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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111-120 订阅

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Deep learning and Histogram Gradient Algorithm to Detect Visual Information Based on Artificial Intelligent 24

Deep Learning and Histogram Gradient Algorithm to Detect Vis...

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5th international conference on Computing, Networks and Internet of things (CNIOT)

作者： Hussein, Nashwan J. Abdulameer, Hasan Ameer Al-taie, Raflaa Hilmi Univ Babylon AL Hussain Univ Coll Med Instrumentat Tech Dept Dept ProgrammingColl Informat Technol Karbala Iraq Univ Babylon Informat Secur Dept Coll Informat Technol Babylon Iraq Univ Babylon Physiol & Organ Funct Dept Coll Med Babylon Iraq

ISBN: (纸本)9798400716751

the realm of computer vision and machine learning is of paramount importance in comprehending human emotions and has garnered considerable interest in the field of face emotion *** recognize the human expression from video is considered a difficult and interesting task since the face is the main means of communication and the most communicative part of the body to display emotions. In this paper provides a detailed investigation of facial expression detection and its application to a proposed real voice expression to make a comparison system that relies on emotions of human, by using deep learning algorithm to recognize and classify emotions shown in face photographs. By using (CNN) (one dimension & two dimensions), with layers and time distributed in order to lead to the efficient capturing of emotion from the video sequences, with system can doing train and testing to the dataset from CREMA -D dataset. the developing a new architecture for video-based emotion recognition. Preprocessing, feature extraction, and classification utilizing 1D-CNN and 2D-CNN models comprise the architectural framework. the 1D-CNN model classifies features after being extracted by the main algorithm of extraction of (HOG), while the 2D-CNN model features extraction and classification simultaneously. the accuracy of the CNN 1D model is 0.81, which indicates that it produces high results. Additionally, the 2D CNN worked superbly, with an accuracy score of 0.68.

关键词： human Expression Face Recognition Emotion Analysis Dimensional CNN

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Generate Adversarial Network Traffic Based on TMUAP for Reversible Transmission of Data 5

Generate Adversarial Network Traffic Based on TMUAP for Reve...

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5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Sun, Lei Ding, Ruiyang Cryptogram Engineering School Information Engineering University Zhengzhou China

ISBN: (纸本)9798350368284

With the constant change of network environment, traditional methods have been unable to meet the task of high-precision network traffic classification, and deep learning methods rely on efficient feature extraction and other advantages have been widely studied and applied. Network traffic classification based on deep learning can automatically extract traffic characteristics and has achieved excellent results in different classification tasks such as encrypted traffic and malicious traffic, but it also faces the threat of countering network traffic. Adversarial network traffic is generated by adding adversarial perturbation to normal network traffic, which can attack the deep learning classifier and make its classification result predict wrong. Most of the adversarial network traffic generated by existing methods destroys the structural properties of network traffic and cannot achieve reversible data transmission of network traffic. An Adversarial network traffic generation algorithm Twice Modular Operation Universal Adversarial Perturbations (TM-UAP) is proposed in this paper, which realizes the reversible data transmission of adversarial network traffic. thus, the effective attack on the network traffic classifier and the data availability of both the sender and receiver of network traffic are realized. Finally, this paper selects the commonly used standard network traffic data sets ISCX20 16 and USTC- TFC2016 to conduct simulation experiments on LeNet5, CNN and other classification models. the results show that the proposed method can not only effectively attack network traffic classifiers, but also achieve reversible data transmission of network traffic. It provides a new method for the practical application research of countering network traffic. © 2024 IEEE.

关键词： adversarial network traffic deep learning reversible transmission of data TM-UAP

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the Problem of Generating Psychological Tests using Artificial Intelligence

The Problem of Generating Psychological Tests using Artifici...

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

作者： Yelizaveta Vitulyova Dinara Matrassulova Mariya Kostsova Anastasiya Grishina Sherniyaz Kabdushev Ibragim Suleimenov al-Farabi Kazakh National university Almaty Kazakhstan Sevastopol State University Sevastopol Crimea V.l. Vernadsky Crimean Federal University Sevastopol Economic and Humanitarian Institute Sevastopol Crimea

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

the paper formulates the problem of generating psychological tests using artificial intelligence and proposes a general principle of its functioning. this method of generating psychological tests (obtaining them automatically) by processing an array of experimental data is primarily intended for completing works of fiction (in particular, science fiction novels). the combination of psychological testing with works of art, in turn, is aimed at analyzing the psychological state of the population on a mass scale. the principle of operation of the proposed system is based on the use of permutation tests, in which the respondent ranks certain images based on his personal preferences, as well as on identifying a set of reference sequences (substitutions in terms of group theory). the latter are selected as the basis for the generated test. the selection of reference sequences is carried out by minimizing errors (deviations) between them and the sequences obtained during experiments. the work also proposes algorithms that make it possible to compact the information obtained through the use of permutation tests, based on the use of functions that take values in Galois fields.

