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Using the data mining technique to predict successful customer engagement of marketing campaigns in social media

International Journal of Business Intelligence and Data Mining 2023年第2期23卷 166-183页

作者： Al Rabaani, Fatema Salim Said, Aiman Moyaid Fageeri, Sallam Osman AlAbdulsalam, Abdul Rahman Khalifa Department of Information System College of Economics Management and Information System University of Nizwa Nizwa Oman Department of Computer Science Sultan Qaboos University Muscat Oman

Marketing in social media platforms plays a vital role in enhancing the return of investment for start-up companies in the fashion industry. Predicting the level of customer engagement of the marketing campaign in social media reveals customers' preferences and public attention towards the marketing campaign. This research proposed using different data mining classifiers to predict the success of online marketing campaigns to reduce the efforts and allocated resources toward achieving the goal of marketing in the fashion industry. The research collected 8,151 marketing campaigns published on Instagram users account for the fashion industry in Oman, formed social-media-based metrics, and formulated data mining algorithms to predict the customer engagement level. The results show that the model induced by neural networks provides the highest accuracy, 96%, in predicting customer engagement levels. The results illustrated that the number of posts published is not essential to gaining additional interactions from the user. © 2023 Inderscience Enterprises Ltd.

关键词： Data mining

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Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

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computers, Materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-Zebari Rizgar R.Zebari Subhi R.M.Zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related *** has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic *** article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these *** presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart *** attain this,the presented ODLTCP-HRRSI model performs two major *** the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic ***,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)*** experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： Remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

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Developing an Attack Model for compromising Privacy over Secure Connection Protocols 6

Developing an Attack Model for compromising Privacy over Sec...

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6th IEEE International Conference on computer and Communication Engineering Technology, CCET 2023

作者： Almakdi, Sultan Alshehri, Mohammed S. College of Computer Science and Information System Najran University Department of Computer Science Najran Saudi Arabia

ISBN: (纸本)9798350340686

Nowadays with the advancement in the IoT, Man-In-The-Middle (MITM) attack is classified as one of the major concerns for network security experts since it is very hard to detect. It directly affects the data being transmitted - through the network- between the endpoints, its secrecy, and reliability. Besides, the adversary can reveal much sensitive information, such as Social Network credentials, websites, and images being browsed by the victim who may not be able to notice that he/she is under attack. MITM can run over secure connections ( using secure protocols like HTTPS) which make it classified as one of the most dangerous and challenging attacks. In the literature, many researchers have proved that MITM attack invoke the integrity of data if HTTP protocol was used, and users' privacy can be revealed. In this research, we prove that users' privacy can be revealed even though a secure connection protocol (HTTPS was used). Firstly, we survey MITM attack, ARPs Spoofing, and prevention mechanisms. Then, we explain how to perform MITM attack, and gather transmitted data from the user. Finally, we analyze the collected data and show how the adversary can obtain victim's private information from the analyzed data. © 2023 IEEE.

关键词： Network security

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6643-6657页

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi’an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad4879 Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Federated Learning Computational Modeling Accuracy Servers Training Data Models Machine Learning Mobile Computing Particle Measurements Convergence Federated Learning Contribution Evaluation Trust Evaluation Model Projection Theory Secure Aggregation Federated Learning Computational Complexity Test Dataset Global Model Selection Of Participants Model Projections Aggregation Method Shapley Value Speed Of The Model Contributions Of Participants Number Of Participants Accuracy Of Model Computational Cost Local Data Learning Task Layer Model Participation Outcomes Updated Model Value Of Participation Model Aggregation Server Aggregates Global Convergence Iterative Rounds Trust Value Local Updates MNIST Dataset Total Amount Of Data Local Training Aggregation Algorithm Complex Model Structure

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Query Optimization Framework for Graph Database in Cloud Dew Environment

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computers, Materials & Continua 2023年第1期74卷 2317-2330页

作者： Tahir Alyas Ali Alzahrani Yazed Alsaawy Khalid Alissa Qaiser Abbas Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahorePakistan Faculty of Computer Science and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Virtual University of PakistanLahorePakistan

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of *** challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes *** in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance *** performance enhancement,compression and query optimization are the two most *** reduces the volume of data,whereas query optimization minimizes execution *** the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more *** paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a *** factors compression and query optimization improve the performance of the *** research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

关键词： Query optimization compression cloud dew decompression graph database

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Public Key Infrastructure Approaches Based on Blockchain 21

Public Key Infrastructure Approaches Based on Blockchain

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Alani, Dena S. Sagheer, Ali M College of Education for Women University of Anbar Anbar Iraq College of Computer Science and Information Technology University of Anbar Department of Computer Networks Systems Anbar Iraq

ISBN: (纸本)9798350374131

Public-key infrastructure or PKI, is the fundamental technology that makes safe online information sharing possible. But PKI is vulnerable to failures of Certificate Authorities (CAs), which could allow illegal certificates to be issued to the end users. The security of the corresponding end users is at danger if a CA is compromised, as seen by numerous recent breaches. Blockchain technology is a new solution that has the ability to address the issues with traditional PKI systems, specifically the removal of single points of failure and the quick response to CA flaws. Blockchain technology facilitates the management and archiving of digital certificates in an unchangeable, publicly accessible ledger, producing a history log that can be completely tracked. In this article, the classical and modern blockchain-based PKI strategies were explained in addition to illustrating the limitations of each strategy. Finally, this article analyzed PKI methods by comparing their features according to a number of parameters. © 2024 IEEE.

关键词： Blockchain

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