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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231-240 订阅

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Enhancing Emotion Recognition: machine learning with Phasic Spectrogram Texture Features 5

Enhancing Emotion Recognition: Machine Learning with Phasic ...

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

作者： Kumar P, Sriram Ronickom, Jac Fredo Agastinose Uttar Pradesh Varanasi India

ISBN: (纸本)9798350338287

this study aims to develop an emotion detection system that involves the phasic decomposition of electrodermal activity (EDA) signals, segmentation, texture feature extraction, and machine learning algorithms. Initially, the EDA signals are obtained from the continuously annotated signals of emotions dataset and decomposed into tonic and phasic signals using convex optimization approach to electrodermal activity decomposition method. the phasic signals are divided into two halves, and each half is further divided into five non-overlapping segmented windows. Spectrograms are generated using the short-time Fourier transform for each segmented window of the phasic signal, and texture features are extracted using the gray-level co-occurrence matrix and gray-level run length matrix. We built machine learning models such as logistic regression, random forest (RF), and extreme gradient boosting using texture features extracted from the spectrogram of each segment of the first, and second half of phasic component of EDA signals. the results of our experiment using a process pipeline that included second-half phasic segments of EDA, spectrogram, texture features, and RF classifier yielded the highest classification results. We achieved an average accuracy, sensitivity, specificity, precision, and f1-score of 78.5%, 52%, 84.61%, 57.31%, and 53.36%, respectively. the results demonstrate the potential of this approach for emotion detection from EDA signals and can be useful in psychology, human-computer interaction, and health monitoring. © 2023 IEEE.

关键词： Adaptive boosting

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Enhancing human - Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: Application to Collision Detection 5th

Enhancing Human - Robot Collaboration in the Industry 5.0 Fr...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Ciampi, Francesco G. Diallo, thierno M. L. Mhenni, Faida Patalano, Stanislao Choley, Jean-Yves Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy ISAE Supmeca Quartz Lab EA7393 3 Rue Fernand Hainaut F-93400 St Ouen France

ISBN: (纸本)9783031807596;9783031807602

In the context of rapidly evolving technology and increased attention to social and environmental dimensions, the industrial sector is transitioning from Industry 4.0 to Industry 5.0. this new paradigm emphasizes human centrality in highly automated environments, necessitating the exploration of collaboration mechanisms between humans and robots. this study investigates the application of Physics-Informed Neural Networks (PINNs) to enhance human-Robot Collaboration (HRC). PINNs integrates the traditional data-driven approach based on machine learning models with a prior physical knowledge of the system, providing a valuable solution when data are scarce or physical models are too complex or incomplete. the study first focuses on the main aspects of interest in the field of HRC through a state-of-the-art analysis, evaluating the application of Physics-Informed Neural Networks (PINNs) in HRC and robotics. It then delves into the definition of PINNs and their implementation. Finally, as a proof of concept, the model is applied to a case study concerning collision detection in a 6 DoF robotic arm. this is achieved by predicting the joint currents and comparing them with the measured values to identify the contribution due to external forces such as collisions. the results demonstrate that the PINNs model outperforms a traditional neural network, achieving an average error below 10%. Additionally, the collision detection application shows an f1_score of 0.80, indicating strong performance.

关键词： Industry 5.0 human-robot collaboration Collision detection Physics-informed neural networks Physics-informed machine learning

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Detection and Classification of Bipolar Disorder Syndrome using Deep learning 5

Detection and Classification of Bipolar Disorder Syndrome us...

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

作者： Charaan, R.M. Dilip Vimala Ithayan, J. Sankar, M. Chithambaramani, R. Sivaprakash, P. Marichamy, D. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Department of Cse Chennai India Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331504403

Neural networks that are inspired by the way a human brain is structured, are known as artificial neural networks represent a unique class of machine learning algorithms. these algorithms allow artificial neural networks to acquire from data, and make resolutions based on the outcome, just like humans do. Non-linear data models, which have a whole load of variables and inputs, can predict new patterns. Artificial neural networks are already leveraged in everything from medical diagnoses to speech recognition as well as the ever-growing field of machine translation. there are multiple processes involved in the deep learning methodology that the method suggests for classifying bipolar disease. First, clinical and demographic data such as the sample's age, gender, symptom severity, and medication history would be included in the dataset of bipolar disorder patient samples and control samples. the dataset would next undergo preprocessing to remove any outliers or missing values. In the meantime, standardization and normalization would be applied to the data to ensure that each variable is on a consistent scale. Data scientists may also decide to use feature selection to determine which variables are most useful for overall optimization for classification goals. In this proposed system accuracy is better as compared to the remaining conventional models. this makes it easier for us to select the elements that are most crucial to making our part a real feature. the two models ANN are the final features. Findings demonstrate that deep learning models, like as artificial neural networks (ANNs), are effective in treating neuroinformatics illnesses since datasets are readily available. When it comes to public relations, moods, and the dataset, the deep learning model predicts the correct classes with more precision. A popular application of ANN involves approximating a random function, thereby providing an inexpensive way to get to various statistics characterizing its distrib

