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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241-250 订阅

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Fake News Detector Using machine learning 1

引用

5th international conference on computer and Communication Technologies, IC3T 2023

作者： Barhate, Madhuri Yadav, Harshita Bhadke, Harshwardhan Kothawade, Harshali Hatedikar, Tejas Havaldar, Aamod Department of Engineering Sciences and Humanities Vishwakarma Institute of Technology Pune411037 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

In this contemporary era, technology is considered to be the best ally of human beings. It indeed is an indifferentiable element of human lives, but it is also the cause of some of the issues around us. One problem of this kind, which requires immediate attention, is spreading of fake news. the fake news is able to spread throughout the population with ease because of advancement in technology. With the increase in technology, people are able to access various kind of information through their cell phones itself, making it quite simple to trick them by providing rumors or unverified information. In this paper, the authors have tried to come up with an effective algorithm in order to tackle the raised issue by designing a fake news detector with the help of machine learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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A comprehensive survey and visual analysis of AIGC education and teaching 24

A comprehensive survey and visual analysis of AIGC education...

引用

2024 international conference on Intelligent Education and computer Technology, IECT 2024

作者： Liu, Xianghong Li, Haoyu Modern Educational Technology Institute South China Normal University Guangdong Guangzhou510631 China

ISBN: (纸本)9798400709920

To comprehensively understand the research landscape of Artificial Intelligence-Generated Content applications in educational scenarios. this study employs the CiteSpace visual bibliometric tool to conduct a quantitative analysis of 366 publications related to AIGC in education and teaching. the findings indicate that research on AIGC in education and teaching focuses primarily on three aspects. the connotation and characteristics of AIGC in education, the educational threats posed by AIGC, and the application scenarios of AIGC in education and teaching. It is evident that education researchers need to address the potential crises introduced by AIGC, actively explore strategies and models suited for various educational scenarios based on its functional characteristics and adapt to contemporary needs. Dominant type Advantages and characteristics Personalized learning support AIGC can conduct one-on-one open-ended question-and-answer sessions with users teacher instructional assistance Generative AI can assist teachers in lesson preparation and teaching[4] timely feedback evaluation Provide immediate feedback to learners to help them identify and correct mistakes in their learning. Multi-dimensional educational interaction Extending into the cyberspace that facilitates human-machine-human interaction. educational resource sharing Efficiently create personalized and cross-modal educational resources based on different resources[5]. © 2024 ACM.

关键词： Adversarial machine learning

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Research and Design of Intelligent Inspection Robot for Large-Scale Chicken Farms

Research and Design of Intelligent Inspection Robot for Larg...

引用

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

作者： Qingle Quan thelma D. Palaoag Hanqing Sun College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

In order to solve the problems of chicken status monitoring and farming decision making faced by intelligent livestock and poultry farming, this study aims to develop an intelligent inspection robot for chicken farms to explore the solution of intelligent inspection of chicken coops using Internet information technology and Internet of things (loT) technology. this study uses a function-driven development approach, using basic hardware such as LiDAR, remote sensing technology devices, sensor fusion hardware, communication devices, power supply, motors and actuators, and ROS (Robot Operating System) embedded development platform and Python language to complete the functional development of the system, and through the running test in the chicken farm to determine the system's usability and the system was tested in a chicken farm to determine the usability and reliability of the *** includes the use of LiDAR, infrared sensors, ultrasonic sensors, etc. to realize basic data acquisition in the chicken house, and a sensor fusion unit to integrate data from different sensors into a common environment model to create a comprehensive view of the robot's surroundings. the robot uses the sensor data to determine its current position, which is typically accomplished through LiDAR and map matching. the robot monitors the sensor data in real time to detect the presence of obstacles or changes in position for real-time obstacle avoidance. the robot can communicate with a remote operator or a centralized control system to transmit real-time data and receive task instructions. the robot can feedback the task execution and collected data to the operator or central system for further analysis and decision making. Users were satisfied with the usability, ease of use and reliability of the system through actual operational tests on the farm. the system realizes real-time monitoring of the chicken house, which can effectively enhance the level of intelligent farming, imp

关键词： Laser radar Inspection Sensor fusion Robot sensing systems Real-time systems Reliability Usability

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human Routine Analyzer Using machine learning 5

Human Routine Analyzer Using Machine Learning

引用

5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Arvindhan, M. Sharma, Akshat Prasad, Atul Kumar Galgotias University Department Of Computer Science Greater Noida India

ISBN: (纸本)9798350330861

this project aims to develop a ML model which will have the ability to analyze and interpret the human body gestures and movements with the help of several sensors like - Gyro Sensor, Accelerometer, etc. and will be able to present all the data collected by it graphically for an easier understanding of the *** model will take the readings provided by the sensors into account and that data will be provided by through the user's smart phone. All that data will be passed onto the model which will then process the data using several ML models like - Random Forest Classifier, KNN Classifier, SVM, and Logistic Regression. Afterward the data processing the model will provide the accuracy score. the final output of the model will lay out the outcome of data processing through graphs like - Bar Chart, Pie Chart, *** model's scope will range from medical to human computer interaction. the model will be making the user aware of their health by keeping track of their running, sleeping, walking, etc. activities and that data can be used by dietitian, or doctors to help them come up with a better remedy if the user is suffering from any kind of disease. © 2023 IEEE.

