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A Systematic Study About Chronic Disease Prediction Strategies using Artificial intelligence based Algorithms 3

A Systematic Study About Chronic Disease Prediction Strategi...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Priya, P. Kavya, S. Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

ISBN: (纸本)9798331532420

The challenge of chronic diseases will take its deserved place among the top global health issues, mainly because they account for 74% of deaths every year, as reported by the WHO. This systematic review will discuss possible applications of machine learning and AI techniques to predict, diagnose, and treat CDs such as diabetes, cancer, cardiovascular diseases, and liver disease. Integration of ML algorithms: RF, SVM, DT, ANN, and boosting techniques has transformed healthcare by improving the quality of predictive information for potential cases, reducing healthcare costs, and contributing to innovative clinical decision support systems. The research focuses on several approaches of ML, namely supervised and unsupervised learning, for predicting patient-specific treatment regimens and hence better outcomes in chronic disease management. Another discussion from the study is on how to apply ML on large, structured and unstructured datasets collected from the healthcare system about the predictive powers of AI-based models in order to be able to address complications arising from CD. Research aims at providing a comprehensive overview of the understanding of predictive models (PMs) under one heading, making an amalgamation of the desired workings of PMs for chronic disease prediction, useful for knowledge collection for future reference. The paper also provides a descriptive analysis of data sources, methodologies, and algorithms adopted, accompanied by a tabular representation of previous studies to avoid redundancy and enhance accuracy. © 2024 IEEE.

关键词： Prediction models

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Explainable Artificial intelligence: A Study of Current State-of-the-Art Techniques for Making ML Models Interpretable and Transparent 3

Explainable Artificial Intelligence: A Study of Current Stat...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Thakur, Ayush Vashisth, Rashmi Tripathi, Sudhanshu Amity Institute of Information Technology Amity University Noida India Amity University Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Artificial intelligence and machine learning are becoming more prevalent in a variety of industries, necessitating an increased the demand for systems that are capable of explaining their decision-making processes to human users. The idea behind of 'Explainable AI' is to create AI systems that can offer clear and reasoned arguments for their activities they perform. This research looks on new approaches for increasing transparency and interpretability in machine learning models. Our focus is on the wide range of different XAI approaches that have been put forth, with a new focus on how broadly applicable they are. Apart from that, we will look into the significant challenges that must be overcome to support the development of ML models that are both transparent and accessible. © 2023 IEEE.

关键词： Ethics Explainable Artificial intelligence Humanin-the-loop systems Model Bias Transparency and Interpret ability

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machine Learning-based Prediction of Parkinson's Disease: A Comparative Analysis of Algorithms 3

Machine Learning-based Prediction of Parkinson's Disease: A ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Om Prakash, P.G. Reddy, Balineni Nehan Sai Kumar Lohith, Seedella Sai Mohankrishna SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350343632

Diagnosing Parkinson's disease (PD) is an important medical diagnosis that calls for the application of cutting-edge methods such as machine learning algorithms. This study explores the application of cutting-edge machine learning techniques for the early detection of Parkinson's disease using a diverse dataset that includes patient and clinical record data. The study calls for rigorous feature selection, model training, data preprocessing, and evaluation because it uses a complicated and nuanced dataset. Using rigorous cross-validation and extensive experimentation, this study assesses the sensitivity, specificity, and accuracy of these machine learning algorithms in identifying subtle patterns suggestive of symptoms of Parkinson's disease. Additionally, traditional diagnostic methods are thoroughly scrutinized for comparative analysis, augmenting the scope of the evaluation *** research findings shed important light on how well machine learning algorithms identify Parkinson's disease, which will significantly improve medical diagnostic methods. This work highlights the critical role that a range of sophisticated machine learning models and meticulous dataset curation play in enhancing the precision and robustness of Parkinson's disease detection systems. © 2023 IEEE.

关键词： Clinical Records Data Preprocessing Diagnostic Approaches Feature Selection Healthcare Parkinson's Disease Detection Sensitivity Specificity Voice Data

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Heritage of India: Advanced Monuments Classification using Artificial intelligence 3

Heritage of India: Advanced Monuments Classification using A...

引用

3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Tiwari, Raj Gaurang Gautam, Vinay Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350321487

Tourism is India's most lucrative service industry. Many historical buildings and cultural practices originated in India. Every year, millions of people go to tourist hotspots, and millions more share photos they have taken there on social media and mobile devices with friends and family. The promotion and dissemination of cultural assets as well as tourism activities and landmarks in urban settings might benefit greatly from the use of artificial intelligence methods and machine learning algorithms, which are already being used in a wide variety of settings. Automatic identification and categorization of landmarks and tourist attractions might be an intriguing use case for these methods in the cultural and touristic sector. This study makes an effort to categorize India's historic sites. Hotels, travel companies, restaurants, government organizations, etc. all benefit from the classification of monuments so that they may better plan for the future and provide facilities that are up to date with visitor expectations. Using a deep learning technique, the suggested approach aims to retrieve characteristics and categorize the Indian landmarks that visitors visited. © 2023 IEEE.

