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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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computer systems science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

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Blockchain Technology Supports Employee Career Management to Increase Transparency of Employee Career Achievements 6

Blockchain Technology Supports Employee Career Management to...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Inayatulloh Yasri Fathurrozi, Ahmad Yasir, R. Muhammad Sinlae, Fried Lestari, Tyastuti Sri Priatna, Wowon Hartono, Indra Kusumadi Noe'man, Achmad Information Systems Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computers Science Department School of Compters Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computer Science Bhayangkara Jakarta Raya University Jakarta Indonesia

ISBN: (纸本)9798331539603

Career planning for employees is an important part for employees to increase motivation and work performance. On the other hand, career management for companies plays an important role in meeting employee needs in the future. The problem is that employees cannot get real-time and valid information about their career progress based on objective company assessments. Many old employees are not promoted to higher positions without an accurate explanation from the company. Thus, a system is needed that can help employees monitor their career progress. So the aim of this research is to build an employee career management model using blockchain technology to increase the transparency of employee career paths in the company. The research uses a qualitative approach through a literature review to obtain information about employee management phenomena and problems. The literature review is also used to study blockchain technology as an alternative solution to career management problems. The research output is a blockchain technology adoption model for corporate career management and simulation with the Hyperledger framework. © 2024 IEEE.

关键词： Employment

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The SEIR Model for Predicting Malware Propagation in computer Networks 5

The SEIR Model for Predicting Malware Propagation in Compute...

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

作者： Kharabsheh, Mohammad Al-Aiash, Issa Mughaid, Ala Almiani, Muder The Hashemite University Faculty of Prince Al-Hussien bin Abdullah II for IT Department of Computer Information Systems Department Zarqa13133 Jordan Gulf University of Science and Technology Kuwait

ISBN: (纸本)9798350354690

The propagation of infectious diseases has long been mathematically modeled to estimate their spread within populations, a concept increasingly applied to cybersecurity to analyze the spread of malware in computer networks. This paper investigates the use of an epidemiological framework, the Susceptible-Exposed-Infected-Recovered (SEIR) model, to predict the spread of malware across computer networks. The model parameters are modified to represent the infection rate and the susceptibility of nodes to be infected with malware, which allows the model to be adapted to various network topologies and how it can simulate the spread of mal ware over time in a specific network making the model a valuable tool for cybersecurity professionals enabling them to anticipate 0 demonstrate the model's potential, synthetic data was generated and used in the simulations, showcasing the model's capability to predict malware propagation within a network. These insights can enhance the strategic deployment of security interventions. © 2024 IEEE.

关键词： Cyber attacks

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data 8

Deep Transfer Hashing for Adaptive Learning on Federated Str...

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8th International Workshop and Tutorial on Interactive Adaptive Learning, IAL@ECML-PKDD 2024

作者： Röder, Manuel Schleif, Frank-Michael Faculty of Computer Science and Business Information Systems TUAS Würzburg-Schweinfurt Würzburg Germany Faculty of Technology Bielefeld University Bielefeld Germany Center for Artificial Intelligence and Robotics Würzburg Germany

This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution. © 2024 Copyright for this paper by its authors.

关键词： Federated learning

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Wormhole Attack Detection Technques in MANET 2

Wormhole Attack Detection Technques in MANET

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bashir, Muhammad Tahir, Sidra Almufareh, Maram Fahaad Hamid, Bushra Qamar, Farkhanda PMAS ARID Agriculture University Rawalpindi Punjab Pakistan Jouf University College of Computer and Information Sciences Department of Information Systems AL-Jouf Sakakah Saudi Arabia Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

MANET is a wireless medium, with no infrastructure, for nodes to communicate within a network. Movement of nodes is a lot more in this network than other networks due to lack of infrastructure (Wireless nature). In a mobile ad hoc network, each node functions as a router and finds the best path forwarding a packet via origin to destination. This network is important in places where building a framework is impossible, like in a military setting. A mobile ad-hoc network (MANET) is a temporary network created by unrestricted nodes that can move around and communicate without a centralized network. In a black hole attack, several or even more attacker node use a tunnel-like private channel to find a wormhole attack. Wormhole tunnel will then start collecting the data packets and transferring them to another location. The proposed work encouraged secure and effective routing in MANET. At that point, we selected 20 important attributes to create a dataset that was labelled with the aid of a unique node address. Apply two well-known machine learning classifiers that categorise data into two groups, specifically normal and malicious, to test sample data in this manner. For feature selection and SVM (Support Vector Machine) classification, respectively, we employ genetic algorithms. The demonstration of the technology was assessed on many quantifiable characteristics and compared to contemporary techniques. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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computer systems science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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