Department of Computer Science

Permanent URI for this collectionhttp://erepository.kibu.ac.ke/handle/123456789/188

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Browsing Department of Computer Science by Subject "Big Data Analytics"

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    Big Data Analytics and Electronic Resource Usage in Academic Libraries: A Case Study of a Private University in Kenya
    (European Scientific Journal, 2019-05-01) Wakahia, Samuel Kairigo
    The purpose of the study was to apply Big Data analytics as a tool for evaluating electronic resources usage in the academic library setup in Kenya with reference to the library of one private university. Log files of postgraduate students were mined from the server where the offsite access platform (ezproxy) has been installed. Descriptive statistical techniques such as mean, standard deviation and percentages were computed. Data was transferred to the Statistical Package for Social Sciences (SPSS) software was aided in the analysis. Results revealed that in terms of usage intensity, total URL count was 2,352, the highest user made 283 downloads and the mean URL count was 49 downloads. Further findings revealed that no user utilized more than 5 databases over a period of one year. The mean usage intensity score for respondents who were trained or orientated on e-resource usage was above average at 69.0 while those who had not received training were below average at 29.8. It was concluded that big data analytics is a necessary and powerful tool for investigating electronic resources seeking and usage trends and patterns within Kenyan university libraries. Through big data analysis and data mining, usage patterns and trends such as usage intensity that might not have accurately been revealed through other tools are unearthed. Big data analytics has revealed user preferences and intensity of utilization of various databases and helped in detection of redundant databases. From the usage patterns, it was clear that the level of utilization of the University library’s e-resource platform was very low. Most of the databases accessible through the platform were redundant. Further, only two databases namely e-book central and ebscohost were the most popular among users while the rest were barely being utilized if at all. For most students, just one or two databases were sufficient in meeting their research needs. An integrated data analytics model for investigating university library’s e-resources usage is proposed.
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    Robotics in Food Manufacturing Industry in the Industry 4.0 Era
    (International Journal of Computer Science and Mobile Computing, 2023-08-30) Barasa, Yonah; Etene, Yonah
    Robotics is used in food manufacturing in multiple processes. Prominent existing applications are: preparation of soil, seeding, water sprinklers, harvest controlling, cutting the harvests, food extractions, processing, packaging, loading/unloading, food warehouse operations, and food retail operations. Robots enhancements in Industry 4.0 framework have enabled cognitive and collaboration capabilities related to dynamics, kinematics, self-diagnostics, and self-control. Robots can identify and change their robotic hands as per process requirement. Robotics in food manufacturing is used to improve food quality and hygiene, increasing quality and its consistency, improving process performance, improving production rate and productivity, production reconfigurability, improving supply chain resilience and responsiveness, improving food traceability, reducing food loss and wastage, and improving workplace safety. Industry 4.0 allows robotic cyber physical systems (RCPS), HMI interfacing with robots through augmented reality thus ensuring remote real time precision monitoring and control of field robots. Robots with attached cameras with machine learning for image visualization analytics can ensure quality assurance and control of food products during all the stages of the food manufacturing logistics. The key drivers for adopting robotics in food manufacturing are cloud manufacturing, Internet of Things (attached with robotic controller cards), virtual reality simulations, augmented reality for HMI controls, additive manufacturing in food processing, integration of Industry 4.0 systems (IoT with blockchains, edge computing, big data analytics, and machine learning), and autonomous automation software. Robotics in food manufacturing using Industry 4.0 capabilities is a positive change. However, there are ethical and financial challenges. Increased usage of robotics can affect employment of food harvesting and manufacturing workers in developed as well as developing economies. Further, economics of using robotics in food manufacturing is not yet justified in developing nations given the high initial costs and prolonged time to break even. Overall, robotics in food manufacturing using Industry 4.0 has many technical and production advantages but ethical and financial viability is an ongoing research domain.