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A Narrative Assistant for Traffic Accidents Based on Large Language Models (LLM) 37

A Narrative Assistant for Traffic Accidents Based on Large L...

引用

37th Annual Conference on Legal Knowledge and Information Systems, JURIX 2024

作者： Kung, Jo-Chi Chang, Chia-Hui Huang, Huai-Hsuan Chien, Kuo-Chun Dept. of Computer Science & Information Engineering National Central University Taiwan

ISBN: (纸本)9781643685625

This study introduces the Collision Clarification Generator (CCG), a Large Language Model-based system designed to assist in documenting traffic accidents. The CCG comprises three modules: Questioning, Information Extraction, and Accident Sequence Generation, which collectively streamline the process of gathering and structuring accident information. The system employs predefined question templates and a standardized Traffic Accident Record Format (TARF) to ensure comprehensive data collection. Evaluation of the CCG involved both human assessment and LLM-based automatic evaluation. Results showed an F1 score of 0.909 in human evaluation, and scores exceeding 7 out of 10 for accuracy and completeness in LLM-based assessment. These findings demonstrate the CCG’s effectiveness in accurately documenting accident information, potentially facilitating subsequent legal and insurance processes. © 2024 The Authors.

关键词： Highway accidents

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Smart Land Use Planning: Integrating Geographic Information Systems (GIS) with Graph Neural Networks (GNN) for Hazard Identification 8

Smart Land Use Planning: Integrating Geographic Information ...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Balaji, S. Ranjani, G. Reddy, C.V.S.N. School of Computer Science Engineering Rvu India Rvce Dept of Ete India

ISBN: (纸本)9798331505462

This paper presents a novel approach to smart land use planning by integrating Geographic Information Systems (GIS) with Graph Neural Networks (GNN). The primary objectives are to conduct satellite imagery analysis for land use patterns, determine suitable zones for commercial and residential development, analyze vulnerability to natural hazards, develop a web-based platform for land use planning, and evaluate the methodology's performance on Indian cities. By leveraging the spatial analysis capabilities of GIS and the predictive power of GNN, we aim to enhance the efficiency and accuracy of urban planning processes. © 2024 IEEE.

关键词： Graph neural networks

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Harmony Forecast: Using Machine Learning to Predict Relationship Quality 1

Harmony Forecast: Using Machine Learning to Predict Relation...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Nallagonda, Harshitha Sai Kondra, Varun Teja Gorla, Srinivas Samantula, Mohankrishna Gitam Deemed to be University Dept. of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350377002

Several things influence human relations. Many of each other's personality traits can have an impact on a relationship between two people. These characteristics have the power to strengthen their love or spark conflict. Personality traits can be used to evaluate the quality of the relationship between the partners. To determine whether the individual will maintain or end the relationship, the relationship's quality has been evaluated based on personality traits. A thorough analysis has been conducted on the personality traits that impact relationships to determine whether they will have an impact, might have an impact or have no impact at all. A study was carried out to investigate the impact of personality traits on relationships. The relationship quality was assessed based on the personality trait which will make the person stay in the relationship, might or might not stay, or not stay in the relationship. The data collected and the pre-existing data combined was used to calculate the relationship quality. © 2024 IEEE.

关键词： Adversarial machine learning

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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ImportChainBD: Blockchain Solutions for Transparent and Accountable Imported Cosmetic Products 3

ImportChainBD: Blockchain Solutions for Transparent and Acco...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Khan, Md. Adib Karmoker, Sagar Rumpa, Ummay Khadiza Rahman, Habibur Hossain, Md. Sabbir Anik, Nur Adnan Chowdhury Hasan, Mahamudul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350354348

To improve the supply-chain management system of Bangladesh for imported cosmetic products this paper proposes a blockchain-based solution. One of the major goals of this project is to increase the tax compliance of the government by stopping counterfeit and illegally imported cosmetic products. Which will enhance the trust of consumers and the brand's reputation. Using this system is quite easy and convenient, as All the information will be stored in the blockchain, so it will become a secure and immutable product tracking system for importers, distributors, and retailers to maintain their ledgers just by printing and scanning a QR code through an IoT device, and consumers can check the authenticity of these cosmetic products just by scanning them. If the product is verified to be suspicious, The Mobile Court Management System has the power to check its origin. Finally, we can say that it will avoid the sale of counterfeit items in the market and increase government revenue on tax. © 2024 IEEE.

