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An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2627-2647页

作者： Tarek Sheltami Gamil ahmed Ansar Yasar Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread ***,deploying IoD for area coverage poses numerous limitations and *** include interference between neighboring drones,the need for directional antennas,and altitude restrictions for *** challenges necessitate the development of efficient *** research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges *** primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring *** deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of *** the other hand,deploying a few drones cannot satisfy the coverage *** accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is *** objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground *** is assumed that each drone is equipped with a directional antenna to optimize coverage in a given *** simulations are conducted to assess the effectiveness of the proposed *** demonstrate that the proposed method achieves maximum coverage with minimum transmission ***,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence *** simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage an

关键词： IoD line of sight optimal deployment IPSO RF model

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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Computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： ahmed Alhussen Arshiya S.Ansari Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial *** study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic *** are wireless networks that are based on mobile devices and may *** distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion *** study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within *** framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real ***-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce *** framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban *** simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and *** results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

Utilizing N-hydroxy-9-octadecenamide as a collector in flotation separation of bastnaesite and fluorite

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Journal of Rare Earths 2024年第8期42卷 1620-1628,I0008页

作者： Zhihao Guo Sultan ahmed Khoso Mengjie Tian Wei Sun School of Minerals Processing and Bioengineering Central South UniversityChangsha410083China State Key Laboratory of Mineral Processing Beijing102600China Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources Wuhan430081China Key Laboratoryof Hunan Province for Cleanand Efficient Utilization of Strategic Calcium-containing Mineral Resources Central South UniversityChangsha410083China Department of Mining Engineering Mehran University of Engineering&TechnologyJamshoro76062Pakistan

Bastnaesite((Ce,La,Pr,Nd)CO_(3)F) is a significant light rare earth mineral found in nature,known for its fine-grained *** is commonly employed for the recovery of fine-grained bastnaesite *** serve as an essential flotation reagent that enhance the surface hydrophobicity of target minerals.A novel collector,N-hydroxy-9-octadecenamide(N-OH-9-ODA),was synthesised in this study.N-OH-9-ODA exhibits superior selectivity compared to the traditional collector oleic acid in the flotation separation of bastnaesite and *** experimental and co mputational results indicate that N-OH-9-ODA exhibits superior selectivity due to its higher adsorption affinity for bastnaesite surface compared to fluorite *** zeta potential and the binding energies of the Ce 3d peaks in the X-ray photoelectron spectrum(XPS) of bastnaesite surface exhibit significant ***,fluorite surface demonstrates minimal alterations in its zeta potential and the binding energies of the Ca 2p peaks in its XPS after its interaction with N-OH-9-ODA.

关键词： Bastnaesite Collector Hydroxamic acids N-hydroxy-9-octadecenamide Oleic acid Rare earths

Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review

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BLOCKCHAIN-RESEARCH AND APPLICATIONS 2024年第3期5卷 184-208页

作者： Saleh, ahmed M. Shamsan Univ Tabuk Dept Informat Technol Tabuk 47512 Saudi Arabia

As the use of artificial intelligence (AI) grows within the field of cybersecurity, so does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of blockchain technology with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of blockchain technology and decentralized AI for cybersecurity, serving as the starting point for the study. This paper begins with an overview of blockchain technology and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between blockchain technology and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled, decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.

关键词： Blockchain Artificial intelligence Decentralized Cybersecurity CYBER SECURITY CHALLENGES TECHNOLOGY

Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

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Computers, Materials & Continua 2024年第8期80卷 1889-1905页

作者： Abdulraqeb Alhammadi Zaid ahmed Shamsan Arijit De Center for Artificial Intelligence and Robotics(CAIRO) Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Electrical Engineering College of EngineeringImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Information Engineering Electronics and TelecommunicationsSapienza UniversityRome00185Italy

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation *** complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational *** localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online *** networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user *** networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to *** article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user *** proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s *** operates in both online and offline *** the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s *** results unequivocally showcase the superior performance of the proposed model when compared to other existing models and *** proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing ***,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

关键词： Localization positioning Bayesian fingerprinting received signal strength(RSS)

A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan ahmed I.Taloba Amr Abozeid ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

Higgs inflation model with non-minimal coupling in hybrid Palatini approach

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Chinese Physics C 2024年第4期48卷 193-201页

作者： Brahim Asfour Aatifa Bargach ahmed Errahmani Taoufik Ouali Laboratory of Physics of Matter and Radiation University of Mohammed firstBP 717OujdaMorocco

In this paper,we propose a hybrid metric Palatini approach in which the Palatini scalar curvature is non minimally coupled to the scalar *** derive Einstein’s field equations,i.e.,the equations of motion of the scalar ***,the background and perturbative parameters are obtained by means of Friedmann equations in the slow roll *** analysis of cosmological perturbations allowed us to obtain the main inflationary parameters,e.g.,the scalar spectral index and tensor to scalar ratio *** this perspective,as an application of our analysis,we consider the Higgs field with quartic potential,which plays the inflaton role,and show that predictions of Higgs hybrid inflation are in good agreement with recent observational data[***.641,61(2020)].

关键词： non-minimal coupling hybrid metric Palatini Higgs inflation

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

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Computers, Materials & Continua 2024年第1期78卷 1399-1422页

作者： Mehmood ahmed Noraini B.Ibrahim Wasif Nisar Adeel ahmed Muhammad Junaid Emmanuel Soriano Flores Divya Anand Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit RajaBatu Pahat86400Malaysia Department of Computer Science COMSATS University Wah Cantt CampusIslamabad47010Pakistan Department of Information Technology The University of HaripurKhyber Pakhtunkhwa22620Pakistan Engineering Research Innovation Group Universidad Europea del AtlanticoSantander39011Spain

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert *** models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate *** addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation ***,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost *** study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these *** proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry *** article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost *** the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing *** findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

关键词： Artificial neural networks COCOMO II cost drivers global software development linear regression software cost estimation

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer