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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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(r, k, Ε)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, k-Anonymity, and Ε-Differential Privacy

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IEEE Access 2025年 13卷 70422-70435页

作者： Kara, Burak Cem Eyupoglu, Can Karakus, Oktay National Defence University Turkish Air Force Academy Department of Computer Engineering İstanbul34149 Turkey Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom

In recent years, there has been a tremendous rise in both the volume and variety of big data, providing enormous potential benefits to businesses that seek to utilize consumer experiences for research or commercial purposes. The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals’ personal information and placed many limits. Data anonymization technologies have become an important solution for businesses trying to generate value from data while adhering to GDPR limitations. To address these challenges, researchers have developed various methods, including k-anonymity and Ε -differential privacy, offering solutions for both industry and academia. However, protecting individuals’ privacy against diverse attack attempts presents significant challenges for anonymization models that rely solely on a single technique, highlighting the need for more adaptable and hybrid approaches. In this study, a new hybrid anonymization algorithm called (r, k, Ε )-anonymization has been proposed, which combines k-anonymity and Ε-differential privacy models in a consistent framework and provides stronger privacy guarantees compared to existing privacy-preserving models. The proposed algorithm is capable of overcoming well-known shortcomings of the k-anonymity and Ε -differential privacy models, and it has been confirmed by extensive tests on real-world datasets. The proposed (r, k, Ε )-anonymization algorithm outperforms k-anonymity and Ε -differential privacy in terms of the average error rate measure, achieving data utility increases of 31.74% and 26.99%, respectively. © 2013 IEEE.

关键词： Anonymity

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

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Tsinghua Science and Technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

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Speeding Up Distributed Learning via Sparse and Flexible Coded Computing

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IEEE Transactions on Information Theory 2025年第4期71卷 3167-3180页

作者： Zhang, Jingzhe He, Xiaofan Dai, Huaiyu Wuhan University Electronic Information School Wuhan430072 China North Carolina State University Department of Electrical and Computer Engineering RaleighNC27695 United States

Plagued by slow or failing workers (also known as stragglers), the speedup gain assumed by distributed learning often falls short. Although substantial efforts have been devoted to mitigating this straggling effect with coding-theoretic techniques, existing pioneering works often suffer from two issues: dense combination and inflexibility. In particular, a code that involves dense combination of sub-tasks may destroy sparsity and lead to heavy workload. In contrast, an inflexible code that conservatively designs its computation procedure according to the presumed maximum number of stragglers may entail unnecessary redundancy when the actual number of stragglers is small. To this end, a generic framework based on matrix splitting is proposed in this work to construct sparse and flexible codes. Specifically, by splitting an original sparse coding matrix into two sparser sub-matrices, a two-layer coded computation that maintains sparsity can be created accordingly. In the meantime, when the actual number of stragglers is small, the computation may be flexibly terminated at layer-one without executing layer-two, thereby avoiding unnecessary computation. Based on this framework, a novel flexible Bernoulli code is proposed. In addition, by deriving a lower bound in closed-form through the lens of a bipartite graph, its decoding probability is shown to be high and asymptotically one. Moreover, extensive simulations including an application in distributed learning of LeNet are conducted to validate the effectiveness of the proposed scheme. © 1963-2012 IEEE.

关键词： Matrix algebra

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Pest and Scratch Detection on Apple Surface Using TricubeNet

Pest and Scratch Detection on Apple Surface Using TricubeNet

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2025 International Conference on electronics, Information, and Communication, ICEIC 2025

作者： Ngoc Han, Nguyen Bui Lee, Ju-Hwan Kim, Jin-Young Kim, Hyoung-Gook Kwangwoon University Department of Electronic Convergence Engineering Seoul Korea Republic of Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798331510756

From orchard to consumer, apples experience multiple static and dynamic stresses, leading to defects such as pest damage and surface scratches. Detecting and sorting defective apples post-harvest is crucial for enhancing market value and minimizing contamination risks. While machine vision-based sorting and grading methods have advanced, they often prioritize easily detectable defects, neglecting undetectable ones. In this work, we apply TricubeNet, a one-stage, anchor-free detector, to detect pests and scratches on the surface of apples. This empirical study addresses a gap in the literature, as TricubeN et has not been previously evaluated on fruit datasets. By carefully tuning the size hyperparameter of the Tricube kernel, we achieve the highest F1 scores of 65.34 %, demonstrating the model's potential for subtle defect detection. © 2025 IEEE.

