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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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Air Canvas: Expressing Creativity in the Real World with OpenCV and Python

Air Canvas: Expressing Creativity in the Real World with Ope...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Shanthalaksmi Revathy, J. Suresh, A. Sanjith Bala, B. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350393378

In today's world, online education has become one of the most important and widely used platform as it has spread all over the world. But online learning also has many challenges. One of the most important problems is to focus on the courses taught. To solve this problem, we created an "air canvas"that allows content to be delivered in real space and allows us to draw our needs by holding our fingers in the air.. It works by using a webcam to capture finger movements. It is built using the famous OpenCV library and python. There are numerous ways to write when drawing, including utilising a keyboard, touch screen, digital pen, pencil, or electric glove. But in the proposed system, Python computer vision programming and gesture detection are used to produce natural human-computer interaction. © 2023 IEEE.

关键词： Python

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Redefining Crime Record Storage: An Advanced Architecture Harnessing the Power of Blockchain technology 26

Redefining Crime Record Storage: An Advanced Architecture Ha...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Hossen Manik, Md. Motaleb Halim Sagor, Anite Habib, Md. Ahsan Mossadek Touhid, Md. Ahmed, Tanim Islam, Md. Zabirul Haque, Fabliha Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350359015

Crime record storing is a significant task for law enforcement agencies. Often, they face challenges in managing and inspecting the records efficiently and securely. Moreover, locally stored data, as hard copy or electronically, can be modified by any entity, which creates a possibility of data alteration. These issues can be resolved by employing a storage architecture that provides an efficient data storage and management system while simultaneously ensuring data integrity. Here, blockchain technology offers a possible alternative to traditional storage architecture by ensuring proper data storage, management, and integrity. It ensures the reliability of information by providing tamper-proof, verifiable records and enhanced data storage and access. This paper proposes a blockchain-based crime record storage framework that facilitates efficient data storage, precise solutions for data alteration, and improves data access operations to a greater extent. To implement the architecture, it utilizes a private blockchain storage that encompasses all the requirements simultaneously. Finally, the experimental evaluations demonstrate the efficacy of the proposed framework including comparison with traditional storage architectures. © 2023 IEEE.

关键词： Blockchain Crime Record Data Access Data Integrity Data Storage

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An Algorithmic and Machine Learning Approach for Yoga Asanas Feedback 3

An Algorithmic and Machine Learning Approach for Yoga Asanas...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Saxena, Prakhar Sharma, Vishnu Yadav, Anuj Singh, Rohit Pratap Singh, Laiphrakpam Dolendro National Institute of Technology Department of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798331523893

Yoga, rooted in the ancient traditions of India, epitomizes the pursuit of holistic harmony, seeking to integrate life's physical, mental, and spiritual aspects. Innovative solutions are emerging to enhance yoga practices in response to the growing interest in integrating artificial intelligence and machine learning into physical well-being. The proposed work addresses the limitations of human-based feedback systems in yoga sessions by proposing an algorithmic approach aided by machine learning models to provide unbiased feedback on Yoga Asanas. Moreover, the proposed work mitigates injuries resulting from incorrect yoga practices and the inherent subjectivity in human assessments and offers feedback on yoga performances based on predefined criteria. © 2025 IEEE.

关键词： feedback machine learning Yoga Pose Recognition

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Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

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computers, Materials & Continua 2022年第5期71卷 2459-2476页

作者： Sonali Dash Sahil Verma Kavita N.Z.Jhanjhi Mehedi Masud Mohammed Baz Department of Electronics and Communication Engineering Raghu Institute of Technology(A)Visakhapatnam531162India Department of Computer Science and Engineering Chandigarh UniversityMohali140413India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye *** diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular *** various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s *** curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge *** bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong *** bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the ***,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the *** C mean thresholding is used for the extraction of *** recommended fusion approach is assessed on DRIVE *** results illustrate that the fusion algorithm preserved the advantages of the both and provides better *** results demonstrate that the recommended method outperforms the traditional approaches.

