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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt 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 Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Fine-grained Energy-efficient Data Center Traffic Scheduling Mechanism Based on Hierarchical Control

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 742-757页

作者： Niu, Tianyu Li, Honghui Li, Weidong Por, Lip Yee College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Hohhot China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

With the continuous expansion of the data center, its energy consumption is also increasing. Aiming at the problem that the high redundancy of modern data center network causes low energy-consumption utilization, this paper proposes an energy-saving traffic scheduling algorithm (IMV-MLF) based on improved multi-layer-virtual-topology combined with maximum link-utilization path priority, which realizes more fine-grained control of each layer of virtual topology in data center network. Firstly, the relevant shap-ing linear programming mathematical model is established according to the optimization goal of energy conservation, which can minimize the energy consumption of the whole network as far as possible under the premise of meeting all network-flow requirements;Then, according to the optimization goal, the existing SDN based energy saving multi-layer-virtual-topology (EMV-SDN) method is improved. Whether the network devices in other layers are enabled is determined by the overall traffic level rather than the traffic level of a single link. On the basis of the opened level, the newly arrived network flows are preferentially allocated to the path with the highest link utilization, and overload link set is introduced, we can effectively use the available link routes with sufficient residual bandwidth to reduce congestion when the topology changes by marking the links whose link utilization reaches the critical value, and further count and close the links and switches which no network flows pass through. The experimental results show that the performance of IMV-MLF algorithm is better than multi-layer virtual topology energy saving (EMV-SDN) algorithm and the equivalent multipath (ECMP) algorithm in saving network energy. This result is helpful to provide another new idea for energy conservation of data center. © 2023, Journal of Network Intelligence.

关键词： Energy utilization

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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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Madinah Smart City system: Managing and Monitoring Traffic, Emergency Services, and Crowds in The Haram 10

Madinah Smart City System: Managing and Monitoring Traffic, ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Benaida, Mohamed Islamic University Faculty of Computer Science and Information System Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Billions of Muslims across the globe consider Madinah in Saudi Arabia to be a holy city, thus it attracts millions of visitors every year. Therefore, cases of overcrowding, slow arrival of emergency services, and increased danger to life, can be regular occurrences if not managed efficiently. Consequently, this study aimed to address these abovementioned factors via the utilisation of many tools and mechanisms including cameras, image processing, tracking system, Artificial intelligence hubs, Network Data, Mobile- enabled internet of things sensors etc., as well as data from previous studies and Saudi government agencies. As a result, four main components were realised in the process of creating our proposed framework: control and analysis of traffic signals, control and analysis of human crowding, possessing smart emergency services in real time, and securing data through its collection, storage, and sharing. The combination of all four components fulfils the goals of this research and provides a more robust and efficient environment via improved coordination between different government departments in Madinah, effective emergency services, and improved speed of decision-making, which saves lives in cases of emergency situations where emergency services can attend to the points of concern efficiently and prevents deaths in events of stampedes via the appropriate control and management of crowds. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data Analytics

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The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions

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Neural Computing and Applications 2024年第4期37卷 1999-2034页

作者： Eke, Christopher Ifeanyi Shuib, Liyana Department of Information System FCSIT University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa State Lafia Nigeria

The healthcare sector has advanced significantly as a result of the ability of artificial intelligence (AI) to solve cognitive problems that once required human intelligence. As artificial intelligence finds more applications in healthcare, trustworthiness must be guaranteed. Even while AI has the potential to improve healthcare, there are still challenging issues because it is yet to be widely adopted, especially when it comes to transparency. Concerns about comprehending the internal workings of AI models, possible biases, model robustness, and generalizability are raised by their opacity which makes them function like black boxes. A solution for worries over the transparency of AI algorithms is explainable AI. Explainable AI seeks to enhance AI explainability and analytical capabilities, particularly in vital industries like healthcare. Even though earlier research has examined several explainable AI-related topics, such as a lexicon, industry-specific overviews, and applications in the healthcare industry, a thorough analysis concentrating on the function of explainable AI in building trust in AI healthcare systems is required. In an effort to close this gap, a systematic literature review that adheres to PRISMA principles that analyze relevant papers that were published between 2015 and 2023 was done in this paper. To determine the critical role that explainable AI plays in fostering trust, this study examines widely utilized methodologies, machine learning and deep learning techniques, datasets, performance measures and validation procedures used in AI healthcare research. In addition, research issues and potential research directions are also discussed in this research. Thus, this systematic review provides a thorough summary of the present status of research on explainability and transparency in AI healthcare systems, thus illuminating crucial factors that affect user trust. The results are intended to assist researchers, policymakers and healthcare professi

关键词： Adversarial machine learning

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Improving Route-Finding Performance of Dijkstra Algorithm and Maintaining Path Visual Cue Using Douglas-Peucker Algorithm 23

Improving Route-Finding Performance of Dijkstra Algorithm an...

