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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

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computer Modeling in engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

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TextNetTopics_TIS: Enhancing TextNetTopics with Random Forest-Based Topic Importance Scoring

TextNetTopics_TIS: Enhancing TextNetTopics with Random Fores...

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2024 Innovations in Intelligent systems and Applications Conference, ASYU 2024

作者： Voskergian, Daniel Bakir-Gungor, Burcu Yousef, Malik Computer Engineering Department Al-Quds University Jerusalem Palestine Department of Computer Engineering Abdullah Gul University Kayseri Turkey Department of Information Systems Zefat College Safad Israel

ISBN: (纸本)9798350379433

TextNetTopics is an innovative Latent Dirichlet Allocation-based topic selection method for training text classification models. One main limitation is its computationally intensive scoring mechanism, especially when applied to many topics. This scoring mechanism involves training a machine learning model (i.e., Random Forest) on each topic using the Monte-Carlo Cross-Validation approach and assigning a score value based on a specific performance metric (e.g., accuracy or F1-score). Moreover, the measured score does not account for the interactions between all features residing in all topics. This paper presents a new topic-scoring mechanism called Topic Importance Scoring. This computationally efficient approach trains a Random Forest model on all topics simultaneously and leverages the extracted feature importance values to give each topic a score reflecting its classification potential. The experiments on three diverse datasets confirm that the proposed method's performance is superior to the Topic Performance Scoring, which was used in the original TextNetTopics method. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Detailed Study on Various Techniques for Detecting Railway Track Anomaly 2

Detailed Study on Various Techniques for Detecting Railway T...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Komarasamy, Dinesh Iyapparaja, M. Mohana Saranya, S. Cibi Chakravarthy, M. Fredolin Tibba, B. Gayathri, R. Kongu Engineering College Department of Computer Science and Engineering Erode Tamil Nadu Perundurai India School of Computer Science Engineering and Information Systems Vit Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

A vital part of today's transportation infrastructure is the railway system that facilitates the massive movement of people and products. With so many people and commodities being transported, the rail tracks could develop defects and flaws over time. The development of these defects might jeopardize the safety and integrity of the rails due to persistent wear and tear, exposure to harsh environmental factors and the enormous loads carried by trains. Hence, to prevent faults and to avoid accidents, it is essential to maintain track safety. Thus, researchers focused on different technologies with different methodologies for detecting the defect that occurred in rail tracks as early as possible. Therefore, this paper summarizes the available methods and technologies for detecting defects and faults in railway tracks. © 2024 IEEE.

关键词： Defects

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Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

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computer systems Science & engineering 2023年第5期45卷 1869-1886页

作者： V.Divya S.Sendil Kumar V.Gokula Krishnan Manoj Kumar School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia Department of Electrical and Electronics Engineering S A Engineering CollegeChennaiTamilnaduIndia Department of Computer Science and Information Technology CVR College of EngineeringMangalpallyHyderabadTelanganaIndia School of Computer Science University of Petroleum and Energy StudiesDehradunIndia

Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.

关键词： Deep learning optimization algorithm signal conducting system schizophrenia convolutional neural network mayfly optimization algorithm

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Strategic facility & space planning utilising Design for Lean Six Sigma

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International Journal of Sustainable engineering 2023年第1期16卷 1-13页

作者： Ó Longaigh, Brían Noonan, John Trubetskaya, Anna McDermott, Olivia Dept of Engineering University of Limerick Limerick Ireland Department of Computer Science and Information Systems University of Limerick Limerick Ireland College of Science & Engineering University of Galway Galway Ireland

This study aims to develop and implement a Strategic Facility Planning process at a highly regulated manufacturing site to optimise manufacturing space and capacity in a facility running out of space. The project demonstrates the application of Design for Lean Six Sigma and a structured Define, Measure, Analyse, Design, and Verify methodology in designing and implementing a process that enables the case study manufacturing site to improve its space utilisation and free up space. The project resulted in increased space usage of approx. 38000 sq. ft. equating to a hard cost saving of over $13 million for the organisation and a cost avoidance of $22 million. The study’s contribution is that it highlights for the first time that the Design for Lean Six Sigma methodology can be utilised for space and facility utilisation and can be leveraged by other manufacturers. This study has academic and managerial implications for identifying best practices for Design for Lean Six Sigma methodology application in Strategic facility planning. This study contributes to the few academic published works to utilise Design for Lean Six Sigma methodology for space utilisation in a highly regulated environment. This study will contribute to managerial practice in aiding other manufacturing organisations with recommendations on utilising Design for Lean Six Sigma and design for improved space utilisation to reduce costs. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Design

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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TBNS: A Novel Tool for Simulating and Analyzing Bursty User Requests in Large-scale Networks 16

TBNS: A Novel Tool for Simulating and Analyzing Bursty User ...

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16th International Conference on information Security and Cryptology, ISCTURKEY 2023

作者： Sansli, Ahmet Tasci, Tugrul Sakarya University Department of Computer Engineering Sakarya Turkey Sakarya University Department of Information Systems Engineering Turkey

ISBN: (纸本)9798350393996

Internet usage has become indispensable for almost every aspect of life in today's world. Broadband Internet access has led to the need to deliver a large number of products and services over the Internet, and thus to the need for efficient delivery of exponentially increasing amounts of digital content from one point to another. In this context, the importance of methods for content distribution and effective management of user requests *** this paper, a tree-based CDN architecture capable of handling a very high number of bursty user requests is proposed and a network simulator software developed to simulate user requests on this architecture is presented. In order to demonstrate the effectiveness of the proposed architecture, a comparison is made with client-server, cache-proxy and traditional CDN methods. Since the existing network simulator tools such as OMNeT++, NS-2, NS-3, and OPNET do not have the capability to simulate the number of user requests considered in this study, the comparison process is carried out through a simulation tool (TBNS) which is capable of simulating large real-world networks up to approximately 223 individual user requests, with an expectation of being able to benefit in delivering solutions to the challenges of scalability, performance, resource utilization, and real-time *** case studies are designed to compare the proposed TBCDN approach to the client-server, the cache-proxy and the traditional CDN methods, in terms of request-handling. While the average transfer times are evaluated in the first case study, the network load causation aspects are measured in the latter. The results show that the proposed TBCDN approach generates lower transfer times as well as causes less network load, especially in case the number of user requests reached at a certain level around 1 million. © 2023 IEEE.

关键词： Proxy caches

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A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

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IEEE Transactions on Services Computing 2023年第4期16卷 1-9页

作者： Li Y. Jian S. Hsieh S. Chung W. Zomaya A.Y. Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Systems The University of Sydney Australia

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods. IEEE

关键词： Cloud computing Cloud data center networks congestion control Data centers Network topology Optical switches Routing Servers software-defined networks Topology

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