, IEEE International Joint

作者： Jawad Yousaf Kamran Javed Mohammed Ghazal Abu Dhabi University Abu Dhabi United Arab Emirates Saudi Data and Artificial Intelligence Authority (SDAIA) National Centre for Artificial Intelligence (NCAI) Riyadh Saudi Arabia Faculty of Computer Sciences and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

Different commercial ESD gun models, although complying with the standard ESD waveform requirements in terms of rise time and current values for standard Pellegrini target, produce different ESD waveforms. The variations of the ESD source and target impedance in real-time with the change in the gun model affects the ESD susceptibility compliance testing results and immunity analysis for the estimation of possible ESD failure in a product. This study presents, for the first time, a novel generic ESD generator model using artificial neural network (ANN) based deep learning techniques. The developed deep learning model incorporates the characteristics of the real-time generated ESD waveforms by various commonly used commercial ESD gun models with different target load impedances. The presented model could be used as a generic ESD source for fast ESD susceptibility and immunity testing’s at the design stage of a product using numerical or circuit-analysis-based tools.

关键词： Deep learning artificial neural networks Immunity testing Electrostatic discharges Generators Real-time systems Numerical models

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LFCKT: A Learning and Forgetting Convolutional Knowledge Tracking Model

LFCKT: A Learning and Forgetting Convolutional Knowledge Tra...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Mengjuan Li Lei Niu Jinhua Zhao Yuchen Wang Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institute Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China

ISBN: (纸本)9798350345636

Personalized exercise recommendation is a key research direction of personalized learning. In personalized exercise recommendation, we recommend suitable exercises for students according to their knowledge mastery status to improve their learning efficiency. Therefore, the accuracy of predicting students’ knowledge state in personalized exercise recommendation affects the goodness of the exercise recommendation. In the process of students’ learning, learning behavior and forgetting behavior are intertwined, and students’ forgetting behavior has a great influence on the knowledge state. In order to accurately model students’ learning and forgetting, we propose a Learning and Forgetting Convolutional Knowledge Tracking model (LFCKT) that takes into account both learning and forgetting behaviors. The model takes into account three factors that affect knowledge forgetting, including the interval time of target knowledge interaction, the count of past target knowledge interaction and student’s state of knowledge. LFCKT model uses students’ answer results as indirect feedback of knowledge mastery in the process of knowledge tracking, and integrates individual personalized learning behavior and individual forgetting behavior. Through experiments on the real online education public dataset, LFCKT can better track students’ knowledge mastery status and has better predictive performance than current knowledge tracking models.

关键词： Knowledge engineering Target tracking Education Predictive models Real-time systems Behavioral sciences

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Holographic Display in Consumer Electronics for Detecting Abnormal IoT Devices Using a Lightweight Security Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Aanjankumar, S. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Poonkuntran, S. Khan, Firoz Quasim, Mohammad Tabrez Basheer, Shakila VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India KPR Institute of Engineering and Technology Department of Information Technology Tamilnadu Coimbatore641407 India Maharastra Pune412115 India Faculty in Center for Information and Communication Sciences Ball state University United States University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia Princess Nourah bint Abdulrahman University Department of Information Systems College of computer and Information science Riyadh11671 Saudi Arabia

IoT (Internet of Things) devices are becoming more popular because they can be connected to mobile phones and communicate with various types of devices (e.g., smart homes, automation, and monitoring applications), which provides a high-quality service with the flexibility of performing operations from anywhere at any time. This research proposes a holographic display in consumer electronics to provide a more interactive experience for the user by providing significant security and privacy framework to the IoT devices which are prone to vulnerabilities. A simple Post-Quantum Cryptography Algorithm (PQCA) is used in this study to find flaws in a quantum lattice that is based on a 3x3 matrix in pre-processing. Also, an edge-based anomaly detection framework is created to work with consumer devices to show holographic data streams and IoT device activity in real time for devices with limited resources and less computational overhead than traditional machine learning models. The suggested method conducts testing on a set of unsupervised IoT devices, which contain data and metrics on user behaviour. The dataset contains 8000 service requests in an IoT environment. It finds security anomalies in 70% of the cases in training phases and 30% of the cases in testing phases, respectively. The proposed system detects vulnerable IoT devices by projecting them in a holographic projection graph, which in turn enhances privacy. The test results showed that the minimum encryption time overhead is 0.00085, and the accuracy achieved is about 97.44%. © 1975-2011 IEEE.

