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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida Sharif Muhammad Imran Sharif Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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An IoT Environment Based Framework for Intelligent Intrusion Detection

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computers, Materials & Continua 2023年第5期75卷 2365-2381页

作者： Hamza Safwan Zeshan Iqbal Rashid Amin Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Deparment of Computer Science UET TaxilaTaxila47080Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Software-defined networking(SDN)represents a paradigm shift in network traffic *** distinguishes between the data and control *** are then used to communicate between these *** controller is central to the management of an SDN network and is subject to security *** research shows how a deep learning algorithm can detect intrusions in SDN-based IoT ***,low accuracy,and efficient feature selection is all *** propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in *** obtain the best and most relevant features,a feature selection technique is *** experiments have revealed that the proposed solution is a superior method for detecting flow-based *** performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s *** show that the adopted methodology outperforms existing models.

关键词： Software-defined networks prediction modeling machine learning deep learning

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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yagi, Ryuichi Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Numerous fields employ correlation-based searches to provide insights about relationships in a database. Herein we propose a correlated DNA subsequence search for DNA databases to extract DNA subsequences that frequently co-occur with a query. Typical search methods must evaluate the entire database for potential candidates, which is computationally expensive. To overcome this, we propose a threshold-based algorithm to achieve efficient computation. The proposed algorithm extracts all DNA subsequences whose correlation is more significant than the threshold and excludes unlikely candidates. Experiments involving real-world DNA databases show that the proposed method achieves a faster search than baseline algorithms while maintaining a high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA

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25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yasuda, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Various fields from biomedicine to sports science employ similarity searches in multivariate time series (MvTS) to identify patterns and trends. Given MvTS and a query time series, a similarity search extracts a set of subsequences in the MvTS that yield the most significant similarity to the query. Although this is a fundamental task, similarity searches become computationally expensive as the length of the MvTS increases. Herein an efficient method is proposed to accelerate the search process. Using hash-based indexes for MvTS, our proposed method effectively approximates the similarity of each subsequence pair, enhancing the computational efficiency. An experiment involving synthetic and real-world MvTS datasets demonstrates that our proposed method has a higher efficiency and search accuracy compared to existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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引用

25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Graph similarity search (GSS) models chemical compounds as a graph database. GSS is an essential tool for drug discovery because they can find similar graphs (compounds) for a query. Existing GSS methods have two critical limitations. First, handling large databases is time consuming. Second, finding compounds with the structure-activity relationship (SAR), which is vital in drug discovery, remains difficult. Herein a novel graph-based method for chemical compound searches is proposed to overcome these limitations. Since compounds with SAR share similar substructures, the proposed method extracts correlated subgraphs included in a query and explores similar compounds. In practical drug discovery task, our method achieves faster searches and improved accuracy compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph Databases

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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Recent Advancements of computer Vision in Healthcare: A Systematic Review

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 563-571页

作者： Islam, Mahmudul Nayan, Nasim Mahmud Islam, Ashraful Sikder, Sankar Rashel, Masud Rana Alam, Md. Zahangir Department of Computer Science and Engineering Independent University Bangladesh Dhaka1229 Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka1229 Bangladesh Department of Mathematics University of Louisiana LafayetteLA70503 United States Department of Mechatronics Engineering University of Évora Évora Portugal

The advancements in computer Vision (CV) techniques have demonstrated significant promise in healthcare applications. The adoption of CV within the healthcare sector has drawn considerable attention. The aim of this study is to systematically review and synthesize the recent advancements and applications of CV techniques in various domains of healthcare. 125 papers were selected initially, and after gradual filtering, 20 papers were selected for the final study. In this study, we have identified five medical domains such as disease detection, drug discovery, surgical procedures, human identity decoding, and remote patient monitoring where CV applications are being successfully implemented. Among these domains, the use of CV in surgical assistance is notable. It capitalizes on the precision and efficiency offered by CV. Deep Learning (DL) models show adaptability and accuracy in medical imaging. The combination of computer vision and sensors enhances real-time surgical skills assessment. This study revealed that CV applications can be utilized for predictive analytics and personalized patient treatments. Standardized performance metrics, ethics, and data governance are crucial for responsible computer vision deployment in healthcare. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Deep learning

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Enhancing identity and access management using Hyperledger Fabric and OAuth 2.0: A block-chain-based approach for security and scalability for healthcare industry

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 49-67页

作者： Sutradhar, Shrabani Karforma, Sunil Bose, Rajesh Roy, Sandip Djebali, Sonia Bhattacharyya, Debnath Department of Computational Sciences Brainware University West Bengal India Department of Computer Sciences The University of Burdwan West Bengal India Department of Computer Science & Engineering JIS University West Bengal India Pôle universitaire Léonard De Vinci Research Center Paris La Défense 92 916 France Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522302 India

Block-chain-based Identity and access management framework is a promising solution to privacy and security issues raised during the exchange of patient data in the healthcare industry. This technology ensures the confidentiality and integrity of sensitive information by providing a decentralized and immutable ledger. In our research, we propose an identity and access management system that employs Hyper-ledger Fabric and OAuth 2.0 for improved security and scalability. This combination allows for transparency and immutability of user transactions and minimizes the risk of fraud and unauthorized access. Additionally, Hyper-ledger Fabric's privacy, security, and scalability features enable granular access control to sensitive information, while OAuth 2.0 authorizes only trusted third-party applications to access specific data on the Fabric network. The proposed approach can handle large volumes of data and support multiple applications, thus providing a secure and scalable solution for managing access to the Fabric network. Moreover, our solution employs Role-based access control based on the patient's role, ensuring privacy and confidentiality. Our statistical analysis demonstrates that the proposed approach can efficiently and securely manage patient identity and access, potentially transforming the healthcare industry by enhancing data interoperability, reducing fraud and errors, and improving patient privacy and security. Furthermore, our solution can facilitate compliance with regulatory requirements such as HIPAA and GDPR. © 2023 The Authors

关键词： Scalability

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Controlling Biomolecular Timekeeping via Regulated Gene Product Degradation 63

Controlling Biomolecular Timekeeping via Regulated Gene Prod...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rezaee, Sayeh Nieto, Cesar Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350316339

Numerous biological timing mechanisms are associated with the duration required for key regulatory molecules to reach critical threshold levels. Recent studies have mainly explored how random fluctuations affect the timing of events as molecules accumulate to reach a high threshold. This study examines the complementary scenario: decrease in protein levels until they reach a low critical threshold. We aim to study the timing precision resulting from different degradation pathways and their inherent stochastic nature, especially under low-copy number conditions. More specifically, we explore how integer-valued protein molecular counts decrease stochastically following Michaelis-Menten kinetics. This approach encompasses both zero-order (where the net degradation rate is independent of protein levels) and first-order (where the rate is proportional to protein levels) decay processes. Our results show that, while zero-order decay can be sluggish in terms of mean timing, it provides the highest precision in timing with the lowest noise in threshold-crossing time. Conversely, upon considering randomness in the initial protein levels, first-order decay demonstrates better precision compared to zero-order. Interestingly, in the presence of multiple noise sources, timing stochasticity can be minimized when Michaelis-Menten decay kinetics operate under sub-saturation conditions. This characterization of timing driven by decreasing copy numbers has implications for improving the precision of biological clocks and understanding the timing of cell-cycle events. © 2024 IEEE.

关键词： Stochastic systems

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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