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

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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Enhanced Text-to-Image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Trustworthy Artificial Intelligence and Automatic Morse Code Based Communication Recognition with Eye Tracking

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Journal of Mobile Multimedia 2023年第6期19卷 1439-1461页

作者： Medichalam, Krishnakanth Vijayarajan, V. Kumar, V. Vinoth Iyer, I. Manimozhi Vanukuri, Yaswanth Kumar Prasath, V.B. Surya Swapna, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore 632014 India Department of Computer Science and Engineering East Point College of Engineering and Technology India Department of Electrical Engineering and Computer Science University of Cincinnati 45221 OH United States Department of ECE Dr MGR Educational and Research Institute Chennai 95 India

Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications. © 2023 River Publishers.

关键词： blinks communication eye detection face detection gestures image processing Morse code

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Blockchain-based Patient Recommendation System for Smart Healthcare

Blockchain-based Patient Recommendation System for Smart Hea...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Mehta, Raj Mehta, Mahek Kakkar, Riya Oza, Parita Agrawal, Smita Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat 382481 India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia

ISBN: (纸本)9798350350548

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc. © 2024 IEEE.

关键词： Electronic health record

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security 2

Innovative Data Encryption Techniques using AI for Wireless ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Artificial Intelligence For Realtime Face Recognition Attendance Using College Classrooms and Buses

Artificial Intelligence For Realtime Face Recognition Attend...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Ahila, A. Hosanna Princye, P. Poonguzhali, A. Kavivendhan Deepa, M. Arthy, A. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Iconix Software Solution Software Analyst Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Teachers take attendance by having pupils sign in or check-in classes and transportation. Student absences often result from individual mistakes. This article examines a technology that records data from classroom photographs of every student's face. This research uses an Adaptive Boost Classifier, Random Forest (RF), and Deep Convolutional Neural Networks (DCNNs). The model performs well on the DCNN model with 88 and 92% accuracy and on the ResNet50 pre-trained model with 97.21% accuracy. After detecting each student's face, they recorded their present status in an Excel document. It kept the best system implementation approach based on performance. © 2024 IEEE.

关键词： Convolutional neural networks

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Photonics Applications and Web engineering – WILGA 2024

Photonics Applications and Web Engineering – WILGA 2024

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Romaniuk, Ryszard S. Smolarz, Andrzej Wójcik, Waldemar Warsaw University of Technology Institute of Electronic Systems Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

Wilga 2024 Summer Symposium on Photonics Applications and Web engineering was the 52th edition of the research and technical meetings series. Traditionally, the annual series of technical conferences and topical sessions lasted the whole week. They usually were split to one winter meeting in January and one or two summer events in May/June, and in September. The Covid epidemic and the war in Ukraine has changed a lot. The hybrid meetings were held in Wilga resort near Warsaw, owned by the Warsaw University of Technology. Now the meetings since 2022 were shifted to Lublin. Nearly 100 participants took part in all of the Wilga 2022 events, most of them being young researchers active in all aspects of photonics, electronics and ICT science and technology. Over 60 participants took part in Wilga 2024 meetings. Wilga Symposium embraces hardware and software technologies associated with photonics like optics, optical engineering, optoelectronics, electronics and electrical engineering, mechatronics, chemical and material engineering, applied physics, industrial solutions and applications. Around 50 papers were presented during Wilga 2024, oral and poster. Out of this number, some papers chosen by authors are published in this volume of Proc. SPIE mostly related to photonics. The major change was that during Wilga 2020, 2021, 2022 and 2024 Symposia, during summer editions, no traditional large young researcher sessions at large were possible to be organized. We hope that this will change and Wilga 2025 will return to a good tradition to organize concurrent social research sessions full of young researchers like was during Wilga 2019 and earlier. © 2024 SPIE.

关键词： Photonic devices

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Fractal dimension analysis for improved decision-making in non-stationary multi-arm bandit problems: a machine learning approach

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The European Physical Journal Special Topics 2025年 1-16页

作者： Nandhini, S. Kala Pandian, J. Arun Department of Computer Applications School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

In this research, we propose a novel approach to addressing the exploration–exploitation dilemma in multi-armed bandit (MAB) algorithms using fractal dimensions. The fractal dimension is used in the algorithms to represent the reward distributions of arms which represents the uncertainty of the arm in receiving the reward. The fractal dimension of the reward distribution is implemented in the most popular MAB optimization algorithms, such as Epsilon-Greedy, Upper Confidence Bound (UCB), Exponential-weight algorithm for Exploration and Exploitation (EXP3), and Thompson Sampling in this study. The algorithm prefers to choose arm with the least fractal dimension, as a lower fractal dimension represents less uncertainty of the arm. The performance of the fractal-enhanced MAB optimization algorithms is compared with traditional algorithms in non-stationary environments with various numbers of arms. The proposed approach provides a novel way to quantify and utilize the uncertainty of each arm in MAB problems.

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