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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： Intelligent systems

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ABSOLUTE MONOTONICITY INVOLVING THE COMPLETE ELLIPTIC INTEGRALS OF THE FIRST KIND WITH APPLICATIONS

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Acta Mathematica Scientia 2022年第3期42卷 847-864页

作者： Zhenhang YANG 田景峰 Engineering Research Center of Intelligent Computing for Complex Energy Systems of Ministry of Education North China Electric Power UniversityBaoding 071003China Zhejiang Society for Electric Power Hangzhou 310014China Department of Mathematics and Physics North China Electric Power UniversityBaoding 071003China

Let K(r)be the complete elliptic integrals of the first kind for r∈(0,1)and f_(p)(x)=[(1−x)^(p)K(√x)].Using the recurrence method,we find the necessary and sufficient conditions for the functions−f′_(p),ln f_(p),−(ln f_(p))^((i))(i=1,2,3)to be absolutely monotonic on(0,1).As applications,we establish some new bounds for the ratios and the product of two complete integrals of the first kind,including the double inequalities exp[r^(2)(1−r^(2))/^(64)]/(1+r)^(1/4)

关键词： Complete elliptic integrals of the first kind absolute monotonicity hypergeometric series recurrence method inequality

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Malpractice Detection in Examination Hall using Deep Learning

Malpractice Detection in Examination Hall using Deep Learnin...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Aruna S K Madhumitha A Selvanayaki Kolandapalayam Shanmugam Senthil Kumar Thangavel Maiga Chang Department of Computer Science and Engineering School of Engineering and Technology Bangalore CHRIST University India Department of Mathematics and Computer Science Ashland University Ohia USA Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Computing Information and Systems Athabasca University Canada

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring.

关键词： YOLO Accuracy Plagiarism Real-time systems Classification algorithms Object recognition Monitoring

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Video-Based Jump Height Estimation in Athletic Performance Testing

Video-Based Jump Height Estimation in Athletic Performance T...

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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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9th International Conference on Advanced computing and Communication systems, ICACCS 2023

作者： Charaan, R. M. Dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, R.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

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Computers, Materials & Continua 2022年第9期72卷 4267-4281页

作者： Jaber S.Alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： Medical data classification feature selection deep learning healthcare sector artificial intelligence

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Active Sensing for Target Tracking: A Bayesian Optimisation Approach

Active Sensing for Target Tracking: A Bayesian Optimisation ...

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International Conference on Information Fusion

作者： Xingchi Liu Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Scientific Computing Department Rutherford Appleton Laboratory Science and Technology Facilities Council UK

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Active sensing plays an essential role in searching and tracking a target without initial target state information. This paper studies the active sensing approach for sensor management problems using multiple unmanned aerial vehicles based on the received signal strength measurements of the target. A Bayesian optimisation-based approach is proposed which adopts the Gaussian process method to model the received signal strength in an area over time and then the expected improvement acquisition function is leveraged to decide where to take new measurements considering the uncertainty of the Gaussian process. A unique contribution of this paper consists of the designed spatial-temporal composite kernel function that accounts for the time-varying nature of the signal strength. Numerical results obtained from different measurement noise levels and varying initial Bayesian optimisation settings demonstrate that the proposed approach can efficiently schedule multiple unmanned aerial vehicles to locate the target within a minimum number of initial data. Particularly, it achieves at most $57 \%$ lower tracking error and $46 \%$ lower lost-track probability as compared to the benchmark approach.

关键词： Target tracking Atmospheric measurements Gaussian processes Particle measurements Autonomous aerial vehicles Sensors Bayes methods Kernel Vehicle dynamics Optimization

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Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach

arXiv

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arXiv 2024年

作者： Alshehri, Abeer Abdulrahman, Amal Alamri, Hajar Miller, Tim Vered, Mor School of Computing and Information Systems The University of Melbourne Melbourne Australia Department of Computer Science and Information Systems King Khalid University Abha Saudi Arabia School of Computing Macquarie University Sydney Australia School of Electrical Engineering and Computer Science The University of Queensland Melbourne Australia School of Computing and Information Systems Monash University Melbourne Australia

Goal recognition (GR) involves inferring an agent’s unobserved goal from a sequence of observations. This is a critical problem in AI with diverse applications. Traditionally, GR has been addressed using’inference to the best explanation’ or abduction, where hypotheses about the agent’s goals are generated as the most plausible explanations for observed behavior. Alternatively, some approaches enhance interpretability by ensuring that an agent’s behavior aligns with an observer’s expectations or by making the reasoning behind decisions more transparent. In this work, we tackle a different challenge: explaining the GR process in a way that is comprehensible to humans. We introduce and evaluate an explainable model for goal recognition (GR) agents, grounded in the theoretical framework and cognitive processes underlying human behavior explanation. Drawing on insights from two human-agent studies, we propose a conceptual framework for human-centered explanations of GR. Using this framework, we develop the eXplainable Goal Recognition (XGR) model, which generates explanations for both why and why not questions. We evaluate the model computationally across eight GR benchmarks and through three user studies. The first study assesses the efficiency of generating human-like explanations within the Sokoban game domain, the second examines perceived explainability in the same domain, and the third evaluates the model’s effectiveness in aiding decision-making in illegal fishing detection. Results demonstrate that the XGR model significantly enhances user understanding, trust, and decision-making compared to baseline models, underscoring its potential to improve human-agent collaboration. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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Computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Keratoconus Severity Staging Using Random Forest and Gradient Boosting Ensemble Techniques 22

Keratoconus Severity Staging Using Random Forest and Gradien...

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22nd IEEE International Multi-Conference on systems, Signals and Devices, SSD 2025

作者： Muhsin, Zahra J. Qahwaji, Rami Ghafir, Ibrahim Al Bdour, Muawyah Alryalat, Saif Alshawabkeh, Mo'Ath Al-Taee, Majid University of Bradford Faculty of Eng. and Digital Technologies United Kingdom Scool of Medicine The University of Jordan Amman Jordan The Hashemite University Department of Ophthalmology Zarqa Jordan Independent Consultant of Computing and Systems Engineering Liverpool United Kingdom

ISBN: (纸本)9798331542726

Accurate keratoconus (KC) staging is vital for improving patient care and guiding clinical decision making. Choosing the right Machine Learning (ML) algorithm is key to effectively tackling this challenge and ensuring optimal performance. This paper presents a detailed comparison of eight ML algorithms commonly used in KC detection, based on a clinical dataset collected by the authors over the past decade. The study investigates each algorithm's effectiveness in distinguishing KC severity stages. Results showed that ensemble learning algorithms outperformed other approaches, with Random Forest (RF) emerging as the top-performing model, achieving the highest validation accuracy of 98.82%. When tested on unseen data, the RF model maintained a comparable accuracy of 98%, demonstrating its robustness, and generalizability in accurately distinguishing between different stages of KC severity. © 2025 IEEE.

关键词： corneal tomography ensemble learning keratoconus machine learning severity staging

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