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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Thara P Mohammed Azneed Muhammad Sanas Jithu Prakash P M Harsh P Naik Divya B Dept of Computer Science and Engineering Aarupadai Veedu Institute of Technology Vinayaka Mission’s Research Foundation(DU) Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

This paper introduces a novel approach to subtitle synchronization using the Whisper ASR (Automatic Speech Recognition) model from OpenAI. The primary aim of this research is to achieve accurate and robust synchronization of subtitles with audio content, even in the presence of uniform or non-uniform delays. The project leverages the capabilities of the Whisper ASR model, which is trained on diverse audio datasets and offers multitasking functionalities including multilingual speech recognition, speech translation, and language identification. Key features of the project include Whisper ASR integration, SRT modification, and accuracy enhancement. The system utilizes FFmpeg for audio extraction and preprocessing, followed by speech recognition using the Whisper ASR model. Preprocessing techniques are applied to enhance the precision of generated timestamps, ensuring precise synchronization. The methodology also involves timestamp adjustment based on text comparison between input and transcribed SRT files, resulting in accurately synchronized subtitles. The project offers a user-friendly interface for input acquisition and interaction, guiding users through the synchronization process. While promising, the system has limitations such as the lack of action detection, character name prediction, and challenges with repetitive word handling. Nonetheless, the Whisper ASR-based Subtitle Synchronization project presents a reliable solution for enhancing subtitle accuracy and accessibility in various video content scenarios.

关键词： Accuracy Multitasking Feature extraction Control systems Delays Synchronization Reliability Automatic speech recognition

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An Empirical Analysis of Relationship between Economic Growth and Freedom: A Cross Country Study using Machine Learning Approach 14

An Empirical Analysis of Relationship between Economic Growt...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shivkumar, S. Gupta, Deepa Deepika, M.G. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Management Dept. of Management Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350335095

While there is sufficient exploration on the role of labour, capital and technology as determinants of economic growth, less is analysed on the role of economic and political freedom as factors influencing the economic growth of a country. The economic growth of some countries is high and in some low. The reason for this variation has been a matter of discussion in economic literature. In this paper, the relationship between economic growth and economic freedom along with labour and capital is analysed. The multi-linear regression model is used to estimate the relationship. The results obtained indicate that there exists a significant relationship between economic growth, economic freedom, labour and capital. The R2 score was 0.929 which states that the dependent variable is explained well by the independent variables. © 2023 IEEE.

关键词： Machine learning

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Optimized Fetal ECG Feature Extraction with Genetic Algorithm Based Heart Rate Detection 4

Optimized Fetal ECG Feature Extraction with Genetic Algorith...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Pokkuluri, Kiran Sree Soni, Nidhi Sahu, Amit Mukherjee, Tilak Prasanthi, Y. Sharma, Disha Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal462044 India Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Haldia Institute of Technology Electronics and Communication Engg. Dept West Bengal Haldia721657 India Kkr & Ksr Institute of Technology and Sciences Dept of CSE-Data Science Guntur India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

Congenital heart problems are the most common primary cause of death that can be attributed to birth defects. It is possible to identify cardiac defects by analyzing the geomorphology of cardiac electric indications. Additionally, the examination of fetal cardiac signals, which does not involve any intrusive procedures, can be an efficient method for monitoring the health of the fetal heart and identifying cardiac abnormalities at an early stage. One method that can be utilized to evaluate the health and condition of the fetus is the ECG signal, which is a graphical copy of the electric potential that is generated in conjunction with the action of the heart. However, there are several technical issues that arise during the process of non-invasive FECG extraction. These issues include interference from maternal electrocardiograms and maternal electromyograms, interference from power lines operating at 50 Hz, and baseline drift. Extraction and analysis of the fetal electrocardiogram signal are the key goals of electronic fetal monitoring;they are the primary objectives. In the process of extracting features from the fetal ECG indication, which is a nonlinear mix of the gentle ECG, the fetal ECG indication, and other interfering signals, digital signal processing techniques constitute a key part of the process. It is possible to find solutions to problems involving abnormality detection by employing data mining techniques and soft computing approaches for categorization. © 2024 IEEE.

关键词： Fetal monitoring

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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Enhancing Health Product Traceability on the Blockchain: A Novel Approach for Supply Chain Management inspection to AI

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Prashanth, Mallellu Sai Uma Maheswari, V. Aluvalu, Rajinikanth Prasad Kantipudi, M.V.V. Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Dept. of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Pune India

INTRODUCTION: Blockchain technology is being investigated as a viable solution due to the industry's growing requirement for accountability and traceability. This study describes a fresh method for tracking down medical products that makes use of a decentralised smart contract network set up on the Ethereum blockchain. In order to enable secure and auditable tracking of health products throughout their lifecycle, the suggested system, named "HealthProductTraceability," makes use of the transparency and immutability of blockchain. OBJECTIVES: The system uses a "Product" struct to hold pertinent data such the product name, batch number, temperature, producer, and distributors. To quickly get product information depending on the batch number, a mapping is used. The use of tools to manufacture items, send them to distributors, and market them is one significant contribution of this *** demanding validation tests, such as verifying that batch numbers are unique and exist before carrying out certain activities, these functions protect the integrity of the traceability system. METHODS: In order to enable interested parties to track the product's travel and temperature changes, the system additionally emits events for product manufacture, distribution, and temperature adjustments. The suggested system is innovative because it can track the temperature of health items from beginning to end on a decentralised, open platform. RESULTS: By utilising blockchain technology, the system lessens reliance on centralised authorities, fosters stakeholder trust, and minimises the likelihood of fraud, forgery, and tampering in the supply chain for health products. The contract's architecture recognises some of the issues with blockchain technology, including scalability and privacy. By investigating solutions like sidechains, off-chain transactions, and enhancements to consensus methods, scalability issues are solved. CONCLUSION: In summary, the suggested HealthProductTraceability

