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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Kumar, K. Ashok Dharshan Sekhar, Chandra Manikanta, S.D. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The current scenario of India's traffic laws poses a number of challenges, each of which may be addressed in a variety of ways. An increasing incidence of motorbike and moped-related fatalities have been linked to the common practice of driving without a headgear in India. The current system relies heavily on CCTV records to keep tabs on traffic offences, forcing cops to manually examine individual frames and, if the rider isn't wearing a helmet, focus in on their numberplate. However, this calls for a significant investment of time and resources, since traffic offences are common and the number of motorcyclists grows daily. What if there were a technology that could detect whether or not a motorcyclist was breaking the law by failing to wear a helmet and, if so, get the vehicle's registration number from the database? This study has been accomplished with recent success using HaaR, HoG, LBP, R-CNN, CNN features, etc. However, the pace, precision, and effectiveness of object identification and categorization are severely constrained by these works. In an effort to automate the process of finding instances of the traffic offense of not donning a headgear and retrieving the numberplate of vehicles, this study develops a Non-Helmet Driver identification method. The core idea is to use three tiers of DL for Image Classification. Individual, motor bike, headgear, and licence plate are all recognisedutilising YOLOv2's increasing levels of sophistication. Then, OCR is used to get the licence plate number. Particularly for the extraction of licence plate numbers, all of these methods are governed by a set of predetermined parameters and limits. Since moving images are used as input, the processing time is critical. By combining the aforementioned methods, we have developed a comprehensive system for identifying helmets and extracting licence plate numbers. © 2023 IEEE.

关键词： Extraction

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Sentimental Analysis Based on User Emotions Using Machine Learning

Sentimental Analysis Based on User Emotions Using Machine Le...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Darwin, D. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350333244

Emotion detection and sentiment analysis algorithms are used in various circumstances, particularly when employing interactive systems, to comprehend the polarity or emotions displayed by individuals. Understanding user emotions is a crucial component of human-computer interaction. More natural relationships would be possible with computers that can recognise emotions. Research in this area has increased dramatically as a result of machine learning's expanding capacity to analyse huge data and extract emotions from it. This research paper examines the use of machine learning algorithms to predict sentiments using a dataset of self-reported information. The models - Naive Bayes classifier and Recurrent Neural Network are investigated for predicting the sentiments of the data and compared in terms of their performance. The results of the experiments identify the most suitable model for predicting the sentiments of the user text. © 2023 IEEE.

关键词： Recurrent neural networks

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An Application for Privacy-Preserving Contact Tracing and Public Risk Assessment Using Blockchain for Covid-19 Pandemic 7th

An Application for Privacy-Preserving Contact Tracing and Pu...

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7th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2022

作者： Shreya, K.R. Nagamani, D.R. Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789811993039

Many nations are debating on the ways to revert to the new regular state of living as the count of reported infections and victims of the novel COVID-19 outbreak drops with each day gradually. One option examined is the use of contact tracking to bring the spreading of the illness reducing and preventing another round of transmission. Contact tracing still has security and privacy difficulties, regardless of the fact that there are multiple solutions, which makes widespread usage challenging. On the basis of that, a unique approach for contact traceability is introduced, utilizing Blockchain like its foundation. User confidentiality may be protected as well as a comprehensive view of the public for all verified instances can be made available to individuals and governmental entities through connecting distributed ledger alongside contacts tracking apps. By calculating the danger of COVID-19 exposure to the wider populace and disclosing it in a ledger, it also explores how open places may assist in contact tracking. With the use of an Android app, the system can detect the position, and this position is kept on the ledger. These places can efficiently notify possible contamination concerns whilst still ensuring the data's confidentiality and reliability. Finally, findings were made when numeric figures are presented in various settings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Optimized Deep Learning Classification Model for Intelligent Edge devices

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Journal of engineering Science and technology Review 2024年第3期17卷 88-94页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Electronics and Communication Engineering REVA University Karnataka Bangalore560064 India

Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations for deploying the model on resource-constraint embedded IoT devices. We propose an efficient neural network compression framework that performs filter pruning, fine-tuning and 8-bit quantization to reduce computational complexity, inference time, and memory footprint. Furthermore, reducing the bit widths of activation and weights helps design a compact deployment model on resource-limited IoT devices such as smartphones. The proposed system is evaluated extensively on the CIFAR-10 dataset for Resnet34 and VGG16 models. In addition, we examine the efficacy of a larger model. The result shows that pruning followed by quantization compresses the neural network and compared to the baseline model, achieved an accuracy of 78.01% for Resnet34 and 82.34% for Vgg16 after pruning and quantization which is © 2024 School of Science, DUTH. All rights reserved.

关键词： Object recognition

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Label Hierarchy Alignment for Improved Hierarchical Text Classification

Label Hierarchy Alignment for Improved Hierarchical Text Cla...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kumar, Ashish Toshniwal, Durga Indian Institute of Technology Roorkee Department of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350324457

Effective text and label representations are crucial for accurate predictions in Hierarchical Text Classification (HTC). However, existing methods face challenges in capturing relevant label representations due to the discrete nature of labels and the complexities of their hierarchical relationships. To address these challenges, we propose Label Hierarchy Alignment (LHA), which ensures that learned label representations conform to the hierarchical label space. In this paper, we present two LHA approaches. The first is adversarial label alignment, which utilizes adversarial learning to enforce a prior distribution on label embeddings. This guides the model to adjust label embeddings in accordance with the hierarchical structure. The second approach is contrastive label alignment, which uses contrastive learning to identify semantic similarities and differences between label nodes across the hierarchy. This enriches the label embeddings with semantic depth, thereby enhancing their alignment with the hierarchical structure. By explicitly addressing various hierarchical aspects, both approaches generate refined label representations that align more effectively with the label hierarchy. We implemented LHA on the current state-of-the-art model and evaluated its performance on two benchmark datasets. The experiment results show that performing LHA improves HTC performance. © 2023 IEEE.

