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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491-500 订阅

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Artificial Intelligence in Space Exploration: Improving Data Analysis and Decision-Making for Planetary Missions

Artificial Intelligence in Space Exploration: Improving Data...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olakunle L. Ojo Adeniran S. Afolalu Yinka Ajiboye Ademola T. Morebise Oladipo E. Abe Ifeoluwa M. Omoyajowo Olugbenga Olumodimu David S. Adeyeye Olugbenga M. Agboola Oyelami Olawunmi Department of Mathematical and Physical Sciences Afe Babalola University Ado Ekiti Nigeria Department of Mechanical & Mechatronics Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Mechanical Engineering Science University of Johannesburg South Africa Department of Mathematical and Physical Sciences Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Physics Federal University Oye Ekiti Students Affair Unit Afe Babalola University Ado Ekiti Nigeria Institute of Cosmology and Gravitation University of Portsmouth UK Department of Geological Sciences Achievers University Owo Ondo State Nigeria Department of Tourism and Events Management Afe Babalola University Ado Ekiti Nigeria Geography Department University of Lagos Lagos Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Artificial Intelligence (AI) has become a transformative force in space exploration, addressing critical challenges and enhancing mission capabilities. this paper reviews AI applications in planetary missions, focusing on autonomous navigation, data processing, scientific discovery, predictive analytics, and mission planning. AI technologies, particularly machine learning and deep learning, have revolutionized decision-making by enabling autonomous operations. Additionally, emerging AI technologies, such as quantum and neuromorphic computing, offer new possibilities for data processing and decision-making. Despite significant progress, challenges remain, including AI system robustness in harsh space environments, human-AI interaction, and multi-agent coordination. Future research must tackle technical and standardization challenges while addressing ethical concerns to fully realize AI's potential in space exploration.

关键词： Deep learning Ethics Quantum computing Neuromorphic engineering Space missions Decision making Transforms Planning Artificial intelligence Autonomous robots

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Fuzzified Risk Assessment Model for Detection and Prediction of Flood Using machine learning Techniques

Fuzzified Risk Assessment Model for Detection and Prediction...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Kayode Abiodun Oladapo Dayo Olufunsho Alowolodu Jide Ebenezer Taiwo Akinsola Department of Computer Science McPherson University Seriki Sotayo Nigeria Department of Cybersecurity Federal University of Technology Akure Nigeria Department of Computer Science First Technical University Ibadan Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Pluvial flooding is a type of flood that has become a threat to human life and the global economy due to the variability in climate change, rapid urbanization and fast-growing spatial development. Studies have shown that machine learning (ML) applications can be used to reduce this risk. However, less attention has been paid to the use of a fuzzy rule-based classification to appraise the performance of ML, based on pluvial flood Conditioning Variables (CVs). therefore, the study developed a fuzzified risk assessment model for the detection and prediction of pluvial flood using ML techniques. the application was developed by categorizing the pluvial flood CVs using the degree of interdependency index. A total of 144,401 records and eight CVs out of 53 were gathered from the United States Geological Survey and Copernicus Climate Data Store from five Local Government Areas (LGAs) in Oyo State, Nigeria. Fuzzy logic was applied to the eight CVs using the triangular method, after which five ML algorithms were used to train the CVs: K-Nearest Neighbours (KNN), Random Forest (RF), Classification and Regression Trees (CART), Naïve Bayes (NB) and Artificial Neural Network (ANN) algorithms. the fuzzy logic re-classified the selected CVs into five classes: no flood, low, moderate, high and very high pluvial flood susceptibility. the performance of the application was evaluated using the 10-fold cross-validation and hold-out techniques, based on accuracy, sensitivity, specificity, precision and Area Under Receiver Operating Characteristics (AUROC) metrics. the study concluded that the fuzzified ML application can be used in detecting and predicting pluvial floods. Consequently, CART which had the best results, when compared to the rest of the classifier models, is recommended for use by experts. this is because the application showed the capacity to assist experts to alert the public on expected risks. Also, it will further assist local authorities and government agencies for ap

关键词： Fuzzy logic Accuracy Local government Predictive models Floods Risk management Indexes Random forests Regression tree analysis Guidelines

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Proposition Development on Incidents of Unethical usage of AI in education sector 23

Proposition Development on Incidents of Unethical usage of A...

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

作者： Malini Mittal Bishnoi Swamynathan Ramakrishnan Vanathi Ramaswamy Dhanushiga K M Mansurali Anifa Humanities Arts and Applied Sciences Amity University United Arab Emirates Business Amity University United Arab Emirates Department of Business Management Erode Arts and science College (Autonomous) India Department Of Computer Applications Bharathiar University India Management Central University of Tamil Nadu India

ISBN: (纸本)9798400709418

AI is increasingly being implemented into educational institutions, promising greater and more effective outcomes for learning and personalized experiences. However, the fast deployment of AI in the education field raises worries about its unethical applications. this paper aims to critically analyse the ethical implications of AI in education, with a particular emphasis on the unethical use of AI technologies. Based on previously published research papers, this study also explores the potential adverse effects of AI in education, such as violations of privacy, algorithmic bias, transparency issues, and less human contact. Furthermore, it analyzes educational institutions', policymakers', and technology developers' ethical duties in assuring the ethical and responsible deployment of AI in education sector. this study also looks into the present context and suggests ethical frameworks to assist stakeholders and practitioners navigate the ethical issues and concerns that are related to the use of AI in education while promoting an educational environment that puts the needs of students, justice, and human centricity first. this study analyzes the existing literature on the Incidents of unethical usage of AI in education sector and based on it, propositions were developed. the propositions developed in this study serve as a foundation for further research, discussions, and decision-making related to the ethical implications of AI in the education sector.

