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Business-based decision support systems have been proposed for a few decades in the e-commerce and textile industries. However, these Decision Support Systems (DSS) have not been so productive in terms of business decision delivery. In our proposed model, we introduce a content-based image retrieval model based on a DSS and recommendations system for the textile industry, either offline or online. We used the Fashion MNIST dataset developed by Zalando to train our deep learning model. Our proposed hybrid model can demonstrate how a DSS can be integrated with a system that can separate customers based on their personal characteristics in order to tailor recommendations of products using behavioral analytics, which is trained based on MBTI personality data and Deap EEG data containing numerous pre-trained EEG brain waves. With this hybrid, a DSS can also show product usage analytics. Our proposed model has achieved the maximum accuracy compared to other proposed state-of-the-art models due to its qualitative analysis. In the first section of our analysis, we used a deep learning algorithm to train our CBIR model based on different classifiers such as VGG-net, Inception-Net, and U-net which have achieved an accuracy of 98.2% with a 2% of minimized error rate. The result was validated using different performance metrics such as F-score, F-weight, Precision, and Recall. The second part of our model has been tested on different machine learning algorithms with an accuracy rate of 89.9%. Thus, the entire model has been trained, validated, and tested separately to gain maximum efficiency. Our proposal for a DSS system, which integrates several subsystems with distinct functional sets and several model subsystems, is what makes this study special. Customer preference is one of the major problems facing merchants in the textile industry. Additionally, it can be extremely difficult for retailers to predict customer interests and preferences to create products that fulfill those needs. The three innovations presented in this work are a conceptual model for personality characterization, utilizing an amalgamation of an ECG classification model, a suggestion for a textile image retrieval model using Denoising Auto-Encoder, and a language model based on the MBTI for customer rating. Additionally, we have proposed a section showing how blockchain integration in data pre-processing can enhance its security and AI-based software quality assurance in a multi-model system. [ABSTRACT FROM AUTHOR]

Motivation is fundamental for students to achieve successful and complete learning. Motivation can be extrinsic, i.e., driven by external rewards, or intrinsic, i.e., driven by internal factors. Intrinsic motivation is the most effective and must be inspired by the task at hand. Here, a novel strategy is presented to increase intrinsic motivation toward activities requiring the use of sample data sets, by automatically extracting the student's social data from a social media network, to create a personalized sample data set. A tool was developed to access Facebook and create a database to be used in practical assignments on database queries. A user study $(N=74)$ measured intrinsic motivation before and after the assignment in three different groups of first-year university students. Each group had to create a set of queries of similar difficulty, with each group using a different database. Intrinsic motivation was found to be significantly higher for the group using their own social media data than for the groups using social media data from an unknown person or a business. The approach presented here can be applied to other subjects, such as data mining, image processing, or statistics. [ABSTRACT FROM AUTHOR]