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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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Image Caption Generation using CNN and Audio Conversion 2

Image Caption Generation using CNN and Audio Conversion

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Priya, K. Babu, R.Vijay Reddy, M.Muralidhar Mohan Reddy, T.H. Maanesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Artificial Intelligence & Data Science Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Arizona state university Department of Data Science Tempe United States

ISBN: (纸本)9798350368413

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation. © 2024 IEEE.

关键词： Convolutional neural networks

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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Synergy of artificial intelligence and Music 6

Synergy of Artificial Intelligence and Music

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Patil, Ruchira Pramod Bhor, Prabhu Anil K. J. Somaiya Institute of Technology Department of Artificial Intelligence and Data Science Sion Mumbai India Datta Meghe College of Engineering Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350359817

This comprehensive paper delves into the intricate relationship between Music and artificial intelligence (AI). It presents an extensive overview of the current developments in this emerging field and explores the potential future applications. The paper goes beyond the surface, emphasizing that music is not just a melodious tune but a complex data set that AI can efficiently manipulate. It investigates how AI can affect human brain functionality through music, discusses AI's role in automated music composition, and highlights its significance in assisting impaired individuals in playing music. Furthermore, the paper predicts the future of AI in the music industry, considering its possibility for transformation. © 2023 IEEE.

关键词： Sentiment analysis

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Robust Real-Time PCB Defect Detection Using Improved DETR

Robust Real-Time PCB Defect Detection Using Improved DETR

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wu, Qingtian Yang, Jinfeng Yang, Ou Zhang, Liming Guan, Yisheng Institute of Applied Artificial Intelligence Guangdong-Hong Kong-Macao Greater Bay Area School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China University of Macau Faculty of Sciences and Technology Department of Computer and Information Science SAR Macau China School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou Guangdong China

ISBN: (纸本)9781665481090

This paper presents a PCB defect detection system utilizing the Real-Time Detection Transformer (RT-DETR) model. Originally designed for general object detection, we adapt RT-DETR for the specific task of PCB defect detection, achieving both high accuracy and rapid inference. Building on this baseline, we introduce re-parameterization techniques to further enhance the model's performance with less parameters computational cost. Additionally, we employ data augmentation strategies to enrich the diversity of the dataset, thereby improving the model's robustness and generalization capability. Through these efforts, the developed PCB defect detection system based on the RT-DETR model meets the expected requirements in terms of high accuracy and real-time inference speed. The code and models will be made publicly available. © 2024 IEEE.

关键词： Object detection

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Automatic White Blood Cells Classification using Optimized Convulutional Neural Network 3

Automatic White Blood Cells Classification using Optimized C...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Mahale, Pragati Prashant Deshmukh, Rakesh K. Pise, Priya Gardi, Manish Subhash Kalinga University Raipur India AISSMS's Institute of Information Technology Department of Information Technology Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India JSPM'S Jayawantrao Sawant College of Engineering Computer Engineering Department Pune411028 India

ISBN: (纸本)9798350381931

Methods based on deep learning are widely used nowadays for data analysis. These procedures involve low time investment and simple tools. In this research, WBC classification was suggested using module weighted optimized CNN. Two transfer learning modules and deformable convolutional (DC) layers characterize our technique, which we use to improve resilience. For WBC classification on low-resolution and noisy data sets, the suggested Optimized CNN (OCNN) demonstrated the best results because to its precise feature extraction and improved network weights. It has the potential to be employed in clinical settings as an alternate technique. White blood cells (WBCs) are immune system cells that may be found throughout the body in the blood, lymph, and other tissues. There is a current upward trend in the prevalence of WBC-related blood disorders such leukemia and lymphoma. The process of analyzing leukocytes in microscopic blood images is the major concern of this research. In this paper, we work on WBCs segmentation from microscopic blood images. © 2024 IEEE.