关键词： Convolutional neural networks

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Integrating VGG16 and LBP for Accurate E-Waste Classification Towards Environmental Sustainability 3

Integrating VGG16 and LBP for Accurate E-Waste Classificatio...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Krishna, N. Hari Kumaraswamy, I. Bose, N. Subhash Chandra Sudhakar, P. Reddy, T. Ravi Sankar Krishnan, G. Hari Government Polytechnic Dept. of Eee Andhra Pradesh Kurnool India School of Engineering Mohan Babu University Dept. of Eee Andhra Pradesh Tirupati India Government Polytechnic Dept. of Eee Andhra Pradesh Rayachoti India

ISBN: (纸本)9798331509675

The growing challenge of electronic waste (e-waste) management necessitates advanced solutions for automated classification and recycling. This study presents a hybrid methodology combining transfer learning with VGG16 and handcrafted Local Binary patterns (LBP) features, enhanced by Principal Component Analysis (PCA) for dimensionality reduction, and classification using a Random Forest Classifier. The methodology achieves a remarkable accuracy of 98.54% on the E-Waste Image Dataset, outperforming existing approaches. Extensive evaluation across multiple metrics, including precision, recall, F1 score, and Cohen's Kappa, demonstrates the robustness of the approach. This research offers an efficient framework for sustainable e-waste classification, supporting environmental conservation efforts. © 2025 IEEE.

关键词： Local binary pattern

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Enhancing Data Security and Data Sensitivity: Leveraging the Synergy of Blockchain Artificial intelligence 3

Enhancing Data Security and Data Sensitivity: Leveraging the...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jain, Vikas Chouhan, Sachin Kate, Vandana Nigam, Nidhi Bhalerao, Shilpa Acropolis Institute of Technology & Research Dept of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350343274

Data security and sensitivity have become significant challenges for organizations worldwide in the digital transformation era. Protecting sensitive user data from cyber-attacks, breaches, and information exposure is of utmost concern. This research paper explores the potential of integrating blockchain, artificial intelligence (AI), and machine learning (ML) technologies to enhance data protection and ensure confidentiality. The introduction provides an overview of data sensitivity and the growing importance of data security in the digital age. It delves into blockchain technology, emphasizing its decentralized, immutable ledger for secure authentication, access control, and tamper-proof data storage. The paper presents various use scenarios, illustrating how blockchain safeguards sensitive user data, including medical records, financial information, and personal identifiers. It further examines AI's role in data security, showcasing its capability to detect threats, analyze anomalies, and authenticate users, thereby blocking unauthorized access to confidential information. Additionally, the study explores machine learning for data sensitivity management, highlighting its prowess in formulating access control policies and data categorization. The research underscores how blockchain, AI, and machine learning synergize to create a comprehensive data security architecture, enabling businesses to leverage the immutability of blockchain and the intelligent data protection of AI/ML. Real-world examples demonstrate the effectiveness of this synergy in enhancing data security and safeguarding against modern threats. © 2023 IEEE.

关键词： Blockchain

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Towards Accurate Crowd Counting Via Smoothed Dilated Convolutions and Transformer 3

Towards Accurate Crowd Counting Via Smoothed Dilated Convolu...

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3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

作者： Zeng, Xin Wang, Huake Zhu, Gaoyi Wu, Yunpeng Zhengzhou Vocational College of Finance and Taxation Zhengzhou China Xi'an Jiaotong University School of Information and Communications Engineering Xi'an China Sichuan University School of Mechanical Engineering Chengdu China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350335262

Density-based methods have shown promising results on crowd counting. Many existing methods seek to extract multi-scale features by dilated convolutions, but always gridding artifacts plague dilated convolutions. In this work, we propose to solve the gridding artifacts via smooth dilated residual block (SDRB). The smoothed dilation technique adds separable and shared convolutions that provide dependency among feature maps. Moreover, we present a residual contextual transformer block (RCTB) for multi-scale feature generation. The RCTB enables the location and recognition of people on the pixel level. Finally, we corroborate the prediction accuracy and the generalization capability with extensive experimental support. Our model enjoys superior performance on three realistic and public benchmarks: JHU-CROWD++, ShanghaiTech, and FDST. © 2023 IEEE.

