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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101-110 订阅

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

引用

5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. there are many types of brain tumor that are identified. the tumors in brain is independent of age and it can grow at anytime. the symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. then classification of the segmented image is done by using Support Vector machine. the results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Automatic Speech Emotion Recognition and Mind Status Classification Based on Deep learning 5

Automatic Speech Emotion Recognition and Mind Status Classif...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Shubhangi, D.C. Gadgay, Baswaraj Nagaratnamma Vishveshwarayya technological University CPGS Department of Computer Science Kalaburagi Karnataka India Vishveshwarayya technological University CPGS Department of Electronics and Communication Karnataka Kalaburagi India

ISBN: (纸本)9798350338287

Speaking to one another is most effectual & natural technique of communication amongst humans. Because of this, several scientists are investigating the possibility of using voice signals as a means of communication between computers and humans. What this implies is that the computer should be able to recognize human speech and voices. there has been a lot of progress in voice recognition, but scientists are still a long way from creating a realistic interaction between computers and humans. the purpose of emotion speech recognition is automatically identifying an individual's emotional state given the utterer's voice. Applications that need human-computer interaction, like speech synthesis & medical analysis, may greatly benefit from voice emotion detection. Validation of the suggested approaches in voice emotion recognition is performed utilising Ravdess speech database in this system. Next, the algorithm determines the individual's mental state based on the emotion detected in their speech. In addition, we present a deep learning- and voice-based methodology for identifying mental health issues. © 2023 IEEE.

关键词： human computer interaction

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A Comprehensive Study on Software Engineering and Emerging Technologies 5

A Comprehensive Study on Software Engineering and Emerging T...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bhatnagar, Vivek Kaur, Tejinder Singh, Gulbir Mishra, Ritu Department of Mmictbm Ambala Haryana Mullana133207 India Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nainital India

ISBN: (纸本)9798331504403

Recently, software engineering field is undergoing a significant transformation driven by the rapid advancement of emerging technologies. AI introduces intelligent automation in code generation, testing, and debugging by significantly enhancing the development efficiency and reducing human error. machine learning (ML) algorithms further enhance the predictive maintenance and performance optimization. Block chain technology enables the creation of decentralized applications (DApps) and smart contracts, ensuring enhanced security, transparency, and automation. Internet of things (IoT) brings a paradigm shift with an exponential increase in connected devices, necessitating robust software solutions for real-time data processing and analytics, while also highlighting the security challenges. © 2024 IEEE.

关键词： Predictive maintenance

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Performance and Reliability of Touch Screen: A Game-Oriented Research

Performance and Reliability of Touch Screen: A Game-Oriented...

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

作者： Rongrong Fu Qianzhou Dong Yuwei Liu China academy of information and communications technology Beijing China

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

With the rapid development of the mobile gaming industry, mobile devices have emerged as imperative platforms for gaming. Touch interaction has gained significant traction within the gaming industry, underscoring the need to scrutinize its performance and reliability to enhance the overall user experience. However, the existing research on touch interaction in gaming scenarios remains limited. this study strives to conduct a thorough investigation into the performance and reliability of touch screens in gaming environments through empirical experimentation. Primarily, a comprehensive set of experimental equipment and testing methods are proposed. then, key performance and reliability indicators pertaining to touch interaction are introduced. To facilitate the research, two distinct genres of high-load online mobile games are selected. Ultimately, the study measures the performance and reliability of touch interaction on smartphones and tablets through gaming scenarios. Our experiments show that, in gaming scenarios, the average click latency of mobile devices ranges from 94ms to 162ms, and the average slide latency ranges from 116ms to 187ms. Under 100 consecutive shooting operations, the continuous click accuracy rate remains above 96%. Additionally, we tested one million touch interactions on two Android phones and encountered only 5 and 10 anomaly cases, respectively.

关键词： Performance evaluation Industries human computer interaction machine learning Touch sensitive screens User experience Reliability

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Categorisation of Post-Sale Automotive Customer Feedback Using Natural Language Processing

Categorisation of Post-Sale Automotive Customer Feedback Usi...

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

作者： Diego Vicente-Estévez Angel Dacal-Nieto Andrés Paradela Víctor Alonso-Ramos Juan José Areal Alberto Riomao CTAG- Centro Tecnológico de Automoción de Galicia O Porriño Spain Stellantis España Vigo Spain

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

Automotive manufacturing is permanently searching new methods to improve its efficiency and quality. A relevant data source to do this is post-sale customer feedback. they can highlight situations in manufacturing processes not detected in the factory, that may repeat in the future. the analysis of these data is currently performed manually by experts, which try to label each new occurrence with known potential causes, in a long and inefficient process. this paper proposes a new system to analyse customer feedback in order to categorise it from their raw description, aiming to facilitate and boost problem solving in the related manufacturing process. It is based in the usage of Natural Language Processing and machine learning algorithms, since most of its description is based on non-standardized text fields, and in different languages. the solution has been validated with Stellantis automotive group data, obtaining that a combination of Decision Trees and Gradient Boosting classifiers provides an acceptable performance of 85% of accuracy, acting as a first solution to a very complex problem, with high potential of impact in the manufacturing efficiency.

