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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Jagadeesan, M. Saravanan, T.M. Selvaraj, P.A. Mounika, V. Ramya, D. Vasantha Kumar, K.S. Kongu Engineering College Perundurai Department of Computer Applications TamilNadu Erode638060 India

ISBN: (纸本)9798350348217

The security risks are increasing due to software flaws. The vulnerability dataset used in this design had taken from the cve web site. Additionally, the updated error description is matched as input, cross-referenced with the dataset and if a match is found, the error order is set up. It is now possible to manage software vulnerability using an automatic bracket system, and the system's security performance will also improve. It will also reduce the chance of system damage and attack. In this design, a brand-new model called the automatic vulnerability bracket model built on bracket algorithms for Convolutional Neural Networks has been put out. 900 neuron input layers, 16 retired layers, and 27 affair neurons are used to build the model. Age range is adjusted, and it is delicate. © 2023 IEEE.

关键词： Network security

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A Comparative Analysis of Image Processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments 5

A Comparative Analysis of Image Processing and Deep Learning...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

关键词： Intelligent systems

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Deep Neural Networks for Comprehensive Multimodal Emotion Recognition

Deep Neural Networks for Comprehensive Multimodal Emotion Re...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Tiwari, Ashutosh Kumar, Satyam Mehrotra, Tushar Singh, Rajneesh Kumar Sharda University School of Engineering and Technology Department of Computer Science and Engineering India Sharda University School of Engineering and Technology Department of Computer Science & Applications India

ISBN: (纸本)9798350323887

Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It's crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below. © 2023 IEEE.

关键词： Emotion Recognition

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Detecting and localizing multiple forgeries with TransGAN

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Iran Journal of computer Science 2025年 1-17页

作者： Singh, Upasana Rathor, Sandeep Kumar, Manoj Department of Computer Engineering and Applications GLA University U.P. Mathura India Department of Information Technology GGV University Chhattisgarh Bilaspur India

The rapid rise of manipulated videos in the digital era necessitates advanced forgery detection and localization techniques. This study presents a novel approach using a GAN-based framework integrated with a 3D Swin-B Transformer Encoder–Decoder architecture for future frame prediction. A ConvLSTM-based Siamese network serves as a discriminator, capturing spatiotemporal inconsistencies, while forgery localization is enhanced through attention maps and a patch-based strategy. Experimental validation on diverse datasets demonstrates the effectiveness of the proposed method, achieving 98.97% detection accuracy and 100% localization accuracy, surpassing existing techniques. The results highlight the robustness of the approach in identifying and localizing various types of video forgeries, making a significant contribution to digital content forensics. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial training Forgery detection Forgery localization GAN-based framework Multimedia forensics Spatiotemporal forgery

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Empowering Diabetic Care: Building an Edge-Enabled Machine Learning Model for Classification 3

Empowering Diabetic Care: Building an Edge-Enabled Machine L...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Suchi Prangya Dash, Subhashree Subudhi, Anisha Agarwal, Shiva Dabata, Prajna Paramita Agarwal, Shashank Computer Science and Engineering Odisha Siksha 'O' Anusandhan Bhubaneswar India Utkarsh Business School Bachelor of Computer Applications Uttar Pradesh Bareilly India

ISBN: (纸本)9798350354379

Diabetes mellitus is a chronic metabolic dis-order characterised by elevated amounts of glucose in the bloodstream. The categorization and forecasting of diabetes are crucial for the effective administration of this prevalent long-term condition. Machine learning (ML) enables real-time monitoring and personalised therapy on edge devices. Machine learning can be employed to predict the likelihood of developing diabetes mellitus using advanced technology. Many symptoms are too subtle to be readily observed. It is a primary factor contributing to global blindness. It has a gradual lethal effect and there is currently no recognised remedy for it. This study explores the potential of edge-based predictive modelling for early identification of diabetes. Neural networks have the ability to accurately detect and analyse patterns and trends within patient data, leading to more accurate predictions of diabetes. Principal component analysis (PCA), quantization, dynamic pruning, and model compression are employed to optimise model performance while working within the limitations of the available re-sources. The proposed methodology is used and evaluated on the Pima Indian diabetic datasets, demonstrating its efficacy in achieving precise classification within the constraints of edge devices. Additionally, the possibility of shifting trained models from cloud servers to edge devices is being examined in order to reduce the computational burden. © 2024 IEEE.

