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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 558 条记录，以下是151-160 订阅

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via machine learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. the Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. this paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. this research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. the exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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Empowering human-machine Communication in Code Generation with NLP 5

Empowering Human-Machine Communication in Code Generation wi...

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5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Aswal, Upendra Singh Rao, Pappala Mohan Supreeth, B.R. Manikandan, G. Srinivasan, R. Tiwari, Mohit Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Andhra University College Of Engineering AU College Of Engineering Computer Science and System Engineering AP Visakhapatnam530003 India Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Department of Computer Science Ullal Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Thandalam India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi India

ISBN: (纸本)9798350351378

the symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). this study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. the possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development. © 2024 IEEE.

关键词： Software design

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machine learning for Diversity and Inclusion: Addressing Biases in HR Practices 5

Machine Learning for Diversity and Inclusion: Addressing Bia...

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5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Anitha Kumari, D. Shaikh, Irfan Abdul Karim Gangadharan, S. Narendra Kiran, P.B. Ramya, S.R. Nargunde, Amarja Satish Vels Institute of Science Technology and Advanced Studies Pallavaram TamilNadu Chennai India Bahrain Polytechnic Faculty of Business Bahrain St. Joseph's College of Engineering Department of Management Studies Chennai India Lovely Professional University Jalandhar-Delhi Mittal School of Business Punjab Phagwara India Karpagam College of Engineering School of Management Studies Tamilnadu Coimbatore India Bharati Vidyapeeth Institute of Management and Rural Development Administration Sangli Bharati Vidyapeeth Bhavan Rajwada Chowk Maharashtra Sangli India

ISBN: (纸本)9798350351378

As organizations strive to foster diverse and inclusive workplaces, the role of technology, particularly machine learning, becomes crucial in addressing biases inherent in human Resources (HR) practices. this research explores the application of machine learning algorithms to mitigate biases in recruitment, performance evaluation, and talent management processes, ultimately promoting diversity and inclusion. Biases, whether conscious or unconscious, can affect decision-making at various stages, leading to underrepresentation and disparities within the workforce. Recognizing the need for innovative solutions, this paper advocates for the integration of machine learning techniques to augment HR processes. By leveraging advanced algorithms, organizations can develop unbiased job descriptions, eliminate resume screening biases, and create diverse candidate shortlists. machine learning models can provide objective insights by analyzing performance metrics, removing subjective biases, and fostering a more equitable assessment of employees. Talent management, the focus of the fourth section, is critical for the retention and development of a diverse workforce. machine learning algorithms can assist in identifying high-potential employees based on merit, skills, and contributions, thus promoting a fair and inclusive talent pipeline. Addressing concerns related to algorithmic biases, the research suggests guidelines for responsible ML implementation to ensure that technology aligns with organizational values and legal requirements. In conclusion, this research underscores the transformative potential of machine learning in creating more equitable and inclusive workplaces. By addressing biases in HR practices, organizations can harness the power of technology to build diverse teams, foster innovation, and promote a culture of inclusion. However, it also emphasizes the need for ongoing research, collaboration, and ethical considerations to maximize the positive impact of machi

关键词： Decision making

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Hidden Figures: Architectural Challenges to Expose Parameters Lost in Code 15th

Hidden Figures: Architectural Challenges to Expose Parameter...

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15th ACMSIGCHI conference on Engineering Interactive Computing Systems (EICS)

作者： Dix, Alan Swansea Univ Computat Foundry Swansea W Glam Wales Cardiff Metropolitan Univ Cardiff Wales

ISBN: (纸本)9783031592348;9783031592355

Many critical user interaction design decisions are made in the heat of detailed development. these include simple parameter choices or more complex weightings in intelligent algorithms. Many would be appropriate for expert design review, user-preference choices or optimisation by machine learning, but they are buried deep in the code. Although the developer may realise this potential, the location of the decision is far removed in the code from where user feedback occurs, data can be collected and machine learning could be applied. this position paper describes several case studies and uses them to frame an architectural challenge for tools and infrastructure to uncover these hidden variables to make them available for machine learning and user inspection.