关键词： Blood

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BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Elumalai, Anupriya Ayyasamy, Ramesh Kumar Kavya, S.P. Dhanasekaran, S. Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India VIT - AP University School of Computer Science and Engineering India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Deemed to Be University Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350348217

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book's interacting entities on blockchain because every consumer could retrace the book's renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants. © 2023 IEEE.

关键词： Smart contract

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Enhancing Histopathological Image Classification through InceptionResNetV2: A Deep Learning Approach 2

Enhancing Histopathological Image Classification through Inc...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahesh, R.T. Shenbagavalli, P. Mohanraj, T. Amala, P. Anitha, K. Thakur, Arastu Department of Computer Science and Engineering Bangalore India Rathinam Technical Campus Department Of Artificial Intelligence And Data Science Coimbatore India Karpagam Academy of Higher Education Coimbatore India Erode Sengunthar Engineering College Erode India CSI College of Engineering Department of Information Technology Nilgiris India

ISBN: (纸本)9798331534950

Histopathological image analysis is a critical task in medical diagnostics, aiding in the accurate classification and staging of diseases. Traditional image analysis methods, while effective, often lack the precision and scalability provided by modern deep learning techniques. This study presents a sophisticated use of the InceptionResNetV2 architecture that has been optimized for the categorization of histological pictures from the colon and lungs. The InceptionResNetV2 model was trained on a comprehensive dataset of histopathological images of lung and colon cancer, which were pre-processed and augmented to enhance their quality and diversity. The model was adjusted to operate without the top layer and included additional dense layers with dropout for regularization to prevent overfitting. A custom learning rate adjustment callback was implemented during training to optimize key performance metrics. The final model achieved remarkable classification accuracy, with precision and recall rates exceeding 99%. This performance underscores the capability of InceptionResNetV2 in handling complex image features associated with histopathological data. The research showed that deep learning might significantly increase medical image analysis's prediction accuracy and efficiency, providing useful assistance for clinical decision-making. The results imply that these models may be expanded to include more medical imaging and diagnostic processes, offering a scalable solution for global healthcare systems. © 2024 IEEE.

关键词： Lung cancer

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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Tomato Leaf Disease Detection through Machine Learning based Parallel Convolutional Neural Networks 7

Tomato Leaf Disease Detection through Machine Learning based...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Shetty, Sahana Selvanayaki, S. Sumathi, T. Nageswari, C.Shobana Nisha Devi, K. Suganyadevi, S. FET-Jain Department of CSE-AIML Cyber Security School of CSE Jayanagar Bangalore India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Dr.Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India R.M.D. Engineering College Department of ECE Kavaraipettai601206 India Bannari Amman Institute of Technology Department: Artificial Intelligence and Data Science Sathyamangalam India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9798350397253

Agriculture is the primary economic activity in a significant number of nations. Yet, plant-related illnesses are the most difficult obstacle for farmers to overcome. The leaf is the portion of the plant that is noticed the most, but the process of segmenting an image of a sick lesion on a leaf suffers from challenges related to uneven lighting and a crowded, complicated natural environment. The combination of colour balancing and superpixel is the foundation of the approach that has been suggested as a solution to these issues. To begin, the picture that is being read in will be converted into a color-balanced image so that the effects of uneven lighting may be removed. Second, using the superpixel operation, compact areas are produced from the modified picture. An experimentally calculated threshold is then applied to the Histogram of Gradients (HOG) and colour channels of the superpixel to separate the undesirable backdrop from the image of the leaf. In order to identify a sick or contaminated picture, K-means clustering is employed. Pyramid of HOG (PHOG), an expanded version of HOG, together with Grey Level Co-occurrence Matrix (GLCM) characteristics are used to depict a sick infected portion of the body. In the end, many disease classifiers are evaluated against one another, and Random Forest (RF) is chosen as the best option. An experiment is carried out to verify the effectiveness of the proposed approach's accuracy using natural photographs obtained from a variety of farms, images downloaded from the internet, and the Plant-Village colour dataset. This is done so that the resilience of the method can be evaluated. © 2023 IEEE.

关键词： Color

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