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The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency–Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching–learning activities are aligned with regulatory body expectations. [ABSTRACT FROM AUTHOR]

Edible Bird Nest (EBN) constitutes a thriving industry in several Southeast Asian nations where its value chain encompasses several critical processes starting with nest harvesting to processing, and finally sales. However, a detailed review addressing EBN production from an intelligent system perspective is currently missing. Hence, this paper aims to document a comprehensive study of the various parts of the EBN value chain, where machine-intelligence has been incorporated into various solutions. We classified all the EBN processes into three primary segments: farming and production, quality control, and market analysis. In farming and production, two key areas emerge. First, there is process analysis which involves Failure Mode and Effect Analysis (FMEA) as well as profit, cost, and efficiency analysis, while swiftlet house monitoring pinpoints the optimal environment for swiftlets to flourish. On the other hand, works related to the second segment of quality control can be divided two primary approaches: image analysis which involves either auto-grading or automatic impurities inspection, and chemical analysis to ascertain the origin and authenticity of the EBN. The third and final domain of market analysis, covers both business strategy and customer behavior analysis. We have identified the integration of cutting-edge intelligent methods in each area while offering recommendations for future work. Our findings also unveiled intricate patterns, networks, relationships, and trends in the application of machine intelligence within the EBN value chain. These insights highlight many underexplored areas as well as several strategic aspects in this emerging industry. [ABSTRACT FROM AUTHOR]

Micro-expression (ME) recognition has attracted numerous interests within the computer vision circle in different contexts particularly, localization, magnification, and recognition. Challenges in these areas remain relevant due to the nature of ME's split-second transition with minute intensity levels. In this paper, a comprehensive state-of-the-art analysis of ME recognition and detection challenges are provided. Contemporary solutions are categorized into low-level, mid-level, and high-level solutions with a review of their characteristics and performances. This paper also provides possible extensions to basic methods, highlight, and predict emerging trends. A thorough analysis of mainstream ME datasets is also provided by elucidating each of their advantages and limitations. This survey gives readers an understanding of ME recognition and an appreciation of future research direction in ME recognition systems. [ABSTRACT FROM AUTHOR]