关键词： Artificial Intelligence education education stakeholders education technology educational infrastructure ethics future of education personalized learning unethical practices

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A Review on Various Facial Expression Recognition Techniques 5

A Review on Various Facial Expression Recognition Techniques

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5th international conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Pranathi, P. Lakshmi, C. Suneetha, M. SRMIST Kattankulathur Dept CSE Chennai Tamil Nadu India SRMIST Kattankulathur Dept Computat Intelligence Chennai Tamil Nadu India VR Siddhartha Engn Coll Dept Informat Technol Kanuru India

ISBN: (纸本)9781665426428

In present days, Facial Expression Acknowledgment (FER) has now become an unbelievable issue in contemporary science study. Facial expression, a vital mode of communication of human emotions, has been studied throughout the world in the fields of Driver Protection, human computer interaction (HCI), deception detection, health care, monitoring etc. Generative Adverse Networks (GANs), with various gestures, can create more one-to-one faces that can be used to improve data base. Facial expression recognition is one of the most effective naturalizing and instant means for people to convey their feelings and intentions. FER is a common research subject which has brought a range of computational vision tasks, such as image generation, video generation, super resolution reconstruction and image translation. Happiness, sorrow, surprise, disgust, fear and rage all constitute six universal facial expressions. Other things such as eye change, breathing and combing the variance in expression will underline all the emotion above. A face recognition device is a behavior request for the identification or authentication of an individual automatically from a digital picture or from a video source. this analysis paper helps to explain methods, strategies and challenges in the real time FER climate, which discuss and examine problems and challenges. this paper finally concludes the latest developments and addresses the problems facing the FER process and the potential of future growth.

关键词： Facial Expression Recognition Image machine learning deep learning optimization

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Predicting learning Behavior Using Hyper-Personalization and Attentiveness Measure

Predicting Learning Behavior Using Hyper-Personalization and...

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Advances in Science and Technology (ICAST), international conference on

作者： Rahil Desai Riya Muni Devanshi Madhani Abhijit Joshi Department of Information Technology Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9781665492645

With advances in educational technology, the use of online courses and e-learning platforms has increased. However, the primary issue with online learning is that learners usually fail to complete the entire course. In addition, the instructor has no mechanism of knowing how diligently the learners complete the course and whether the course requires any refinement. Our proposed system addresses these concerns by using hyper-personalization, which aims to create a personalized curriculum for each learner based on their knowledge, areas of weakness, and interests. the customized curriculum development is accompanied by a feedback system that collects facial expressions and employs Convolutional Neural networks (CNN), Recurrent Neural networks (RNN), and other deep learning techniques to assess the learner's attentiveness during the course and determine whether they find the course interesting. this mechanism will assist the instructor in receiving appropriate input on the course, which will be useful for them to improve on the areas of concern. the proposed system thus designs a tailored curriculum for each learner and monitors their attentiveness to assist instructors with the course refinement.

关键词： Deep learning human computer interaction Recurrent neural networks machine learning algorithms Electronic learning Curriculum development Prediction algorithms

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Artificial Intelligence and Mixed Reality Technology for Interactive Display of Images in Smart Area

Artificial Intelligence and Mixed Reality Technology for Int...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： T. Kathikeyan S Revathi B R Supreeth J Sasidevi Mohammed Ahmed Sunanda Das Sona College of Technology Salem Tamilnadu India Erode Sengunthar Engineering College Erode Tamilnadu India Oxford Degree and PG College Bangalore Karnataka India Department of CSE K Ramakrishnan College of Engineering Samayapuram Trichy India Oriental Institute of Science and Technology Bhopal Madhya Pradesh India Jain University Bangalore Karnataka India

the adoption of immersive technologies has altered the traditional shopping experience. We report the results of a study that set out to see how people react to a 17 million artificial intelligence (AI) embedded mixed reality (MR) exhibit in a shopping and entertainment complex that brings together cutting-edge technology and traditional retail. Our research shows that MR enjoyment, spatial immersion, and consumers’ perceptions of unique experiences are all positively impacted by the quality of AI (i.e. voice synthesis and recognition using machine learning) connected with an augmented object. All of these things work together to pique consumers’ interest and elicit favorable reactions, including the desire to make a purchase and the want to tell others about the experience. In conclusion, this research demonstrates that there are new opportunities to increase customer engagement thanks to interactive AI and MR technology.

