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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Avupati, Sai Likhitha Harshitha, Abbineni Jeedigunta, Sai Pranavi Sai Chikitha Chowdary, Dirisala Pushpa, B. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397376

Traffic rules are often neglected by people and are taken for granted which results in road accidents and can sometimes end up taking the lives of people or can make them handicapped. Therefore, traffic rules have been introduced for the efficient movement of vehicles, to prevent accidents, and for pedestrian safety. They also help in reducing traffic congestion and decrease the duration of traveling, making transport efficient. With the increase in the number of vehicles moving on roads, it is important to follow the traffic rules to avoid unwanted consequences. But most people tend to violate traffic rules. To enforce the traffic rules at all times, there should be an efficient monitoring system. Thus, in order to identify traffic rule violations accurately, we put forward a project that identifies these violations in real time. In this system, we deal with detecting multiple traffic violations like triple riding, not wearing a helmet, over-speeding, and using a phone while driving with the help of You Only Look Once (YOLOv5) and Haar Cascade. This would be helpful for the traffic police to monitor roads more precisely and take the necessary action against the drivers who violate the traffic rules effectively. © 2023 IEEE.

关键词： Object detection

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Fake Review Detection and Classification Using Improved Convolutional Neural Network on Amazon Dataset 3

Fake Review Detection and Classification Using Improved Conv...

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

作者： Sasikala, C. Ramya, S. RajeshKumar, S. Ramachandramoorthy, K.B. VijayaKumar, S. Umapathi, K. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Fake reviews are a growing concern for e-commerce websites and other online platforms. To tackle this issue, researchers have developed an advanced convolutional neural network system that can detect and classify fake reviews. This system has been specifically designed to analyze the Amazon review dataset, and by integrating various text pre-processing methods and an innovative attention mechanism, it has significantly improved its classification accuracy. In fact, the proposed model outperforms existing state-of-the-art techniques in terms of accuracy and F1-score, with an impressive accuracy of 0.97. These results demonstrate the effectiveness of the improved CNN model in accurately detecting and classifying fake reviews, and provide a valuable tool for maintaining the authenticity and trustworthiness of online reviews. © 2023 IEEE.

关键词： Convolutional neural networks

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Blockchain based System for Secure Property Registration

Blockchain based System for Secure Property Registration

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Mehta, Nishu Rani, Rinkle Kalra, Nidhi Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350333602

The procedure of registering a property involves paying stamp duty, and registering the sales deed for the property you have purchased. Property registration is done at the office of the sub-registrar who has jurisdiction over the area in which you purchased it. Land/property registration is a long process necessitating numerous paper checks, in-person meetings, and cash exchanges, which results in significant delays and is both time- and money-consuming. In India, currently government is practicing the paper-based land registration system which raises significant problems like high time intricacy, centralized authority, physical visit to property site for authentication, high cost of transactions, proneness to human error, frauds involving property documents, lack of communication, third-party participation, ineffectiveness, timing issues and issue with ownership. These issues are getting worse every day;hence the registry procedure system needs to be improved. In this study, to overcome such challenges we will develop a tamper-proof, fraud-resistant, automated Blockchain - based system to create, issue, securely store, share and verify the Land Registration Records. The proposed system will also provide a digital locker all the stakeholders for keeping land record certificates. All the actions taken by different entities involved in the land record registration will be time-stamped, verified by the different nodes, and stored as a block which has a unique hash value, so that neither the hackers/spammers nor the insiders can issue a counterfeit certificate on a later date or modify/tamper the original certificate. © 2023 IEEE.

关键词： Smart contract

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Enhanced word vector space with ensemble deep learning model for COVID-19 Hindi text sentiment analysis

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Multimedia Tools and Applications 2025年第9期84卷 5861-5882页

作者： Jain, Vipin Kashyap, Kanchan Lata Department of SCSE VIT Bhopal University Madhya Pradesh Sehore466114 India Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

The SARS-CoV-2 virus has spread worldwide since March 2020 and became a global pandemic. Millions of people worldwide have infected with this virus. This research work proposed an enhanced word vector space and deep learning-based hybrid model for COVID-19 Hindi text sentiment analysis. Total 36,863,393 Hindi tweets are collected for analysis. First, various natural language processing techniques are applied for text preprocessing. Next, The word vector space is enhanced by combining various word embedding techniques namely, Word2vec, Fasttext, Glove, POS2Vec, WordPosition2Vec, Lexicon2Vec, and Elmo. Finally, developed a hybrid model for sentiment classification. According to the experimental findings, highest 89.60%, 78.70%, 77.23% and 91.64% precision, recall, F-score, and accuracy, respectively, is given by proposed model. The obtained results indicate that the proposed approach is highly efficient in classifying Hindi sentiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vector spaces

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Exploiting Human-AI Dependence for Learning to Defer 41

Exploiting Human-AI Dependence for Learning to Defer

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Zixi Cao, Yuzhou Feng, Lei College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore Information Systems Technology Design Pillar Singapore University of Technology and Design Singapore

The learning to defer (L2D) framework allows models to defer their decisions to human experts. For L2D, the Bayes optimality is the basic requirement of theoretical guarantees for the design of consistent surrogate loss functions, which requires the minimizer (i.e., learned classifier) by the surrogate loss to be the Bayes optimality. However, we find that the original form of Bayes optimality fails to consider the dependence between the model and the expert, and such a dependence could be further exploited to design a better consistent loss for L2D. In this paper, we provide a new formulation for the Bayes optimality called dependent Bayes optimality, which reveals the dependence pattern in determining whether to defer. Based on the dependent Bayes optimality, we further present a deferral principle for L2D. Following the guidance of the deferral principle, we propose a novel consistent surrogate loss. Comprehensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our proposed method. Copyright 2024 by the author(s)

