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Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)

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China Communications 2022年第6期19卷 219-247页

作者： A.Balamurugan Sengathir Janakiraman M.Deva Priya A.Christy Jeba Malar Department of Computer Science&Engineering KPR Institute of Engineering and TechnologyCoimbatoreTamilnaduIndia Department of Information Technology CVR College of EngineeringHyderabadTelanganaIndia Department of Computer Science&Engineering Sri Eshwar College of EngineeringCoimbatoreTamilnaduIndia Department of Information Technology Sri Krishna College of TechnologyCoimbatoreTamilnaduIndia

Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless *** this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival ***,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical *** meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are *** this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data *** robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink *** specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of *** high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality *** simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

关键词： Marine Predators Optimization Algorithm(MPOA) Particle Swarm Optimization(PSO) Optimal Cluster-based Routing Cluster Head(CH)selection Wireless Sensor Networks(WSNs)

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Assessment, Categorisation and Prediction of the Landslide-Affected Regions Using Soft Computing and Clustering Techniques

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Journal of The Institution of Engineers (India): Series B 2023年第3期104卷 579-602页

作者： Quraishi, Md. Iqbal Choudhury, J. Paul Department of Information Technology Kalyani Government Engineering College West Bengal Kalyani Nadia India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata India

Landslides are known natural hazard that significantly impacts human lives by damaging assets and infrastructures. It is tough to collect field data due to the non-access of various remote places. Collection of landslide images affected by those faraway places can be a way out for post-landslide assessment. Once a landslide impacts a region, the heavily affected places are more prone to getting affected again in the future. Post-landslide assessment by the proposed model can provide a survey. It contains region-wise, heavily affected, medium-affected landslide accounts and non-affected regions, which can help future decisions like road, building construction, and other socio-economic decisions for the specific regions. Even in the worst landslide again, the region will get minor damage to properties and human lives in the future. The model applies image processing, soft computing, and clustering tools on those collected images and finally produces a prediction, post-assessment and categorisation in terms of heavily affected, medium-affected landslide, and non-affected regions. One hundred fifty statistical features including ripplet coefficient, fractal dimension, autocorrelation coefficient, gray level co-occurrence matrix, gray level run length matrix, Gabor coefficient, moments, histogram, and directional contrast are used in the model. A linear support vector machine for selecting principal features uses seven feature selection methods: Fisher score, FSV, infFS, Laplacian, L0, MCFS, and MUTinfFS. Soft computing models like fuzzy systems, neural networks and genetic algorithms, and hybridisation are applied. Akaike’s information criteria and Bayesian information criteria are used for the best model selection. Classification based on clustering algorithm, viz. partitioning around medoids, K-means, hierarchical single, hierarchical complete, hierarchical centroid, and affinity propagation clustering algorithms, is used. Finally, the optimal number of the clusters i

关键词： K-means clustering

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Prioritizing Network-On-Chip Routers for Countermeasure Techniques against Flooding Denial-of-Service Attacks:A Fuzzy Multi-Criteria Decision-Making Approach

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computer Modeling in engineering & Sciences 2025年第3期142卷 2661-2689页

作者： Ahmed Abbas Jasim Al-Hchaimi Yousif Raad Muhsen Wisam Hazim Gwad Entisar Soliman Alkayal Riyadh Rahef Nuiaa Al Ogaili Zaid Abdi Alkareem Alyasseri Alhamzah Alnoor Department of Electromechanical Systems Engineering Thi-Qar Technical CollegeSouthern Technical UniversityBasra61001Iraq Department of Civil Engineering Wasit UniversityAl Kut52001WasitIraq Technical Engineering College Al-Ayen UniversityThi-Qar64001Iraq Department of Artificial Intelligence Engineering College of EngineeringAlnoor UniversityNinavah41012Iraq Information Technology Department Faculty of Computing and Information Technology at Rabigh King Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyWasit UniversityAl kut52001WasitIraq Information Technology Research and Development Center University of KufaNajaf54001Iraq College of Engineering University of Warith Al-AnbiyaaKarbala56001Iraq Management Department College of Business Administration(COBA)A’Sharqiyah University(ASU)Ibra400Oman

The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an *** this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA ***,an MCDM approach is essential to address these emerging *** the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the *** results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of *** contrast,the Hop count is identified as the least significant criterion,with a weighted score of *** remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring *** terms of ranking,SvN-FDOSM has two approaches:individual and *** the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under *** sensitivity analysis provides a high stability in ranking among all 10 *** approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.

