A reproducible educational plan to teach mini autonomous race car programming

Eken, Süleyman; Şara, Muhammed; Satılmış, Yusuf; Karslı, Münir; Tufan, Muhammet Furkan; Menhour, Houssem; Sayar, Ahmet

doi:10.1177/0020720920907879

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…There were 27 students attending this survey. The survey is categorized into "Learning Value", "Value Added", "Design and Other Issues", and the important open-ended questions about taking the perspective of the students follows the survey perspective of [29]. "Learning Value", "Value Added", and "Design and Other Issues" are created as a five-level Likert-type scale survey, ranging from 'strongly disagree' to 'strongly agree'.…”

Section: Evaluation Of the Educational Content And Discussionmentioning

confidence: 99%

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Soygazi

2023

Sustainability

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Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

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Section: Evaluation Of the Educational Content And Discussionmentioning

confidence: 99%

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Soygazi

2023

Sustainability

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“…Reproducibility also enables researchers and scientists to verify the accuracy of the results they are seeing and make sure they are reliable. By reproducing results, water management teams can ensure that their decisions are based on accurate, reliable data [78,79].…”

Section: Reproducibilitymentioning

confidence: 99%

Water Quality Estimation and Anomaly Detection: A Review

Balta,

Balta Kaç,

Balta

et al. 2023

EAI Endorsed Trans IoT

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Critical infrastructures that provide irreplaceable services are systems that contain industrial control systems (ICS) that can cause great economic losses, security vulnerabilities and disruption of public order when the information in it is corrupted. These ICSs, which were previously isolated, have now become systems that contain online sensors, wireless networks and artificial intelligence technologies. This situation has also increased the scope of attacks by malicious people who intend to carry out industrial espionage and sabotage these systems. In this study, water quality estimation systems and anomaly detection are comprehensively examined. In this direction, the statistics of the studies in the literature, the methods for water quality anomaly detection, the existing data sets, and the difficulties encountered in the water systems to achieve better water management are discussed. Principle findings of this research can be summarized as follows: (i) new methodologies and architectures have improved water quality assessment through anomaly detection, (ii) different datasets including multi-modal information have been presented, and (iii) remaining challenges and prospects have been investigated.

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“…7 shows performance results. Association of Tennis Professionals (ATP) Matches dataset 15 is used for classification task. Individual csv files for ATP tournaments from 2000 to 2017 may be found in these databases.…”

Section: Case Study 2: Machine Learning Practices On Different Sport ...mentioning

confidence: 99%

A Dockerized big data architecture for sports analytics

Özgüven

Gönener

Eken

2022

ComSIS

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The big data revolution has had an impact on sports analytics as well. Many large corporations have begun to see the financial benefits of integrating sports analytics with big data. When we rely on central processing systems to aggregate and analyze large amounts of sport data from many sources, we compromise the accuracy and timeliness of the data. As a response to these issues, distributed systems come to the rescue, and the MapReduce paradigm holds promise for large scale data analytics. We describe a big data architecture based on Docker containers with Apache Spark in this paper. We evaluate the architecture on four data-intensive case studies in sport analytics including structured analysis, streaming, machine learning approaches, and graph-based analysis.

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A reproducible educational plan to teach mini autonomous race car programming

Cited by 9 publications

References 27 publications

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Water Quality Estimation and Anomaly Detection: A Review

A Dockerized big data architecture for sports analytics

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