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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 560 条记录，以下是401-410 订阅

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Research on Classification Identification of Surface EMG Signals Based on Deep learning

Research on Classification Identification of Surface EMG Sig...

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Artificial Intelligence and computer Engineering (ICAICE), international conference on

作者： Yumeng Lu Long Ling Zhenjie Zhang Jingping Deng School of Intelligent Manufacturing and Aeronautics Zhuhai College of Science and Technology Zhuhai Guangdong China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

With the continuous development of robot technology in the field of industrial production, the introduction of biological electrical signals to control robots will not only provide a new way of human-computer interaction operation, but also improve the security and stability of the system. Among them, SEMG has a high signal-to-noise ratio, and the robot control accuracy based on SEMG is higher compared to other bioelectrical signals. this paper studies the classification and identification method of sEMG signals. Classified and of signals by constructing a one-dimensional convolutional neural network model. By studying the one-dimensional convolutional neural network from the horizontal and vertical dimensions respectively, the experimental results show that the accuracy of the horizontal dimension recognition is higher, which can reach 93.28%.

关键词： Training Solid modeling Accuracy Service robots Robot control Neural networks Solids Stability analysis Convolutional neural networks Surface treatment

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A Novel Digital and Intelligent Automatic Kebab machine Based on Grilling Recommendation Model

A Novel Digital and Intelligent Automatic Kebab Machine Base...

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

作者： Weiqiang Zhou Shupeng Mao Miaomiao Li Fanglei Du School of Computer Science Qufu Normal University Rizhao China School of Electronic Information Engineering Rizhao Polytechnic Rizhao China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

In a fast-paced contemporary life, people pay more attention to the rapidity of food. Kebab as a kind of food is loved by people, at present many kebab store dining efficiency is low, kebabs are in short supply, while the existing kebab equipment in the level of intelligence is still deficient, unable to meet the needs of consumers and investors. In order to solve the above problems, grilling automation can be realized with the help of intelligent grilling methods. In this paper, we design a new digital and intelligent kebab grilling machine based on the grilling recommendation model, which combines hardware design and software development to improve efficiency through intelligent grilling. We designed a kebab machine with a layered architecture, which not only has the function of grilling but also can monitor the grilling process in real-time. the software platform receives the kebab matching queue through an external interface and generates matching information through the bake recommendation model, which is displayed on the interface of the software platform. the kebab machine interacts with the software platform in real-time and uploads the kebab process data to the software platform for statistics and storage. Based on these data, data visualization tools are used to analyze the changes in the ripeness of different types of kebabs and to study the relationship between grilling time, grilling temperature, and kebab ripeness.

关键词： Temperature sensors Temperature measurement Temperature distribution Software Real-time systems Data models Optimization Monitoring Software development management Resistance heating

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A Gamified Platform for Educating Children About their Legal Rights

A Gamified Platform for Educating Children About Their Legal...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： B. Ramasubba Reddy P. Neelima M.Sunil Kumar C. Sushama D. Ganesh Y. Yethish Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Department of Computer science and Engineering School of Engineering & Technology SPMVV Tirupati Dept of CSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

In order to raise children’s understanding of their legal rights, this article suggests a gamified online platform. there is a lack of scale, involvement, and interaction in the present human rights education initiatives. the proposed platform’s decision-making ability and support vector machine algorithms work together to provide children with age-appropriate, interactive content about their fundamental rights in the form of quizzes, tales, and situations. Various groups of youngsters demonstrate considerable advances in their rights knowledge, according to evaluations. the gamified approach’s revolutionary use of artificial intelligence for adaptive learning and its widespread accessibility give it great promise for transforming child rights protection in India and providing children with legal information on a large scale. the platform offers an appealing solution that is focused on children and fills significant holes in current awareness campaigns. It has the potential to make a significant difference in protecting children’s rights.