关键词： Smartphones

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Sign Language Recognition Using Convolutional Neural Network Based on VGG-16 Deep learning Approach 5th

Sign Language Recognition Using Convolutional Neural Network...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Athish, M.J. Sowmya, S. Lakshaya, E. Preetha, S. Rishiya Shankar, S.U. Sendhil Kumar, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819761050

Communication is a significant part of our lives, as it facilitates interaction among individuals. Language serves as a mode of communication for all individuals for expressing their thoughts and interpretations. However, hearing-impaired individuals often face hurdles in using spoken language. the technology for recognizing sign language made a commendable effort to improve communication levels among hearing-impaired individuals. In the current era of deep learning, which is a subset of machine learning, Convolutional Neural Network models have been developed and are now in use for gesture recognition. this recognition is based on the CNN model and VGG-16 model. It is used to hone the temporal features. the dataset used here is American Sign Language Dataset (A–Z and 0–9). the focus of this work is to develop a sign language recognition system to fulfill the need for communication of impaired people in society. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Detection of Autism Spectrum Disorder Using Optimized Extreme learning machine Technique 5th

Detection of Autism Spectrum Disorder Using Optimized Extrem...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Chintalapati, Phaneendra Varma Babu, Gurujukota Ramesh Kumar, Kode Satish Chintha, Venkata Ramana Sree, Pokkuluri Kiran Anoch, Bellamgubba Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9783031770777

Numerous disorders that cannot be diagnosed medically have emerged throughout the world, including Autism Spectrum Disorder (ASD). It impacts on the numerous aspects of behavior, such as social and language abilities in addition to stereotyped and repetitive behavior. this illness is a serious neurodevelopmental disorder. the symptoms of ASD and many other mental diseases are remarkably similar, making an accurate diagnosis of ASD challenging and time-consuming. the increasing use of the Optimized Extreme learning machine technology (OELM) in the prediction of many human diseases suggests that early diagnosis based on various physiological and health variables could be possible. this analysis attempts to develop a categorization model that can most accurately predict the risk of Autism. It was examined if it would be possible to evaluate and predict characteristics of autism spectrum disorders in toddler, child, adolescent, and adult age groups using the Optimized Extreme learning machine Technique. therefore, improving people′s overall mental health requires an early identification of autism spectrum disorders. the effectiveness of the models built for ASD detection was examined using a number of performance assessment criteria. the revolutionary concept of Optimized Extreme learning machines (OELM) for the early identification of autism disorders is presented in this analysis. the OELM algorithm offers reliable and precise predictions for autism diseases. ROC curve for this describes system shows better results in comparision. the suggested approach better the others in terms of F1- Score, precision, sensitivity, and accuracy. these algorithms are also evaluated on Kaggle ASD datasets and contrasted with other machine learning algorithms. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Exploring Risk Factors for Cardiovascular Disease: Insights from NHANES Database Analysis 5th

Exploring Risk Factors for Cardiovascular Disease: Insights ...

引用

5th international conference on Data and Information Sciences, ICDIS 2023

作者： Parashar, Gaurav Chaudhary, Alka Pandey, Dilkeshwar AMITY Institute of Information Technology AMITY University UP Noida India Department of Computer Science and Engineering KIET Group of Institutions UP Ghaziabad India

ISBN: (纸本)9789819969050

the primary factors contributing to mortality in the human population encompass a range of interrelated medical conditions, such as cardiovascular disease (CVD) and associated comorbidities. the main aim of the study is to investigate major factors in developing CVD. In the study, we have used National Health and Nutrition Examination Survey (NHANES) dataset to gather data between the years 2007 and 2012. A machine learning (ML) ensemble approach was utilized to collect and analyze data about the patient’s demographics, lifestyle characteristics, physical examination, and laboratory test results and achieved an accuracy of 91.49%. Furthermore, research has demonstrated a strong correlation between CVD and factors like BMI, BP, HDL, LDL, basophils, WBC, RBC, hemoglobin, and monocytes. the implications of these findings suggest that medical practitioners consider the identified risk factors while assessing a patient’s risk of CVD and implementing preventative measures. Further, investigation is required to validate these findings and identify additional predictors and biomarkers associated with CVD. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： machine learning

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Efficient Boxing Punch Classification: Fine-Grained Skeleton-based Recognition Made Light 5

Efficient Boxing Punch Classification: Fine-Grained Skeleton...

