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12th international conference on Soft Computing for Problem Solving, SocProS 2023

ISBN: (纸本)9789819731794

the proceedings contain 62 papers. the special focus in this conference is on . the topics include: Study of Optimality Strategies for Two-Person Game Model Under Interval Uncertainty;L-BFGS Optimization-Based human Body Posture Rectification—A Smart interaction for computer-Guided Workout;optimizing Pneumonia Detection from Scarce Chest X-Ray Data: A Comparative Analysis of Advanced Augmentation Techniques Using Deep learning;unleashing the Potential of Deep learning for Precise Nuclei Segmentation and Classification in H &E-Stained Whole Slide Images;Submitted for Special Session#3: "Softness in AI Computing Techniques for Not so Easy Computational Problems" Matrix Games Having Linguistic q-Rung Orthopair Fuzzy Payoffs;research Analysis on Current Advances in Parkinson’s Disease Detection Using Signal Processing and machine learning-Based Techniques;Analysis of Different Inference Implementations for Deep learning Model on ADITYA-U Tokamak;Deciphering the Pathways Towards Analysis of Existing HealthCare Services by Use of AI to Respiratory Diseases Concerning Cough Study: Future Challenges and Applications;predicting Impact Strength of Natural Fiber Composites Using Optimized Gradient Boosting Approach;multiobjective Diabetic Diet Model Using Neutrosophic Fuzzy Programming;performance Analysis of Ann-Based Multi-Input Hybrid Power Conversion System;ECCO: Cloud Energy Optimization and Load Balancing;recommender Process Based on Trust-Distrust Factor for Signed Social Networks;GA Based Order Abatement Technique for Linear Dynamic Systems;Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET;chaotic Swarm Bat Algorithm with Improved Search;Random Forest Modeling for Prediction of thrust Force During Drilling of Flax/PP Composite;an Application of Sentiment Analysis to Analyze the Performance of Players on Endorsed Brands Using Social Media;Automatic Number Plate Detection System for Indian Vehicles Using Yolov5 and EasyOCR.

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12th international conference on Soft Computing for Problem Solving, SocProS 2023

12th International Conference on Soft Computing for Problem ...

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12th international conference on Soft Computing for Problem Solving, SocProS 2023

ISBN: (纸本)9789819732913

the proceedings contain papers. the special focus in this conference is on . the topics include: Study of Optimality Strategies for Two-Person Game Model Under Interval Uncertainty;L-BFGS Optimization-Based human Body Posture Rectification—A Smart interaction for computer-Guided Workout;optimizing Pneumonia Detection from Scarce Chest X-Ray Data: A Comparative Analysis of Advanced Augmentation Techniques Using Deep learning;unleashing the Potential of Deep learning for Precise Nuclei Segmentation and Classification in H &E-Stained Whole Slide Images;Submitted for Special Session#3: "Softness in AI Computing Techniques for Not so Easy Computational Problems" Matrix Games Having Linguistic q-Rung Orthopair Fuzzy Payoffs;research Analysis on Current Advances in Parkinson’s Disease Detection Using Signal Processing and machine learning-Based Techniques;Analysis of Different Inference Implementations for Deep learning Model on ADITYA-U Tokamak;Deciphering the Pathways Towards Analysis of Existing HealthCare Services by Use of AI to Respiratory Diseases Concerning Cough Study: Future Challenges and Applications;predicting Impact Strength of Natural Fiber Composites Using Optimized Gradient Boosting Approach;multiobjective Diabetic Diet Model Using Neutrosophic Fuzzy Programming;performance Analysis of Ann-Based Multi-Input Hybrid Power Conversion System;ECCO: Cloud Energy Optimization and Load Balancing;recommender Process Based on Trust-Distrust Factor for Signed Social Networks;GA Based Order Abatement Technique for Linear Dynamic Systems;Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET;chaotic Swarm Bat Algorithm with Improved Search;Random Forest Modeling for Prediction of thrust Force During Drilling of Flax/PP Composite;an Application of Sentiment Analysis to Analyze the Performance of Players on Endorsed Brands Using Social Media;Automatic Number Plate Detection System for Indian Vehicles Using Yolov5 and EasyOCR.

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EmoDetect: A learning-Centred Affective Database for Detecting Student Frustration in Online learning

EmoDetect: A Learning-Centred Affective Database for Detecti...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Mohammad Mustaneer Rahman Muhammad Umair Munir Md Mujibur Rahman Md Badiuzzaman Information and Communication Technology University of Tasmania Tasmania Australia Faculty of Information Technology University of Central Punjab Pakistan Faculty of Information Technology and Computing University of Malaya Malaysia UNSW Sydney Australia

the role of facial expressions in human interaction is essential, with significant strides being made in the development of systems designed to automatically recognise these expressions in the context of human-computer interaction (HCI). However, there is a noticeable lack of databases dedicated to training these automatic detectors for learning-centric affect detection in online learning. Existing annotated databases often draw primarily on images sourced from movies, the internet, or simulated expressions in controlled laboratory environments, and they fail to capture learners’ facial expressions at different stages and in varying contexts of genuine learning experiences. To address this gap, we present EmoDetect, a novel database comprising video recordings of 30 students’ facial expressions and screen-captured interactions with an online learning system as they engage in online learning tasks. By analysing these videos, we have identified facial action units related to frustration and discovered new action units and body language specific to learning that can aid in detecting learning-centred frustration. these findings enhance our understanding of automatic recognition of students’ affective states in online learning and offer valuable data for future research.

