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Classification and prediction of student performance data using various machine learning algorithms

Materials Today: Proceedings 2023年 80卷 3782-3785页

作者： Pallathadka, Harikumar Wenda, Alex Ramirez-Asís, Edwin Asís-López, Maximiliano Flores-Albornoz, Judith Phasinam, Khongdet Manipur International University Manipur Imphal India Department of Electrical Engineering Faculty of Science and Technology State Islamic University of Sultan Syarif Kasim Riau Indonesia Management and turism Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Computer Science and Engineering Science Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Environmental Engineering Environmental Sciences Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru Faculty of Food and Agricultural Technology Pibulsongkram Rajabhat University Phitsanulok Thailand

In today's competitive world, it is critical for an institute to forecast student performance, classify individuals based on their talents, and attempt to enhance their performance in future tests. Students should be advised well in advance to concentrate their efforts in a specific area in order to improve their academic achievement. This type of analysis assists an institute in lowering its failure rates. Based on their prior performance in comparable courses, this study predicts students' performance in a course. Data mining is a collection of techniques used to uncover hidden patterns in massive amounts of existing data. These patterns may be valuable for analysis and prediction. Education data mining refers to the collection of data mining applications in the field of education. These applications are concerned with the analysis of data from students and teachers. The analysis might be used for categorization or prediction. Machine learning such as Nave Bayes, ID3, C4.5, and SVM are investigated. UCI machinery student performance data set is used in experimental study. Algorithms are analysed on certain parameters like- accuracy, error rate. © 2021

关键词： Forecasting

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Pragmatic Analysis of IoMT Network Modelling Techniques from a Statistical Perspective

Pragmatic Analysis of IoMT Network Modelling Techniques from...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Fuke, Radhika P. Mahajan, R.P. Sage University Department of Information Technology Phd Scholar Indore India Sage University Department of Computer Science Engineering and It Professor Indore India

ISBN: (纸本)9781665484251

Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci

关键词： Scalability

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An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings

An Integrated Mobile Vision System for Enhancing the Interac...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Chen, Jin Ramnath, Satesh Samaroo, Tyron Maksakuli, Fani Ruci, Arber Sturdivant, E’edresha Zhu, Zhigang Nearabl Inc New YorkNY10023 United States Computer Science Department The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper presents a mobile-based solution that integrates 3D vision and voice interaction to assist people who are blind or have low vision to explore and interact with their surroundings. The key components of the system are the two 3D vision modules: the 3D object detection module integrates a deep-learning based 2D object detector with ARKit-based point cloud generation, and an interest direction recognition module integrates hand/finger recognition and ARKit-based 3D direction estimation. The integrated system consists of a voice interface, a task scheduler, and an instruction generator. The voice interface contains a customized user request mapping module that maps the user’s input voice into one of the four primary system operation modes (exploration, search, navigation, and settings adjustment). The task scheduler coordinates with two web services that host the two vision modules to allocate resources for computation based on the user request and network connectivity strength. Finally, the instruction generator computes the corresponding instructions based on the user request and results from the two vision modules. The system is capable of running in real time on mobile devices. We have shown preliminary experimental results on the performance of the voice to user request mapping module and the two vision modules. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Palmprint recognition

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Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index

arXiv

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arXiv 2023年

作者： Singh, Anuradha Yadav, Jyoti Shrestha, Sarahana Varde, Aparna S. Environmental Science and Management PhD Program Department of Computer Science Montclair State University MontclairNJ07043 United States

Understanding adoption of new technology by its consumers helps us deal with the challenges it faces in any new market. Moreover, the impact it creates on society in terms of the environment, health, and justice is significantly based on the extent of adoption. Alternative fuel vehicles (AFVs) is such an area that faces challenges in terms of consumers’ diverse social status and resistance to change. In order to achieve a cleaner transportation sector, we need to address these challenges. In this paper, we conduct a study via machine learning techniques to correlate the adoption of AFVs across various regions, their socioeconomic ranking, and their impact on the air quality index (AQI);and furthermore to predict the AQI as per the region’s AFV adoption. This is an empirical study with predictive modeling based on a regional panel data analysis where we use real US census data, air quality data, and data on the number of AFVs purchased per region. Research in this area can help to promote appropriate policies for AFV adoption in the future with due justice to different population groups. This work exemplifies a modest utilization of AI techniques to enhance social good. More specifically, it makes a considerable impact on energy, climate, transportation, and environmental sustainability. © 2023, CC BY.

关键词： Population statistics

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[Wearable Smart Cardiac Care: Take Vipasyana for Example]

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Hu li za zhi The journal of nursing 2024年第3期71卷 13-19页

作者： Hsi-Lu Chao Sau-Hsuan Wu PhD Associate Professor Department of Computer Science National Yang Ming Chiao Tung University Taiwan ROC. hsiluchao@nycu.edu.tw. PhD Professor Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan ROC.