关键词： Convolution

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Prediction of Auto Insurance Claims as an Imbalance Data Distribution Problem 3rd

Prediction of Auto Insurance Claims as an Imbalance Data ...

引用

3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Singh, Akhilesh Kumar Singh, Sumit Kumar Singh, Koushlendra Kumar SCSE Department Galgotias University Yamuna Expy Opposite Buddha International Circuit Sector 17A Uttar Pradesh Greater Noida203201 India Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Adityapur Jamshedpur831014 India

ISBN: (纸本)9789819743582

Claim accuracy is one of the major concerns of both clients and insurance companies. Claim accuracy is directly associated with predictive modeling in the insurance business. Predictive modeling applications have played a vital role in the insurance business in areas such as fraud detection and claim prediction. Predictive modeling heavily depends on the data distribution. The present work addresses the challenges of an imbalanced dataset in the context of insurance claim prediction for autos. Prediction of the occurrence of claims in auto insurance has been employed in the present work. XGBoost, LR, SVM, RF, KNN, DT, and GNV have been used to calculate the claim prediction. The predictive model and random oversampling, random undersampling, and SMOTE have been applied, respectively. It has been observed that XGBoost is giving good results in all three cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Classification Confusion matrix Imbalance data Predictive modeling

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DistilBERT Hotel Rating Prediction Model Based on an Ensemble Learning Framework 3

DistilBERT Hotel Rating Prediction Model Based on an Ensembl...

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3rd international conference on Electronics and Information Technology, EIT 2024

作者： Yuan, Yifu Nanjing Audit University International Joint Audit Institute Jiangsu Nanjing210029 China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369151

The swift advancements in artificial intelligence and natural language processing have significantly impacted the hospitality industry by enabling more precise sentiment analysis and recommendation systems. This study presents a novel hotel rating prediction model leveraging DistilBERT, a distilled version of BERT, within an ensemble learning framework. DistilBERT retains 97% of BERT's performance while being 60% faster and 40% smaller, making it ideal for real-time applications in resource-constrained environments. Our ensemble model combines two DistilBERT models, one processing review titles and the other processing review texts, to capture comprehensive semantic information from both sources. The dataset used comprises approximately 10,000 hotel reviews, preprocessed to ensure quality and consistency. Through rigorous training and evaluation, including the use of cross-validation and random search for hyperparameter tuning, our model demonstrated superior performance compared to traditional machine learning models and other pre-trained language models such as BERT, RoBERTa, and ALBERT. The DistilBERT ensemble model achieved high accuracy, precision, recall, and F1 score, showcasing its effectiveness in handling complex text classification tasks. This research provides valuable insights for the hospitality industry, enabling better prediction of customer satisfaction and enhancing service quality. Future work could explore additional features, multi-lingual capabilities, and further optimization to improve model performance. © 2024 IEEE.

关键词： Customer satisfaction

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KiL 2023: 3rd international Workshop on Knowledge-infused Learning 23

KiL 2023: 3rd International Workshop on Knowledge-infused Le...

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29th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD)

作者： Gaur, Manas Tsamoura, Efthymia Sreedharan, Sarath Mittal, Sudip Univ Maryland Baltimore Cty Baltimore MD 21228 USA Samsung AI Res Cambridge England Colorado State Univ Ft Collins CO 80523 USA Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (纸本)9798400701030

Recent prolific advances in artificial intelligence through the incorporation of domain knowledge have constituted a new paradigm for AI and data mining communities. For example, the human feedback-based language generation in ChatGPT (a large language model (LLM)), the use of Protein Bank in DeepMind's AlphaFold, and the use of 23 rules of safety in DeepMind's Sparrow have demonstrated the success of teaming human knowledge and AI. In addition, the knowledge retrieval-guided language modeling methods have strengthened the association between knowledge and AI. However, translating research methods and resources into practice presents a new challenge for the machine learning and data/knowledge mining communities. For example, in DARPA's Explainable AI seminar, the need for explainable contextual adaptation is seen as the 3rd phase of AI, facilitating the interplay between data and knowledge for explainability, safety, and, eventually, trust. However, policymakers and practitioners assert serious usability and privacy concerns that constrain adoption, notably in high-consequence domains, such as cybersecurity, healthcare, and other social good domains. In addition, limitations in output quality, measurement, and interactive ability, including both the provision of explanations and the acceptance of user preferences, result in low adoption rates in such domains. This workshop aims to accelerate our pace towards creating innovative methods for integrating knowledge into contemporary AI and data science methods and develop metrics for assessing performance in various applications.

关键词： Neurosymbolic AI Knowledge-infused Learning Explainable AI Safe AI Language Models Games Programming Languages

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