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Weighted rank aggregation based on ranker accuracies for feature selection

Soft Computing 2025年第4期29卷 1981-2001页

作者： Abdolrazzagh-Nezhad, Majid Kherad, Mahdi Faculty of Engineering Department of Computer Engineering Bozorgmehr University of Qaenat Qaen Iran Department of Computer Science Faculty of Computer and Industrial Engineering Birjand University of Technology Birjand Iran Faculty of Engineering Department of Computer Engineering University of Qom Qom Iran

Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The obtained results by the proposed method have higher values of classifier measures than the existing baseline Feature Selection methods and rank aggregation-based multi-filter methods in the most datasets. The experiments show that rank aggregation-based Feature Selection methods outperform individual methods. The proposed method also shows the weight of each Filter-based Feature Selection method, in which the MRMR method has a higher weight than other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wiener filtering

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

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Alexandria Engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Blockchain-Driven Secure Data Sharing Framework for Edge Computing Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 978-997页

作者： Fuad A.M.Al-Yarimi Ramzi Salah Khaled Mohamoud Department of Computer Science King Khalid UniversityMuhayel Aseer 62529Saudi Arabia Faculty of Information Science and Technology University Kebangassan MalaysiaBangi 43600Malaysia Department of Information System King Khalid UniversityMuhayel Aseer 62529Saudi Arabia

This study examines secure and effective data sharing methods for edge computing *** methods of sharing data at the edge have issues with security,speed,and *** goal is to develop a Blockchain-based Secure Data Sharing Framework(BSDSF)capable of improving data integrity,latency,and overall network efficiency for edge-cloud computing *** proposes using blockchain technology with Byzantine Fault Tolerance(BFT)and smart contract-based validation as a new method of secure data *** has a two-tiered consensus protocol to meet the needs of edge computing,which requires instantaneous *** employs Byzantine fault tolerance to deal with errors and protect against *** contracts automate validation and consensus operations,while edge computing processes data at the attack *** validation and failure detection methods monitor network quality and dependability,while system security ensures secure communication between *** is an important step toward digital freedom and trust by protecting security and improving transaction *** framework demonstrates a reduction in transaction latency by up to 30％and an increase in throughput by 25％compared to traditional edge computing models,positioning BSDSF as a pivotal solution for fostering digital freedom and trust in edge computing environments.

关键词： Byzantine Fault Tolerance(BFT) blockchain-driven edge computing networks consensus contract data validation contract node validation contract

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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DEXO: A Secure and Fair Exchange Mechanism for Decentralized IoT Data Markets

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IEEE Internet of Things Journal 2025年第11期12卷 16095-16111页

作者： Li, Yue Alom, Ifteher Sun, Wenhai Xiao, Yang University of Kentucky Department of Computer Science LexingtonKY United States Purdue University Department of Computer and Information Technology West LafayetteIN United States

Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart contract-assisted fair exchange. For the first time, DEXO ensures end-to-end data confidentiality, source verifiability, and fairness of the exchange process with strong resilience against participant collusion. We implemented a prototype of the DEXO system to demonstrate feasibility. The evaluation shows a moderate deployment cost and significantly improved blockchain operation efficiency compared to a popular data exchange mechanism. © 2025 IEEE.

关键词： Internet of Things Blockchains Smart contracts Soft sensors Security Servers Resilience Decentralized applications Data models Oceans

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Assessing the Teachers’ Readiness for Integrating Augmented Reality in K-12 Education: A Comparative Analysis

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 22-44页

作者： Ewais, Ahmed Dalipi, Fisnik Abualrob, Marwan Ferati, Mexhid Kurti, Arianit Computer Science Department Faculty of Information Technology Arab American University Jenin Palestine Department of Informatics Faculty of Technology Linnaeus University Växjö Sweden Elementary Education Department Faculty of Arts Arab American University Jenin Palestine

The integration of augmented reality (AR) into educational environments will depend on its perceived effectiveness in enhancing teaching practices and the attitudes toward the use of this technology. Therefore, the main objective is to investigate the teachers’ attitude and motivation to adopt AR in educational settings, which also looks at a cross-cultural context. Furthermore, this research reveals different aspects that have an impact on teachers’ attitudes toward adopting AR in the teaching process. To investigate this, we conducted a study with 87 K-12 teachers belonging to two different education systems, i.e., Sweden and Palestine. The mixed-methods approach enhances the validity of the study and provides a broad understanding through numerical data, while qualitative insights offer deeper explanations of the findings. The results indicate a statistically significant difference in teachers’ attitudes about AR, with a mean 3.99 for teachers coming from Palestine showing a more positive attitude towards AR-supported learning. Therefore, it is important for educational institutions and application developers to consider a range of learning and teaching methods, as well as specific needs, throughout the process of developing and incorporating AR into the curriculum. © 2025 by the authors of this article.

关键词： attitudes augmented reality (AR) education learning schools teachers

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