关键词： Contrastive learning

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Get It Right: Improving Comprehensibility with Adaptable Speech Expression of a humanoid Service Robot 1

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6th international conference on Applied machine learning and Data Analytics, AMLDA 2023

作者： Sievers, thomas Möller, Ralf Institute of Information Systems University of Lübeck Lübeck23562 Germany

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

As humanoid service robots are becoming more and more perceptible in public service settings for instance as a guide to welcome visitors or to explain a procedure to follow, it is desirable to improve the comprehensibility of complex issues for human customers and to adapt the level of difficulty of the information provided as well as the language used to individual requirements. this work examines a case study using a humanoid social robot Pepper performing support for customers in a public service environment offering advice and information. An application architecture is proposed that improves the intelligibility of the information received by providing the possibility to translate this information into easy language and/or into another spoken language. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： human robot interaction

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Multimodal Emotion Recognition in human–computer interaction Using MFF-CNN 1

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8th international conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Ahmad, Akram Singh, Vaishali Upreti, Kamal Department of Computer Science Maharishi University of Information Technology Lucknow India Department of Computer Science Christ University Ghaziabad India

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

the rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing human–computer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the human–computer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. this paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. this research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Extended Graph Assessment Metrics for Regression and Weighted Graphs 5th

Extended Graph Assessment Metrics for Regression and Weighte...

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5th international Workshop on Graphs in Biomedical Image Analysis (GRAIL) / Workshop on Overlapped Celi on Tissue - Cell Detection from Cell-Tissue interaction Challenge (OCELOT) / 5th MICCAI Workshop / 1st MICCAI Challenge Workshopinternational conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Mueller, Tamara T. Starck, Sophie Feiner, Leonhard F. Bintsi, Kyriaki-Margarita Rueckert, Daniel Kaissis, Georgios Tech Univ Munich Inst AI Med & Healthcare Fac Informat Munich Germany Tech Univ Munich Dept Diagnost & Intervent Radiol Fac Med Munich Germany Imperial Coll London Dept Comp BioMedIA London England Helmholtz Zentrum Munich Inst Machine Learning Biomed Imaging Munich Germany

ISBN: (纸本)9783031550874;9783031550881

When re-structuring patient cohorts into so-called population graphs, initially independent patients can be incorporated into one interconnected graph structure. this population graph can then be used for medical downstream tasks using graph neural networks (GNNs). the construction of a suitable graph structure is a challenging step in the learning pipeline that can have a severe impact on model performance. To this end, different graph assessment metrics have been introduced to evaluate graph structures. However, these metrics are limited to classification tasks and discrete adjacency matrices, only covering a small subset of real-world applications. In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and weighted graphs. We focus on two GAMs in particular: homophily and cross-class neighbourhood similarity (CCNS). We extend the notion of GAMs to more than one hop, define homophily for regression tasks, as well as continuous adjacency matrices, and propose a lightweight CCNS distance for discrete and continuous adjacency matrices. We show the correlation of these metrics with model performance on different medical population graphs and under different learning settings, using the TADPOLE and UKBB datasets1(the source code can be found at https://***/tamaramueller/ExtendedGAMs).

关键词： Graph neural networks graph assessment metrics medical population graphs

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An Automatic Navigation System for New Technical Advanced Drones for Different Alpplications 5

An Automatic Navigation System for New Technical Advanced Dr...

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

作者： Talwandi, Navjot Singh thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Autonomous pilot technology integrated into drones has ushered in a new era of unmanned aircraft systems, creating a multitude of applications this abstract examines the importance of the importance and potential of autonomous pilot aircraft, underscoring its transformative impact on industries ranging from agriculture to surveillance and beyond Navigation requires intelligent a unmanned aerial vehicles (UAVs) will be equipped with sophisticated artificial and sensor systems, enabling them to operate autonomously without constant human intervention A rapidly growing field in robotics and aerospace technology Tires It is showing to do so, the paper provides a comprehensive overview, including key concepts, challenges, and recent developments in autonomous automation enter the box. It also discusses, and educates, the critical importance of real-time operations, the critical role of obstacle detection and avoidance systems, and the use of GPS and optical-based navigation delves into how machine learning and artificial intelligence enhance the autonomy of drones. In addition, the paper explores the many potential applications and benefits of autonomous drone communications, from search and rescue missions to precision agricultural delivery Growing interest and investment in this technology in 2013 highlights its potential to transform industries, promising a future marked by improved safety and performance. © 2024 IEEE.