关键词： Blood vessel extraction curvelet transform fast bilateral filter C mean thresholding

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Merging the Strengths of GPT-4 and DeepLab for Semantic Text Understanding in Digital Textbooks

Merging the Strengths of GPT-4 and DeepLab for Semantic Text...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Anusha, B. Borah, Asha Rani Mishra, Gaurav Thethi, H Pal Al-Allak, Mustafa Abdulhussein Vidhya, B. Veda Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Computer Science Engineering Bangalore India Amity School Of Engineering And Technology Amity University Greater Noida India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Najaf Iraq Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

Gathering all the necessary data is the major objective of this study so that a comprehensive picture can be painted of how new technologies might improve students' understanding of semantic content in digital textbooks. The recommendations for this approach make use of TIFA, CEG, and ACA, or the Adaptive Complexity Adjustment. This approach combines the best features of various algorithms. TIFA makes these learning aids easy to completely comprehend by combining written and visual material. Answering students' queries and providing answers that make sense in the context is CEG's responsibility. To make learning more tailored to everyone, ACA changes the difficulty of the content depending on performance metrics. Conversely, when students ask CEG questions, it finds answers. We compare the proposed method, Semantix Blend, to two more popular approaches, Textbook Classic and Trad Edu. Interpreting text, evaluating visual information, ensuring consistency in context, enabling customisation, making content more accessible, enhancing learning results, and many more advantages are all possible with Semantix Blend. The study's findings will contribute to the improvement of digital textbooks, leading to improved instruction for students in the long term. © 2024 IEEE.

关键词： Semantics

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SecGes: A Secure Biometric-Driven Hand Gesture Recognition System for Smart Wheelchair Movement 17

SecGes: A Secure Biometric-Driven Hand Gesture Recognition S...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Kaur, Gurleen Gupta, Bakul Saini, Devanshi Mohindru, Kashika Nehra, Anushka Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350307672

In this paper, we present, SecGes, an innovative solution to enhance the mobility and accessibility of smart wheelchairs by integrating deep learning-based hand gesture recognition with the YOLO Version-8 DL model. The main objective is to create an intuitive and efficient control system for wheelchairs, allowing users to navigate their environment effortlessly. We incorporate contactless fingerprint biometric authentication using HSV and YCbCr based Image Segmentation and CNN classification to ensure secure and personalized access to the wheelchair. This dual system not only empowers users ability to control their mobility through intuitive hand gestures but also prioritizes safety and security through biometric authentication. We leverage deep learning techniques to design a robust and adaptable framework having potential to significantly improve the quality of life of an individual with mobility impairments. The results obtained demonstrate the feasibility and effectiveness of the proposed approach in smart wheelchair technology, with promising implications for broader assistive technology applications. © 2023 IEEE.

关键词： Deep learning

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An Explainable Machine Learning Model for Diagnosis of Chronic Kidney Disease Using Regular Health Informatics 5

An Explainable Machine Learning Model for Diagnosis of Chron...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Paul, Susmita Mamun, Md. Al Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350394290

People all over the globe are impacted by the serious public health problem known as chronic kidney disease (CKD). It is associated with unfavorable health outcomes that can occasionally result in renal failure and other chronic illnesses including cardiovascular disease. Early identification and appropriate treatment may be able to lessen the burden of CKD. In recent years, several clinical decision-making systems on CKD detection have been developed by various researchers to facilitate and confirm an efficient diagnosis. In this research, CKD advancement is assessed directly from a dataset comprising regular health check information for 1 million participants. We have utilized Chi-Square, Minimum Redundancy Maximum Relevance (mRMR), and Recursive Feature Elimination (RFE) feature selection algorithms to select the most crucial features. K-nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) are three state-of-the-art classification algorithms used in this study. The findings have been calculated for each classifier utilizing the selected features from the feature selection algorithms. The following measures are used to assess the performance of the models: accuracy, sensitivity, specificity, F1-score, and Area Under the Curve (AUC). The performance of each classification method is impressive. The SVM method, however, has outperformed all other used algorithms and has achieved 99.29% accuracy. An AI-based model explanation method, SHapley Additive exPlanations (SHAP), has been used to disclose the importance of the features in classification. Among the top features contributing most to the prediction model, age, sex, HMG, smoking state, height, waist, drinking state, and ALT is cost-effective regular health information. © 2023 IEEE.

关键词： Feature Selection

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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