关键词： human computer interaction Art Codes Neural networks Psychology machine learning Parallel processing Software Galois fields Testing

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Real-Time Stress Monitoring using IoT and machine learning for Cognitive Wellness 5

Real-Time Stress Monitoring using IoT and Machine Learning f...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： thangam, S. Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Kumari, J. Jesy Janet Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350379945

Stress monitoring and management became some of the most vital requirements for maintaining overall well-being in high-stress environments. this work deals with the challenging issue of real-time stress detection using a fusion of the Internet of things and machine learning technology implemented on a Raspberry Pi. Using the proposed model, the following physiological signals are collected using a Pulse Sensor for heart rate, a DS18B20 sensor for temperature, a DHT22 sensor for humidity, and a Grove GSR sensor for galvanic skin response (GSR). Based on the collected data, a hybrid model of Support Vector machines and Random forests was trained, which would ultimately classify stress into different levels. A Snake game dynamics were controlled through a joystick. Using the predicted stress levels, the game difficulty is automatically adjusted, to provide real-time biofeedback for the user's cognitive wellness and stress management. this ensures that the user is calm and focused. the system effectively adapted to the gaming environment based on the stress levels of the player and it will serve as a promising tool to enhance cognitive wellness through game interaction. © 2024 IEEE.

关键词： Biofeedback

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the Appeal, Efficacy, and Ethics of Using Text- and Video-Generating AI in the learning Process of College Students: Predictive Insights and Student Perceptions 16th

The Appeal, Efficacy, and Ethics of Using Text- and Video-Ge...

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16th international conference on Social Computing and Social Media (SCSM) Held as Part of the 26th international conference on human-computer interaction (HCII)

作者： Brzezinska, Magdalena WSB Merito Univ Poznan Poland

ISBN: (纸本)9783031613043;9783031613050

this article explores the subjective appeal and effectiveness of three selected instances of generative artificial intelligence (generative AI) in the perception of sophomore undergraduate students at WSB Merito University in Poznan, Poland. thoughtful introduction of AI tools to instruction is discussed, with the underlying goal of preparing college students for life during the 5th industrial revolution and providing them with skills needed for the evolving job market. the author presents a case study involving a curriculum-related group assignment designed to implement text- and video-generating AI in a Culture and History of English-language college course, while student autonomy, creativity, engagement, and deep thinking are highlighted. the task execution phase is followed by a qualitative and quantitative survey-based analysis conducted to evaluate the attractiveness and efficacy of AI in the learning process as viewed by the students. Additionally, the ethical aspect of (mis)using artificial intelligence is addressed. the findings contribute to the ongoing discourse on the integration of AI in tertiary education and shed light on the potential advantages and hazards of incorporating AI tools into a humanities class.

关键词： Generative AI Text- and Video-generating AI College Education

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Gesture Recognition on Textile-Based Pressure Sensor Array 5

Gesture Recognition on Textile-Based Pressure Sensor Array

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5th international conference in Electronic Engineering, Information Technology and Education, EEITE 2024

作者： Celik, Ilknur Cetin, Munire Sibel Cetin, Kadir Ozlem Atalay, Ozgur Atalay, Asli Tuncay Ince, Gokhan Istanbul Technical University Computer Engineering Turkey Istanbul Technical University Textile Engineering Turkey

ISBN: (纸本)9798350372878

Wearable technologies are in pursuit of designing portable devices worn by people. In this paper, gesture recognition on a sleeve having a capacitive textile-based pressure sensor array has been developed. machine learning models are generated to recognize a set of gestures by accumulating capacitance values read from a sensor array during the execution of gestures. the performance of the gesture recognition system is evaluated in the experiments conducted with human test subjects. Real-time gesture prediction is achieved using the trained model. the performed gestures and predicted data are collected and analyzed, resulting in accuracies up to 86'. © 2024 IEEE.

关键词： Gesture recognition

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Research on human Resource Intelligent Recommendation Method Based on Improved Algorithms 5

Research on Human Resource Intelligent Recommendation Method...