关键词： Quantum cryptography

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artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports

arXiv

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arXiv 2025年

作者： Wesely, Sophia Hofer, Ella Curth, Robin Paryani, Shyam Mills, Nicole Ueberschär, Olaf Westermayr, Julia Leipzig University Faculty of Chemistry and Mineralogy Institute of Physical and Theoretical Chemistry Leipzig Germany Magdeburg-Stendal University of Applied Sciences Department of Engineering and Industrial Design Magdeburg Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden Leipzig Germany University of North Florida Brooks College of Health JacksonvilleFL United States University of North Florida Athletics Department JacksonvilleFL United States Institute for Applied Training Science Leipzig Germany

Over the past four decades, cheerleading has evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling—encompassing team synchronicity, ground interactions, choreography, and artistic expression—makes objective assessment challenging. artificial intelligence (AI) has revolutionized various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Results indicate that certain machine learning models can effectively identify different tumbling elements despite inter-individual variability and data noise, achieving high accuracy. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports, providing objective metrics that complement traditional judging methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Sports

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An Approach to Optimize Lab-Seat Allocation Problem Based on Multi-Agent Negotiation

An Approach to Optimize Lab-Seat Allocation Problem Based on...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Kai Li Lei Niu Yang Yang Yuchen Wang Faculty of Artificial Intelligence Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institude Faculty of Artificial Intelligence Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institude Central China Normal University Wuhan China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China

ISBN: (纸本)9798350345636

China’s higher education level is rising year by year, the traditional method of allocating “One seat per person” cannot cope with the growth in the number of students in universities. Existing approaches focus more on space utilization and less on the students’ feelings. But in fact, students are more willing to go to the lab and are more productive if they have a satisfactory lab-seat. Therefore, new and more effective methods are needed. This paper uses multi-agent negotiation method to solve the problem. Students and laboratory administrator are independent agents and the negotiations between agents determine the final allocation. Both parties adjust their offer during the negotiation process using a concession strategy based on time constraints and result in a max overall utility finally.

关键词： Education Resource management Time factors

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Optimizing Wireless Performance in Wearable Flexible Electronics: Antenna Strategies

Optimizing Wireless Performance in Wearable Flexible Electro...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Jacob Abraham S. Parameswari D. Nirmala B. Panjavarnam A. Rajasekar P Ashok Department of Electronics B P C College Ernakulam Kerala India Department of Electronics and Communication Engineering Sairam Institute of Technology Chennai Tamilnadu India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Sairam Institute of Technology Chennai Tamilnadu India Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

Flexible electronics refers to the utilization of pliable plastic substrates in the construction of elastic and bendable electrical circuits. Unlike conventional semiconductors and glass substrates, flexible electronics open up possibilities for entirely new product designs. This innovation hinges on the fabrication of electronic components on flexible plastic substrates like transparent conductive polyester film or polyimide, facilitating the assembly of electronic circuits. Remarkably, the same components employed in crafting rigid printed circuit boards can be repurposed for fashioning flexible electronic assemblies, allowing the board to flex and conform to desired shapes. In an alternative approach to flexible electronics, the standard silicon substrate must undergo thinning to a few tens of micrometers through etching processes, thereby achieving a bending radius of less than 5mm. The burgeoning growth of wearable technology, spanning global positioning systems (GPS), communication devices, health monitoring systems, and military applications, has propelled the rapid expansion of flexible antenna design and modeling. This surge reflects the pivotal role that flexible electronics play in enabling the evolution of these crucial industries.

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A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized database

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Computer Modeling in engineering & sciences 2024年第4期139卷 593-631页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Abdullah Alamri Chang-Wook Lee Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)PuneMaharashtra412115India Electronics&Telecommunication Dr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis Centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyAustralia Earth Observation Centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Science Education Kangwon National UniversityChuncheon-si24341Korea

Infection of leukemia in humans causes many complications in its later *** impairs bone marrow’s ability to produce *** diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this *** binary classification is employed to distinguish between normal and leukemiainfected *** addition,various subtypes of leukemia require different *** sub-classes must also be detected to obtain an accurate diagnosis of the type of *** entails using multi-class classification to determine the leukemia *** is usually done using a microscopic examination of these blood *** to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for *** utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of *** advanced deep-learning methods are also *** to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding *** study introduces a deep learning-machine learning combined approach for leukemia *** uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning *** transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature *** extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia *** the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the *** d

关键词： Leukemia diagnosis deep learning machine learning random forest XGBoost

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

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2023 7th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Wenxue Li Chi Cheng Peihua Xu Renfeng Liu Yun Zhang Huabing Zhou College of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Meteorological Service Center Wuhan China Faculty of Artificial Intelligence Education School of Educational Information Technology Central China Normal University Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models.

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OpenLS-DGF: An Adaptive Open-Source dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Correction to: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2024年 1-1页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

Typesetter overlooked corrections regarding affiliations of author Rakesh Kumar and needs to be correctly read as only affiliated to: Department of Mathematics, Lovely Professional University, Phagwara, Punjab 144411, India. The original article has been corrected. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

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