关键词： Training human computer interaction Consumer behavior Mixed reality Entertainment industry Virtual reality Speech recognition

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Face Detection and Recognition of the Seven Emotions via Facial Expression: Integration of machine learning Algorithm into the NAO Robot 5

Face Detection and Recognition of the Seven Emotions via Fac...

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5th international conference on Control and Robotics Engineering (ICCRE)

作者： Reyes, Rosula S. J. Depano, Keanu M. Velasco, Aaron Matthew A. Kwong, John Chris T. Oppus, Carlos M. Ateneo Manila Univ Dept Elect Comp & Commun Engn Quezon City Philippines

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167916

this study revolves around NAO, a programmable and interactive robot, to do Emotion Recognition via Facial Expression, and to give an appropriate response for the identified emotion whilst accumulating images to further enhance the accuracy. To develop this cohesive system, the authors utilized computer Vision and Haar-Cascade Classifier along with the feature descriptors, Histogram of Oriented Gradient and Local Binary Pattern to train the Support Vector machine learning Algorithm. Gathering and increasing data with Asian/Filipino participants whilst being added to the CK+ database in different stages of retraining, optimization, and undertaking cross-fold validation yielded improved results with the highest average accuracy across the seven emotions of 87.14%. Suggesting that adding images with a specific ethnicity yields a higher accuracy model for a more diverse testing dataset and in the wild image classifications. With the machine learning model capable of accurately recognizing the emotion and reinforcing it with the accumulated local images, NAO equipped with emotion recognition capabilities can better aid and support the individuals in need.

关键词： NAO NAOqi Choregraphe Suite human-Robot interaction Support Vector machine Histogram of Oriented Gradient Local Binary Pattern Emotion Recognition via Facial Expression

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A Way Towards Explainable AI Using Neuro-Fuzzy System

A Way Towards Explainable AI Using Neuro-Fuzzy System

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international conference on Information Systems and computer Networks (ISCON)

作者： Hitendra Garg GLA University Mathura India

ISBN: (纸本)9781665447874

In the field of AI, machine learning (ML) plays important role. Mostly ML models are like black box, in which we give some set of inputs to the model and that model trained itself to do prediction or classification. But these models especially complex models like Deep learning models not able to justify the outcome, means how the outputs come. the unexplainability of artificial intelligence models draws a lot of attention to researchers, as it limits the applications in various fields like medical, finance, research etc. So, there should be a method which makes any AI model trustable and transparent, so that any individual or government agency can use AI technology without any hesitation. In this paper a new model of XAI is introduced, in which a Neuro-fuzzy hybrid model is present. this new concept idea can be used to describe the working system of any ML model.

关键词： human computer interaction Deep learning Computational modeling Government Decision making Finance Predictive models

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Detecting psychological stress using machine learning over social media interaction 5

Detecting psychological stress using machine learning over s...

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5th international conference on Communication and Electronics Systems, ICCES 2020

作者： Meshram, Shweta Babu, Rajesh Adhikari, Jayanth Department of Computer Science & Engineering Tulsiramji Gaikwad Patil College of Engineering and Technology Nagpur Maharashtra India

ISBN: (纸本)9781728153711

Many of the population now face stress leading to psychological issues. therefore, stress factors must be identified before a big health problem is involved. BP, heart failure, or death are usually caused by excessive stress. the social media data like posting on Facebook profiles, twits on twitter, etc are used to recognize human stress, as people communicate their social media feelings, promote the acquirement of social data, and detect stress based on their behavior. As traditional solutions are highly time-consuming and expensive. Twitter data collection and user tweets are also posted on the site. User stress states are called anxious or unstressed users using the algorithm of the convolutional neural network (CNN). © 2020 IEEE.

关键词： Convolutional neural networks

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Various Approaches of human Activity Recognition: A Review

Various Approaches of Human Activity Recognition: A Review

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international conference on Computing Methodologies and Communication (ICCMC)

作者： Tina Anmol Kumar Sharma Siddharth Tomar Kapil Gupta National Institute of Technology Kurukshetra Haryana India

ISBN: (纸本)9781665403603;9781665447751

In the past few decades, recognizing the activities of an individual is remaining as the most challenging task in the computer science domain. human activity recognition (HAR) is the study of identifying the actions of agents based on digital images and sensor data. Identifying human activity from digital images and sensor data is a considerably difficult task due to background disorder, partial occlusion, changes in scale, perspective problems, lighting, appearance, etc. To solve this problem, significant research has been performed in this area. Many applications of the HAR system includes video surveillance systems, human-computer interaction, etc. this work gives a concise survey on some new research advancements in the field of human activity recognition. Moreover, this work provides a comparative study on three popular approaches of activity recognition viz. vision-based (pose estimation), wearable devices, and radio signal-based approach. Also, it includes a comprehensive analysis on some existing datasets of HAR. the work includes a couple of pros and cons on the aforementioned approaches of activity recognition based on their performance and shows the research significance of vision-based system.

关键词： Performance evaluation Digital images Wearable computers Pose estimation Lighting machine learning Activity recognition

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