Hassan Usman Katsina Polytechnic

About: Hassan Usman Katsina Polytechnic is a education organization based out in Katsina, Nigeria. It is known for research contribution in the topics: Ultimate tensile strength & Computer science. The organization has 49 authors who have published 60 publications receiving 395 citations.

Recommendation system based on deep learning methods: a systematic review and new directions

Abstract: These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications such as Natural Language Processing (NLP) and image processing, more efforts have been made by the researchers to exploit deep learning methods for improving the performance of RS. However, despite the several research works on deep learning based RS, very few secondary studies were conducted in the field. Therefore, this study aims to provide a systematic literature review (SLR) of deep learning based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the field. This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications. The paper particularly adopts an SLR approach based on the standard guidelines of the SLR designed by Kitchemen-ham which uses selection method and provides detail analysis of the research publications. Several publications were gathered and after inclusion/exclusion criteria and the quality assessment, the selected papers were finally used for the review. The results of the review indicated that autoencoder (AE) models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs) models. Also, the results showed that Movie Lenses is the most popularly used datasets for the deep learning-based RS evaluation followed by the Amazon review datasets. Based on the results, the movie and e-commerce have been indicated as the most common domains for RS and that precision and Root Mean Squared Error are the most commonly used metrics for evaluating the performance of the deep leaning based RSs.

Weighted aspect-based opinion mining using deep learning for recommender system

Abstract: The main goal of Aspect-Based Opinion Mining is to extract product's aspects and the associated user opinions from the user text review. Although this serves as vital source information for enhancing rating prediction performance, few studies have attempted to fully utilize it for better accuracy of recommendation systems. Most of these studies typically assign equal weights to all aspects in the opinion mining process, however, in practices; users tend to give different priority on different aspects of the product when reaching overall ratings. In addition, most of the existing methods typically rely on handcrafted, rule-based or double propagation methods in the opinion mining process which are known to be time-consuming and often inclined to errors. This could affect the reliability and performance of the recommender systems (RS). Therefore, in this paper, we propose a weighted Aspect-based Opinion mining using Deep learning method for Recommender system (AODR) that can extract product's aspects and the underlying weighted user opinions from the review text using a deep learning method and then fuse them into extended collaborative filtering (CF) technique for improving the RS. The proposed method is basically comprised of two components: (1) Aspect-based opinion mining module which aims to extract the product aspects from the review text to generate aspect rating matrix. (2) Recommendation generation component that uses tensor factorization (TF) technique to compute weighted aspect ratings and finally infer the overall rating prediction. We evaluate the proposed model in terms of both aspect extraction and recommendation performance. Experiment results on different datasets show that our AODR model achieves better results compared to the baselines.

Mechanical Properties of Fibre-Metal Laminates Made of Natural/Synthetic Fibre Composites

Abstract: Mechanical properties are among the properties to be considered in designing and fabricating any composite to be used as a firewall blanket in the designated fire zone of an aircraft engine The main focus of this work was to study the tensile, compression, and flexural strengths of the combination of natural/synthetic fibres with metal laminates as reinforcement in a polymer matrix The materials included flax fibres, kenaf fibres, carbon fibres, aluminium alloy 2024, and epoxy The two-hybrid fibre metal laminate composites were made from different layers of natural/synthetic fibres with aluminium alloy of the same thickness The composites were made from carbon and flax fibre-reinforced aluminium alloy (CAFRALL) and carbon and kenaf fibre-reinforced aluminium alloy (CAKRALL) Based on the results obtained from the mechanical tests, the CAFRALL produced better mechanical properties, where it had the highest modulus of elasticity of 44 GPa Furthermore, the CAFRALL was 148% and 204% greater than the CAKRALL in terms of the tensile and compressive strengths, respectively, and it had a 337% lower flexural strength The results obtained in the study shows that both composites met the minimum characteristics required for use in the fire-designated zone of an aircraft engine due to their suitable mechanical properties