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Distance Matrix and Markov Chain Based Sensor Localization in WSN

computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

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computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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Leveraging Machine Learning to Predict Asthma following Vaccination 5

Leveraging Machine Learning to Predict Asthma following Vacc...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Gull, Seema C. NaveenKumar, C.G. Gull, Karuna C. Sanskruti, V.B. Laddi, Mahantesh Karnataka State Open University Department of Studies in Computer Science Mysuru India S.G.Balekundri Institute of Technology Department of Computer Science & Business Systems Belagavi India M.S. Sheshgiri College Engineering and Technology Department of Biomedical Engineering Belagavi India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350376685

This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is important to monitor and prevent issues such as asthma. The goals for future research include the creation of risk-adjusted models that may predict the risk of asthma following vaccination, depending on patients' demographic information and the characteristics of the vaccines. The research uses several sophisticated techniques such as Artificial-Neural-Networks (ANN), Random-Forests (RF), Naïve-Bayes (NB), and logistic regression (LR) to improve the ability to predict and identify prevention measures and healthcare management. Thus, a significant innovation of this work is the comprehensive assessment of these algorithms when exposed to noise, which is a common issue in many medical data sets. The study also showed that Random-Forest and ANN models were significantly more accurate and yielded larger ROC AUC scores than other models;moreover, they were much less sensitive to noise with noise values ranging from 0 to 0.5. This places RF and ANN on a better pedestal to offer a better prediction of post-vaccination asthma than the other models, especially in a scenario where there is the possibility of data contamination. The present findings can thus help create better and more resistant models of prediction that are useful in better health management and treatment of asthma over and after vaccination. © 2024 IEEE.

关键词： Logistic regression

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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How transformations of representation for input data can affect the properties of induced decision reducts and rules 27

How transformations of representation for input data can aff...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Decision reducts and rules belong to forms used for the representation of knowledge learnt from input data while using a rough set approach in the exploration stage. As with any patterns that capture properties of data, the size is considered as the most important indicator of their quality, as a more general and concise description allows for improved understanding and simplified interpretation. The size of a reduct is given by its cardinality, and for a rule it is a number of included conditions, both referred to as lengths. These lengths undergo transformations along with data transformations, so such processing as discretisation leads to changed characteristics of existing patterns and through them also reducts and rules. The paper presents research with contributions in the observations focused on the influence of supervised and unsupervised discretisation on properties of attributes, induced decision reducts, and rules, offering a deeper insight into relations among them. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Data exploration decision reduct decision rule discretisation knowledge representation rough set theory transformation

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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The Impact of 5G on the Transformation and Communication of Smart Entities

The Impact of 5G on the Transformation and Communication of ...

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International Conference on Smart business and technology, ICSBT 2024

作者： Talbi, Chaymae Rahmouni, M’hamed Tahiri Alaoui, Moulay Lakbir Belhiah, Meryam Lagmiri, Souad Najoua Ziti, Soumia IPSS Intelligent Processing and Security of Systems Faculty of Science Mohammed V University Rabat1014 RP Morocco Mohammed V University Rabat Morocco IRSM Computer Science Networks Security and Management Higher Institute of Management Administration and Computer Engineering Rabat10040 Morocco

ISBN: (纸本)9783031866975

The advent of 5G technology marks a significant milestone in the evolution of smart businesses and technologies. This chapter explores the transformative effects of 5G on the communication and operational paradigms of intelligent entities. By providing unprecedented speed, low latency, and high connectivity, 5G facilitates real-time data exchange and enhances the integration of Internet of Things (IoT) devices. This, in turn, empowers smart cities, industries, and enterprises to achieve greater efficiency, innovation, and responsiveness. The chapter delves into specific case studies across various sectors, including healthcare, transportation, and manufacturing, demonstrating how 5G drives digital transformation. In healthcare, for example, 5G enables remote surgeries and real-time patient monitoring. In transportation, it supports autonomous vehicles and smart traffic management systems. In manufacturing, it enhances automation and predictive maintenance. The theoretical analysis presented highlights the critical role of 5G in supporting advanced applications such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI). These technologies benefit from the high bandwidth and low latency provided by 5G, enabling new levels of interaction and data processing. The findings underscore the potential of 5G to revolutionize communication infrastructure and operational methodologies, paving the way for future advancements in smart technologies. This chapter concludes by discussing the challenges and future prospects of 5G deployment, including issues related to security, infrastructure investment, and regulatory frameworks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Should supervised discretisation always be trusted unreservedly? On combining characteristics of supervised and unsupervised discretisation algorithms in two-step processing 27

Should supervised discretisation always be trusted unreserve...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Authorship attribution Data representation Pattern recognition Stylometry Supervised discretisation Unsupervised discretisation

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