关键词： Support vector machines Electric potential Adaptive learning Law Decision making Protection Artificial intelligence

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Analysis on machine learning Strategies for Carcinoma Detection Biomarker 23

Analysis on Machine Learning Strategies for Carcinoma Detect...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Shairal Verma Prem Kumari Verma Nagendra Pratap Singh Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology India CSE NIT Jalandhar India Computer science and engineering NIT Jalandhar India

ISBN: (纸本)9798400709418

Biomarkers are substances that are identifiable and can potentially be used to show if a disease is present or is progressing. Biomarkers have the potential to diagnose cancer, forecast its course, and direct decisions regarding treatment. It has enabled the discovery of genes, plasma metabolites, and miRNA biomarkers for various cancers. A potent method for finding and analyzing biomarkers in data sets is machine learning. A component of artificial intelligence, machine learning is still a crucial and significant step in the diagnosis of several illnesses in the human body. In this rapidly expanding research, an increasing number of medical professionals are depending on artificial intelligence to identify and diagnose illnesses inside the body. the only issue is that they are striving to improve both precision and accuracy. thus, we have provided an overview of the technologies used in the case of biomarkers used to detect cancer in the body in this article. this article will provide an overview of the work completed so far and assist future researchers in discovering new avenues for exploration within the specific research areas they cover.

关键词： Biomarkers

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Continuous Finger Joint Angle Estimation by a sEMG-FMG-Driven Explainable Gate-Recurrent Unit Network with Cross-Modality Multihead Attention

Continuous Finger Joint Angle Estimation by a sEMG-FMG-Drive...

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Artificial Intelligence and computer Engineering (ICAICE), international conference on

作者： Zhouping Chen Hong Wang Northeastern University Shenyang China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Decoding movement intention is crucial for human-machine interaction (HMI). However, accurate finger joint angle (FJA) estimation is still a challenge. To address this problem, in this study, a gate-recurrent unit network with attention (GRU-Attention) was proposed, and hybrid surface electromyography (sEMG) and force myography (FMG) modality was selected as decoding information source due to its promising potential in improving estimation accuracy. the model utilizes two-stream GRU network to encode and decode information, and adds cross-modality multihead attention (CMMA) mechanism after the encoder block to fuse multimodal information. Five hand movements were estimated. Ablation studies demonstrated that adding the CMMA mechanism and multimodality can effectively improve estimation accuracy, and adding the CMMA mechanism was superior to that of multihead self-attention mechanism. Moreover, the average NRMSE of the proposed model (0.0633±0.0030) was significantly less than that of the convolution neural networks and long short-term memory CNN-LSTM model (0.0726±0.0034, $p$ <0.001) and the CNN and Transformer model (0.0686±0.0033, $p$ =0.027). Moreover, the proposed model has an explainable prediction mechanism, as feature importance was quantified by attention weights.

关键词： Hands Representation learning Accuracy Fuses Estimation Logic gates Predictive models Transformers Decoding Reliability

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Deep learningBased Emotion Detection from EEG Signals for humanmachineinteraction 23

Deep LearningBased Emotion Detection from EEG Signals for Hu...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Saurabh Raj Nitin N. Sakhare Yogesh Sharma Monika Soni Anup Bhange Pradeep Kundlik Deshmukh Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) India Department of Computer Engineering BRACT'S Vishwakarma Institute of Information Technology India Computer Engineering VIIT India Auricle Global Society of Education and Research India Department of Computer Science & Engineering KDK College of Engineering India Department of Computer Engineering School of Computational Sciences COEP Technological University India

ISBN: (纸本)9798400709418

Detecting emotions helps create more intuitive and responsive technology interfaces between humans and machines. this study introduces deep learning-based emotion detection using EEG signals. Our research uses Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a proposed hybrid of CNN and LSTM (ConvLSTM) along with Particle Swarm Optimization (PSO) for hyperparameter optimization. the presented study aims to improve EEG-based emotion detection, which will help create more unified and emotionally intelligent human-machine interfaces. Recent deep learning advances have advanced emotion detection. these advances have allowed systems to understand and respond to human emotions. Electroencephalography (EEG) signals can capture neural patterns linked to different emotions, giving a unique view of the brain's emotional state. However, extracting meaningful emotional information from these complex signals is difficult. EEG spatial patterns are captured using CNN and LSTM networks model data temporal dependencies. A hybrid ConvLSTM model integrates spatial and temporal information for a complete EEG analysis. the study presents a method that helps to detect emotions from EEG signals with accuracy of 99.63%. this study's high accuracy is a significant advancement in the field and has great potential for improving human-machine interaction systems. Due to the use of deep learning-based emotion detection, these systems are able to intuitively respond to the feelings of their users. they allow for deeper and more genuine connections to be made between humans and machines. the presented methodology achieves remarkable precision and lays the groundwork for emotionally intelligent technologies that can impact virtual assistants, healthcare, and entertainment. this research advances emotionally intelligent human-machine interfaces, paving the way for technology to understand and respond to human emotions with precision and sensitivity.