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Startup Success and Failure Prediction Algorithm Using k-Means Clustering and Artificial Neural Network

Startup Success and Failure Prediction Algorithm Using k-Mea...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Misra, Ajai Kumar Jat, Dharm Singh Mishra, Durgesh Kumar Namibia University of Science and Technology Computer Science Windhoek Namibia Sri Aurobindo Institute of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9798350314878

Startups or new businesses are the growth indicators of a country. Additionally, growing startups are a good opportunity for investors to get high returns in less time and investment amount. In this context, startup success and failure prediction are essential for investors and business administrators. The manual analysis and tracking of a company's growth is challenging for an investor. Therefore, in this paper, a machine learning model has been presented to accurately predict the project of a start-up's success and failure in terms of 'Operating', 'Acquired', 'Closed', and 'IPO'. In this context, the CrunchBase dataset has been used for experimentation. The proposed model includes a modified k-means clustering algorithm and Artificial Neural Network for predicting the start-up's success and failure. Based on the experimental analysis in different scenarios the optimal prediction accuracy of 89% has been recorded. Therefore the proposed model provides acceptable accurate results. © 2023 IEEE.

关键词： Machine learning

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Exploring Author Style in Nakba Short Stories: A Comparative Study of Transformer-Based Models 1

Exploring Author Style in Nakba Short Stories: A Comparative...

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1st International Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

作者： Hamed, Osama Zaidkilani, Nadeem Department of Computer Systems Engineering Palestine Technical University-Kadoorie Palestine Department of Computer Engineering and Security Mathematics University Rovira i Virgili Spain Department of Engineering and Technology Al-Zaytona University of Science and Technology Palestine

ISBN: (纸本)9798891762169

Measuring semantic similarity and analyzing authorial style are fundamental tasks in Natural Language Processing (NLP), with applications in text classification, cultural analysis, and literary studies. This paper investigates the semantic similarity and stylistic features of Nakba short stories, a key component of Palestinian literature, using transformer-based models, AraBERT, BERT, and RoBERTa. The models effectively capture nuanced linguistic structures, cultural contexts, and stylistic variations in Arabic narratives, outperforming the traditional TF-IDF baseline. By comparing stories of similar length, we minimize biases and ensure a fair evaluation of both semantic and stylistic relationships. Experimental results indicate that RoBERTa achieves slightly higher performance, highlighting its ability to distinguish subtle stylistic patterns. This study demonstrates the potential of AI-driven tools to provide more in-depth insights into Arabic literature, and contributes to the systematic analysis of both semantic and stylistic elements in Nakba narratives. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Detecting Traffic Police Hand Gestures with MoveNet Thunder for Self-Driving Cars 8

Detecting Traffic Police Hand Gestures with MoveNet Thunder ...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Samjebasjeswin, K. Raj, Samson Arun Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331512248

This study introduces a novel Traffic Police Hand Gesture Recognition System specifically designed for autonomous vehicles in India, utilizing TensorFlow's MoveNet Thunder model. In Indian urban environments, where traffic police frequently use hand gestures to manage traffic, autonomous vehicles face significant challenges in accurately interpreting these signals. This paper presents the development of a deep learning-based system that recognizes and interprets traffic police gestures such as 'Stop cars from the left and right', 'stop cars from the front', 'stop cars from the back', 'start cars from the left', 'start cars from the right', 'Vehicles should be started at T-Point', 'Stop cars from the front and rear', 'Unidentified Position'. The methodology involves creating a custom dataset of approximately 8,000 labeled images of traffic police performing gestures under varied environmental conditions, which was used to train the MoveNet Thunder model. The system relies on camera-based inputs for real-time gesture detection and processing. Extensive testing in the Carla simulator demonstrated the system's effectiveness, achieving an accuracy of 89% in gesture recognition. The results indicate significant improvements in the interpretability and reliability of autonomous vehicle responses to manual traffic signals, contributing to safer and more efficient vehicle navigation in environments with human-directed traffic control. This research lays the groundwork for future applications of gesture recognition in autonomous vehicles, particularly in complex urban traffic settings. © 2025 IEEE.

关键词： autonomous vehicles Gesture recognition MoveNet Thunder real-time detection TensorFlow

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Smart Agricultural Greenhouse System: A Context-Aware Application

Smart Agricultural Greenhouse System: A Context-Aware Applic...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Swain, Sujata Niyogi, Rajdeep School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science IIT Roorkee Roorkee India

ISBN: (纸本)9783031513374

Smart applications are getting more powerful and cheaper cost due to the advancement in sensor technology. In this chapter, we have considered a smart greenhouse application. The important parameters of the smart greenhouse are to control light, temperature, humidity, and water. Greenhouse can provide a favourable growth environment for plants. This smart greenhouse system is a context-aware application. When the context changes in the environment, the application automatically adapts it and does the changes seamlessly without user intervention. In this chapter, we propose a model for the smart greenhouse system. The proposed model can adapt to context changes such as temperature rises, and it automatically turns on the AC seamlessly. The proposed model is based on the machine learning approach. The model maintains the greenhouse temperature, humidity, and sunlight duration in the suitable ranges of the plant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context Greenhouse Machine learning NLP Sensors Web service

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