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Faculty of Engineering and Built Environment

Permanent URI for this communityhttp://ir-dev.dut.ac.za/handle/10321/9

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Author: search.filters.author.Pillay, NelendranHas files: Yes

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    An evaluation of digital and artificial intelligent tools in an electronic and computer engineering curriculum at a university of technology in South Africa
    (2023-12-08) Maharaj, Shaveen; Pillay, Nelendran
    Digital tools have become integral to higher education, offering a wide array of opportunities for improving the learning experience. This study explores the adoption and impact of digital tools in engineering education. The study employed a mixed-methods approach, utilizing quantitative data and qualitative data collection. Participants included both staff and students in a comprehensive analysis. The integration of technology in higher education has witnessed significant growth, encompassing educational software, learning management systems (LMS), and online platforms. In engineering education, tools like Moodle, MATLAB, Turnitin, Simulink, and MS Teams have gained prominence (Joksimović & Milosavljević, 2016). However, their effectiveness in achieving educational goals remains to be fully evaluated. One key advantage of digital tools is personalized learning. Advancements in technology, including artificial intelligence (AI), enable adaptive learning software like ChatGPT to tailor lesson plans based on individual needs. Furthermore, digital tools expand students' access to resources, such as online lectures, readings, and simulations, supplementing traditional teaching methods. They also facilitate collaborative learning and group projects through platforms like Moodle and Blackboard, enabling effective communication, document sharing, and teamwork. The findings of this study shed light on the current landscape of digital tools in engineering education. A SWOT analysis is applied to inform future strategies. The study emphasizes the need for a comprehensive evaluation of the effectiveness of these tools and their potential to transform engineering education. This research contributes to the ongoing dialogue on optimizing digital tools for personalized learning and collaborative education in engineering programs. It underscores the importance of evaluating their impact and tailoring their use to enhance the overall educational experience.
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    An assessment of environmental RF noise due to IoT deployment
    (MDPI AG, 2023-09) Ingala, Dominique G.K.; Pillay, Nelendran; Pillay, Aritha
    The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends.
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    Comparative study of binary classifiers for reducing false negative detection of melanoma in skin lesions
    (IEEE, 2022-10-27) Jooravan, Amith; Reddy, Serendra; Pillay, Nelendran
    Reliable and accurate classification of a skin lesion is essential to the early diagnosis of skin cancer, especially melanoma. Traditional classification methods require performing a biopsy on the lesion. The overlap of benign and malignant clinical features may lead to incorrect melanoma diagnosis and/or excising an excessive number of benign lesions. This paper focuses on the use of machine learning to aid physicians with the non-invasive classification methodology of skin lesions, whilst prioritising the minimization of false negative classification. The clinical features used are based on the ABCD rule, representing the asymmetry, border, colour and diameter of the lesion. The dermoscopic images chosen are of melanoma lesions less than 0,76mm in thickness which corresponds to the early stages of cancer. The investigated classification methods include K-Nearest neighbours (KNN), Naïve Bayes and linear support vector machine. (LSVM). This research proposes the use of a LSVM machine learning algorithm to classify a skin lesion as being either melanoma or non-melanoma with the lowest false negative rate of the investigated classification. Classification accuracy of 85% and a false negative rate of 5% is achieved.
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    Autonomous classification and spatial location of objects from stereoscopic image sequences for the visually impaired
    (IEEE, 2022-07-20) Sivate, Themba M.; Pillay, Nelendran; Moorgas, Kevin; Singh, Navin
    One of the main problems faced by visually impaired individuals is the inability or difficulty to identify objects. A visually impaired person usually wears glasses that help to enlarge or focus on nearby objects, and therefore heavily relies on physical touch to identify an object. There are challenges when walking on the road or navigating to a specific location since the vision is lost or reduced thereby increasing the risk of an accident. This paper proposes a simple portable machine vision system for assisting the visually impaired by providing auditory feedback of nearby objects in real-time. The proposed system consists of three main hardware components consisting of a single board computer, a wireless camera, and an earpiece module. YOLACT object detection library was used to detect objects from the captured image. The objects are converted to an audio signal using the Festival Speech Synthesis System. Experimental results show that the system is efficient and capable of providing audio feedback of detected objects to the visually impaired person in real-time.
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    Machine learning classifiers based on HoG features extracted from locomotive neutral section images
    (IEEE, 2022-10-27) Mcineka, Christopher Thembinkosi; Pillay, Nelendran
    This paper presents a comparative study on machine learning algorithms for neutral section image classification. The classifiers are trained by employing the Histogram of Oriented Gradient features that are extracted from the neutral section dataset [1]. A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 88kV three-phase supply coming from the national grid. While the main purpose of the neutral section is to separate phase voltages, electric locomotives can traverse through these phases by switching On and Off. This auto-switching is possible through induction magnets installed in between the rails and with magnet detection sensors installed underneath the locomotives. However, a computer vision model has been developed, trained, and tested with a neutral section dataset containing images having open and close markers [1]. This paper, therefore, utilises this dataset to provide performance comparison on several machine learning classification algorithms viz. Decision Tree, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Ensemble, Naïve Bayes, and Convolutional Neural Network. A confusion matrix, F1- measure and computation time are employed to measure the performance of each classifier. The MATLAB Classification Learner application was used to obtain the results. The results show that the Linear Support Vector Machine performs best when considering performance and prediction speed. The Linear Support Vector Machine achieved a training accuracy of 93.40% with a test accuracy reaching 94% at a prediction speed of 75 objects per second (computation time).