关键词： human computer interaction Manufacturing processes machine learning algorithms Soft sensors Natural language processing Production facilities Manufacturing

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Optimized Plant Species Classification through MobileNet-Enhanced Hybrid Models 5

Optimized Plant Species Classification through MobileNet-Enh...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Lasya, S. Jyothsna, S. Pushpa, B.R. Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

Medicinal plants are used to cure various diseases such as asthma, cancer, heart disease etc. Plant identification plays a pivotal role in various domains such as agriculture, ecology, and environmental conservation. Manual identification of plant species is tedious and time consuming as there exist inter class similarity and intra class variations among the plant species that may lead to human error. Hence automatic identification is required based on machine learning and deep learning models. In this study, we propose two novel hybrid models for accurate plant species classification, leveraging the combination of deep learning architectures. the first hybrid model integrates InceptionNetV3 and MobileNet, achieving an accuracy of 88.11%, while the second hybrid model combines AlexNet and MobileNet, outperforming the first model with an accuracy of 93.86%. Both hybrid models employ deep learning classifiers. this research underscores the efficacy of hybrid models in enhancing plant species classification accuracy and contributes towards advancing the plant identification. the experiments are carried out using self-built datasets that are captured in unconstrained environment. © 2024 IEEE.

关键词： Convolutional neural networks

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ICA based Noise Reduction in Mobile Phone Speech Communications 5

ICA based Noise Reduction in Mobile Phone Speech Communicati...

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Obillaneni, Lavanya Lahari Nallamothu, Navya Sree Geda, Chandini Challamalla, Mounisha Singh, Nilu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350367171

Speech signals captured during video conferencing, mobile phones, teleconferencing etc., include noise in speech signals. Many algorithms exist for noise reduction, such as, time-varying adaptive filter and multiband adaptive gain control etc. Independent Component Analysis (ICA) is a statistical computational technique used for the cancellation of ambient noise in the selected part of a speech signal. Background noise reduction is a good application in human-machine interaction. the adverse impact on a mobile phone call is noisy location. Hence, to improve speech quality in mobile phones, noise decreasing techniques are proposed. this study has presented a literature on noise reduction techniques and algorithms and advantages of it. © 2024 IEEE.

关键词： Noise abatement

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interactions between planners' and PPC systems: Derivation of simulation scenarios with consideration of cognitive bias and disruptions 5

Interactions between planners' and PPC systems: Derivation o...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Rannertshauser, Patrick Antons, Oliver Arlinghaus, Julia Otto von Guericke Univ Univ Pl D-39106 Magdeburg Germany

Despite an increase in the availability of digital solutions, planners' remain at the center of the production planning and control (PPC) decision-making process and have to make numerous decisions involving different information. Due to the inherent complexities, planners' are supported by PPC systems to fulfil their tasks. Research has shown that humans are influenced by cognitive bias when making decisions, especially in uncertain and complex environments and when disruptions such as machine failures occur. Researchers have therefore proposed debiasing methods to reduce human errors in decision-making. However, there is currently no environment within this research to test these proposed methods. In this study, we aimed to take the first step toward closing this research gap. We combined existing PPC research with a systematic literature review on disruptions in production as well as perspectives from practice through interviews with 12 experts. We investigated the interactions between planners' and PPC systems with consideration of different cognitive biases and disturbances to derive relevant simulation uses cases. (c) 2024 the Authors. Published by Elsevier B.V.

关键词： cognitive bias disruption production planning and control interaction scenarios

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Enhanced Retail Shopper Behavioural Analysis using human machine interaction and Model Validation 5

Enhanced Retail Shopper Behavioural Analysis using Human Mac...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bogala, Mahendra Reddy Uppu, Venkata Adinarayana Rao Vanapalli, Gowri Lakshmi Godey, V. K. Kasthuri Kumar, Sunil Kotni, V. V. Devi Prasad Gitam School of Business Visakhapatnam India Gitam School of Business Department of Operations Visakhapatnam India Gitam School of Business Department of Finance Visakhapatnam India Gitam School of Business Department of Entreprenuership Visakhapatnam India Gitam School of Business Department of Marketing Visakhapatnam India

ISBN: (纸本)9798350383522

this study project examines how the retail mix factors effect consumer behavior and analyzes and validates the model of how brain-computer interface (BCI) may be integrated for better analysis and human-machine interaction in retail. through the use of statistical analysis and data collection, it looks at the significance of retail mix components. the study also examines how BCI technology may be used in the retail industry, with a focus on enhancing human-machine interaction and customer behavior analysis. the potential for BCI integration in the retail sector and a better knowledge of retail customer behavior are both advanced by this study. © 2024 IEEE.

关键词： behavior of retail shoppers consumer behavior factors influencing consumer behavior factors influencing the behavior of retail consumers retail retail shoppers

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machine learning Based Integrated Audio and Text Modalities for Enhanced Emotional Analysis 5

Machine Learning Based Integrated Audio and Text Modalities ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Agarkhed, Jayashree Vishalakshmi Pda College of Engineering Computer Science and Engineering Kalaburagi Karnataka585102 India

ISBN: (纸本)9798350321425

Emotions play a crucial role in human communication, and the ability to automatically recognize them has broad applications in various domains, including human-computer interaction, affective computing, and virtual reality. Bimodal speech emotion recognition which aims to accurately recognize and interpret emotions expressed in speech signals. this article reviews the state-of-the-art techniques and methodologies employed in multimodal speech emotion recognition, highlighting the advancements in feature extraction, fusion strategies, and machine learning algorithms. Additionally, emerging trends and future directions in this field are discussed, focusing on the challenges and opportunities for improving the performance and robustness of multimodal speech emotion recognition systems. © 2023 IEEE.

关键词： Feature extraction

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