关键词： Hierarchical Text Classification Multi-Label Classification Representation Learning

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Summarization of Video Clips using Subtitles 4

Summarization of Video Clips using Subtitles

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4th International Conference on Communication Systems, Computing and IT Applications, CSCITA 2023

作者： Anil, Eleesa Sebastian, Sherine Johnson, Janice Rane, Janhavi Priya Karunakaran, K. Computer Engineering Department St. Francis Institute of Technology Mumbai India

ISBN: (纸本)9781665459877

Due to the ever growing world of high speed internet, videos have become a common medium for information on the web. When we want to gain information about anything from educational topics to entertainment we prefer watching videos instead of reading long paragraphs. With the vast diversity of videos available on the internet today on every single topic possible it gets confusing to find the right content for our needs. People end up wasting time on trying to find a good video instead of on the actual work the video is needed for. Video content being such a big part of our information source today it is necessary to have a system that will enable users to understand a gist of the video instead of having to sit through hours of content just to find nothing useful. The primary objective of this given paper is to propose a method to create a video summary in a way that it contains only the necessary and important information in a concise format by using various NLP algorithms such as Textrank, LexRank and LSA(Latent Semantic Analysis). © 2023 IEEE.

关键词： Natural language processing systems

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Misinformation Detection Through Authentication of Content Creators 18th

Misinformation Detection Through Authentication of Content C...

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18th International Conference on Data Science and Artificial Intelligence, ICDSAI 2022

作者： KSudhama, Kruthika Siddamsetti, Sree Gayathri G, Pooja Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9783031161773

Recent technological advancements have made content modification and recreation easier and practically undetectable without suitable verification techniques. Users can change data from social media with photo, video, and text editing tools and share the updated content in a different context. As a result, online social media platforms are suitable for distributing fake news and misinformation. Misinformation can take several forms, including one or more types of multimedia, such as text, photos, videos. The modified contents provide fake evidence to the user, leading to various misconceptions. Generally, fake news has eye-catching headlines attracting the readers. These are called click-baits. The content of these click-baits often differs from what the headlines suggest. There are also many fake websites whose IP addresses are slightly modified versions of popular news agencies. Users easily get fooled to open the website as its address seems legitimate. These issues indicate the importance of identifying legitimate content creators from non-legitimate ones. This chapter focuses on authenticating the legitimate content creators verified by a trusted entity using certificates and blockchain technology. Also, check for fakeness in their content using natural language processing and image processing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Forwarding Strategy Performance Using NFD Pipeline Measurements on Vehicular Named Data Networking (V-NDN) 9

Forwarding Strategy Performance Using NFD Pipeline Measureme...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Syambas, Nana Rachmana Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia

ISBN: (纸本)9798350305029

The Named Data Networking (NDN) paradigm has been used as a promising vehicle communication model, namely the Vehicular Named Data Networking (V-NDN) model. In NDN, delivery of interest in the NDN network is done by selecting an existing interface. The problem is as to which interface is right in terms of fulfilling the request that suits the user. In this study, we compare the effectiveness of each forwarding strategy by creating new scenarios, such as variations in the number of interests and simulation time. We also measure the effectiveness of the forwarding strategy using the parameters specified in the NFD Forwarding Pipeline. The conclusion from the experiment is that superior forwarding strategy recommendations are based on content store (CS) management and data compatibility obtained based on consumer demand for producers. © 2023 IEEE.

关键词： Pipelines

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Test Suite Minimization for Regression Testing Using Keyword Match Technique 7th

Test Suite Minimization for Regression Testing Using Keyword...

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7th International Conference on Information and Communication technology for Competitive Strategies , ICTCS 2022

作者： Freeda, R. Adline Rajendran, P. Selvi Department of Computer Science and Engineering Hindustan Institute of Science and Technology Chennai India

ISBN: (纸本)9789811996375

Regression testing is a crucial phase of the testing practice as it ensures stability and functionality of the system. It repeats test cases from the previous versions to ensure that the original function of the system is not affected by the new changes. The agile methodologies had gained popularity in the recent years which stresses the key importance of regression testing in protecting software quality, and regression testing has made significant development in the recent years. In an agile framework, the ideal scenario for regression testing is to run a regression suite of test cases after each sprint and release, which consumes money and time. In the proposed system, using the keyword match technique, we provide a test case coverage mechanism that predicts the maximum coverage of test cases over the requirements in this work. Following that, test cases are chosen based on maximum coverage. The approach improves both prioritization and decision-making. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Propensity modelling for predicting customer behaviour using clustering techniques for effective business analysis 2

Propensity modelling for predicting customer behaviour using...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Panati, Harish Reddy Joy, Emmanuel Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350347982

The digital world generates massive volumes of data, and it is critical to analyse them. With machine learning and powerful visualization tools, the competition to innovate and succeed in this industry has made a significant leap towards tailoring data-driven solutions. Analyzing these data has never been easier and the insights gathered can transform many businesses. In this paper, four different clustering techniques were compared namely, K-means, Agglomerative, Birch, and Spectral clustering. Three separate performance metrics were used to find the effectiveness of these algorithms: the Silhouette coefficient, the Calinski-Harabasz score, and the Davies-Bouldin index. Based on the analysis, it was found that the K-means clustering algorithm outscored the other three by a higher performance measure score. The insights gathered from clustering led to the formulation of critical business strategies. This study also provides an in-depth look into expressing corporate strategies with advanced machine learning technologies. There are several applications to it, including classification, clustering, prediction, and big data visualization. © 2023 IEEE.

关键词： Data visualization

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