关键词： Intelligent interfaces machine learning user interface architecture

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Emotion Classification to Augment the Emotional Intelligence of Autistic Individuals 5

Emotion Classification to Augment the Emotional Intelligence...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Guruvammal, S. Kesavamoorthy, R. Anju, M.I. Chellatamilan, T. Jegatha Deborah, L. Jain University Dept. of Cse Kanakapura India Cmr Institute of Technology Dept. of Cse Bengaluru India Srm Institute of Science and Technology Dept. of Ece Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India University College of Engineering Computer Science and Engineering Tindivanam India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

the collaboration of machine learning (ML) algorithms and cognitive psychological frameworks supports the enhancement of emotional intelligence. In light of Plutchik's emotional wheel, this study investigates how to identify human emotions. By utilizing 24 different emotions, 8 high-intensive (secondary) emotions are identified. We employed 9 different classification algorithms K-nearest neighbors (KNN), decision trees (DT), naïve bayes (NB), support vector machine (SVM), Logistic regression, random forest (RF), artificial neural network (ANN), gradient boosting machines (GBM), and bagging classifier (BC) to identify the emotions accurately and the performance of them are compared. this research uses the labelled dataset consisting of 11 columns as similar word 1, similar word 2, typical sensation 1, typical sensation 2, opposite intensity low, opposite intensity medium, opposite intensity high, tells, helps, emotion and cluster. © 2024 IEEE.

关键词： Random forests

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. this paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). the employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. the research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. the model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting Flow via a machine learning Model in a MOOC Context 1

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5th international conference on Deep learning theory and Applications (DeLTA)

作者： Ramirez Luelmo, Sergio Ivan El Mawas, Nour Bachelet, Remi Heutte, Jean Univ Lille Trigone CIREL Ctr Interuniv Rech Educ Lille Campus Cite SciBatiments B5-B6 Villeneuve Dascq France Univ Lorraine CREM UR Ctr Rech Sur Mediat Metz Ile Du Saulcy France Univ Lille Nord France Cent Lille Villeneuve Dascq France

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

Flow is a human psychological state positively correlated to self-efficacy, motivation, engagement, and academic achievement, all of which positively affect learning. However, automatic, real-time flow detection is extremely difficult, a challenge particularly exacerbated in a Massively Online Open Course (MOOC) context, where the distant, and asynchronous components rejoin the educational and online context. We approach this issue by training a machine learning (ML) model to detect flow transparent and automatically in a MOOC. We pair the results of the EduFlow2 and Flow-Q questionnaires (n = 1 553, two years data collection), and their MOOC log data (French MOOC "Gestion de Projet" [Project Management]) to a ML pipeline to create a ML model that detects flow (ROC = 0.68 and PRC = 0.87) in a MOOC context. this ML model detects flow (0.85) with a greater Precision than its absence (0.34).

关键词： MOOC Flow machine learning

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A Study on the Cognition of Symbolic Imagery in computer Graphics Synthetic Imagery

A Study on the Cognition of Symbolic Imagery in Computer Gra...

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machine learning and human-computer interaction (mlhmi), international conference on

作者： Huang Yu-Che Huang Ping-Hsien Department of Industrial Design Chaoyang University of Technology Taichung Taiwan Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

In computer graphics, symbolic imagery plays a vital role and has an inevitable impact on creation, communication and emotional expression. through symbolic imagery, we are able to convey complex concepts and emotions, while also providing users with a wealth of meaningful information and a rich user experience. Here are just a few of the effects of computer-drawn symbolic imagery: Symbolic imagery can convey complex concepts and messages using simple graphics or images. For example, icons and logos are often used to quickly convey the functionality of an app or website, helping users quickly understand its purpose. In addition, symbolic imagery plays a key role in brand building. A unique and distinctive logo can help differentiate a brand from its competitors and establish a brand image in the minds of users. these symbolic images may be seen in logos, advertising, and product design. this research will focus on the image cognitive characteristics of ordinary people through the integration of computer image synthesis technology. In user interface design, symbolic imagery is used to improve the usability and operability of the system. Observed symbolic imagery can help quickly understand how users operate, thereby increasing usage and user satisfaction. Finally, I hope that computer graphics will not only be part of the visual design, but also a way of communication and expression, which can profoundly affect the user's experience and product acceptance. In the design process, the rational use of symbolic imagery will help enhance the value and appeal of the product. We hope to provide a reference for future machine learning through the study of computer images through image symbol recognition

