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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-rashidy, Nora El-Ghamry, Amir ElSayed, Nesma E. Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Faculty of Computers and Information Mansoura University Mansoura Egypt School of Engineering and Computer Science Hosted By Global Academic Foundation University of Hertfordshire Garden City Egypt Faculty of Computer Science and Engineering New Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Blood transfusion is in constant demand, as it is required for several medical procedures and life-or-death operations. This study is motivated by the fact that the need for blood transfusions is steadily on the rise because of accidents, operations, diseases, etc. such that the ability to accurately predict the number of blood donors allows medical personnel to predict the future blood supply and plan accordingly to recruit enough volunteers to meet demand. We attempt to handle this supply–demand gap by using a predictive model that identifies potential donors. We model the probability of a person donating blood, depending on five specific features, using machine learning techniques. This paper implements a machine learning pipeline that includes data preprocessing and donor classification. Several ensemble models have been developed. The results show that the random forest model led to the best test set accuracy (96.3%), which beat other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Machine learning

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A Cloud-Based Trust Computing Model for the Social Internet of Things

A Cloud-Based Trust Computing Model for the Social Internet ...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Ali-Eldin, Amr M.T. Mansoura University Faculty of Engineering Computer Engineering and Control Systems Department Mansoura Egypt

ISBN: (纸本)9781665412438

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach. © 2021 IEEE.

关键词： Internet of things

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A Dynamic Cooperative Multi-Agent Online Coverage Path Planning Algorithm 16

A Dynamic Cooperative Multi-Agent Online Coverage Path Plann...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Sadek, Mina G. El-Garhy, Ahmed M. Mohamed, Amr E. Helwan University Faculty of Engineering Computer Systems Engineering Dept. Cairo Egypt

ISBN: (纸本)9781665408677

This paper proposes a novel approach for finding an optimized solution for the online coverage path planning in unknown environments problem employing cooperative multi robotic agents. The suggested approach lessens the time required for solving the coverage problem by using cooperative multiple robotic agents working together to achieve enhanced complete coverage while sustaining minimum time, and the minimum number of repeated cells and respecting the service time. The distributed multi-agent planning with coordination process is optimized using dynamic programming with a rolling horizon limited look-ahead policy for online capability. In addition to expanding the usual single-agent action tree to hold a multi-agent scheme for possible collision-free sub-trajectories generation con-cerning quality, repetitions count, length per single agent to plan and execute cooperatively for covering a given unknown sur-roundings. Regarding coverage optimization, we utilize a multi-objective optimization genetic based algorithm with uniform cost search for the single-agent coverage sub-trajectory planning. In multi-agent-based systems, communication introduces overhead and have a direct influence on the system's robustness. Hence, we proposed a voting and coalition formation based on the K-means clustering algorithm for minimizing the communication henceforth improving the system's robustness. Simulation results highlight an enhancement regarding speeding up the complete coverage process compared to optimum single-agent approaches while advancing near-optimal utilization and efficiency minimizing repetitions in different unknown environments. © 2021 IEEE.

关键词： Multiobjective optimization

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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TCCFD: An Efficient Tree-based Framework for Credit Card Fraud Detection 16

TCCFD: An Efficient Tree-based Framework for Credit Card Fra...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Own, Reem M. Salem, Sameh A. Mohamed, Amr E. Helwan University Faculty of Engineering Computer Systems Engineering Dept. Cairo Egypt

ISBN: (纸本)9781665408677

Due to digitalization, credit card fraud has become one of the most prevalent global risks. The global economy loses billions of dollars annually due to credit card fraud. Financial institutions adopt a strategic approach to credit card fraud mitigation, relying on business rules to combat the occurrence of fraud and sophisticated analysis to detect fraud. However, Fraudsters are altering their patterns regularly with the help of new technologies. Thus, organizations continually seek to detect new fraud patterns. The detection of credit card fraud has improved tremendously through using artificial intelligence, particularly machine learning techniques. We introduce through this paper an efficient framework that adopts various tree-based machine learning models to detect fraudulent transactions. We apply the proposed framework to a highly asymmetric real-world dataset. The proposed framework integrating the extra trees classifier with the borderline synthetic minority oversampling technique surpasses others and previously developed models with 99.96 % accuracy and 96% area under the ROC curve. The proposed framework asserts its efficacy and reliability in classification while also addressing the class imbalance issue. © 2021 IEEE.

关键词： Machine learning

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Hyper-Heuristic Strategy for Input-Output-Based Interaction Testing

Hyper-Heuristic Strategy for Input-Output-Based Interaction ...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020

作者： Din, Fakhrud Zamli, Kamal Z. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang26300 Malaysia Department of Computer Science & IT University of Malakand Lower Dir District KPK Pakistan

ISBN: (纸本)9789813345966

Software testing aims at exploring faults within software in order to ensure it meets all necessary specifications. Test case design strategies play key role in software testing. Classical test case design strategies, however, do not sufficiently include support for exploration of faults due to interaction between parameter values. New strategies known as t-way strategies (where t expresses interaction strength) have been developed for finding interaction faults. However, existing t-way strategies for input-output-based relationship (IOR) interaction testing mostly adopt greedy algorithms which often generate poor quality test data. Therefore, this paper presents the design of a new IOR test suite generation strategy called IOR_HH based on the exponential Monte Carlo with counter (EMCQ) hyper-heuristic. EMCQ is a parameter free hyper-heuristic which works as controller of the three implemented low-level meta-heuristic operators, namely crossover, peer learning and global pollination in the proposed IOR_HH strategy. Experimental results demonstrate the impact of the proposed strategy against existing computational strategies for IOR interaction testing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software testing

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Are We in Flow? Measuring and Supporting Simultaneous Flow in Duos of Elderly Cyclists

Are We in Flow? Measuring and Supporting Simultaneous Flow i...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Mario Boot Laura Kahnt Dees Postma Mehmet Baran Ulak Karst Geurs Paul Havinga Transport Engineering & Management Faculty of Engineering Technology University of Twente Enschede The Netherlands Interaction Technology Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Pervasive Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands

Cyclists can experience psychological flow while cycling. Experiencing flow simultaneously is foundational to reaching shared flow in groups, which has unique and highly desirable characteristics. Notably, in research on affective computing and cycling experience, very little is known about flow in duos of cyclists. To address this knowledge gap, this study investigates if and how simultaneous flow (SF) during cycling experiences can be measured via sensor data and supported by personalizing assistance levels of e-bike motors. We collected heart rate, cadence, and position data, as well as self-reports of individual flow, from 10 duos of elderly cyclists, a demographic with increasing e-bike usage. Our XGBoost and Shapley values analysis shows that SF can be identified in heart rate, cadence, and position data. The personalization of motor assistance seemed to disrupt SF in our sample, possibly because our duos were well-adjusted already. Our findings support the development of real-time, objective identification of SF, which helps expert evaluations and biofeedback systems. Altogether, to the best of our knowledge, this is the first study to offer a valuable and innovative approach for measuring and supporting SF.

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A Novel Cryptocurrency Prediction Method Using Optimum CNN

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computers, Materials & Continua 2022年第4期71卷 1051-1063页

作者： Syed H.Hasan Syeda Huyam Hasan Mohammed Salih Ahmed Syed Hamid Hasan Iowa State University AmesUSA University of Texas at Arlington USA Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

In recent years,cryptocurrency has become gradually more significant in economic regions *** cryptocurrencies,records are stored using a cryptographic *** main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social *** is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media ***,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet *** paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and *** results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

关键词： Cryptocurrency litecoin monero bitcoin ethereum twitter optimal CNN price prediction sentiment analysis

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