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Rough Fermatean fuzzy decision-based approach for modelling IDS classifiers in the federated learning of IoMT applications

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Neural Computing and Applications 2023年第30期35卷 22531-22549页

作者： Albahri, O.S. Al-Samarraay, Mohammed S. AlSattar, H.A. Alamoodi, A.H. Zaidan, A.A. Albahri, A.S. Zaidan, B.B. Jasim, Ali Najm Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya12733 Iraq Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Perak Tanjong Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Iraqi Commission for Computers and Informatics Baghdad10022 Iraq Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Foundation of Alshuhda Baghdad Iraq MEU Research Unit Middle East University Amman Jordan

Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the sheer volume of network traffic, high-dimensional datasets and the necessity for real-time detection. Although machine learning integration assists IDSs in overcoming these challenges, modelling difficulties persist due to varied evaluation criteria and levels of conflict and importance. Multi-criteria decision-making (MCDM) solutions have been utilised in IoMT and IDS, yet they fall short in capturing the subjective judgements of experts and rely on normalisation approaches, which can impact results. This study seeks to address these issues through the integration of robust MCDM methodologies, namely fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM). Utilising rough Fermatean fuzzy sets (RFFSs), this integration produces precise solutions with reduced uncertainty. Our methodology involves adopting a decision matrix for IDS classifiers based on integrated evaluation criteria, followed by deriving new formulations and developments for RFFSs-based FDOSM and FWZIC for the modelling and weighting of criteria, respectively. Evaluations using datasets involving 125,973 records and 41 features across 17 evaluation criteria revealed that accuracy–security and training time–performance weights yielded the highest scores, whereas false negative rate–security and CPU time–performance criteria received the lowest weights. The random forest emerged as the optimal IDS classifier. Systematic modelling, sensitivity analysis and comparative studies confirmed the robustness of our results. © 2023, Crown.

关键词： Integration

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Hybrid Approach to Identify Druglikeness Leading Compounds against COVID-19 3CL Protease

arXiv

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arXiv 2022年

作者： Aqeel, Imra Majid, Abdul Biomedical Informatics Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages. © 2022, CC BY.

关键词： SARS

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Routing on the Shortest Pairs of Disjoint Paths

Routing on the Shortest Pairs of Disjoint Paths

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IFIP Networking Conference

作者： Pé ter Babarczi Gá bor Ré tvá ri Lajos Ró nyai Janos Tapolcai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics E&#x00F6 tv&#x00F6 s Lorand Research Network Budapest Hungary

Recent trends point towards communication networks will be multi-path in nature to increase failure resilience, support load-balancing and provide alternate paths for congestion avoidance. We argue that the transition from single-path to multi-path routing should be as seamless as possible in order to lower the deployability barrier for network operators. Therefore, in this paper we are focusing on the problem of routing along the shortest pairs of disjoint paths between each source-destination pair over the currently deployed link-state routing architecture. We show that the union of disjoint path-pairs towards a given destination has a special structure, and we propose an efficient tag encoding scheme which requires only one extra forwarding table entry per router per destination. Our numerical evaluations demonstrate that in real-world topologies usually only 4 bit tags are sufficient in the packet headers to route on the disjoint path-pairs. Finally, we show that our tags automatically encode additional paths beyond the shortest pair of disjoint paths, including the shortest paths themselves, which enables incremental deployment of the proposed method.

关键词： Focusing Tagging Routing Data structures Market research Load management Encoding

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KNOWLEDGE-BASED DATASET FOR TRAINING PE MALWARE DETECTION MODELS

arXiv

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arXiv 2022年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kl’uka, Ján Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava Slovakia

Ontologies are a standard for semantic schemata in many knowledge-intensive domains of human interest. They are now becoming increasingly important also in areas until very recently dominated by subsymbolic representations and machine-learning-based data processing. One such area is information security, and more specifically malware detection. We propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE, Windows binary format) malware files. The ontology was inspired by the structure of the data in the EMBER dataset and it currently covers the data intended for static malware analysis. With this proposal, we hope to achieve: a) a unified semantic representation for PE malware datasets that are available or will be published in the future;(b) applicability of symbolic, neural-symbolic, or otherwise explainable approaches in the PE Malware domain that may lead to improved interpretability of results which may now be characterized by the terms defined in the ontology;and (c) by joint publishing of semantically treated EMBER data, including fractional datasets, also improved reproducibility of experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Ontology

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Stability from graph symmetrization arguments in generalized Turán problems

arXiv

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arXiv 2023年

作者： Gerbner, Dániel Karim, Hilal Hama Alfréd Rényi Institute of Mathematics Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

Given graphs H and F, ex(n,H,F) denotes the largest number of copies of H in F-free n-vertex graphs. Let χ(H) 0 there exists δ > 0 such that if an n-vertex F-free graph G contains at least ex(n,H,F) − δnh copies of H, then the edit distance of G and the r-partite Turán graph is at most Εn2. We say that H is weakly F-Turán-stable if the same holds with the Turán graph replaced by any complete r-partite graph T. It is known that such stability implies exact results in several cases. We show that complete multipartite graphs with chromatic number at most r are weakly Kr+1-Turán-stable. Answering a question of Morrison, Nir, Norin, Rza zewski and Wesolek positively, we show that for every graph H, if r is large enough, then H is Kr+1-Turán-stable. Finally, we prove that if H is bipartite, then it is weakly C2k+1-Turán-stable for k large enough. Copyright © 2023, The Authors. All rights reserved.

