Classifying mental disorders through clinicians' subjective approach based on three-way decisions.

The most widely used technique for psychiatric diagnosis is a contemporary manual-based procedure based on prevailing culture-bound data for the classification of mental disorders. However, it has several inherent faults, including the misdiagnosis of complex patient phenomena and others. A potential mental patient from a minority culture could present with atypical symptoms that would be missed by the standard approach. Using the three-way decisions (3WD) as a framework, we propose a unified model that represents the subjective approach (CSA) of clinicians (psychiatrists and psychologists) consisting of three components: qualitative analysis, quantitative analysis, and evaluation-based analysis. The results of the qualitative and quantitative investigation are a classification list and a set of numerical weights based on malady severity levels according to the clinician's highest level of assumptions. Moreover, we construct a comparative classification of diseases into three categories with varying levels of importance; a three-way evaluation-based model is utilized in this study in order to better comprehend and communicate these results. This proposed method enables clinicians to consider identical data-driven individual behavioral symptoms of patients to be integrated with the current manual-based process as a complementary diagnostic instrument to improve the accuracy of mental disorder diagnosis.

Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks.

Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't always accurate. We developed an intelligent model for jute pests identification based on transfer learning (TL) and deep convolutional neural networks (DCNN) to solve this practical problem. The proposed DCNN model can realize fast and accurate automatic identification of jute pests based on photographs. Specifically, the VGG19 CNN model was trained by TL on the ImageNet database. A well-structured image dataset of four dominant jute pests is also established. Our model shows a final accuracy of 95.86% on the four most vital jute pest classes. The model's performance is further demonstrated by the precision, recall, F1-score, and confusion matrix results. The proposed model is integrated into Android and IOS applications for practical uses.