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Lightweight Deep learning Models for Robust Hand Gesture Recognition 5th

Lightweight Deep Learning Models for Robust Hand Gesture Rec...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Nisha Sonu Narayan, Satya Gajrani, Jyoti Department of Computer Science and Engineering Engineering College Ajmer Rajasthan Ajmer India

ISBN: (纸本)9789819761050

Hand gesture recognition is a burgeoning area of study with wide-ranging applications, including human–computer interaction, sign language translation, and virtual reality control. In this work, we introduce an innovative lightweight DL model designed for robust HGR and provide an in-depth analysis of its classification performance. We meticulously evaluate key performance metrics such as precision, detection rate, Jaccard index, and false positive rate and compare these results with existing methodologies. Our proposed model stands out by surpassing the performance of current models. It exhibits higher accuracy rates during both training and validation, a substantially improved detection rate, and an almost perfect Jaccard index. Most impressively, our model achieves a remarkable false positive rate of zero, showcasing its exceptional reliability in distinguishing between positive and negative instances. these findings underscore the effectiveness of our approach in addressing challenges related to overlapping gestures, unbalanced class distributions, and precise HGR. In our conclusion, we identify areas for potential enhancement and further research. To clarify the novelty and research gap of our proposed model, we emphasise its lightweight nature and articulate how it differs from existing methods. this distinction lies in its capacity to deliver accurate and efficient HGR while imposing minimal computational burden, setting it apart from more resource-intensive approaches. In summary, our study and potential to revolutionise practical applications that demand dependable and effective hand gesture recognition systems, bridging existing gaps in the area. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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Research on Big Data Financial Fraud Detection System Based on machine learning

Research on Big Data Financial Fraud Detection System Based ...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Yutong Li SciencesPo Paris School of Management and Innovation Paris France

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

To solve the problem of fraud detection in financial transaction data, this paper proposes an innovative detection scheme that integrates machine learning and big data analysis technology. We combine the subtleties of time series analysis with the power of modern data analysis to design a model that is both efficient and accurate in identifying financial fraud. First, advanced data acquisition technology is used to capture high-frequency financial transaction data in real time to ensure the freshness and integrity of data. then, the ARIMA (autoregressive integral moving average) model is used to analyze these time series data in depth to reveal the hidden information behind the trading patterns and trends. On this basis, the LightGBM (Lightweight Gradient Elevator) algorithm is introduced, which is a machine learning tool that performs well when dealing with high-dimensional, sparse data, especially for complex data structures in the financial sector. By integrating the time series features mined by ARIMA with multi-dimensional information such as credit history and geographical location of both parties, as the input of LightGBM model, we construct a multi-modal fraud detection framework. Empirical research shows that compared with using ARIMA model alone or traditional machine learning methods, our hybrid model has achieved a significant breakthrough in the accuracy and response speed of fraud detection, which can not only perceptively capture abnormal transaction activities, but also proactively warn potential financial risk points.

关键词： Analytical models Accuracy Time series analysis Finance machine learning Big Data Data models Real-time systems Fraud Principal component analysis

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A Survey on - Accessible Visual Information Retrieval (AVIR)

A Survey on - Accessible Visual Information Retrieval (AVIR)

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), international conference on

作者： Ms. Anu Mohan M Santhosh Krishna B V Simi Roshan S Yartha Likitha Reddy Jinith Kumar G S Computer science and engineering New Horizon College of Engineering Bangalore India

this research study analyzes Accessible Visual Information Retrieval (AVIR), an evolving domain at the crossroads of computer vision, accessibility, and information retrieval. this research study focuses on analyzing the critical role of AVIR in democratizing access to visual information, enabling inclusivity in various domains such as education, navigation, and digital content consumption. this study intends to explore methodologies that augment accessibility, including image captioning, audio descriptions, and multimodal interfaces, to cater to diverse user requirements. Furthermore, this work addressesthe inherent challenges in AVIR, such as ensuring accuracy in content descriptions, adapting to dynamic visual contexts, and optimizing user interfaces for seamless interaction. Finally, novel techniques leveraging machine learning, natural language processing, and human-computer interaction paradigms are proposed to overcome these challenges. Moreover, this study underscores the ethical implications and societal impact of AVIR advancements, emphasizing the importance of designing systems that not only improve accessibility but also respect privacy, cultural diversity, and individual preferences. through a comprehensive synthesis of advancements and challenges, this study provides a roadmap for researchers, developers, and practitioners in furthering the accessibility of visual information and aims to foster collaborations and innovations in AVIR that are both technically robust and ethically sound, ultimately advancing the goal of a more inclusive and accessible digital landscape for all.

