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Insurance Amount Prediction Based on Accidental Car Damage Level using AI 8

Insurance Amount Prediction Based on Accidental Car Damage L...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kumar, D. Manoj Reddy, Chadipriala Nithin Kumar Sai, Gunisetty Venkata Chowdary, Puligundla Sujan Srm Institute of Science and Technology Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350396638

This document presents a machine learning-based approach for predicting the insurance amount for a car damage claim based on the level of damage caused by an accident. The proposed model uses a dataset of past car damage claims, including the level of damage and the corresponding insurance amount paid out. The dataset is subjected to the application of a number of machine learning algorithms, such as decision trees, random forests, and neural networks. The effectiveness of these algorithms is assessed using a variety of measures, including accuracy, precision, recall, and F1-score. The results show that the model achieves high accuracy and F1-score, indicating its effectiveness in predicting the insurance amount based on the level of damage. This proposed approach has the potential to improve the accuracy and efficiency of the insurance claims process, ultimately benefiting both insurance companies and their clients. Insurance companies use a variety of factors to determine the amount of compensation paid out to a customer for a car damage claim. One of the most important factors is the level of damage caused by the accident. However, accurately assessing the level of damage can be a difficult undertaking, as it requires a high level of expertise and experience. To address this challenge, machine learning techniques can be used to build models that can predict the insurance amount based on the level of damage. In order to do this, a model must be trained using past data to identify trends and connections between the severity of the harm and the associated insurance payouts. Overall, insurance amount prediction based on accidental car damage level using AI is a promising area of research and has the potential to benefit both insurance companies and their clients. © 2023 IEEE.

关键词： Insurance

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Automated Detection of Biases within the Healthcare System Using Clustering and Logistic Regression 15

Automated Detection of Biases within the Healthcare System U...

引用

15th International Conference on computer and Automation engineering, ICCAE 2023

作者： Prakhar, Jyoti Haider, Md. Tanwir Uddin National Institute of Technology Patna Computer Science and Engineering Patna India

ISBN: (纸本)9798350396225

Big data play a vital role in decision-making, especially in the healthcare system such as in cardiovascular disease. However, the findings of algorithms used in decision-making show some disparity as compared to the existing findings of physicians. This is due to the biases in the big data set used for the healthcare system. This will lead to misdiagnosing certain protected groups or attributes like gender. Therefore, it is the major problem to detect biases in a large dataset. In this paper, we have proposed a model and implemented it to detect biases in the large data set of cardiovascular disease. This model uses statistical performance metrics to measure the biases in the dataset. The result shows that if we apply the clustering mechanism with logistic regression along with statistical performance metrics it gives a better result to detect biases in the dataset. © 2023 IEEE.

关键词： Decision making

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Design and Implementation oF a Single Sign-In Identity (SSID) oN tHE Banking System Using a Blockchain Algorithm

Design and Implementation oF a Single Sign-In Identity (SSID...

引用

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Joshua, Ishola Oluwaseun Arowolo, Michael Olaolu Adebiyi, Marion O. Gbolagade, Kazeem Alagbe Olaniyan, Julius O. Aderemi, Emmanuel Tunbosun Landmark University Department of Computer Science Omu Aran Nigeria University of Missouri Department of Electrical Engineering and Computer Science Columbia United States Institute of Professional and Continuous Education Kwara State University Department of Computer Science Malete Nigeria Landmark University Department of Computer Scince Nigeria

ISBN: (纸本)9798350358155

This paper presents the design and implementation of a Single Sign-In Identity (SSID) on the banking system using a blockchain algorithm. The traditional banking system often requires customers to remember multiple login credentials for different banking services, resulting in inconvenience and potential security vulnerabilities. To address this issue, the proposed system leverages blockchain technology to provide a secure and efficient method for managing user identities within the banking domain. The SSID system simplify authentication for banking customers by providing a unified login across multiple platforms. It utilizes a blockchain algorithm to ensure the integrity and immutability of user identities and transactions, enhancing security through decentralization. The system involves creating blockchain ledger. Smart contracts manage authentication and validate user identities. Benefits of the SSID system include improved user experience, reduced reliance on passwords, and enhanced protection against identity theft and fraud. Blockchain technology also provides transparency, auditability, and accountability in banking. A system is developed and tested in a simulated banking environment to evaluate system performance, security, and usability, considering factors like scalability and transaction speed. Overall, implementing a Single Sign-In Identity system using a blockchain algorithm has the potential to revolutionize banking by offering a secure, efficient, and user-centric authentication mechanism. © 2024 IEEE.