关键词： EEG signals

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machine learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability

Machine Learning Approach for Evaluating Industry-Based Empl...

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Recent Trends in computer Science and Technology (ICRTCST), international conference on

作者： Prakash Chandra Swain Seema Balan Lakshmi. S. R Ankur Choudhary Pramod Kumar Patjoshi J Raja School of Social Financial & Human Sciences Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar Odisha India Department of Management Studies Rajalakshmi Engineering College Chennai Department of Business & Management Christ University Bangalore Karnataka Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Management Centurion University of Technology and Management BBSR Campus Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. the research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. the financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. this highlights the difficulty of judging businesses. the implications of these results for job-seekers, investors, and businesses are varied. the study also highlights the significance of ethics, openness, and addressing biases in assessment. this study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations.

关键词： Logistic regression Ethics Linear regression Employment machine learning Companies Predictive models

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An Analysis on Virtual Mouse Control using human Eye

An Analysis on Virtual Mouse Control using Human Eye

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： P. M. Naidu N. Muthukumaran S. Chandralekha K. Tejaswini Reddy K.Sri Vaishnavi Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Technological advances have enabled hands-free computer interaction, especially for people with limited hand function, such as amputees and paraplegics. However, current solutions face limitations in terms of input methods, accuracy, functionality, and accessibility. the proposed model aims to solve these problems by developing a reliable, user-friendly, and accurate system by utilizing facial expressions, especially eye and mouth movements. the proposed system aims to improve reliability and expressiveness by combining two modalities. Using advanced image processing technology, errors caused by lighting changes are minimized to provide a smooth user experience. the virtual mouse interface provides comprehensive features such as scrolling, clicking, and typing, improving the experience of users with different needs. By providing a reliable, versatile, and convenient hands-free interaction system, this study aims to give users greater independence, improve communication, and increase their participation in the digital world.

关键词： Accuracy Image processing computer network reliability Computational modeling Mouth Lighting User experience

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Enhancing Emotion Recognition: machine learning with Phasic Spectrogram Texture Features

Enhancing Emotion Recognition: Machine Learning with Phasic ...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE international conference on

作者： Sriram Kumar P Jac Fredo Agastinose Ronickom School of Bio-medical Engineering Indian Institute of Technology (BHU) Varanasi Uttar Pradesh India

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.

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Hand Gesture based AI Controller for Presentation, Virtual Drawing and System Volume Management

Hand Gesture based AI Controller for Presentation, Virtual D...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Akshay Kumar Richa Pandey Khursheed Alam Harishchander Anandaram Kapil Joshi Sumit Chaudhary Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE Graphic Era Hill University Haldwani India Department of Mathematics A.H. Siddiqi Center for Advanced research in Applied mathematics and Physics Sharda University Grater Noida India Department of Artificial Intelligence Amrita school of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

the use of wireless control systems for human-computer interaction has grown in popularity recently. Earlier, people used to stick to input tools like a keyboard or mouse, which restrict one’s movement and interaction options, but now, newer technologies like computer vision, machine learning and AI have opened new and interactive ways of interacting with computers. this paper presents a hand gestures approach that not only can control presentations but is also useful for facilitating drawing and managing system volume which enrich the user experience and engagement by offering an interface that eliminates the need for keyboard and mouse. With the help of this algorithm, the computer system accurately responds to hand movements in real time. With this model, one can easily navigate through slides, play, or pause videos, and perform multiple presentation related tasks using hand gestures. It boosts mobility and enhances audience engagement by removing the limitations posed by traditional input methods. It also allows users to digitally draw on the system. Users can sketch diagrams, annotate slides, and unleash their creativity without requiring drawing tablets. It uses Google’s mediapipe module to detect hand landmarks. We incorporated specific gestures that enable the model to initiate a particular action. It showed an accuracy of $\mathbf{9 8 \%}$ under normal lighting conditions, which slightly drops to $95 \%$ and $\mathbf{9 6 \%}$ in low and bright light, respectively, while the overall accuracy remains at $96.33 \%$.

关键词： human computer interaction Wireless communication Accuracy Computational modeling Keyboards Control systems Solids

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