关键词： NoC-based MPSoC security flooding DoS attack MCDM FDOSM FWZIC fuzzy set

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Enhancing Adaptive Data Management Middleware: Techniques for Federated Cloud Environments 8th

Enhancing Adaptive Data Management Middleware: Techniques fo...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Kolekar, Vikas K. Sakhare, Sachin R. Department of Computer Engineering SMT. Kashibai Navale College of Engineering Pune411041 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune411048 India

ISBN: (纸本)9783031736162

The adaptive data management middleware is crucial for facilitating efficient and dynamic data management in distributed computing environments. Nevertheless, the widespread adoption of federated cloud environments brings about new complexities and difficulties in data administration, requiring inventive methods to improve the functionalities of current middleware systems. This research paper introduces innovative strategies for enhancing adaptive data management middleware that is especially designed for federated cloud environments. The proposed methodology addresses several fundamental components of adaptive data management middleware. Initially, proposed the methods for dynamically provisioning and allocating resources to enhance the efficiency of data processing and storage across federated cloud environments. In addition, provided the strategies for implementing data governance and enforcing security measures to meet regulatory standards and protect confidential information in federated cloud environments. Furthermore, investigated the techniques for duplicating and synchronization data to improve the accessibility and uniformity of data in federated environments. To evaluate the results of the proposed techniques, we conducted comprehensive experimental evaluations using simulated federated cloud infrastructures. The results demonstrate the substantial enhancements in the performance of data management, utilisation of resources, and capacity to scale when compared to existing methods. Lastly discussed the implications of our findings and emphasised potential areas for further investigation in this rapidly evolving domain. © IFIP International Federation for information Processing 2025.

关键词： Cloud platforms

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Combining Historical Data with Future Predictions to Assess Urban Noise and Air Quality 2

Combining Historical Data with Future Predictions to Assess ...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Siva Prasad, Veeram Venkata Bhanu Chand, K. Aruna Devi, R Uma Krishna, Jonnalagadda Srinivasarao, Badde Sai Spurthi Institute of Technology Computer Science And Engineering India Swarnandhra College of Engineering and Technology Department of Information Technology India India

ISBN: (纸本)9798350364699

Analysing Urban Noise and Air Quality by Integrating Past Data Accompanying Projections for the Future. By joining predicting models accompanying Internet of Things technology, this study tackles the question of judging buzz and air kind in urban regions. To do this, a model was used to devise noise and PM2.5 heat maps. Open-beginning traffic data was again used for correct contextualization. Using this form, find the districts with extreme levels of contamination and crashes are likely situated on busy roads and technical zones. R2 principles of 0.93 for PM2.5 and 0.90 for blast showed that the forecasting models, which involve conclusion trees and convolutional affecting animate nerve organ networks, were completely correct in guessing the levels of dirtiness and crash. These findings demonstrate the significance of actively seeking out methods or policies that have no adverse impact on environmental issues in reports when formulating municipal plans. Furthermore, the study proposes strategies to enhance the city's conservation of natural resources, drawing on the conclusive findings. These procedures contain the exercise of reduced-issuance zones and the promotion of green fields. A joined and data-compelled plan is important for compensating policy selections in city preservation of natural resources, and this study creates a solid gift to both scientific knowledge and useful use in these fields. © 2024 IEEE.

关键词： Air quality

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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A pressure therapy method: Monaural beats sonic resonance

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IEEE Potentials 2024年第3期43卷 17-22页

作者： Lin, Tsen-Fang Chen, Liang-Bi Southern Taiwan University of Science and Technology Department of Popular Music Industry Tainan710301 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

This article introduces a non-invasive pressure therapy method using monaural beats (MB) sonic resonance to promote relaxation and reduce stress. Unlike binaural beats (BB), MB requires no headphones, enhancing accessibility. By targeting ultralow brainwave frequencies, MB effectively relieves anxiety and stress. Compared to BB and other infrasound methods, MB offers advantages in terms of cost, comfort, and flexibility. Potential applications in IoT and AIoT systems for adaptive therapies are also discussed, with further research needed to realize their therapeutic potential fully. © 2024 IEEE.

关键词： Noninvasive medical procedures

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第2期5卷 48-58页

作者： Abdulshaheed, Haider Rasheed Abdulrahman, Mayasa M. Al Barazanchi, Israa Ibraheem Tawfeq, Jamal Fadhil Computer Engineering Techniques Department Baghdad College Economic Sciences University Baghdad Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Department of Communication Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking human physiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs depend on the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing the quality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faulty sensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN is prepared to employ a crossbreed fuzzy-genetic calculation to optimize its execution in distinguishing defective sensor hubs. The proposed approach is assessed employing a dataset collected from a real-world WBAN. The comes about appears that the GLANN-based approach beats existing strategies in terms of exactness and proficiency. The proposed approach has potential applications within the field of healthcare, where exact and solid observing of human physiological parameters is basic for conclusion and treatment. By and large, this ponder presents a promising approach to progressing the unwavering quality and exactness of WBANs by identifying flawed sensor hubs utilizing GLANN . © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Sensor nodes

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