关键词： Blockchain

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Fracture Detection in Radiology with Resnet-152 & CNN 9

Fracture Detection in Radiology with Resnet-152 & CNN

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Chauhan, Shivam Chauhan, Rahul Rawat, Ruchira Aluvala, Srinivas Singh, Rahul Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

In the field of medicine, fracture identification in medical imaging is a crucial task that has significant effects on patient diagnosis and therapy. Deep learning methods have demonstrated amazing promise in improving fracture diagnosis efficiency and accuracy in recent years. This survey paper offers a thorough examination of the most recent developments, difficulties, and breakthroughs in deep learning-based fracture identification. We explore the key concepts of deep learning and how it applies to many types of medical imaging, including MRIs, CT scans, and X-rays. This survey covers widely used deep learning concepts including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and more recent developments like DenseNet and *** goal is to give researchers, medical professionals, and policymakers a thorough grasp of the state of deep learning-based fracture diagnosis by analyzing a wide spectrum of recent publications. The authors highlight areas of prospective future research as well as how these developments might affect clinical practice. Will also discuss about the benefits deep learning brings to this field. In conclusion, the authors will discuss the future directions of this technology. © 2024 IEEE.

关键词： computerized tomography

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Hyperparameter Optimization of Machine Learning Models for Monitoring Menstrual Mental Health 1

Hyperparameter Optimization of Machine Learning Models for M...

引用

1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Chawla, Tisha Mittal, Shubh Srinivasan, Kathiravan Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350335774

Monitoring and tracking mood disorders associated with Menstruation are becoming crucial to avoid impairment in the mental health of a woman, prevent disruption in family life and relationships, and allow women to maintain a proper balance in their work life. Every sentiment linked during the menstruation cycle can be identified for improving fecundability and health management. This study aims to analyze the feelings of women during Premenstrual dysphoric disorder (PMDD) and the actual menstrual cycle using text-mining methods. We used sentimental analysis in a Reddit dataset to identify positive and negative emotions in women. In the proposed framework, public sentiments were first processed and data was balanced using Synthetic Minority Over-sampling Technique (SMOTE) followed by classification using different machine learning frameworks like Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, AdaBoost Classifier and Boosting Classifier. The findings of this study exhibit that women evoke both positive and negative sentiments. However, compared to other feelings the negative emotion is at its inception. The result shows that Random Forest with tuned hyperparameters gives us the best accuracy of 94% when compared to other baseline and ensemble machine learning models with different feature extraction techniques. © 2023 IEEE.

关键词： Support vector machines

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FreeSpeech- Assistive Technology Tool for Augmentative and Alternative Communication in Individuals With Autism Spectrum Disorder 11

FreeSpeech- Assistive Technology Tool for Augmentative and A...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Sheejamol, P.T. Pandey, Harikesh Mary Chacko, Anu National Institute of Technology Calicut Computer Science and Engineering Calicut India

ISBN: (纸本)9798350326147

The quality of life of people can be greatly enhanced by using appropriate Assistive technology (AT). Augmentative and alternative communication (AAC) is a specific type of AT that can help people with language difficulties communicate better using technology. Currently, many such tools exist on the market, but most of them were created specifically for English- speaking people. It has been observed that the number of people diagnosed with such disorders has increased both in urban and rural areas. It may not be possible for all such people to be able to use English;hence, support for tools in their native language is needed. In this work, we studied the requirements of an AAC tool and proposed a generalized design 'Free Speech' that can be customized to support any language. The design is evaluated by building a proof of concept tool to support Hindi. © 2023 IEEE.

关键词： Speech communication

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Vehicle Point Cloud Segmentation Based on Deep Learning 9

Vehicle Point Cloud Segmentation Based on Deep Learning

引用

9th International Conference on computer and Communications, ICCC 2023

作者： Xu, Xiaodong Yi, Guohong Li, Jianting Wu, Bingqian Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350317251

A vehicle point cloud segmentation model is designed to address the common issue of real-time acquisition of point cloud of vehicle body and wheels in intelligent parking systems. Firstly, a Multi resolution Feature Pruning Segmentation Network (MFPS-Net) is proposed. This method takes pure point cloud information as input, adopts PointNet++ trunk network, and integrates channel level pruning to accurately segment vehicle point clouds while improving model segmentation efficiency. Then, in order to verify the effectiveness of the model, a dataset with real labels was generated from the vehicle point cloud using bilateral LiDAR data collected from the vehicle buffer. Finally, point cloud segmentation experiments were conducted. The experimental results showed that the accuracy of vehicle point cloud segmentation reached 92.01%. © 2023 IEEE.

