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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Keerthana, E. Rekha, K.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

Advanced Multimodal Conversational Bot powered by the LLama 2 model, which has 13 billion parameters. The bot can provide personalized song recommendations that are flexible across languages. It uses text, and audio inputs to understand users from different linguistic backgrounds. It accurately assesses the user's mood in multiple languages through sentiment analysis and refines emotional context using image and video analysis. This allows it to offer song recommendations considering the user's mood, preferences, and realtime context, regardless of their chosen language. The bot also handles incomplete or unclear queries in multiple languages, ensuring precise and relevant song suggestions. A custom dataset tailored for music recommendations is used for finetuning, enhancing the bot's ability to provide personalized and contextually relevant song suggestions. © 2024 IEEE.

关键词： Bot (Internet)

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NLP based Resume Analysis and Adaptive Skill Assessment System 3

NLP based Resume Analysis and Adaptive Skill Assessment Syst...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Julian, Anitha Haripriya, K. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The proposed work introduces an innovative recruitment approach integrating LinkedIn web scraping to compile a comprehensive dataset of job descriptions and essential skills. Utilizing a pioneering Word2Vec-driven machine learning model, it efficiently categorizes job descriptions, transforming hiring practices. Job seekers benefit from a dedicated platform offering concise resume summaries and a tailored algorithm for precise role matching. Automation and personalization converge to craft custom interview questions, bridging the recruiter-job seeker gap. The goal is to enhance hiring efficiency, optimize career opportunities and talent acquisition. Performance metrics validate the model's accuracy and effectiveness in job title predictions, ensuring informed decisions for both recruiters and job seekers. © 2024 IEEE.

关键词： Efficiency

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IoT-Based Healthcare System: A Blockchain Approach 4th

IoT-Based Healthcare System: A Blockchain Approach

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Komal Kour, S. Reddy, Guradi Vinay Sravani, N.V.S. Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9789819994410

This study introduces a blockchain-based Internet of Things (IoT)-based healthcare system. By utilizing the immutable and decentralized characteristics of blockchain technology, the suggested system addresses the issues of data security, privacy, and interoperability in health care. By integrating IoT devices with blockchain, the system ensures secure and trusted management of healthcare data, enabling efficient data exchange among stakeholders. The paper presents the architecture, components, and mechanisms of the blockchain-based IoT healthcare system, highlighting its potential benefits and future directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Health care

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health Records Personalized healthcare Sentiment analysis Symptom

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Fired_from_NLP at AraFinNLP 2024: Dual-Phase-BERT - A Fine-Tuned Transformer-Based Model for Multi-Dialect Intent Detection in The Financial Domain for The Arabic Language 2

Fired_from_NLP at AraFinNLP 2024: Dual-Phase-BERT - A Fine-T...

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2nd Arabic Natural Language Processing Conference, ArabicNLP 2024

作者： Chowdhury, Md. Sajid Alam Chowdhury, Mostak Mahmud Shanto, Anik Mahmud Murad, Hasan Das, Udoy Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891761322

In the financial industry, identifying user intent from text inputs is crucial for various tasks such as automated trading, sentiment analysis, and customer support. One important component of natural language processing (NLP) is intent detection, which is significant to the finance sector. Limited studies have been conducted in the field of finance using languages with limited resources like Arabic, despite notable works being done in high-resource languages like English. To advance Arabic NLP in the financial domain, the organizer of AraFinNLP 2024 has arranged a shared task for detecting banking intents from the queries in various Arabic dialects, introducing a novel dataset named ArBanking77 which includes a collection of banking queries categorized into 77 distinct intents classes. To accomplish this task, we have presented a hierarchical approach called Dual-Phase-BERT in which the detection of dialects is carried out first, followed by the detection of banking intents. Using the provided ArBanking77 dataset, we have trained and evaluated several conventional machine learning, and deep learning models along with some cutting-edge transformer-based models. Among these models, our proposed Dual-Phase-BERT model has ranked 7th out of all competitors, scoring 0.801 on the scale of F1-score on the test set. ©2024 Association for Computational Linguistics.