关键词： Cosmetics

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Unraveling Patterns in Healthcare Fraud through Comprehensive Analysis 18

Unraveling Patterns in Healthcare Fraud through Comprehensiv...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chirchi, Khushi E. Kavya, B. Dayananda Sagar University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9789380544519

This study addresses healthcare provider fraud in Medicare, employing advanced machine learning models on a diverse dataset to predict potential fraud. The goal is to contribute insights for effective fraud detection and mitigate its impact on overall healthcare costs. Utilizing Logistic Regression, Random Forest, and addressing class imbalance through SMOTE, we discern patterns in provider behavior. Evaluation metrics, including confusion matrices, accuracy, sensitivity, specificity, Kappa values, AUC, and F1-scores, comprehensively assess each model's performance. Findings highlight distinct behavior patterns and underscore SMOTE's effectiveness in mitigating class imbalance challenges. Comparative analysis discusses algorithm strengths and weaknesses, offering insights for real-world implementation, impacting fraud prevention strategies for insurance companies, healthcare providers, and beneficiaries. In summary, this research makes a noteworthy contribution to the detection of healthcare fraud, offering insights for effective strategies. The incorporation of SMOTE enhances model robustness, leading to improved fraud detection capabilities and, consequently, reducing the impact of fraud on healthcare costs. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANN AUC GNN Kappa values Logistic Regression ML NB Random Forest SMOTE SVM

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Deep Learning for Enhanced Consumer Behavior Analysis and Predictive Accuracy

Deep Learning for Enhanced Consumer Behavior Analysis and Pr...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Bharti, Jigyasa Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The accelerating digital transformation in retail necessitates advanced analytical approaches to effectively comprehend and predict consumer behavior. Existing methodologies in retail data mining often fall short in handling the complexity and heterogeneity of data, resulting in suboptimal predictive accuracy and operational efficiency. This work addresses these limitations by introducing a novel deep learning framework that synergizes the capabilities of Graph Neural Networks (GNNs) with Q Learning and VARMAx models. The proposed model adeptly fuses data from diverse sources such as Twitter, Amazon, Facebook, and Flipkart, creating a comprehensive analysis platform. By leveraging the relational data structure processing power of GNNs and the dynamic decision-making proficiency of Q Learning, along with the time series predictive strength of VARMAx models, this approach offers a more holistic understanding of consumer trends. The efficacy of this model is demonstrated through extensive testing on varied data samples encompassing social media, e-commerce platforms, and tweets. The results are compelling: an 8.3% increase in accuracy, 8.5% enhancement in precision, 6.5% improvement in recall, and a significant 10.4% reduction in delay compared to existing classification methods. The impact of this work is far-reaching, offering retailers a robust tool for strategic decision-making, enhanced customer insight, and a competitive edge in the rapidly evolving digital marketplace. This approach not only propels the retail industry forward but also sets a new benchmark in the application of deep learning for consumer behavior analysis. © 2024 IEEE.

关键词： Data mining

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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Automatic and Multilingual Speech Recognition and Translation by using Google Cloud API 5

Automatic and Multilingual Speech Recognition and Translatio...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Yellamma, Pachipala Varun, Potla Raghu Narayana, Nunna Charan Naga Lakhshmi Chowdary, Yogendra Manikanth, Polisetty Sai, Kunderu Hemanth Ganesh Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350395235

The speech recognition is plays a vital role in the technology. The proposed work introduces a web application that leverages state-of-the-art technologies for audio-to-text recognition and multilingual text translation. Developed using Flask, the application integrates the googletrans library for translation, speech-recognition for audio processing, and *** for seamless database integration. Users have the convenience of uploading audio files, which undergo automatic transcription with language detection. The recognized text is subsequently translated into languages such as Hindi, Tamil, and Telugu, offering users the flexibility to choose their desired target language. Additionally, the system ensures secure storage of both transcribed speech and its translations in a MySQL database for future retrieval. The user-friendly web interface simplifies the entire process, making it a valuable tool for language learning, content localization, and accessibility services. The project effectively highlights the harmonious integration of audio recognition and machine translation technologies, delivering a powerful solution for various applications. © 2024 IEEE.

关键词： Speech recognition

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Medlet-Block: Secured Health-Based Cloudlet Platform Using Blockchain 16

Medlet-Block: Secured Health-Based Cloudlet Platform Using B...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Gupta, Atrayee Agarwal, Varun Techno Main Saltlake Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350383119

Medical information cannot be easily shared and exchanged between various healthcare systems and providers due to a lack of interoperability, delays, inefficiencies, and above all lack of security. This paper provides a design of an edge-based blockchain for a secured health-care system which takes care of confidentiality, integrity, non-repudiation and other security challenges. © 2024 IEEE.

关键词： Block Health-care Symmetric key Transaction

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