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Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

computer Systems Science & engineering 2022年第4期41卷 255-269页

作者： Wei Zhuang Yixian Shen Chunming Gao Lu Li Haoran Sang Fei Qian School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Engineering&Technology University of WashingtonTacomaWA 98402USA China General Nuclear Power Group Nanjing210028China Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210029China

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from *** this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless *** with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count *** the dis-tance between access point(AP)and monitor point(MP)is typically non-ﬁxed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different *** order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefﬁcient matrix of subcarriers which are insensitive to the change of received signal *** conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

关键词： CSI device-free deep learning crowd counting Wi-Fi wireless sensing

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs 6

Firefly Swarm Intelligence Based Automatic Clustering and Tr...

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6th International Conference on Communication and information Systems, ICCIS 2022

作者： Chen, Siji Jiang, Bo Xu, Hong Ding, Yan Wang, Xin School of Computer Science and Technology Chongqing China The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China School of Communication and Information Engineering CQUPT Chongqing China

ISBN: (数字)9781665463850

ISBN: (纸本)9781665463850

Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple clusters and various optimal strategies can be adopted to achieve strong network performance. In this paper, we propose a firefly swarm intelligence based automatic clustering and tracking algorithm (FSIACT) for UANETs, which is inspired by the collective behavior of fireflies. Firstly, we propose the fitness function consisting of link survival possibility, average distance and residual energy, and utilize it as the light intensity of the firefly. Secondly, firefly algorithm (FA) is put forward for cluster head (CH) selection and cluster management. Based on the characteristics of the FA, the whole swarm can be automatically divided into several clusters and cluster members (CMs) are willing to track the CH in the cluster. It is verified in simulations that the proposed algorithm achieves the lower handover rate of CHs, longer link expiration time (LET) and longer node lifetime. © 2022 IEEE.

关键词： Swarm intelligence

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Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

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computers, Materials & Continua 2023年第3期74卷 6461-6484页

作者： Abeer Bashab Ashraf Osman Ibrahim Ibrahim Abakar Tarigo Hashem Karan Aggarwal Fadhil Mukhlif Fuad A.Ghaleb Abdelzahir Abdelmaboud Faculty of Computer Science&Information Technology Alzaiem Alazhari University13311KhartoumSudan Faculty Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400SabahMalaysia Department of Computer Science University of SharjahSharjah27272United Arab Emirates Electronic and Communication Engineering Department Maharishi Markandeshwar Deemed to be UniversityMullanaAmbala133207HaryanaIndia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Information Systems King Khaled UniversityMuhayel61913AseerSaudi Arabia

University timetabling problems are a yearly challenging task and are faced repeatedly each *** problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal *** techniques have been used to solve university timetabling problems,and most of them use optimization *** paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling *** comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the *** is noted that meta-heuristic methodologies are widely used in the ***,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling ***,trends and future directions in university timetabling problems are *** paper provides good information for students,researchers,and specialists interested in this area of *** challenges and possibilities for future research prospects are also explored.

关键词： University timetabling timetabling approaches meta-heuristics combinatorial optimization

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Revolutionizing Loan Approval: Harnessing the Power of K-Nearest Neighbors for Predictive Eligibility

Revolutionizing Loan Approval: Harnessing the Power of K-Nea...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Mhd Saeed Sharif Madhav Theeng Tamang Niyas Jaheer Hussain Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London UK College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

A bank has become a basic need of our daily lives. People use banks for several tasks, such as depositing or withdrawing money and borrowing loans. The number of loan applications is rising significantly. It is crucial for the bank and financial sectors to determine loan eligibility and approve loans to suitable applicants only. This study attempts to design a system which can easily predict loan eligibility with the application of several machine learning (ML) algorithms. We employed the five different machine learning algorithms to achieve this research's aim. These algorithms were trained using the pre-processed dataset to create predictive models. F1 score, accuracy, Recall and Precision are used to evaluate the performance of the proposed approaches. The result showed that K-nearest neighbor (KNN) demonstrated outstanding performance, with an accuracy rate of 88.89%. The Random Forest (RF) model had an accuracy of 84.44%, whereas the Support Vector Machine (SVM) and Linear regression (LR) models achieved 82.22% and 80% accuracy, respectively. The Decision Tree (DT) algorithms showed the worst performance among the five algorithms, with an accuracy of 79.88%. The achievement of this research focuses on the possibility of machine learning algorithms as an efficient method for predicting loan eligibility in banks, contributing to the advancement of financial technology by automating and optimizing key banking processes, reducing processing times, and enhancing overall bank efficiency.