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical Engineering and computer science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

关键词： Apache storm big data real-time streaming framework artificial intelligence heart failure rehabilitation care intelligent cardiac healthcare internet of things virtual reality-based 3D ward round system

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Vegetation Coverage and Urban Amenity Mapping Using computer Vision and Machine Learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Vegetation mapping

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Parental Responses to Aggressive Child Behavior towards Robots, Smart Speakers, and Tablets

Parental Responses to Aggressive Child Behavior towards Robo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Keziah Naggita Elsa Athiley Beza Desta Sarah Sebo Computer Science PhD Student Toyota Technological Institute at Chicago Computer Science Undergraduate Student University of Chicago Assistant Computer Science University of Chicago

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

The increasing growth of robots and other technological devices in homes makes it critical to understand child- device interactions within the home, especially given the real possibility of child aggression towards these devices. To explore factors that currently and will, in the future, shape child-robot interaction in the home related to children’s aggressive behavior, we conducted a 2 x 3 x 3 between-subjects crowdsourced study (N = 332) that examined how parents would respond and perceive their child interacting with different technological devices. Participants were shown a video clip of a person interacting with a technological device (robot, smart speaker, or tablet), exhibiting either aggressive or neutral behavior, and interacting with the device in one of three interaction modalities (audio, physical, or audio+physical). Imagining that the person in the video was their child, parents who observed aggressive behavior compared with neutral behavior indicated greater concern, a higher likelihood to intervene, distinct intervention methods, a higher perception of device mistreatment, and greater sympathy for the device. Despite hypothesizing that the robot would be seen as the most anthropomorphic, animate and, warm device, participant ratings of the robot were no different than the smart speaker, however, both devices were rated more highly on those dimensions than the tablet.

关键词： Shape Behavioral sciences Robots

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A Balanced Pedagogical Approach toward AI Readiness Education for STEM Learners: Instilling a balanced view of AI capabilities through active learning in both traditional classroom and self-directed online environments 22

A Balanced Pedagogical Approach toward AI Readiness Educatio...

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Proceedings of the 2022 6th International Conference on Education and E-Learning

作者： Alvis C. Fong Ajay K. Gupta Steve M. Carr Shameek Bhattacharjee Michael Harnar Department of Computer Science Western Michigan University USA Interdisciplinary PhD in Evaluation Program Western Michigan University USA

ISBN: (纸本)9781450398428

Artificial intelligence (AI) is increasingly being applied to disciplines beyond computer science (CS). Engineers, statisticians, business analysts, biologists, physicists, physicians, and pharmacists, are among the many non-CS professionals who leverage the power of AI algorithms and systems for solving their domain-specific problems. Although AI has been found useful for solving a wide range of previously unsolvable problems, there are important limitations associated with contemporary AI. It is therefore important to inform current and future AI users regarding both strengths and weaknesses of AI in its current form, as well as what AI will be like in the foreseeable future. In this paper, the authors describe a pedagogical approach toward educating AI users from a range of STEM disciplines so that they can best exploit what AI has to offer. Specifically, a balanced approach is taken to ensure that learners gain knowledge and skills in what AI can or cannot do for them. A growing suite of experiential learning modules, which complement existing educational resources, serve as a vehicle for getting STEM learners ready for a future workplace characterized by significant use of AI technologies. These learning modules promote active learning and can be applied in a traditional classroom setting, self-directed online study, or a mix of the two modes. The paper ends with a presentation of encouraging results of actual use of the experiential learning modules in a mixed mode setting across multiple quantitative disciplines. All project artifacts, including the developed experiential learning modules, recommended uses, and best practices, are freely available on the project website. Interested educators and researchers are welcome to use the available resources and/or contribute to the on-going research.

关键词： artificial intelligence readiness active learning STEM education experiential learning computing education

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A Model and Tool for Community Engagement: Case Study: Community Engagement in the Bisotun World Heritage Site 14

A Model and Tool for Community Engagement: Case Study: Commu...

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14th AVI-CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI-CH 2022

作者： Nasrolahi, Ahmad Messina, Vito Gena, Cristina Tech4Culture PhD Program University of Torino Turin Italy Historical Studies Department University of Torino Turin Italy Computer Science Department University of Torino Turin Italy

Participating local community in cultural heritage management has always been a concern since the Venice Charter so far (1964). In addition, the Faro Convention (2005) shifted focus from cultural heritage values to the values of cultural heritage for society. In this case, there is required to design a simple method and tool in order to facilitate the interaction between local people and authority. This paper introduces a method and tool (iCommunity) that are now using in community-centered approach in the Bisotun World Heritage Site. The main goal of this application is to find a simple and applicable method and tool for inclusion the local community in decision-making processes in cultural heritage management. The proposed model is designed based on the general public participation principles, on the one hand, and the users' need and requirement in the world heritage site, on the other hand. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Decision making

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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