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5th international conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Yang, Jiao Hankou University Wuhan China

ISBN: (纸本)9798331527662

the information age's continuous growth and expansion have seen several potential workers seeking information about job openings online. this has resulted in the escalation of human resource data and information on relevant talent information, contributing significantly to the growth of information overload. Besides this, improved learning algorithms have made significant strides and developments in different fields, spanning human resource management, education, and the medical field. To address the issue of information overload within the human resource management sector, this paper seeks to propose a design model for a human resource recommendation algorithm that functions on improved learning algorithms. the comprehensive consideration of the improved learning algorithm is informed by the need to consider a broader scope of behaviours and characteristics of the job features and individuals seeking to fill these positions. As established in this case, implementing the improved learning algorithms considers the assembly of varied learning models merged in designing a human resource intelligent recommendation system. Findings revealed a significant improvement in human resource management recommendation systems upon including improved learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Rainfall Prediction using Random Forest and XGBoost-A comparative study 5

Rainfall Prediction using Random Forest and XGBoost-A compar...

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5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sharma, Deepa Rattan, Punam School of Computer Applications Lovely Professional University Punjab Jallandhar India

ISBN: (纸本)9798350389609

there are numerous aspects of human life where knowing how much rain to expect might be beneficial. Heavy rainfall events such as flash floods and landslides, as well as droughts, can be predicted with effective rainfall forecasting. Because of reliable weather forecasts, the infrastructure required to capture rainwater and cultivate crops may be planned ahead of time. With the advent of big data, new possibilities have arisen for weather prediction and research. A variety of machine learning and Deep learning algorithms enable accurate weather forecasting. this study aims to investigate the efficacy of Random Forest in forecasting rainfall in Jammu Region of Union Territory of Jammu and Kashmir and compare its performance with XGBoost using RMSE as performance metric. Hyperparameter Tuning is used to enhance the efficiency of the algorithms. the dataset is collected for a period of 10 years from 2012 to 2022. Random Forest comes out to be slightly better than XGBoost when compared on test data by having less RMSE. © 2024 IEEE.

关键词： Rain

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A Comparative Study of machine learning Algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier Models 5

A Comparative Study of Machine Learning Algorithms for Predi...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Dodda, Ratnam Raghavendra, C. Aashritha, Marpu MacHerla, Hima Varshini Kuntla, Anusha Reddy Hyderabad India Hyderabad India Cvr College of Engineering Department of Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798350379945

Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. this study investigates the effectiveness of three prominent machine learning algorithms Sequential, Random Forest, and Decision Tree Classifier in forecasting customer churn. Utilizing a dataset encompassing historical customer information such as demographics, transaction records, and interaction patterns, we preprocess the data by addressing missing values, encoding categorical variables, and scaling numerical features. Subsequently, each model is trained on the processed dataset, and their performance is assessed using accuracy as the evaluation metric. Our findings demonstrate promising predictive capabilities, with the Sequential model achieving an accuracy score of 94%, followed by Random Forest with 92%, and Decision Tree Classifier with 91%. Additionally, we conduct a comparative analysis of these models, focusing on interpretability, computational complexity, and robustness. While the Sequential model excels in predictive accuracy by capturing temporal dependencies in sequential data, Random Forest and Decision Tree Classifiers offer simplicity and interpretability, catering to scenarios where model transparency is paramount. © 2024 IEEE.

关键词： Sales

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A Framework to Detect human Emotion using Deep learning Techniques 5

A Framework to Detect Human Emotion using Deep Learning Tech...

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5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Tiwari, Poorva Aravinth, J. Amrita Vishwa Vidyapeetham Amrita School of Engineering Coimbatore Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350376753

Detecting the emotions in speech is a complex challenge, particularly in selecting databases, identifying key speech features, and choosing effective models. In the human computer interaction (HCI) field, recognizing emotional speech has become essential. Our study explores using the MEL Frequency Cepstral Coefficient (MFCC) for feature extraction, capturing speech nuances related to emotions. We apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to address the complexities of emotional speech recognition. these models are adept at processing temporal sequences, making them suitable for speech pattern analysis. We use the TESS (Toronto Emotional Speech Set) database, which includes six emotions: indifference, fear, happiness, surprise, sadness, and anger. Our goal is to develop a model that accurately identifies these emotions. Our model, tested on the TESS database, achieved approximately 97% accuracy, marking notable progress in speech-based emotion detection. © 2024 IEEE.

关键词： Long short-term memory

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