SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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[ISSN 2519-5883]
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Communities in DSpace

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Now showing 1 - 5 of 7

Recent Submissions

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Drowsiness detection system during driving using IoT and Machine Learning
(Strathmore University, 2024) Somo, A. M.
The interest in implementing drowsiness detection systems through the integration of IoT and Machine Learning, especially in the automotive and transportation sector is growing significantly. By utilizing this technology, it becomes possible to monitor and identify instances of driver drowsiness, addressing safety concerns related to fatigue related accidents. However, the widespread adoption and application of these drowsiness detection systems encounters some challenges such as poor telecommunication for network connectivity for IoT devices and ensuring efficient resource utilization within the constraints of Machine Learning. These are the main challenges faced by drowsiness detection systems during driving. This study designs and implements an efficient drowsiness detection system that utilizes Machine Learning and IoT technologies. The approach will involve the deployment of an IoT connected sensor, which is a camera within the vehicle’s environment. This sensor will collect real-time data on the driver’s eye movements. This raw data is then preprocessed to extract the relevant features and then processed information will be fed into the Machine Learning model. This model, which is optimized for low-resource environments will be able to perform real time drowsiness classification. Our model will employ CV2, KNN and Dlib algorithms independently. The purpose of implementing these distinct machine learning algorithms is to conduct a comprehensive assessment and comparison of their performance. By doing so, we will be able to determine which algorithm yields the best results in terms of accuracy, thus enabling us to make an informed decision. The implemented solution will aim to enhance transparency and consistency in the acquisition of drowsiness related data. This initiative will make things easier for drivers and demonstrate how we can use IoT and Machine Learning technologies to solve problems around detecting drowsiness. By using both hardware and software, the system will show how we can use IoT concepts to solve common problems in drowsiness detection. The hardware we're using includes a computer camera as the sensors, and we'll also use the OpenCV framework libraries to train the machine learning model. The collected data associated with the drowsiness levels will then be transmitted to a central server for real time analysis. The data will undergo thorough processing and assessment to identify patterns of drowsiness instances. Furthermore, a User-friendly python interface will be developed to provide clients with visual insights into the detected drowsiness instances. Keywords – Internet of things (IoT)
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Utilizing Convolution Neural Networks for enhanced lung cancer classification through CT scan analysis
(Strathmore University, 2024) Korir, P. J.
Lung cancer is the major cause of cancer mortality, which poses significant challenges to accurate and timely diagnosis, especially in resource constrained regions like Kenya. The traditional method of diagnosing lung cancer through Computed Tomography (CT) scans often involves manual interpretation, leading to potential delays and inaccuracies. This research aims to harness the power of Artificial Intelligence (AI) to improve the diagnostic process. This research study developed a Convolution Neural Network (CNN) model for enhanced classification of cancer utilizing CT scan images by fine-tuning the pre-trained ResNet50 architecture. Utilizing Pytorch, a leading deep learning framework for computer vision, the model was trained on a curated dataset from the public Lung Image Database Consortium (LIDC), a medical imaging database for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis The collected CT scan image include various types of lung cancer, such as adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal tissue. Data pre-processing techniques such as resizing, normalization, converting and data augmentation techniques were used to ensure compatibility with the pre-trained model. The model’s performance was evaluated with a range of metrics, demonstrating an accuracy of 87.5%, precision of 80.97%, and an F1 score of 77.4%. These results indicate a promising capability for the model to accurately classify types of lung cancer, supporting its potential use in clinical settings. The pre-trained model was then integrated into a web-based application using the Flask framework, with a frontend designed with Vue.js to provide an intuitive user experience for image upload functionality. The Flask API facilitates communication between the frontend and the ResNet 50-based machine learning model. When a CT scan image is uploaded, it is sent to the Flask backend as an HTTP request. The Flask application processes these requests, extracting the image data and preparing it for analysis by interfacing with the ResNet 50 model, which then classifies the images and retrieves the results.
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A Model for mapping crime hotspots using neural networks: a case of Nairobi
(Strathmore University, 2024) Echessa, R. G.
Since the inception of the first modernized police agency, the primary objective of police organizations has been to prevent crime. Law enforcement, police, and crime reduction agencies commonly used hotspot mapping, an analytical technique, to visually determine the locations where a crime was most prevalent. This assisted in decision-making to determine the deployment of resources in target areas. This study aimed to investigate crime mapping techniques in crime analysis and suggest ways to enhance the implementation of crime mapping in Nairobi. Beginning with a historical analysis of GIS and crime mapping, the study then moved on to a consideration of the significance of geography in dealing with crime concerns. Neural Networks and K-Means machine learning models were used, and data was collected through quantitative and qualitative means in two phases. X was utilized in the first phase to collect information from the general public and important informants. The second phase involved collecting crime hotspot coordinates using a participative Geographic Information System. The study focused on utilizing social media data and machine learning techniques, particularly the KMEANS with NN (Neural Network) model, to identify and map crime hotspots in Nairobi. By analyzing crime-related tweets and categorizing them as either positive, negative or neutral using this NN (Neural Network) model then clustering them as either high risk or low risk using K-Means, the study achieved high accuracy, precision, recall, and Fl-Score, suggesting the effectiveness of this approach for crime prediction and prevention. Keywords: Hotspot mapping, X, Machine learning, crime, Neural Networks, K-Means