关键词： Deep learning

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Adaptive Network-Based Pedestrian Detection in Haze Environment 2

Adaptive Network-Based Pedestrian Detection in Haze Environm...

引用

2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Jiang, Shukai Chen, Xiangyang Wang, Leiluo Ye, Zi Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350331417

The task of image dehazing, which involves the extraction of atmospheric haze from an image, presents a significant challenge due to the necessity of haze removal without detriment to the inherent quality of the image. The density of haze is subject to considerable variation, and as a result, extant dehazing algorithms frequently struggle to effectively manage differing haze densities. This often culminates in issues such as color distortion, loss of detail, and the emergence of other artifacts. In response to these challenges, this study introduces an innovative adaptive image dehazing algorithm. This algorithm employs a Generative Adversarial Network (GAN) as its foundational structure. Furthermore, an attention mechanism is integrated into the algorithm with the aim of preventing detail loss and concentrating on salient feature information. The proposed algorithm is subjected to rigorous evaluation across diverse datasets. The results demonstrate that it surpasses existing methods in terms of both image quality and robustness in relation to varying haze densities. © 2023 IEEE.

关键词： adaptive neural network attention mechanism GAN image dehaze

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Machine Learning based efficient Bankruptcy Prediction Model

Machine Learning based efficient Bankruptcy Prediction Model

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Khemka, Devanshu Kaippada, Ranjeesh Nikhil, Parasu Sai Suseela, S. School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

Bankruptcy is a severe financial problem for companies that can lead to significant losses for stakeholders, including employees, investors, and creditors. Early bankruptcy prediction is crucial to reduce default risk and avert financial losses. Using the Taiwan Company Bankruptcy Dataset (1999-2009), we investigate the challenge of making an accurate prediction of company failure in this research article. The dataset contains financial ratios and other company characteristics, such as liquidity ratios, solvency ratios, and profitability ratios. In order to predict the likelihood of bankruptcy, this article examines the application of several machine learning techniques, such as SVM, Naive Bayes, Decision Tree and Logistic Regression. This project's primary objective is to develop an efficient and accurate bankruptcy prediction model that financial institutions can use to identify companies that are likely to default on their financial obligations. © 2023 IEEE.

关键词： Bankruptcy Decision Tree Logistic Regression Machine Learning Techniques Random Forest SVM

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A Transparent Diagnosis Model for Diabetic Retinopathy Using Explainable AI

A Transparent Diagnosis Model for Diabetic Retinopathy Using...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Deo, Arpit Gupta, Priyasha Rajput, Ajeet Singh Sil, Devyani Chandorikar, Chirag Pawar, Mirth Medi-Caps University Department of Computer Science and Engineering Madhya Pradesh Indore453331 India Institute of Engineering & Technology Devi Ahilya Vishwavidyalaya Department of Computer Engineering Madhya Pradesh Indore452017 India

ISBN: (纸本)9798331512965

Diabetic retinopathy is the leading cause of vision problems and partial blindness, and its signs are complicated and variable, making detection difficult. Standard diagnosis is mainly reliant on expert interpretation of retinal images, which can lead to bias and mistake, highlighting the need for improved diagnostic approaches. Despite the fact that conventional AI technologies considerably improve diagnostic skills, their black box aspect limits healthcare practitioners' confidence and optimizes their usage in practice. Grad-CAM++ is a new technique that addresses the black box problem by improving model knowledge, allowing users to grasp the reasoning behind specific decisions. Using XAI approaches, this work creates a diagnostic model for diagnosing diabetic retinopathy, hence improving transparency. The proposed model's convergent plots validate the learning process, which results in 94.4% better diagnostic precision than previous approaches. © 2025 IEEE.

关键词： artificial intelligence convolutional neural network diabetic retinopathy Gradient-Weighted Class Activation Mapping (Grad-CAM++)

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