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8th International Conference on Sustainable information Engineering and Technology, SIET 2023

作者： Pinandito, Aryo Kharisma, Agi Putra Akbar, Muhammad Aminul Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Technology Laboratory Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (纸本)9798400708503

Smartphones and their mobile applications have become an inseparable part of modern daily life. One of them is a public transportation app that helps people commute with public transportation. Many public transportation service providers have mobile applications as a companion for their customers to improve their quality of service. The application provides useful information and/or important features regarding their service, such as timetables or route finding for public transit. Such features run a slow algorithm to provide real-time or time-based information to the users. Dijkstra is one example of an algorithm that allows users to find the shortest path from one location to another in a public transit network. However, Dijkstra is categorized as a greedy algorithm because it looks at all possible solutions. It takes longer for Dijkstra to find the optimum solution when the number of points in a network graph is high. This study attempted to improve the overall route-finding processing time and reduce the memory footprint while keeping the visual cue of a route path reasonably consistent by utilizing Douglas-Peucker line simplification algorithm to simplify the path and reduce the number of points that Dijkstra algorithm processes. The result suggested that Douglas-Peucker simplification process took 0.5% additional memory footprint and put 5.13% additional processing time into the overall route-finding processing time. However, it reduces the overall memory footprint and processing time by 72.56% and 80.83%, respectively. The result conveyed a significant improvement in the practical implementation of Douglas-Peucker algorithm to reduce the input for the Dijkstra algorithm in finding the optimal route, thereby improving the overall route-finding performance. © 2023 ACM.

关键词： Transportation routes

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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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computer Modeling in Engineering & sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第2期4卷 315-326页

作者： Hariguna, Taqwa Ruangkanjanases, Athapol Department Information System Faculty of Computer Science Universitas Amikom Purwokerto Purwokerto Indonesia Chulalongkorn Business School Chulalongkorn University Bangkok Thailand

This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN algorithms to create a more adaptable and accurate sentiment scoring model. Additionally, the primary data from ChatGPT greatly enhances the creation of precise and relevant training data across various topics and languages. Despite these achievements, there remains a need for further exploration of testing methods to mitigate the impact of data limitations on result generalizability. Moreover, it is acknowledged that the model's effectiveness may be diminished when applied to languages other than English. Nevertheless, this research provides a promising avenue for users seeking enhanced and precise sentiment analysis by integrating KNN, RNN, and CNN algorithms with ChatGPT. The findings of this study can serve as a solid foundation for future research endeavors in the advancement of sophisticated and effective sentiment analysis technologies. © 2023, Ital Publication. All rights reserved.

关键词： Sentiment analysis

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Optimizing Broadcast Utilization for Efficient Disaster Management Using Wireless Ad Hoc Networks and Novel Energy-Saving Algorithms

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第20期18卷 142-156页

作者： Alzyoud, Faisal Y. Tarawneh, Monther Almaghthawi, Ahmed Altalidi, Askar Asiri, Layla Alrehaili, Meaad Department of Computer Science Faculty of Information Technology Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila TechnicaUniversity Tafila Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Department of Information Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Departement of Computer Information System Applied college at Mahayil King Khalid University Abha Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

Globalization, industry, and population growth is considered the main reasons for climate change, so when some natural calamity or disaster occurs in any area, it leads to isolating this area from the rest of the region. Wireless ad hoc networks are important in disaster management to reduce losses and costs as they reduce the response, aiding time to save lives. However, they suffer from limited energy, which controls the lifetime of the sensor. Different methods are applied to save energy and increase battery life, such as sleep-awake schedules which assume similar initial energy for all nodes. Domestic partition in a unit disk graph is the production of the maximum possible number of such disjoint dominants. However, it assumes different initial energy levels for all nodes, which is another problem. In this paper, a new broadcast approach based on minimum set data broadcasting is proposed to mini-mize the number of broadcasting messages to increase the battery life during disasters and enhance rescue operations. The proposed minimum rebroadcast algorithm (MSRA) method balances coverage and utilization efficiency by dynamically adjusting node selection based on network conditions. The results show that MSRA significantly reduces power consumption and transmission latency, opening the way to find a new solution for disaster management using wireless ad hoc networks. © 2024 by the authors of this article.

关键词： Mobile ad hoc networks

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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