关键词： Deep learning

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Attention Contours: VR in ADHD Assessment 10

Attention Contours: VR in ADHD Assessment

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajarajeswari, P. Jothikumar, Karthikeyan Gomathi, V. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350353068

Virtual reality (VR) technology offers potential opportunities for the assessment and intervention of Attention-Deficit Hyperactivity Disorder (ADHD). This research article presents a study evaluating the effectiveness and usability of a VR-based attention assessment tool designed to measure attention profiles across diverse populations in diverse settings. The study aims to evaluate the effectiveness and usability of the tool in healthcare settings through user trials involving participants with varying levels of VR experience. The user experiences, such as comfort levels, the incidence of motion sickness, ease of controls, immersion, sense of presence, and overall satisfaction, are evaluated in terms of VR features. Results indicated positive feedback from participants on the developed tool. These findings highlight the potential of the tool as a valuable tool for attention assessment in healthcare settings. This study accentuates the need for VR technology in healthcare to enhance attention disorder assessment and management, opening avenues for future research and implementation. © 2024 IEEE.

关键词： Virtual reality

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging 5

Supply Chain Management Using Internet of Things and Blockch...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Enhanced Cardio Vascular Disease prediction model using Contextual Feature Learning approach 15

Enhanced Cardio Vascular Disease prediction model using Cont...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Aarthirani, M. Rejitha, D. Arulmozhiselvan, L. Meenakshi College of Engineering Department of Computer science and engineering Chennai India

ISBN: (纸本)9798350370249

Cardio Vascular Disease poses a substantial worldwide health problem, highlighting the urgent requirement for the development of precise and more efficient diagnostic techniques. Existing studies have provided useful insights in this area, but there is still a need to improve the models for prediction and fill the gaps in the current detection methods. Traditional models often overlook the subtle nuances and interdependencies between different health determinants. The Contextual Feature Learning model introduces a sophisticated advancement in Machine Learning, tailored to elevate the accuracy of Cardio Vascular Disease predictions. This study aims to introduce a prediction model for Cardio Vascular Disease using the deep Contextual Feature Learning techniques. The proposed Cardio Vascular Disease model achieves the accuracy, precision, recall, F1 score and AUC of 100%, 95.7%, 97.6%, 96.3% and 96.9%. The findings exhibit that Contextual Feature Learning model effectively captures and use complex feature interferences, prompting highly accurate predictions. © 2024 IEEE.

关键词： Cardio Vascular Disease Contextual Feature Learning Deep Learning Heart Disease Dataset Machine Learning

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A Comprehensive Review on Data Visualization Techniques for Real-Time Information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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Revitalizing The Blue Frontier: Survey On Strategies And Innovations For Empowering Marine Species Conservation 4

Revitalizing The Blue Frontier: Survey On Strategies And Inn...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sorna Shanthi, D. Vijay, K. Praveen, N. Priyan, B. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

This paper provides a thorough survey of strategies and innovations crucial for sustaining marine species. Examining challenges such as overfishing and pollution, it highlights the severe threats to marine biodiversity. Emphasizing empowerment, the study explores the ecological, economic, and social importance of marine conservation, focusing on technological advancements like machine learning and deep learning. It introduces a visionary proposed system, poised at the intersection of technology and conservation. Harnessing methodologies like deep learning, this innovative system embodies a holistic approach to tackle identified challenges, aspiring to redefine marine species conservation. A crucial component of this proposed system is the user interface, which serves as the interactive gateway, facilitating seamless user engagement, ensuring a more inclusive and impactful approach. The paper concludes by emphasizing the transformative potential of the proposed system, calling for its judicious use to safeguard the well-being of marine life and our planet. © 2024 IEEE.

关键词： Biodiversity

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