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Personalised Emotion Detection from Text Using Machine Learning 6th

Personalised Emotion Detection from Text Using Machine Learn...

6th EAI International Conference on computer science and Engineering on Digital Transformation in Organizations: New Challenges, COMPSE 2022

作者： Bhavya, A.V. Dhanush, R.H. Sangeetha, J. Jose, Arun Cyril Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031347498

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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Facial Emotion Analysis for Evaluating the Suitability of Online Learning Modules: A Case Study on a MERN-Based Platform 10

Facial Emotion Analysis for Evaluating the Suitability of On...

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10th International Conference on Education and technology, ICET 2024

作者： Wardaya, Utara Setya Bachtiar, Fitra Abdurrachman Pradana, Fajar Marutschke, Daniel Moritz Brawijaya University Faculty of Computer Science Information System Department Malang Indonesia Brawijaya University Faculty of Computer Science Informatics Department Malang Indonesia Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan

ISBN: (纸本)9798331522483

Learning is a process that involves the progressive transfer of knowledge, encompassing both the physical and psychological components of the learner. Emotions play a crucial role in the success of learning, yet they are often overlooked in the context of online education. Facial emotion detection technology and emotion analysis offer a solution for effectively recognizing and accommodating students' emotions in digital learning environments. The use of a Learning Management system (LMS) based on the MERN architecture (MongoDB, ***, ***, ***) is also critical in supporting the flexibility and responsiveness of online learning platforms. This research employed the Agile model, which was systematically planned and organized. The Agile model is an approach that allows for continuous adjustments during development. The aim of this study is to integrate artificial intelligence into an e-learning system to detect and evaluate students' emotions, thereby enabling the development of more effective online learning methods. The design process following the Agile Method involved needs analysis, modeling with UML (Use Case, Activity Diagram, Sequence Diagram, and Class Diagram), as well as database and code implementation. The backend development focused on database management, server configuration, and API integration, while the frontend was designed for a responsive user interface. Testing was conducted using Black Box and White Box methods, which demonstrated 100% validity for system functionality, while user feedback through UAT yielded a 94% validity score. The results indicate that emotion detection functions effectively in e-learning, and emotion analysis can enhance the quality of learning, with positive emotional states leading to better outcomes. © 2024 IEEE.

关键词： Black-box testing

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Image Recognition technology Supported Environment Monitoring Smart Fan 7

Image Recognition Technology Supported Environment Monitorin...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Yang, Sheng-Yuan Lee, Hsiu-Chia Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user's position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user's home. The fan proves the feasibility of the conceptual architecture. © 2024 IEEE.

关键词： Image recognition

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Multi-Agent system Based on Stacking Technique for Rice Yield Prediction 3

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Seireg, Hayam R. Omar, Yasser M. Elmahalawy, Ahmed Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf Egypt Aastmt Faculty of Computing and Information Technology Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350323511

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA. © 2023 IEEE.

关键词： Multi agent systems

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SinhaLearn: NLP, CNN, and OCR Based Data Driven Approach for Enhancing Sinhala Proficiency of Grade 5 Scholarship Students 10

SinhaLearn: NLP, CNN, and OCR Based Data Driven Approach for...

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10th International Multidisciplinary Engineering Research Conference on Moratuwa Engineering Research, MERCon 2024

作者： Francis, Juliana V Bellanavithana, Yasiru L Pulasinghe, Thisal K Silva, Rashmika D Rathnayake, Samadhi Karunathilaka, Supipi V Sri Lanka Institute of Information Technology Department of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331529048

The Sinhala language, deeply rooted in Pali and Sanskrit, holds immense cultural significance for the Sinhalese community in Sri Lanka. However, its intricate morphology and diglossic nature, where written and spoken forms diverge, pose significant challenges. In response, we present 'SinhaLearn' an automated system designed specifically to enhance Sinhala language proficiency for the Grade 5 Scholarship examination domain. Our system leverages advanced Natural Language Processing algorithms, Optical Character Recognition technology, Convolutional Neural Networks, and Part-ofSpeech tagging. Key functionalities include Sinhala Handwriting Capturing and Recognition, real-time error detection and correction in spelling and grammar within Sinhala Present Tense sentences, simplified explanations for complex Sinhala vocabulary aligned with the Grade 5 curriculum, and automated assessment of handwritten responses with instant feedback and scoring. Notably, informed by a comprehensive literature review, our research has made significant improvements by blending rule-based methods with hybrid components, effectively addressing critical gaps in the field. While existing systems often concentrate on singular functionalities such as spelling analysis, grammar analysis, dictionary functions, or content display, our 'SinhaLearn' system provides a holistic solution by integrating these aspects. This comprehensive approach sets a new benchmark for automated systems in Sinhala language proficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid course recommendation system using SVD, NMF and attention-based neural network

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International Journal of information technology (Singapore) 2025年第4期17卷 2449-2456页

作者： Haque, Mohd Mustafeez ul Kotaiah, Bonthu Ahamed, Jameel Department of Computer Science and Information Technology Maulana Azad National Urdu University Hyderabad Gachibowli India Department of Computer Science Central Tribal University of Andhra Pradesh Konda Karakam India