关键词： Fruits

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Sustainable Solutions for Organ Donation and Procurement via Regional Collaboration System 6

Sustainable Solutions for Organ Donation and Procurement via...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Mily, Farzana Akter Arefin, Md Shamsul Department of Computer Science & Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electrical & Electronic Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9798331522667

The severe shortage of organs requires the efficient management and collaboration of practices. Therefore, this paper introduces the Regional Collaboration System (RCS) to deal with this critical issue. The system aims at enhancing organ donation and procurement with the perspective of coordination of regional healthcare organizations. The primary purpose is to assist patients in finding suitable hospitals, donors, and doctors, in their region to carry out a seamless transplantation process. This system has been developed using Visual Studio 2022, SQL Server 2019 Express, and C# for a robust and scalable solution. Key features include an administrative panel, advanced search options, and the user interface to make it easy for administrators, patients, donors, and doctors to use. Moreover, the system also provides a medical history management platform that enables the patients to maintain a record of their history for future use via the administrative panel. This comprehensive approach is designed to improve transplant outcomes, minimize waiting times, and enhance organ availability. By effectively addressing the crisis in organ shortages, RCS holds a great potential to bring revolutionary changes in the existing structure of organ donation and transplantation, thereby saving more lives and hence fostering equality in access to health resources. © 2025 IEEE.

关键词： Records management

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Feasibility Assessment of a Hybrid Energy System with Hydrogen Production for Rural Limpopo Province Communities 33

Feasibility Assessment of a Hybrid Energy System with Hydrog...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Netshilonwe, Pfesesani Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Department of Physics Thohoyandou South Africa Cape Peninsula University of Technology Department of Electrical Engineering Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

The assessment of hybrid energy systems for generating electricity and green hydrogen for cooking and heating is crucial for advancing green technology integration. The annual increases in electricity tariffs have prompted individuals to revert to traditional methods of cooking and heating. Research conducted by climatologists indicates that these conventional practices are environmentally harmful and contribute to adverse climate change. This study is aimed at rural communities of Limpopo province to find new ways to bring affordable, reliable electricity and green hydrogen energy for cooking and heating purposes. The study focuses on implementing population size-dependent hybrid energy systems to produce green hydrogen. Four communities in Limpopo province, Ga-Masekwa, Ka-Dzingidzingi, Duthuni, and Mookgopong non-urban, have been randomly selected for this assessment. The Herman-Beta method, in conjunction with Homer Pro software, is employed to estimate maximum loads in selected communities. This approach facilitates the simulation of various hybrid energy system configurations, allowing for a comprehensive analysis of energy generation and consumption dynamics. Configurations comprised of PV/H2/Grid and PV/BES/H2/Grid are evaluated to determine the best hybrid energy system for each community based on economic performance. Results obtained indicate that the PV/H2/Grid configuration is the most cost-effective, with the lowest NPC and LCOE, offering a high return on investment for potential investors. The NPC of this configuration in all communities is R3.01M, R88.3M, R162M, and R299M, respectively. The corresponding LCOEs are 1.02 R/kWh, 1.2 R/kWh, 1.18 R/kWh, and 1.12 R/kWh. However, it is noted that green hydrogen production requires a significant amount of energy, making the hybrid system run more expensive than it would be otherwise. Successful green hydrogen use in rural communities can lead to economic growth, sustainable cities, reduced emissions, and a tr

关键词： Hybrid power

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