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Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

arXiv

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arXiv 2023年

作者： Ji, Yuelyu Gao, Yuhe Bao, Runxue Li, Qi Liu, Disheng Sun, Yiming Ye, Ye Department of Information Science School of Computing and Information University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh United States Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh United States School of Business State University of New York at New Paltz New Paltz United States

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

关键词： COVID-19

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Causal Machine Learning in IoT-based engineering Problems: A Tool Comparison in the Case of Household Energy Consumption

arXiv

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arXiv 2025年

作者： Kosioris, N.-L. Nikoletseas, S. Filios, G. Panagiotou, S. Department of Computer Engineering and Informatics University of Patras Greece Connect Line S.A. Athens Greece

The rapid increase in computing power and the ability to store Big Data in the infrastructure has enabled predictions in a large variety of domains by Machine Learning. However, in many cases, existing Machine Learning tools are considered insufficient or incorrect since they exploit only probabilistic dependencies rather than inference logic. Causal Machine Learning methods seem to close this gap. In this paper, two prevalent tools based on Causal Machine Learning methods are compared, as well as their mathematical underpinning background. The operation of the tools is demonstrated by examining their response to 18 queries, based on the IDEAL Household Energy Dataset, published by the University of Edinburgh. First, it was important to evaluate the causal relations assumption that allowed the use of this approach;this was based on the preexisting scientific knowledge of the domain and was implemented by use of the in-built validation tools. Results were encouraging and may easily be extended to other domains. © 2025, CC BY-NC-SA.

关键词： Learning algorithms

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Exploring the Benefits and Drawbacks of Machine Learning in Cybersecurity to Strengthen Cybersecurity Defences

Exploring the Benefits and Drawbacks of Machine Learning in ...

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Annual Software Technology Conference

作者： Touraj Khodadadi Mazdak Zamani Saman Shojae Chaeikar Yashar Javadianasl Marieh Talebkhah Mojtaba Alizadeh Department of Information Technology Malaysia University of Science and Technology Selangor Malaysia School of Business and Information Sciences Felician University Rutherford NJ USA Australian Institute of Higher Education Macquarie University Sydney Australia Razak Faculty of Technology and Informatics Universiti Technology Malaysia Kuala Lumpur Malaysia Department of Computer and Communication Systems Engineering Universiti Putra Malaysian (UPM) Seri Kembangan Malaysia Department of Computer Engineering) Lorestan University Khorramabad Iran

Although the initial motivations for carrying out cyberattacks stay the same, cybercriminals demonstrated a heightened level of sophistication in their methodologies due to the growing number of people, and deluge of data, devices, and programs in the contemporary company. The efficacy of conventional cybersecurity measures in identifying and addressing emerging cyber threats is diminishing. In light of the escalating cyber-threat landscape, there is a pressing need to deploy sophisticated tools and technologies that can effectively identify, examine, and promptly respond to emerging attacks and threats. Artificial intelligence (AI) has become a ground¬breaking technology with enormous promise across various industries. One area where AI has made a considerable influence is cybersecurity. AI, notably Machine learning (ML), enhances cybersecurity against more complex attacks and addresses significant concerns such as real-time attack detection, data leakage prevention, malware protection, vulnerability assessments, and many more. There is a growing interest in ML-based solutions that automate the identification of attacks and address sophisticated cybersecurity challenges. This article examines the advantages of the most promising ML applications now in use while highlighting the drawbacks and factors businesses should carefully consider when using ML- powered tools in cybersecurity and ensuring they are used alongside other security procedures.

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

引用

8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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The Dynamic Movement of Disaster Management Systems Based on Vehicle Networks and Applied on the Healthcare System

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Applied Bionics and Biomechanics 2021年 2021卷 5710294页

作者： Alazzam, Malik Bader Alassery, Fawaz Faculty of Computer Science and Informatics Amman Arab University Jordan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia

In order to save human life and assets, the emergency management system (DMS) requires roving rescue teams to respond promptly and effectively. Installation and restoration of appropriate communication infrastructure are important for reducing the effect of disasters and enabling and coordinating information flow among relief teams working in the region. This paper describes a data collection system based on vehicular cloud network services that incorporates the advantages of both architectures of vehicular ad hoc networks (VANETs) with the cloud to establish vehicular cloud networks (VCNs). Vehicles in the current plan perform tasks like monitoring the environment, gathering data, and transmitting data to the control center depending on their positions and instructions. To build a disaster management system, the proposed system uses hybrid wireless networking, which includes both a central system and ad hoc networks. The implementation results show that the suggested system is more dependable and efficient;even light density is improved in terms of reachability with few hops. Furthermore, as compared to the existing system, the suggested system has a lower end-to-end delay and a higher packet delivery ratio. © 2021 Malik Bader Alazzam and Fawaz Alassery.

关键词： Vehicular ad hoc networks

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