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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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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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'RNN-Driven Prognosis of Heart Disease Based on Health Parameters' 2

'RNN-Driven Prognosis of Heart Disease Based on Health Param...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Singh, Inderjeet Singh, Ajay Pal Kumar, Alok Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification. © 2024 IEEE.

关键词： Recurrent neural networks

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Harmonizing the Musical Metaverse: unveiling needs, tools, and challenges from experts’ point of view

Harmonizing the Musical Metaverse: unveiling needs, tools, a...

引用

International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Boem, Alberto Tomasetti, Matteo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy

The Musical Metaverse (MM) represents an innovative frontier for the field of New Interfaces for Musical Expression (NIME). The MM holds the potential to redefine areas such as musical composition and performance via immersive environments based on technologically mediated social interactions. Despite substantial research on single-user immersive systems, the intersection of NIME and the MM remains largely unexplored. In this paper, we systematically explore this domain by examining previous and current approaches, alongside conducting interviews with eleven experts who have created multi-user immersive musical environments and authored publications on this topic. The goal is to map such an uncharted territory by collecting valuable insights and leveraging the perspective of experts to provide an understanding of the potentials and challenges inherent in creating immersive social environments for musical activities. Our results reveal that existing multi-user immersive environments make use of diverse implementation approaches but face challenges due to the absence of standardized technology stacks, particularly in networking and data synchronization. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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Software Code De-Compilation Techniques and Approaches: A Comparative Study 25

Software Code De-Compilation Techniques and Approaches: A Co...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Hassan, Mohammad Fawareh, Faid AlNoor Elrashidi, Ali Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. of Computer Science Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language. © 2024 IEEE.

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Intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

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

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 IEEE.

关键词： Earthquakes

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A Random Forest Framework for Predicting Cardiovascular Disease in Diverse Populations' 2

A Random Forest Framework for Predicting Cardiovascular Dise...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

According to the latest information from WHO, the World Health Organization, heart disease remains a major global health challenge, resulting in millions of deaths each year. Heart disease presents in various forms, including Hypertensive Heart Disease, Congestive Heart Failure (CHF), Coronary Artery Disease, Cardiomyopathy, and Arrhythmia. Accurate classification of these conditions is crucial for reducing mortality rates and improving patient outcomes. This study utilizes an enhanced Random Forest classifier to improve the accuracy of heart disease classification. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to rectify the disparities in class. and Polynomial Feature engineering was employed to capture complex interactions within the data. The dataset underwent comprehensive preprocessing, including feature normalization and label encoding, followed by extensive hyperparameter tuning using GridSearchCV. The optimized model achieved a high accuracy of 92% and a Mean Squared Error (MSE) of 0.05. Key performance indicators, including an F1 score of 0.87, recall of 0.88, and accuracy of 0.92, highlight the model's efficacy in differentiating between various forms of heart disease. Future research could explore incorporating additional clinical data to further enhance the model's accuracy and applicability across diverse patient populations. © 2024 IEEE.

关键词： Clinical research

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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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Sentiment Analysis with Support Vector Machines for Travel Agency Ranking and Traveler Decision Support in Sri Lanka 8

Sentiment Analysis with Support Vector Machines for Travel A...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Jayawardana, D.D.M. Meedin, G.S.N. Rathnayake, H.U.W. The Open University of Sri Lanka OUSL Faculty of Engineering Dept. of Electrical and Computer Engineering Jaela Sri Lanka The Open University of Sri Lanka OUSL Faculty of Engineering Dept. of Electrical and Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798331509170

Travelers often rely on online reviews to plan trips, but the large volume of data can hinder informed decision-making, especially in the Sri Lankan tourism industry. This research develops a sentiment analysis-based system to help travelers navigate reviews and make informed choices. The system uses Natural Language Processing (NLP) techniques to preprocess and structure user reviews, categorizing them into subcategories like transportation, accommodation, food and beverage, entertainment, recommendations, purpose, and travel agencys. Support Vector Machine (SVM) and Multinomial Naive Bayes (MultinomialNB) classifiers are employed to assess sentiment, classifying reviews as positive, neutral, or negative. The integration of NLP and SVM ensures high accuracy, efficiency, and interpretability in sentiment analysis, enhancing the model's performance. The designed platform presents travelers with an easy-To-understand bar chart summarizing travel agency reviews and providing actionable insights into service quality. Key metrics like precision, recall, and score validate the model's effectiveness. By organizing feedback into insights, the system empowers travelers to make better decisions and helps travel agencies identify areas for improvement. The scalable approach allows for adaptation to larger datasets and various tourism contexts, making it a reliable tool for comparing Sri Lankan travel agencies and improving the overall tourism experience. © 2024 IEEE.

关键词： Support vector machines

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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