Study of Critical Success Factors in Engineering Education Curriculum Development Using Six-Sigma Methodology

Highly skilful and competent human capital especially engineers are critically needed to accelerate and contribute efficiently towards the development of technology in view of spearheading much of Malaysia’s transformation agendas. Hence, Institutions of Higher Learning (IHL) are required to play a pivotal role to produce graduates, who are equipped with sound engineering based knowledge, technical competencies along with related desired attributes to support the nation’s transformation roadmap. As employability of graduates forms as one of the important Key Result Areas (KRA) of all IHL, interesting active learners teaching methodologies such as Outcome Based Education (OBE), Problem Based Learning (PBL), Project Base Learning, Case Based Learning (CBL) and TRIZ has been infused in the curriculum to ensure that connectivity prevails between classroom instructions and the curriculum contents besides producing industry ready graduates. However, rapid technological development at work front has created an impact towards employment of graduates, which calls for an immediate redefining of current technical attributes to cultivate elements of creativity or “out of box” thinking approach to spark ideas of highly innovative that are of commercial value to generate revenue for the sustainability of businesses. Hence, this study is to determine the critical success factors required in engineering education curriculum to produce engineering graduates of global minded workforce where adaptability to new conditions and creativity for innovations are much sought after by industries. (Abstract by authors

Investigating university-industry partnership of higher engineering education using cause-effect analysis and multi-criteria decision making: A Malaysian perspective

In recent years, there has been growing interest towards integrating industry into the teaching and learning processes. This is due to many factors including increased concerns about the mismatch between the skills and abilities of the talent pool, strengthening partnership and improving quality of engineering education. Thus, greater emphasis on the teaching and learning processes to enhance the students’ learning experience leads to the university-industry partnership to the forefront interest of the university. On the other hand, exclusion of industry’s engagement in the teaching and learning processes have been identified as the main source of chronic criticism on the higher engineering education segment in recent years. This study demonstrates a research model that hypothesised the influence of teaching and learning domains on the university-industry partnership towards enhancing the learning experience of the engineering students. Using the structural equation modelling (SEM), the hypothesis was tested on the primary data collected from 212 communities of the industry. Furthermore, the study investigated the preference of industry on the type of linkages to foster university-industry partnership using analytical hierarchy process (AHP). The results revealed that nine out of the thirteen hypotheses had significant associations including six direct paths and three indirect effects in the model. The findings indicated the need for industry-university partnership in three main constructs including cooperation in education, the mobility of people and intellectual enhancement. Moreover, internship programme was the important linkage in achieving the overall university-industry partnerships goals, followed by the staff training programme, academic development, consultancy work, student learning activity and publication activity. In summary, the study demonstrates that teaching and learning relevance could be enhanced through optimizing industry’s enrichment activities into the learning process, improving the measures for accreditation in narrowing the gap between theory and practice and proactively improving the quality of teaching by exploring the staff training programmes

Study of the Effectiveness of the Implementation of Washington Accord in Malaysia's Engineering Undergraduate Programme Using SEM

Transformation of engineering undergraduate programs towards global practice of outcomes based accreditation (OBA) entails a huge challenge to universities as there is mounting pressure for infusion of desirable and yet measurable graduate competencies into its offered programs in order to fulfil professional bodies accreditation criteria. Hence, in view of providing a sense of direction to the local engineering education community, Washington Accord (WA) ensures that accredited programmes synonymously mean that its graduates are equipped with 12 professional abilities obtained through an innovative outcomes-based engineering programme. However, further complication unfolds as industries are continuously evolving to meet rapid global demand and practice; hence defining set of competencies that can accurately map attributes outlined by industry with the aim of addressing “skill-gap” will continue to be a challenging endeavour for higher education sector. This paper intends to investigate the effectiveness of the OBA criteria outlined by WA in terms of attainment of graduate attributes which would enable graduates to take on challenging careers in the industry. Hence, the study will focus on identifying the relationship between the five outcome-based accredited criteria and the attainment of twelve graduate attributes outlined by WA in view of producing a conceptual framework which are then tested using Structural Equation Modelling (SEM). (Abstract by authors