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Role of emotional intelligence in transgenerational succession among family businesses in Nairobi County
(Strathmore University, 2024) Kairu, I.
Transgenerational succession has been a major concern for many businesses within the world due to conflicts between the owners, their families and management teams. This has consistently derailed the operations of the institutions. In Kenya, more than 70% of businesses are family-owned, but only 10%-15% survive past two generations, hence there is need to understand what can help to improve the succession within these family firms. This survey sought to establish the role of emotional intelligence during transgenerational succession in family businesses in Nairobi County. This was studied within the lenses of key emotional intelligence aspects; self-awareness, self-management, social awareness and self-regulation in transgenerational succession in family businesses. The research was premised on the social exchange theory, emotional contagion theory and the family systems theory. The study used a positivist paradigm and a descriptive research design in the investigation. Population of the survey was the registered (530) firms under the Association of Family-Owned Businesses. Purposive sampling was used in the selection of participants with only firms that have undergone transgenerational succession being included in the research. A sample of 228 firms that have gone through succession was considered for the research. A structured research questionnaire was utilized in the data collection with both drop and pick method as well as use of Google forms. The study instrument was pretested to determine its reliability and validity. Analysis of the study data was conducted using descriptive and inferential statistics. The findings showed that there was a weak positive correlation between emotional intelligence (i.e., self-awareness, self-management, social awareness, self-regulation) and transgenerational succession. Regression results revealed that overall, there was a positive and statistically significant relationship between emotional intelligence and transgenerational succession in family businesses. However, the relationship with individual measures of emotional intelligence offered varied results. Self-awareness, self-management and self-regulation had a positive and significant effect on the transgenerational succession in family businesses. On the other hand, social-awareness did not have a significant effect on transgenerational succession in the family businesses studied. Based on these conclusions, the study recommended that family businesses should create a supportive environment where individuals feel comfortable expressing their thoughts and feelings. Additionally, the study recommends that family businesses should prioritize the establishment of clear protocols to facilitate positive resolution of disputes as well as promote trust and confidence among stakeholders during transgenerational succession. The study notes that emotional intelligence may have varying long term effects and therefore recommends longitudinal studies tracking family businesses over multiple generations. Keywords: Self-Awareness, Self-Management, Social Awareness and Self-Regulation,
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Assess factors influencing adoption of digital transformation among manufacturing sector firms in Nairobi region
(Strathmore University, 2024) Kipkirui, F. M.
The rapid changes in technological advancement implications are for companies to keep up with the trends and exploit them to their advantage. The manufacturing sector has been signaled as one of the industries that have been slow in its digital transformation. Kenya’s manufacturing sector has not been able to leverage digital transformation to enhance its performance and is behind in meeting its Vision 2030 goals. Therefore, this study assessed the influence of technological, organisational, and environmental factors on DT in Kenya’s manufacturing firms in the Nairobi region. The study was anchored on the technology organisation and environment (TOE) Framework and Diffusion of Innovation (DOI) theory. A positivist research philosophy fits with the study’s objective and is thus adopted. A descriptive correlational design is used as the study aims to describe the association between factors that may influence digital transformation. The target population was 725 firms from which a sample size of 176 was selected as the units of analysis. In each of the 176 firms, a senior manager involved in strategy implementation was purposively and conveniently sampled. The data was gathered using a Likert scale-based questionnaire that was checked for validity and reliability in a pilot study from which the internal consistency of items was assessed. The output indicated that technology, organisation, and environment factors together explained 47.5 % of the change in DT adoption in manufacturing firms and was significant at the 95 % confidence level. Independently, technological factors had a .577 positive and statistically significant effect on DT adoption. The study therefore concludes that increasing technological factors in manufacturing firms will contribute to an increase in DT adoption while organizational and environmental factors do not have any effects on DT adoption. the study recommends that manufacturing firms focus on using technology that has affords them a relative advantage over the existing technology. Keywords: Digital transformation, Manufacturing, Technological, Organizational, Environmental factors.