Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification

Ghane, Mostafa; Ang, Mei Choo; Nilashi, Mehrbakhsh; Sorooshian, Shahryar

doi:10.1016/j.bbe.2022.07.002

Cited by 22 publications

(9 citation statements)

References 102 publications

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“…Decision Tree (DT) is a machine learning algorithm that applies to create decision rules like a tree structure. DT is a supervised learning method that can be used for classification and regression (Ghane et al 2022). DT uses rules to make decisions such as a tree structure, having branches in decisionmaking so that the results obtained are maximized (Spirkovska 1993).…”

Section: Methodsmentioning

confidence: 99%

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Setiawan,

Hadiyanto,

Widodo

2024

J. emerg. sci. eng.

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Shrimp cultivation is one type of cultivation that has a significant impact on the social status of coastal communities. Shrimp farming traditionally faces several challenges, including water pollution, imbalances in temperature, feed, media, and costs. Monitoring the condition of shrimp in the cultivation environment is very necessary to determine the condition of shrimp in the water. Classification of shrimp and fish is the first step in monitoring the condition of shrimp underwater. This research proposes the development of a method for classifying shrimp and fish underwater using feature extraction and machine learning. The flow of this research is: (1) preparing data from ROI detection results, (2) extraction process of morphometric characteristics P and T, (3) calculating the value of morphometric characteristics P and T, (4) data breakdown for training data and testing data, (5) Model creation process, data training and data testing using SVM, RF, DT, and KNN, (6) Evaluation of classification results using a confusion matrix. From this research, it was found that the Random Forest method obtained the highest accuracy, namely 0.93. From this matrix, the values obtained are True Positive = 349, False Positive = 28, True Negative = 223, False Negative = 0.

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Section: Methodsmentioning

confidence: 99%

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Setiawan,

Hadiyanto,

Widodo

2024

J. emerg. sci. eng.

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“…The metrics of accuracy, precision, recall, and F-measure, commonly employed for classification assessment, can be computed as elucidated by Ref. [ 119 ]. The four abovementioned indicators in some cases can also be used in clustering assessments in the way Berdyugina and Cavallucci [ 83 ] computed statistic measures for contradiction identification versus human extraction.…”

Section: Data Extraction and Analysismentioning

confidence: 99%

Semantic TRIZ feasibility in technology development, innovation, and production: A systematic review

Ghane,

Ang,

Cavallucci

et al. 2024

Heliyon

Self Cite

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“…BPNN is a model that imitates the workings of the human brain, consists of an input layer, a hidden layer and an output layer, to get good results, BPNN requires training with a long time and large data [55]. PCR is predictive modeling approach which involves the use of Principal Component Analysis (PCA) and MLR, has been increasingly employed in various applications and industries [56]. It is also used to model predictions with several independent variables [57].…”

Section: 𝑐𝑓 =mentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification

Cited by 22 publications

References 102 publications

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Semantic TRIZ feasibility in technology development, innovation, and production: A systematic review

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

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