Design of an Automated System for Door Set Measurement Using IoT Technologies: A Manufacturer’s Perspective

Small and medium-sized based manufacturing industry, despite being the most important industry in an economy is still grappling with unsteady processes, futile effort on controlling disturbances and erroneous deviation of its end-product resulting in waste of raw materials. Thus, necessitates to push Industry 4.0 (I4.0) higher at their agenda to increase manufacturing efficiency. Considering this, the manufacturer in this study is a mid-tier supplier of high-quality wood door for residential spaces and who frequently deals with modular and customizable door sets. This study makes the following contributions: (1) develop a microcontroller-driven automated system to accurately measure dimensions of door sets; (2) establish a communication to store-retrieve raw data using IoT technologies and (3) develop graphical user interface as diagnostic tool that generates statistical reports as data analytics. A low-cost ESP8266 (ESP) microcontroller Wi-Fi module interfaces with a rotary encoder used to monitor the displacement of door set for error deviation. Data is sent using IoT-based ThingSpeak application. Results satisfactorily record accuracy on error deviation which set between 0 and 0.6 mm based on the percentages of doors. Statistical reports, such as error deviation, percentage of doors within the allowed error, and production rate were remotely accessed to gauge productivity status. Emerging technologies of automation and Internet of Things that underpin concepts introduced by I4.0 are viewed as an antidote to manufacturing issues as it facilitates the creation of smart monitoring and controlling system for improved productivity yield

Effect of artificial night lighting on the growth of loose head lettuce in hydroponic system

Supplemental LEDs lighting technology has been used as the promising lighting source in hydroponic cultures for sustainable production in urban agriculture. It could be the solution to address the growing concern about food safety, environmental impacts, bad weather, and efficient energy usage in agricultural production. In this study, the response of loose head lettuce toward the irradiance of the supplemental red-blue LED light with different power (Watt [W]) was investigated by comparing the treated lettuce with the lettuce cultured under only natural light. The lettuce plants were treated with red LED (640-660 nm) + blue LED (440-450 nm). The power output of the LEDs was specified to 3, 6, 9, 15, and 20 W. The lettuce plants were hydroponically cultured with 8 h red-blue LEDs light exposure (from 12 to 8 am) and 16 h without the red-blue LEDs light exposure (from 8 pm to 12 am) at average air temperatures of 31/28 ºC (day/ night) for 50 days (7 weeks). On the harvesting day, the average shoot heights of the lettuce that was treated with 3, 6, 9, 15, 20 LEDs and natural light were 25.00, 24.75, 20.75, 19.88, 17.63, and 12.63 cm, respectively. The lettuce that was exposed to the 3 W LEDs had the highest shoot height compared to those that were exposed to LEDs with other power outputs. The average fresh weights of the lettuce that was treated with 3, 6, 9, 15, 20 W LEDs and natural light were 27.25, 24.75, 21.25, 19.88, 18.38, and 15.75 g, respectively. The results showed that the fresh weight of the lettuce that was irradiated with 3 W LED light was significantly higher compared to the lettuce that was exposed to LEDs with other power outputs. Hence, it can be concluded that supplementary LEDs lighting technology can be used as an alternative lighting source to improve the growth of lettuce in hydroponic systems. Moreover, the use of 3 W LEDs in hydroponic systems could yield a higher shoot weight and fresh weight

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set

An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization

Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier

Smart Assistive Trolley for Elderly Care and Independence

As people get older their shopping experience gets harder, as they have to keep pushing a trolley that is made of steel, as well as they keep on adding items through their shopping journey the trolley gets heavier. As a result, this project aims to help these senior citizens by providing a robotic trolley that follows them during their journey (through face detection) without the need of any physical interreference, in addition to calculating the walking distance providing the property of showing how many meters they have walked. The trolley has been fabricated with the walking distance estimation feature that has been accomplished by the ultrasonic sensor after the camera detects the user and tracks him. This strategy is achieved by using an OpenCV library that is particularly could be used in Python programming language. The results have shown a great improvement in the elderly individuals’ lives, as it supports them by giving more comfort and extra liberty to the shopping experience