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Design and Implementation of EEG-fNIRS Based Brain-computer Interface Paradigm for Musical Imagery

Design and Implementation of EEG-fNIRS Based Brain-Computer ...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Weiyue Zhu Lin Fang Xinmiao Cao Wenhao Li Kai Wu Jing Zhou School of Biomedical Sciences and Engineering South China University of Technology Guangzhou China School of Material Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

In this experiment, EEG-fNIRS simultaneous acquisition technology was used to design and implement a brain-computer interface paradigm for musical imagery, and the self-designed experimental paradigm was utilized for sampling with a size of 40 college student subjects, data processing, time-frequency domain feature extraction, statistical analysis based on EEG, fNIRS in the resting, white noise, listening, and recall phases, and neurovascular coupling were carried out. Finally, music recognition based on six songs was performed by machine learning, resulting in the best recognized song as well as its features. In the future, the construction of a multimodal database based on simple elements of musical imagery is planned.

关键词： Couplings Hands Time-frequency analysis Temporal lobe Statistical analysis Music White noise Electroencephalography Brain-computer interfaces Synchronization

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Evaluating User Experience in E-learning Platforms: An NLP-Enhanced Analysis of Duolingo Reviews

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international JOURNAL OF human-computer interaction 2024年

作者： Kong, Lingchen Koh, Do Hyong Antonenko, Pavlo Univ Florida Coll Educ Norman Hall2711D1221 SW 5th Ave Gainesville FL 32601 USA

this study presents a novel framework to evaluate the usability and user experience of e-learning platforms, focusing on the Duolingo language learning app. By integrating advanced Natural Language Processing (NLP) techniques such as Large Language Models (LLMs), Latent Dirichlet Allocation (LDA), and sentence embedding models, we identified thirteen key usability and user experience topics from a large dataset of user reviews. Our findings reveal that Duolingo is highly rated since users value its enjoyable learning experiences and effectiveness. However, technical issues, lack of explanations, and frustrating features were identified as areas for improvement. the study demonstrates the effectiveness of our framework in analyzing user feedback at scale, offering valuable insights for improving user experience in e-learning platforms and other domains.

关键词： Natural language processing (NLP) usability testing user experience E-learning machine learning

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Evaluation of Visualization Concepts for Explainable machine learning Methods in the Context of Manufacturing 5

Evaluation of Visualization Concepts for Explainable Machine...

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5th international conference on computer-human interaction Research and Applications (CHIRA)

作者： Gerling, Alexander Seiffer, Christian Ziekow, Holger Schreier, Ulf Hess, Andreas Abdeslam, Djaffar Ould Furtwangen Univ Appl Sci Business Informat Syst D-78120 Furtwangen Germany Univ Haute Alsace IRIMAS Lab F-68100 Mulhouse France Univ Strasbourg F-67081 Strasbourg France

ISBN: (纸本)9789897585388

machine learning (ML) is increasingly used in the manufacturing domain to identify the root cause of product errors. A product error can be difficult to identify and most ML models are not easy to understand. therefore, we investigated visualization techniques for use in manufacturing. We conducted several interviews with quality engineers and a group of students to determine the usefulness of 15 different visualizations. these are mostly state-of-the-art visualizations or adjusted visualizations for our use case. the objective is to prevent misinterpretations of results and to help making decisions more quickly. the most popular visualizations were the Surrogate Decision Tree Model and the Scatter Plot because they show simple illustrations that are easy to understand. We also discuss eight combinations of visualizations to better identify the root cause of an error.

关键词： machine learning Explainable ML Manufacturing Domain Expert Interviews

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Evolution of human-machine Translation: From Early Interface to Modern AI

Evolution of Human-Machine Translation: From Early Interface...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Yetunde Esther Ogunwale Department of Computer Science University of Ilesa Ilesa Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

the evolution of human-machine interaction (HMI) has been a transformative journey, marked by significant advancements in technology and a deepening understanding of user needs. this review paper explores the historical progression of HMI, tracing its origins from early mechanical and command-line interfaces to the sophisticated, AI-driven systems of today. By examining key milestones and technological breakthroughs, the paper highlights the transition from basic interaction models to more intuitive, adaptive, and user-centric designs. the review also discusses the impact of these developments on various industries, alongside the challenges and opportunities that have emerged in the quest for seamless human-machine collaboration. the paper concludes with insights into the future trajectory of HMI, emphasizing the potential of emerging technologies to further revolutionize the way humans interact with machines.

关键词： human computer interaction Privacy Ethics Reviews Personal voice assistants Virtual reality Market research Natural language processing User experience Artificial intelligence

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