An electronic records management system (ERMS) is tightly linked with most of the daily activities of educational organisations and leads to enhance their performance and decision-making. The aim of this article is to identify the significant factors that could influence the ERMS adoption in higher professional education (HPE). The methodology of this article started with identifying the factors through theory analysis and literature and also recommended by experts. Technology–organisation–environment (TOE) theory was used for factor classification. Qualitative approach was used through the interview with experts to validate and verify the proposed framework. This article presents the results of a study which identifies the issues involved in the utilisation and adoption of ERMS. More than 100 previous works and six well-known theories were critically reviewed to identify the main factors for successful ERMS adoption in different areas with the aim of proposing a taxonomic framework that can depict and identify the main factors that have an impact on the success of ERMS adoption. The proposed framework includes 11 factors categorised into three dimensions. The framework is validated and verified by experts. The adoption factors identified here provide a sound theoretical basis for research to understand, support and facilitate the adoption of ERMS to HPE benefit. The proposed framework could help to improve educational outcomes and the successful implementation of ERMS.

This review paper addresses the research question of the significance of explainability in AI and the role of integrating KG and RL to enhance Explainable Recommender Systems (XRS). It surveys articles published from January 2015 to March 2024 on XRS, focusing on knowledge graphs (KGs) and reinforcement learning (RL) for achieving explainability in recommender systems. Employing a systematic methodology, it introduces a custom Python-based web scraper to efficiently navigate and extract relevant academic research papers from IEEE, ScienceDirect (Elsevier), ACM, and Springer online databases. The study encompasses the PRISMA methodology to conduct a thorough analysis and identify pertinent research works. This systematic literature review aims to provide a unified view of the field by reviewing eight existing XRS literature reviews and 29 pertinent XRS studies involving KG and RL from the specified period. It categorizes and analyses relevant research papers based on their implementation methodologies and explores significant contributions, encompassing perspectives on model-agnostic and model-intrinsic explanations.

Binarization is an important process in image enhancement and analysis. Currently, numerous binarization techniques have been reported in the literature. These binarization methods produce binary images from color or gray-level images. This article highlights an extensive review on various binarization approaches which are also referred to as thresholding methods. These methods are grouped into seven categories according to the employed features and techniques: histogram shape-based, clustering-based, entropy-based, object-attribute-based, spatial, local and hybrid methods. Most active binarization researchers exploit several initial information from the source image such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation and local gray level surface with a special attention to statistical information description features of image used in recent thresholding techniques.