Search results for: Mpho Gololo

Authors: Kwazikwenkosi Sikhakhane, Suvendi Rimer, Mpho Gololo, Khmaies Oahada, Adnan Abu-Mahfouz

Abstract:

Medical ultrasound imaging is a crucial diagnostic technique due to its affordability and non-invasiveness compared to other imaging methods. However, the presence of speckle noise, which is a form of multiplicative noise, poses a significant obstacle to obtaining clear and accurate images in ultrasound imaging. Speckle noise reduces image quality by decreasing contrast, resolution, and signal-to-noise ratio (SNR). This makes it difficult for medical professionals to interpret ultrasound images accurately. To address this issue, various techniques have been developed to reduce speckle noise in ultrasound images, which improves image quality. This paper aims to review some of these techniques, highlighting the advantages and disadvantages of each algorithm and identifying the scenarios in which they work most effectively.

Keywords: image processing, noise, speckle, ultrasound

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

Abstract:

Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Mpho Agnes Mbeo, Patient Rambe

Abstract:

The thirteenth year of the Central University of Technology, Free State’s (CUT) transition from a vocational and professional training orientation institution (i.e. a technikon) into a university with a strong research focus has neither been a smooth nor an easy one. At the heart of this transition was the need to transform the psychological faculties of academic and research staffs compliment who were accustomed to training graduates for industrial placement. The lack of a culture of research that fully embraces a strong ethos of conducting world-class research needed to be addressed. The induction and socialisation of academic staff into the development and execution of cutting-edge research also required the provision of research support and the creation of a conducive academic environment for research, both for emerging and non-research active academics. Drawing on ten cases, comprising four heads of departments, three prolific established researchers, and three emerging researchers, this study explores the challenges faced in establishing a strong research culture at the university. Furthermore, it gives an account of the extent to which the current research interventions have addressed the perceivably “missing research culture”, and the implications of these interventions for knowledge management. Evidence suggests that the endowment of an ideal institutional research environment (comprising strong internet networks, persistent connectivity on and off campus), research peer mentorship, and growing publication outputs should be matched by a coherent research incentive culture and strong research leadership. This is critical to building new knowledge and entrenching knowledge management founded on communities of practice and scholarly networking through the documentation and communication of research findings. The study concludes that the multiple policy documents set for the different domains of research may be creating pressure on researchers to engage research activities and increase output at the expense of research quality.

Keywords: Central University of Technology, performance, publication, research culture, university

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Authors: Lintle Mohase, Ninikoe Lebusa, Mpho Stephen Mafa

Abstract:

Wheat is an economically important cereal crop. However, the changing climatic conditions that intensify drought in production areas, and additional pest infestation, such as the Russian wheat aphid (RWA, Diuraphis noxia), severely hamper its production. Drought and pest management require an additional water supply through irrigation and applying inorganic nutrients (including silicon) as alternative strategies to mitigate the stress effects. Therefore, other approaches are needed to enhance wheat productivity during drought stress and aphid abundance. Two wheat cultivars were raised under greenhouse conditions, exposed to drought stress, and treated with silicon before infestation with the South African RWA biotype 2 (RWASA2). The morphological evaluations showed that severe drought or a combination of drought and infestation significantly reduced the plant height of wheat cultivars. Silicon treatment did not alleviate the growth reduction. The biochemical responses were measured using spectrophotometric assays with specific substrates. An evaluation of the enzyme activities associated with oxidative stress and defence responses indicated that drought stress increased NADPH oxidase activity, while silicon treatment significantly reduced it in drought-stressed and infested plants. At 48 and 72 hours sampling periods, a combination of silicon, drought and infestation treatment significantly increased peroxidase activity compared to drought and infestation treatment. The treatment also increased β-1,3-glucanase activity 72 hours after infestation. In addition, silicon and drought treatment increased glucose but reduced sucrose accumulation. Furthermore, silicon, drought, and infestation treatment combinations reduced the sucrose content. Finally, silicon significantly increased the trehalose content under severe drought and infestation, evident at 48 and 72-hour sampling periods. Our findings shed light on silicon’s ability to induce protective biochemical responses during drought and aphid infestation.

Keywords: drought, enzyme activity, silicon, soluble sugars, Russian wheat aphid, wheat

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 121