关键词： human computer interaction Visualization Image recognition Image synthesis Symbols machine learning Cognition

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Audio Deepfakes: Feature Extraction and Model Evaluation for Detection 5

Audio Deepfakes: Feature Extraction and Model Evaluation for...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Bhukya, Ramesh K. Raj, Aditya Raja, Danish N. Department of Electronics and Communication Engineering Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India Department of Computer Science Islamic University of Science and Technology Awantipora Jammu and Kashmir Pulwama192122 India

ISBN: (纸本)9798350361155

Cutting-edge AI-driven tools are currently employed for replicating human voices, leading to the emergence of audio deepfakes. Initially designed to enhance experiences like audiobooks, the potential misuse of audio deepfakes poses significant risks to public safety. Despite advancements in detecting video deepfakes, identifying audio deepfakes remains challenging. We employ methods to extract features such as Mel-frequency cepstral coefficients, chromagrams, spectral contrast, tonnetz, and Mel-spectrograms from audio samples. the extracted deep features are concatenated together and then employed in machine learning and deep learning models to evaluate the ASVspoof2021 dataset to categorize it into bonafide and spoofed speech utterances. the experimental results reveal that the multilayer perceptron model achieves the highest accuracy rate of 96.073%, while SVM, k-NN, XGBoost, and RF produce accuracy rates of 93.58%, 94.58%, 93.544%, and 93.58%, respectively. Additionally, DL models, including CNN, achieve an accuracy rate of 98.2%, DNN of 97.6%, and LSTM of 97.7%, showcasing the superiority of CNN in identifying deepfake audio amidst the evolving landscape of synthetic media. © 2024 IEEE.

关键词： Feature extraction

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Statistical and machine learning Tools for Consonance Detection 5

Statistical and Machine Learning Tools for Consonance Detect...

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5th international conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Barile, Paolo Alizada, Aysel Bassano, Clara Univ Salerno Comp Sci Dept DI Salerno Italy Sapienza Univ Rome Management Dept Rome Italy Univ Salerno Pharm Dept DIFARMA Salerno Italy

ISBN: (纸本)9798350385083;9798350385076

the study of emotions via Artificial Intelligence (AI) is considered a hot topic as technological development is gradually turning its gaze more and more toward understanding and emulating human beings. Emotion detection, however, runs the risk of being a useless investigative tool if it is not accompanied by solid theoretical foundations to reinterpret AI-detected emotions through a broader perspective. the Viable Systems Approach (ASV) may likely be the missing link between studying the emotional sphere and understanding the social dynamics that characterize human beings during each interaction. the concepts of consonance and resonance, typical of ASV, make it possible to describe patterns of information acquisition and to understand the dynamics underlying each individual's decision-making. the purpose of our study is to demonstrate, through field experimentation, the possibility of detecting consonance within groups of people in an automatic way, using statistical analysis techniques and Facial Expression Recognition (FER) algorithms. the trial was conducted in two phases: In the first one, a structured questionnaire categorized participants into distinct value clusters based on their strong beliefs;in the second one FER techniques were used to gauge the change in the initial level of consonance within the same clusters during a common contextual experience. Results demonstrated the possibility of detecting the initial level of consonance and its context-driven variation (resonance) both qualitatively and (at least partially) quantitatively. Research findings certified the feasibility of consonance measurement, opening the way to new usage perspectives for emotion detection tools in AI.

关键词： Viable Systems Approach (VSA) emotion detection Facial Expression Recognition (FER) consonance resonance

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