关键词： Support vector machines Radio frequency Machine learning algorithms Accuracy Fintech Banking Predictive models Nearest neighbor methods Prediction algorithms Decision trees

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Data Heterogeneity Challenge in Consumer Electronics: A Federated Learning Solution

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Zhenyu Wu, Yunting Ge, Huanhuan Wang, Jinlong Lyu, Zhihan Qingdao University of Technology School of Information and Control Engineering Shandong Qingdao China School of Information and Control Engineering Innovation Institute for Sustainable Maritime Architecture Research and Technology Shandong Qingdao China School of Computer Science and Technology Xidian University Xi’an710126 China

Federated learning(FL) enables collaborative model training across consumer electronic devices while preserving data privacy. However, the non-independent and identically distributed nature of data in real-world scenarios significantly impacts personalization performance. To address this challenge, we propose pFedPKD, in this framework, we first employ model pruning to simplify the global model, thereby enhancing its adaptability and improving the efficiency of knowledge transfer between different electronics devices. Moreover, we introduce an attention-based knowledge transfer mechanism, which leverages knowledge distillation to transfer the attention map and predictions of the pruned global model to the local models, thus improving their generalization capability and performance. Extensive experiments on three datasets demonstrate that pFedPKD consistently outperforms state-of-the-art methods, achieving higher personalization accuracy and robustness under heterogeneous data distributions. This work provides an efficient solution for applying FL in consumer electronics, offering improved adaptability and resource efficiency. © 1975-2011 IEEE.

关键词： Knowledge transfer

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Accurate Data Match and Call Method for the Thermal Compensation Database of the Reflector Antenna

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research in Astronomy and Astrophysics 2022年第5期22卷 98-108页

作者： Yuefei Yan Song Xue Xinlan Hu Peiyuan Lian Yan Wang Lin Li Qian Xu Na Wang Wulin Zhao Yuanpeng Zheng Congsi Wang Key Laboratory of Electronic Equipment Structure Design Ministry of EducationXidian UniversityXi'an 710071China School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi'an 710055China School of Physics and Technology Xinjiang UniversityUrumqi830046China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China The 39th Research Institute of China Electronics Technology Group Corporation Xi'an 710065China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050081China

The influence of thermal deformation on the performance of reflector antennas has become increasingly significant with the increasing aperture and working *** use of a thermal compensation database is an efficient method to compensate for the deformation caused by the non-uniform temperature ***,how to efficiently and accurately match and call the database remains as one of the tough challenges for the antenna thermal compensation system to achieve real time ***,this study proposes a data match and call method for the thermal compensation database of the reflector antenna,matching the database from three aspects:the overall rms match of temperature data,the similarity area match of each data sample,and the key area match of key structural *** validation of this method is demonstrated in an *** difference between the pointing adjustment amount calculated by the matched data and the collected data was found to be less than 1",which satisfied the requirements of practical engineering,thus achieving real-time thermal compensation of the antenna.

关键词： instrumentation miscellaneous-methods miscellaneous-telescopes

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A Comparative Study of Sales Prediction Using Machine Learning Models: Integration of PySpark and Power BI

A Comparative Study of Sales Prediction Using Machine Learni...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： MhD Saeed Sharif Madhav Theeng Tamang Anup Nepal Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model's data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making.

关键词： Training Accuracy Machine learning algorithms Biological system modeling Computational modeling Data visualization Predictive models Data models Forecasting Random forests

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Application of Clustering Algorithms to Enhance Personalized Learning Through Recommendation Model

Application of Clustering Algorithms to Enhance Personalized...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Mhd Saeed Sharif Madhav Theeng Tamang Dyuthi Mani Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Training Technological innovation Recurrent neural networks Collaborative filtering Clustering algorithms Predictive models Prediction algorithms Problem-solving Pupils Recommender systems

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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