关键词： and applications artificial intelligence Autonomous drones GPS machine learning navigation obstacle avoidance real-time data processing vision-based navigation

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Improving COVID-19 Forecasts using Hybrid machine learning Models 5

Improving COVID-19 Forecasts using Hybrid Machine Learning M...

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5th IEEE international conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Onuoha, Michael Kulkarni, Aditya Saxena, Navrati San Jose State University Dept. of Computer Science San JoseCA United States Software Engineer III Walmart Inc. RedmondWA United States

ISBN: (纸本)9798350352863

the spread of COVID-19 has impacted human life in every aspect. Because of the uncertainty of the virus's future impact, highly advanced machine learning approaches are needed to enhance the accuracy of COVID-19 forecasts. therefore, we propose the following hybrid models to address the challenge: CNN-LSTM-Prophet, GRU-LSTM-Prophet, and RNN-LSTM-Prophet. the RNN-LSTM-Prophet model performs the best in predicting daily COVID-19 cases in the United States as it has the lowest MSE, RMSE, and MAE values. Moreover, all of the hybrid models perform better than the individual LSTM and Prophet models. Based on the benchmark results, RNN-LSTM-Prophet performs the best compared to the other models in this study. the hybrid model also performs better than the baseline as the MSE, RMSE, and MAE are lower by 2.48%, 1.24%, and 1.85% respectively. Governments worldwide can use RNN-LSTM-Prophet to better determine COVID-19's future trajectory and then implement the appropriate policies and protocols. Moreover, the model can be used to deal with new diseases on the rise. © 2024 IEEE.

关键词： COVID-19

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More than Noise: Assessing the Viscosity of Food Products Based on Sound Emission 1

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

作者： Schiller, Dominik Mertes, Silvan Achzet, Marcel Hellmann, Fabio Schlagowski, Ruben Andre, Elisabeth Augsburg Univ Chair Human Ctr Artificial Intelligence Augsburg Germany Augsburg Univ Chair Mech Engn Augsburg Germany

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

In the era of Industry 4.0, manufacturing is rapidly shifting towards automation, particularly in processes such as quality control, production lines, and logistics. However, the food industry poses distinctive challenges to automation due to the variability in raw materials and stringent hygiene standards. Sensory analysis is crucial in maintaining consistent quality and safety while manufacturing food products. this paper focuses on the automatic estimation of viscosity, a key parameter in many industry quality aspects of food products. An often overlooked aspect is the potential correlation between viscosity and sound emissions. While conventional methods for determining viscosity require expensive equipment, this research investigates the possibility of analyzing the acoustic emission when a liquid is sucked through a vacuum pump to determine the viscosity. By simulating industry-like food products with varying viscosity through different flour and water mixtures, we aim to investigate the feasibility of developing an automatic, deep-learning-based system for real-time viscosity estimation in manufacturing processes. Our results indicate that our proposed methodology can automatically determine the difference in viscosity, showing the feasibility of using sound emission analysis as a tool for viscosity estimation in manufacturing processes.

关键词： machine learning Industry 4.0 Viscosity estimation

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Gene Expression Data Analysis using Supervised machine learning Algorithm in Data Mining for Breast Cancer Prediction 5

Gene Expression Data Analysis using Supervised Machine Learn...

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

作者： Nathiya, S. Sumitha, J. Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350367171

the human body is composed of cells, and each cell has a variety of components. the human body's cells are essential for both the construction and operation of living things. Cells in the human body are capable of dividing and self-destructing as necessary. this natural functioning can occasionally alter;for example, certain cells may grow uncontrollably or improperly, giving rise to tumors. When these tumors develop into malignant ones and spread to nearby body parts, the condition is known as cancer. Specifically, if the cells in the breast can grow uncontrollably then this condition is known as breast cancer. A gene is a piece of genetic material that is present in every human cell. the genes that code for the proteins that determine how each cell functions. Gene expression is the name given to this function that produce protein. Occasionally, variations in the genetic material, or mutated genes, produce alterations in the values of gene expression. Breast cancer is caused by these altered gene expression levels. Some diagnostic techniques are necessary to detect this altered gene expression for breast cancer, and they are highly effective in helping medical professionals to pinpoint the disease's biomarker. thus, the goal of this research is to use gene expression data to create a model that can accurately identify breast cancer biomarkers. the suggested model in this study, a pruned neural network method, is designed to predict breast cancer while requiring less memory and execution time and offering the best accuracy. this suggested model is evaluated against current techniques, including support vector machines and decision trees, and it outperforms them with a 98 % accuracy rate. Having a larger number of gene characteristics and fewer samples is a drawback of using gene expression data. therefore, the wrapper approaches are employed as a preprocessing strategy in this research work to remove the undesirable genes. the gene expression data is preprocessed be

关键词： Support vector machines

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