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Knee Osteoarthritis (KOA) is a type of chronic disease that commonly occurs in older, obese citizens and those with a sedentary lifestyle. This disease causes damage to knee cartilage and causes pain so that the patient's activity is reduced. Radiologists classify the KOA severity based on Joint Space Narrowing (JSN) and the presence or absence of osteophytes into five stages from healthy knee (stage 0) to the worst damage (stage 4). We developed a methodology that aims to accelerate the classification of KOA severity based on information obtained from X-ray images and to reduce the subjectivity of radiologists. This paper describes an automated KOA diagnostic model using hyper-parameter Deep Convolutional Neural Networks (DCNN). Based on our experimental result, it shows the accuracy of the proposed method outperforms other KOA severity classification algorithms, which also discussed deep learning, namely 77.24%. This value is the average result of the accuracy of each fold from each stage of the KOA severity level where we use three-folds cross validation as a method of evaluating system performance. Thus computationally, this method is efficient in automatic diagnosis and has the potential to be a clinician application aid to specify the KOA severity.

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Despite proper soil health management, pests and diseases control is also an important task in corn farming for improving both the quality and the quality of the crops production. One way to address this challenge is to identify the emerging symptoms accurately to help define the appropriate solution. Downy, Leaf Blight, Rot midrib, Rot Stem, Leaf Rust, Burnt, Dwarf, Leaf spot are among the common symptoms on corn crops. Failure to identify these symptoms in the early stage could result adverse effects on the corn crops and reduce the production in the long run. This study therefore aims to classify corn disease symptoms to know the real disease. In this study, Decision trees and Frequent Pattern Growth (FP Growth) are employed. Analysis from the data indicated accuracy values of decision tree J4.5 (ID3) of 95%, J.48 of 96% and Random Forest of 95%. While for the relation pattern with 9 attributes and 2 classes, obtained the most often arise rule is the mycelium symptom or cotton with the leaves color turning red / brown / ash / chlorotic with accuracy of 92%.