引用

5th international conference on computer Vision and Computational Intelligence, CVCI 2024

作者： Baghel, Vipul Rithihas, Nagisetti Sarvanan, M. Srinivasan, Babji Hegde, Ravi Sadananda Dept. of Electrical Engineering IIT Gandhinagar Gujarat India Dept. of Electronics and Communication Engineering IIT Kharagpur West Bengal India Dept. of Applied Mechanics and Biomedical Engineering IIT Madras Tamil Nadu India

ISBN: (数字)9781510680067

ISBN: (纸本)9781510680050

Sports analytics is a field of study that utilizes camera and sensor data to monitor the athlete’s performance and health to optimize the player's strategy and increase the success rate. Coaches rely on analytics to scout opponents and optimize play calls in gameplay. With the advancement in artificial intelligence, accessible and in-depth data collection has been enabled. the well-grounded technique for performance evaluation in sports analytics is human Pose Estimation (HPE). Our focus is on real-time action recognition in combat sports like boxing. Existing state-of-the-art deep learning models are heavily parameterized, so can’t be used in real-time in any low-end devices. Apart from this, fine-grained classification in highly dynamic activities in sports are typically performed using sensors only. Our proposed machine learning based pipeline provides real-time fine-grained solution for 14 boxing punch types of classification using RGB video only. Our approach includes the implementation of three novel and generalized motion dynamics features that encode velocity as well as acceleration of the pose sequences., 1) Unified-Axis Angular Encoding (UAE), 2) 2D Motion Dynamics Descriptors (2DMDD), 3) Fifth-order Angular Encoding (FAE). We employed classical machine learning algorithms I.e., Support Vector machine (SVM), Random Forest (RF), and K Nearest Neighbours (KNN) to make a lightweight model and test it on YouTube videos. the average accuracies of pipeline using the proposed features are found to be 55%, 92% and 84% for UAE, 2DMDD, and FAE respectively. Using KNN, we have achieved 99% accuracy on 10-fold cross-validation by using FAE features. © 2024 SPIE.

关键词： computer vision

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Smart Surveillence Drone with Navigation System 5

Smart Surveillence Drone with Navigation System

引用

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

作者： Padmavathi, A. Vyasasreevedansh, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

At present, in this time of technological advancement and an urgent necessity for advanced spying abilities, the 'Smart Surveillance Drone with Navigation System' program is leading the way in redefining the field of surveillance and safeguarding. the way in which this project is leading is through the integration of the most advanced drone technology, complex navigation systems, and real-time computer vision, striving to create an independent drone model that can be identified and followed by human subjects in real-time while travelling between predetermined waypoints. the key aims of the program involve the assembling and integrating of a range of sensors, such as GPS, IMU, cameras, ultrasonic sensors, and LiDAR, alongside the production of independent navigation algorithms and computer vision technology. By combining cutting-edge technologies such as wireless communication, real-time data processing, and machine learning, the revolutionising of surveillance in several domains is being set out by this project. the adaptability and reliability of the system will be verified by rigorous field testing, while the experience will be enhanced by user feedback and training. With such advancements, the future of disaster response, agriculture, search and rescue operations, and security is promised to be innovative and efficient. this project that's set to change how we think about surveillance and security is captured by this short summary, and the way is being paved for a safer and more secure world. © 2024 IEEE.

关键词： Autonomous Surveillance Navigation System Real-time Object Detection Security Innovation Surveillance Drone

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Revolutionizing Data Annotation with Convergence of Deep learning and Active learning to Enhance Credibility on Twitter Datasets 1

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

作者： Nair, Vinita Pareek, Jyoti Department of Computer Science Gujarat University Ahmedabad380009 India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

Active learning (AL) aims to enhance a model's performance by efficiently selecting samples for annotation, thus requiring fewer annotations overall. Conversely, deep learning (DL) can handle vast amounts of data, making them suitable for annotating large datasets quickly and efficiently enabling the model to learn and extract high-quality features. One of the most substantial obstructions in machine learning today is the integration of human-annotated data to train supervised algorithms effectively. Gathering extensive and precisely annotated datasets demands significant resources, particularly in fields such as natural language processing, encompassing tasks like speech recognition, medical imaging, knowledge extraction, text classification, and many more. Active learning addresses this issue by strategically selecting and annotating the most informative data from the unlabeled dataset. Henceforth, AL and DL highlight their pivotal role in providing effective solutions to data annotation challenge for large datasets. We have proposed a deep learning-driven active learning model that utilizes recurrent neural networks (RNNs) to annotate tweets based on credibility. We have identified and integrated sentiment polarity as the informativeness criterion, facilitating the comprehensive automation of the active learning process. the model demonstrated promising performance, as the RNN achieved an accuracy of 90%, surpassing the 78% accuracy achieved by logistic regression. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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