关键词： Adversarial machine learning

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Metaheuristic-Enhanced Feature Selection for High-Accuracy Intrusion Detection in Cloud Computing

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Procedia computer Science 2025年 259卷 640-649页

作者： Avinash Kumar Surendra Kumar Department of Computer Engineering and Applications GLA University Mathura India

The demand for novel Intrusion Detection Systems (IDS) has risen due to the increasing volume, velocity, and variety of data collected through modern wireless networks. Metaheuristic algorithms provide a viable method to improve IDS performance regarding optimal feature selection. The integration of methods with Machine Learning (ML) in the development of an Intrusion Detection System (IDS) facilitates improved detection accuracy, diminished false positives and negatives, and increased efficiency in network monitoring. This paper presents a hybridization of Grey Wolf Optimization and Lion Optimization, termed GWO-LOA-RF Optimization Algorithm, integrated with a Machine Learning Assisted Intrusion Detection System. The primary objective of the GWO-LOA-RF system is the effective identification and categorization of intrusions to ensure security. Data normalization is primarily conducted to scale the incoming data into a functional format. The GWO-LOA-RF approach features the GWO-LOA-RF base. The random feature selection method can be utilized to identify an optimal subset of features. We have derived multiple insights on feature selection scores in relation to accuracy, recall, precision and F-score as well as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error for the two datasets, CICDDOS2019 and UNSW-NB15. The duration of feature selection has been emphasized to illustrate the most efficient algorithm combinations. We have ultimately presented hybrid algorithmic to organizational IDS requirements, along with distinct solutions for each.

关键词： Intrusion Detection System Grey Wolf Optimization Lion Optimization Mean Absolute Error Mean Squared Error

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Optimizing Retinal Fundus Images for the Automated Diabetic Retinopathy Diagnosis 2

Optimizing Retinal Fundus Images for the Automated Diabetic ...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Awad, Samer Gharaibeh, Yazan Obeid, Abeer Awwad, Sari The Hashemite University Department of Biomedical Engineering Zarqa Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan

ISBN: (纸本)9798331539542

Diabetic retinopathy (DR) is a common eye condition arising from the complications associated with diabetes. The diagnosis of DR is traditionally carried out by experienced clinicians who screen fundus images to identify numerous small features. This process is challenging, time-consuming, and susceptible to errors. Various studies have successfully utilized different machine learning techniques for the automated diagnosis of DR. The performance of DR diagnosis can be influenced by the preprocessing of the images. In this study, the impact of unifying image resolution and field of view on the performance of machine learning-based automated DR diagnosis was evaluated. This approach ensured that the sizes of the various internal parts of the retina were effectively standardized across different images. Over 32,000 images from two different datasets obtained from *** were utilized. Two distinct classification experiments were conducted on the two datasets using an Inception convolutional neural network, both with and without the proposed method. The proposed method resulted in accuracies of 78.8% and 96.8% for dataset 1 and dataset 2, respectively, compared to accuracies of 78.1% and 95.9% without this method. © 2024 IEEE.

关键词： Adversarial machine learning

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Moving Target Defense - Challenges, Problems and Suitability for Internet of Things 4

Moving Target Defense - Challenges, Problems and Suitability...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Gurung, Dipendra Gurung, Sandeep Pradhan, Mohan Pratap Sikkim Manipal Institute of Technology Department of Computer Applications Majitar India Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Sikkim University Department of Computer Applications Gangtok India

ISBN: (纸本)9798350323771

Traditionally cyber attackers have always had an upper hand over defenders as the latter can only react in the event of an attack. Moving Target Defense (MTD) has emerged as a potential game changer by reversing the advantage from attackers to defenders. Together with traditional defenses, MTD is expected to take cyber security up a notch. MTD solutions have been proposed for various levels, namely operating system, network, runtime platform, etc. However, improvement of existing solutions and development of new solutions are required. This paper presents an overview of MTD, the various MTD approaches, attacks mitigated, open research problems in MTD and MTD for the Internet of Things (IoT). © 2023 IEEE.

关键词： Internet of things

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Performance Analysis of Deep Learning Pretrained Image Classifiation Models 1

Performance Analysis of Deep Learning Pretrained Image Class...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Deepa Alli, A. Sheetal Prasad Km, Shiva Christ University Department of Computer Science Bengaluru India Presidency College Department of Computer Applications Bengaluru India Presidency College Department of Computer Science and Engineering Bengaluru India Rao Bahadur y Mahabaleswarappa Engineering College Department of Computer Applications Karnataka Ballari India

ISBN: (纸本)9798350316469

Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning Image classification

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