Technological advancement has led to online learning platforms, and with these, recommending relevant courses to the users have caused challenges such as data sparsity and cold start problem. As a result of these challenges, traditional recommendation systems fail thus providing less relevant and personalized courses recommendation. Thus, the given paper proposes a hybrid course recommendation system which utilizes singular value decomposition (SVD), non-negative matrix factorization (NMF) and Attention-based Neural Network. Because of this combination, the model can produce extremely personalized recommendations based on user’s interactions with the application and inherent features of the courses. Dynamic Profile Matching is used to addresses the cold start problem by dynamically adjusting to new users or objects, resulting in useful suggestions even with limited interaction data. Mean absolute error (MAE) and root mean square error (RMSE) are used to assess the performance of the proposed system. The results show that recommendation accuracy has improved significantly, with the proposed hybrid model’s RMSE of 0.6998 and MAE of 0.6737 being 17% better than those of the conventional models. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Attention mechanism Dynamic profile matching Neural network NMF Recommendation system SVD

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ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIVE TECHNOLOGIES 9

ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIV...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer science and information systems 2024, MCCSIS 2024

作者： Lu, Ssu-Hsuan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan

ISBN: (纸本)9789898704597

With the aging population, health issues have become a significant challenge for society. The increasing demand for remote medical services stems from changing interests in reducing healthcare costs and the sudden outbreak of COVID-19 globally. As the Internet of Things (IoT) and cloud technology advance rapidly worldwide, this trend has driven the digital transformation of biotechnology and pharmaceutical-related industries. Health care is shifting from a focus on medical institutions to a decentralized approach, emphasizing care facilities and home-based services. However, remote medical care brings about certain challenges, such as limited appointment availability due to network constraints, concerns about internet security, data encryption, privacy issues, and lack of awareness among people in remote areas. With the continuous development and widespread adoption of technology, smart home systems have become an integral part of modern life. In this project, the system emphasizes interaction with the elderly and utilizes the multifunctionality of touch screens. It provides various features through a simple and user-friendly interface, including health monitoring, outpatient services, life assistance, and social interaction functions. In the future, the system can incorporate more interactive features for the elderly, such as mini-games to enhance cognitive abilities, hand-eye coordination, or rehabilitation training. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Touch screens

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The Influence of Distance Learning Contracted Live Teaching Strategies on Learning Effectiveness, Learning Motivation, Self-efficacy, and Cognitive Load

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Journal of Internet technology 2024年第3期25卷 403-413页

作者： Kuo, Yu-Chen Lin, Hao-Chiang Koong Lin, Yu-Hsuan Li, Yi-Jia Department of Computer Science & Information Management Soochow University Taiwan Department of Information and Learning Technology National University of Tainan Taiwan Department of Digital Media and Education Industry National Taitung University Taiwan

Distance learning lacks person-to-person interaction, which makes learning less interesting. While peer interaction can gain the support, assistance, and discussion of peer empathy, which can effectively motivate learning. This study proposes a remote peer live learning strategy, which allows learners to conduct peer learning anytime and anywhere via the Internet and uses peer-to-peer video methods to conduct peer-to-peer live teaching. Explores the influence of adding peer factors on their learning effectiveness and learning motivation. The findings of this study are as follows: (1) The learners who use peer live teaching strategy, regardless of whether they are high or low achievement, can effectively improve their learning effectiveness, learning motivation and reduce their cognition compared with those who use general distance teaching. (2) Students who utilized the peer-to-peer live teaching platform exhibited notably higher levels of technology acceptance compared to their counterparts enrolled in the conventional distance learning system. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Distance education

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MAPFUNet:Multi-attention Perception-Fusion U-Netfor Liver Tumor Segmentation

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Journal of Bionic Engineering 2024年第5期21卷 2515-2539页

作者： Junding Sun Biao Wang Xiaosheng Wu Chaosheng Tang Shuihua Wang Yudong Zhang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo451460China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia

The second-leading cause of cancer-related deaths globally is liver *** treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT *** improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be *** deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale *** propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature ***,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,*** experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor *** also extended MAPFUNet to brain tumor segmentation on the BraTS2019 *** results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively.

关键词： Liver tumor segmentation Small tumors Position information Attention Multi-scale features

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Machine Learning Models for Predicting Movie Ticket Pricing

Machine Learning Models for Predicting Movie Ticket Pricing

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

作者： Obagbuwa, Ibidun Christiana Mwanza, Alfred John Sol Platjee University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350358155

Predicting the price of a movie ticket is essential in the dispensations of data analytics and changed customer entertainment behavior. Increasing demand for high-quality 3D screen movies is undergirded by advancements in movie digitization technology in visual effects, driving the movie theatre market. Watching movies with family and friends in cinemas is one of the best entertainment mediums after having a hectic schedule. However, it is difficult to go somewhere without knowing how much you will pay for a specific movie, hence the need for tools to address this customer-side demand. People need to see the prices of tickets online at any time. On the other hand, the movie proprietor aims to make maximum revenue. This study proposes a system modeled to predict the price of the ticket for a specific movie and provided with validations to ensure consistency, reliability, and, most importantly, the correctness of the information. Movie ticket pricing was predicted using different machine learning algorithms to model our system and compare performance to determine which algorithm has the most accurate results. Random forest, Decision trees, and KNN had the highest accuracy. © 2024 IEEE.

关键词： Random forests

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