IoT data analytics in dynamic environments: From an automated machine learning perspective

Yang, Li; Shami, Abdallah

doi:10.1016/j.engappai.2022.105366

Cited by 20 publications

(4 citation statements)

References 140 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, for a classification model, SVM may have a higher predictive ability (Pai et al, 2017). Yang and Lu (2012) also examined sporting game outcomes using SVM. They sought to predict playoff results of National Basketball Association (NBA) games using the previous 10 seasons' data (2002-2011).…”

Section: Machine Learning and Game Outcome Predictionmentioning

confidence: 99%

March Madness prediction: Different machine learning approaches with non‐box score statistics

2023

View full text Add to dashboard Cite

The popularity of analytical research specializing in forecasting of March Madness saw an increase in the past decades. While the influence of nongame statistics on the game outcome has become a great interest in sports analytics, little research has focused on situational factors in predicting sports tournament outcomes. Therefore, this study is to examine the use of different machine learning algorithms, including artificial neural network (ANN), k‐nearest neighbors (kNN), support vector machine (SVM), logistic regression, and random forest (RF), to forecast the winning in a matchup between any two given teams during the March Madness tournaments. Our data include 1370 observations with 685 tournament games from 2006 to 2007 to 2016 to 2017 seasons. The results show that neural networks outperformed all other classifiers (67% of accuracy), followed by SVM (65%), kNN (63%), logistic regression (63%), and RF (61%).

show abstract

Section: Machine Learning and Game Outcome Predictionmentioning

confidence: 99%

March Madness prediction: Different machine learning approaches with non‐box score statistics

2023

View full text Add to dashboard Cite

show abstract

“…The survey in [ 22 ] is exceptional; it examined current methods for choosing, optimizing, and updating models in the field of automated ML. This was done to find the most suitable solutions for each stage of using ML algorithms for data analysis in the IoT and present a summary of it.…”

Section: Related Workmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics

Mohammed

Sultan²,

Lee

et al. 2023

Sensors

View full text Add to dashboard Cite

The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.

show abstract

“…Optimization of gradient boosted trees can be challenging due to the high number of hyperparameters and the bias-variance tradeoff [36]. Techniques such as autoML methods [191], [192], and recent coding assistance tools like ChatGPT for advanced data analysis [193] could simplify this process. Gradient boosted trees offer an additional advantage in light of the increasing call for the communication of model uncertainties in healthcare and other domains [194], [195].…”

Section: Machine Learning Pipelinementioning

confidence: 99%

“…Fully automatic segmentation has also emerged as a viable alternative to manual segmentation. Recent studies demonstrated that the challenges associated with its development in PCa are surmountable [191], [192]. A comparative analysis of a PCa prediction model using the DLM auto-fixed VOI and a model using automatic lesion segmentation would be insightful, especially given Chapter 5's suggestion that radiomics might benefit from a more homogenous segmentation approach.…”

Section: Improving Radiomics Speedmentioning

confidence: 99%

The Value and Challenges of Radiomics Artificial Intelligence for Magnetic Resonance Based Prostate Cancer Imaging

Bleker

View full text Add to dashboard Cite

show abstract

IoT data analytics in dynamic environments: From an automated machine learning perspective

Cited by 20 publications

References 140 publications

March Madness prediction: Different machine learning approaches with non‐box score statistics

March Madness prediction: Different machine learning approaches with non‐box score statistics

Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics

The Value and Challenges of Radiomics Artificial Intelligence for Magnetic Resonance Based Prostate Cancer Imaging

Contact Info

Product

Resources

About