关键词： Smart contract

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Image to Audio Using LLM for Blind People

Image to Audio Using LLM for Blind People

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Data, Computation and Communication (ICDCC), International Conference on

作者： Kumaresan T Geetha A Rajeshkumar G Benita Gracia Thangam J Malathi Eswaran Thivya Rajkumar Dept. of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Dept. of Computer Science and Engineering Presidency University Bangalore India Dept. of Artificial Intelligence and Data science Velalar college of Engineering and Technology Erode India Dept. of Computer Technology Kongu engineering College Perundurai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Blind people face many difficulties while engaging with their surroundings. This project is all about creating innovative solutions for the blind people by providing the information in the format of audio. Image-to-Audio transformation system specifically designed for natural language understanding using Large Language Model (LLM).The system will capture a picture or video of the situation present in front of the blind person and then provide descriptions about the images. The objective is to provide guidelines for blind people by understanding their surroundings through an LLM model and provide audio response. The CLIP model analyzes the objects, scenes, text etc.. and provides relevant information. The extracted visual data is then passed through GTTs (Google-Text-To-Speech) to create rich audio descriptions. It provides accurate and detailed representation of the visual content, allowing blind people to understand their surroundings. The project involves integrating convolutional neural networks and Large Language Model (e.g GPT-4) technologies to create a real-time image-to-text system for blind individuals. The CNN model is used to calculate the distance of the object from the user. We are integrating the Google Assistants to open the app automatically by the blind people with help of their voice.

关键词： Visualization Navigation Large language models Blindness Real-time systems Natural language processing Internet Convolutional neural networks Multimedia communication Monitoring

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Image to Audio Using LLM for Blind People 1

Image to Audio Using LLM for Blind People

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Kumaresan, T. Geetha, A. Rajeshkumar, G. Benita Gracia Thangam, J. Eswaran, Malathi Rajkumar, Thivya Bannari Amman Institute of Technology Dept. of Computer Science and Engineering Sathyamangalam India Presidency University Dept. of Computer Science and Engineering Bangalore India Velalar college of Engineering and Technology Dept. of Artificial Intelligence and Data science Erode India Kongu engineering College Dept. of Computer Technology Perundurai India

ISBN: (纸本)9798331532956

关键词： Image retrieval

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RoseVision: An Android Application To Detect Rose Leaf Diseases Using Modified Convolutional Neural Network 26

RoseVision: An Android Application To Detect Rose Leaf Disea...

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

作者： Nayan, Saidul Islamm Rahman, Syed Mahfuzur Mahbub, Nosin Ibna University of Global Village Dept. of Computer Science and Engineering Barishal8200 Bangladesh Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh

ISBN: (纸本)9798350359015

Every year 28% of the global cut rose production is hampered by various rose plant diseases. Early detection and classification could diminish the cost of cultivation lost. However, without proper knowledge and expert assistance, farmers are facing challenges to identify the diseases in time to take action. Previously, a few works have been conducted on this. So far, no study has been done to develop an efficient automatic mobile application detection system with faster, lighter, and more accurate predictions. In this study, we presented an artificially intelligent mobile application using image processing to identify rose leaf diseases. The primary goal of this work is to create a simple but exact identification with minimal computing power and time. We proposed a Modified Convolutional Neural Network (MCNN) to classify different rose leaf diseases. The proposed MCNN model outperforms the famous pre-trained models including VGG16, VGG19, DenseNet121, MobileNetV2, Resnet50, InceptionV3, and Extreme Inception (Xception) by achieving 97.94% test accuracy. Additionally, based on the trained MCNN model, we developed an android-based mobile application that will allow the farmers to quickly identify the disease using their android phones. © 2023 IEEE.

关键词： Convolutional neural networks

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Neural Style Transfer: A Comparative Study

Neural Style Transfer: A Comparative Study

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International Conference on Computing and Networking Technology (ICCNT)

作者： M Suryamritha Varshini Balaji Srinidhi Kannan Tripty Singh Mansi Sharma Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Dept. of Computer Science and Engineering Graphic Era(Deemed to be) University India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model’s layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model’s ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing.

关键词： computer vision Analytical models Image synthesis Computational modeling Neural networks computer architecture Feature extraction Convolutional neural networks Residual neural networks

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Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control 10

Geographical Self-Organizing Map Clustering in Large-Scale U...

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10th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2024

作者： Elouni, Maha Rakha, Hesham A. Menendez, Monica Abdelghaffar, Hossam M. Department of Computer Science Randolph-Macon College AshlandVA United States Charles E. Via Jr. Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897587030

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Traffic congestion

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