Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori

Mohapatra, Debaniranjan; Tripathy, Jogeswar; Mohanty, Kalyan Kumar; Nayak, Debasish Swapnesh Kumar

doi:10.1109/iccmc51019.2021.9418049

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…However, they have disadvantages such as high computational intensity and the need to select appropriate dimensions. Association rule learning includes Apriori [60,61], Eclat [61], and FP-Growth [62,63], and provide relationship discovery, efficiency in large-scale datasets, and suitability for itemset size. They have disadvantages such as the amount of Table 2 shows the advantages or disadvantages of unsupervised learning algorithms.…”

Section: Unsupervised Learningmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.

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Section: Unsupervised Learningmentioning

confidence: 99%

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Yun,

Lee

et al. 2024

Electronics

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“…The basic algorithms employed in ML are mostly statistical. DL is a subsection of ML that deals with the application of neural networks, with the deep term referring to the count of layers in the network [16,17].…”

Section: Ai For Cvd Detection and Predictionmentioning

confidence: 99%

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

Debasish Swapnesh Kumar Nayak

2023

IJRITCC

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One-third of all deaths worldwide yearly are attributable to cardiovascular disease (CVD). In contrast to the 7% of the wealthy who experience premature death, 43% of the poor do. Lifestyle diseases like obesity and diabetes are to blame. The importance of early identification of heart disease was demonstrated, and premature mortality was kept to a minimum. Combining clinical and biochemical data is essential for the early diagnosis of heart illness. Numerous IoT-enabled wearable healthcare applications have been created and released in recent years. Although the ability of wearable devices to share patient health data is expanding, it remains challenging to predict and identify health problems. Security, data storage, and patient monitoring are all part of the system. Artificial intelligence (AI) therapies may one day change the face of cardiology by providing doctors with cutting-edge data analysis and therapeutic decision-making resources. As the volume and complexity of data continue to increase, AI tools like machine learning (ML) and deep learning (DL) can assist medical professionals in learning more. Suppose we want to provide medical care to the elderly and those with chronic illnesses in the comfort of their own homes. In that case, we must upgrade our communication and information technology systems. The implemented DNN model's accuracy is amazing at 95.34 % and can yield other noteworthy outcomes when used to identify CVDs. We discuss and suggest the most suitable AI-IoT models for early CVD prediction and detection to reduce computational costs and increase time efficiency.

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“…For example, problems such as algorithm e ciency, data quality, and data privacy protection need to be solved using advanced technologies and methods. Therefore, AR mining is a research eld full of challenges and opportunities, which will play an increasingly important role in the future data era [30].…”

Section: Application Of Apropri Ars Algorithm In Sports Data Manageme...mentioning

confidence: 99%

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

Li,

Liu,

Chen

et al. 2023

Preprint

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This paper combines the Apriori association rule algorithm and Web application development technology to optimize and upgrade the management system to optimize the current college sports data information management system. On the one hand, the novel log mining technology in web application development technology is introduced. This technology has an excellent performance in improving system performance and understanding user behavior to discuss students’ access habits and content through processing sports data. On the other hand, combined with log mining technology to optimize the Apriori algorithm, the association between sports data information is found through the optimization algorithm. The retrieval accuracy and time are improved, which is convenient for the webmaster to grasp the details of the system. Finally, experiments are used to verify the reliability and effectiveness of the optimized system. The experimental results show that before the algorithm optimization, with the increase in the amount of information, the running time of the Apriori algorithm almost shows a multiplication trend. However, the optimized algorithm has improved its execution efficiency by at least 10–15%, which can verify that the optimized algorithm also exhibits good performance when the amount of information is enormous. Compared with traditional management systems, the optimized system has dramatically improved information retrieval time and accuracy, with an average retrieval accuracy of 98.3% and a retrieval time improvement of 23%. This is because adding the association algorithm improves the correlation between the information. It improves the retrieval accuracy of the system and shortens the retrieval time. Therefore, the technology and algorithm studied here have specific application value in the sports information management system and provide a methodological reference for the information management of other subjects.

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Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori

Cited by 11 publications

References 8 publications

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

Web Log Mining Techniques to Optimize Apriori Association Rule Algorithm in Sports Data Information Management

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