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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shanmugam, Diya Dhandapani Syed, Safiya Fathima Dinesh, Sahana Chitrakala, S. College of Engineering Guindy Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

In this paper, a method to generate speech from a muted video is proposed. Recent advances in computer vision and deep learning have allowed the development of lip-reading technology that can recognize and interpret lip movements in videos. However, one of the main challenges in visual-to-speech synthesis is distinguishing between homophenes, or sets of words that look identical on a person's lips but contain different sounds. To address this challenge, the proposed model utilizes a speech generation framework that uses an external memory called Visual Audio Recall (VA-Recall) to generate speech from silent video. The VA-Recall system allows the system to effectively synthesize speech from a speaker by recalling the most relevant auditory information that matches the visual input context. The proposed method further generates subtitles for the reconstructed audio. Overall, this process allows for a more natural and contextually appropriate speech synthesis system. © 2023 IEEE.

关键词： Speech synthesis

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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Multi-agent Task Assignment in Unmanned Aerial Vehicle Edge Computing based on Deep Learning Approach 3

Multi-agent Task Assignment in Unmanned Aerial Vehicle Edge ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Samuel, Amalorpava Mary Rajee Yamuna Devi, M.M. Madhusudhanan, S. Dept. of Electrical and Information Engineering University of Witwatersrand Johannesburg South Africa Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundatio Vijayawada India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331532420

Unmanned aerial vehicles (UAVs) are used as supportive edge computing for sparsely located user equipment on a large scale. In this work, we propose and address a collaborative edge computing system involving multiple UAVs as agents in deep reinforcement learning (DRL) approach due to the restricted computation and energy capabilities of UAVs. The challenge of task offloading is being tackled to reduce the total delays in execution and energy usage by simultaneously planning the paths, assigning computation tasks, and managing communication resources of UAVs. Additionally, Lyapunov optimization is incorporated for the system stability of mobile edge computing assisted by multiple UAVs. Exploring a multi-agent deep reinforcement learning framework to achieve a combined strategy to manage task allocation, and power management. The sum rate results of the evaluation show that our method for task offloading using multi-UAV and multi-EC achieves superior performance with 25 % increase when compared to other optimization methods and ability to reduce cost and delay. © 2024 IEEE.

关键词： Energy utilization

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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UAV-based Firefighting by Multi-agent Reinforcement Learning 13

UAV-based Firefighting by Multi-agent Reinforcement Learning

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Tanha, Reza Shami Hooshmand, Mohsen Afsharchi, Mohsen Dept. Computer Science and It Zanjan Iran University of Zanjan Dept. Electrical and Computer Engineering Zanjan Iran

ISBN: (纸本)9798350330151

Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among the best options to do the job of firefighting. Their orchestration of effective fire extinguishing is a must and essential. This work proposes a multi-agent reinforcement learning method using an actor-critic structure to extinguish the fire. While the learning is centralized, their execution is decentralized. Moreover, we define two models of fire spreading based on the discrete-time Markov process. The results show that the proposed method outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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A systematic review of decentralized finance protocols

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International Journal of Intelligent Networks 2023年第1期4卷 171-181页

作者： Shah, Kaushal Lathiya, Dhruvil Lukhi, Naimish Parmar, Keyur Sanghvi, Harshal Dept. of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Dept. of CEECS Florida Atlantic University Boca Raton United States

Decentralized finance (DeFi) has revolutionized the financial industry in recent years. Industries such as banking, insurance, and investment are experiencing a significant shift due to the growth of DeFi. The decentralized finance market is expanding exponentially, emphasizing the potential of digital currencies and decentralized platforms in providing an alternative to the traditional finance paradigm. It eliminates the need for costly intermediaries, reduces transaction fees, and increases accessibility to financial services for everyone, regardless of their geographic location or economic status. With the enormous increase in cryptocurrency investment, individuals and institutions have started to use DeFi. In this context, understanding the architecture and working mechanisms of different DeFi protocols becomes crucial in creating new and innovative products. This review paper explores various DeFi protocols, including derivatives, decentralized exchanges (DEX), lending and borrowing, asset management, and stablecoins. It analyses their internal structure and composability, providing insights into how these protocols can be combined to create new and innovative DeFi products and explore the potential of DeFi in providing an alternative to the traditional finance paradigm. © 2023 The Authors

关键词： Asset management

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Vehicle Theft Detection Using Neural Pattern Recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Optimizing Human Activity Recognition for Precision in Yoga Practice Analysis 8

Optimizing Human Activity Recognition for Precision in Yoga ...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. Rodrigues, Maria Viola Ritesh Muhammed Elham, P.N. Mannan, Mohammed Abdul St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

The proposed system on enhancing human activity recognition in yoga presents an innovative solution using advanced technologies to accurately and quickly analyze data of yoga poses. Through meticulous data collection and preprocessing, including noise removal, normalization, and handling missing data, the system ensures a clean and standardized dataset. Pose estimation algorithms contribute to accurately mapping the body's posture, extracting essential features like joint angles and body orientation. The features form the basis for a powerful machine learning model, trained on a labelled dataset and employing neural networks, enabling the system to recognize yoga poses with remarkable precision. The study embraces an iterative enhancement process, continuously refining the model based on feedback and performance metrics, ensuring ongoing improvements in accuracy. Ultimately, the proposed system represents a cutting-edge solution for advancing human activity recognition in yoga, providing practitioners with real- time feedback and guidance, fostering improved techniques, and enhancing the overall yoga experience. © 2024 IEEE.

关键词： Mapping

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Screening and Ranking resume's using Stacked Model 1

Screening and Ranking resume's using Stacked Model

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Kavitha, D. Padmavathi, B. Sushanth, V. Yashwanth, P. Yuvaraj, R. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9798350348057

Finding the top candidates for a position is the aim of the resume screening process. The application must make use of machine learning methodologies as well as natural language processing to rate candidates in real time. Natural language processing (NLP) and machine learning methods are used to rate resumes. The output would be the resume of a top candidate, with resumes and job descriptions serving as the input. The output's results are obtained instantly. String matching, Cosine Similarity, and TF-IDF will all be done with Mon. Existing systems are straightforward and efficient, but they lack precision, efficiency, and processing capacity. © 2023 IEEE.

关键词： Natural language processing systems

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