Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering

Munna, Tahsir Ahmed; Alam, Mirza Mohtashim; Allayear, Shaikh Muhammad; Sarker, Kaushik; Ara, Sheikh Joly Ferdaus

doi:10.1007/978-3-030-03402-3_31

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Measure the quality of clustering using metrics like WCSS, silhouette score, or others. One can choose the "K" that provides the best trade-off between clustering quality and model complexity [20]. It is useful method for time compensation.…”

Section: K-meansmentioning

confidence: 99%

Algorithms of Machine Learning and Application for Signal Compensation

Peng

2023

HSET

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The advent of machine learning has inaugurated a new epoch, where computers acquire patterns and relationships from data, obviating the need for explicit programming. In this context, supervised learning stands as a cornerstone. This study investigates the importance of decision trees, K-Means, and boosting in the context of signal compensation scenarios. The synergy between these techniques is profound. Decision trees frequently serve as prime contenders for base learners in ensemble approaches like boosting, augmenting predictive precision while encapsulating complex temporal associations. Furthermore, K-Means' ability to segment data into temporal clusters can facilitate preprocessing, thereby enhancing subsequent analysis and boosting model efficacy. Within practical applications, these techniques synergistically address time compensation challenges. Imagine a scenario where historical data is harnessed to forecast time delays in financial transactions. Employing supervised learning through decision trees, key features contributing to delays could be discerned. Boosting could subsequently refine this prediction model by prioritizing instances with temporal disparities, thereby enhancing its accuracy. In parallel, K-Means could segment data into time-related clusters, revealing insights into the temporal patterns governing these delays. In summation, the triumvirate of supervised learning, unsupervised learning, and ensemble learning, enriched by decision trees, K-Means, and boosting, form the bedrock of machine learning's application in time compensation domains.

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Section: K-meansmentioning

confidence: 99%

Algorithms of Machine Learning and Application for Signal Compensation

Peng

2023

HSET

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“…The number of hidden layers and neurons used to achieve an optimal solution varies per situation, and it is usually a trial-and-error process. The most common approach is the use of K-fold cross-validation, as seen in [58][59][60]. However, for evaluation, some k number of nodes need to be defined, which can be obtained by a simple formula,…”

Section: The Convolutional Neural Networkmentioning

confidence: 99%

A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach

Dangut

Jennions

King

et al. 2022

Neural Comput & Applic

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The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.

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“…It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame. [8][9][10][11][12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

“…The positioner will amplify the air supply from actuator to precisely open the valve for the given controller output. The signals being operated are followed universally like 4mA to 20mA, 3-15 psi or (1-5) Volts and So on but often (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) mA is used in industrial automation. In the current scenario a pneumatic control valve with positioner is used to collect the image data for building an optimum model using machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches Using Phog and Smote of Image Feature Extraction Technique for Predicting Control Valve Stem Position

Kalaiselvi¹,

B²,

A.³

2022

INDJCSE

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Control valves are a progressively vital element of recent industrial instrumentation all over the world since these control valves are basically pneumatic devices composed of all mechanical parts their performance is less compared to the ideal one and due to constant moving parts wear and tear degrades over time. Control valves usually called the final control element in any process are used to control continuous flow, level, pressure, and temperature. The signal received from the conventional controller gives the signal for opening the CV (Control Valve) partially, fully open, or fully closed. The control signal is proportional to the magnitude of error with respect to time. The control valve is opened and closed automatically by giving the pressure to open and close using I/P converters in cascade with Pneumatic, Hydraulic, or electrical actuators with positioners. A plant can perform optimally if the performance of the control valve is monitored and maintained. The aim of this article is to initiate the studies in the direction of obtaining accuracy in stem positioning through image inputs and to demonstrate this yielding output as control valve stem position using machine learning algorithms, part of artificial intelligence enables identifying the stem position in less time using digital image processing. The proposed systems comprise of images of different stem positions is fed to the Weka software tool, preprocessed using Pyramid Histogram of Oriented Gradients (PHOG) feature filter and trained using pre-planned classifiers, the performance accuracy of stem levels by experiment-based attribute selection, iteration on ranking thresholds and SMOTE with triple iterations on class levels is calculated. The results show the performance with the maximum accuracy of 92.4051% and weighted average Receiver Operator Characteristics (ROC) values of 0.978. Hence such smart measurements using Machine learning algorithms which is a part of artificial intelligence provide us a vital role in predicting the CV stem position in less time using image processing filters.

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Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering

Cited by 6 publications

References 10 publications

Algorithms of Machine Learning and Application for Signal Compensation

Algorithms of Machine Learning and Application for Signal Compensation

A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach

Machine Learning Approaches Using Phog and Smote of Image Feature Extraction Technique for Predicting Control Valve Stem Position

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