Pan African Conference on Science, Computing and Telecommunications (PACT) 2017

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 20
  • Item
    Foundations of an autonomic manager for maintaining quality of service in enterprise data warehouses
    (Strathmore University, 2017) Omondi, Allan O.; Ateya, Ismail L.; Wanyembi, Gregory N.
    Data stored in an Enterprise Data Warehouse (EDW) is an essential asset to enterprises. Through efficient access to data (where efficiency is quantitatively measured in terms of speed), SMEs can enhance their growth, productivity, and global competitiveness. This can in turn lead to a positive impact on a country's Gross Domestic Product. The purpose of this paper is to present the building blocks required to maximize the speed of data access from EDWs in a self-adaptive manner. Reinforcement Learning (RL) in a fully observable, stochastic environment is proposed. The subsequent solution to a Markov Decision Process is highlighted as the core part of the RL.
  • Item
    An Application of association rule learning in recommender systems for e-Commerce and its effect on marketing
    (Strathmore University, 2017) Mbugua, Anne W.; Omondi, Allan O.
    High annual customer churn rates and low customer attractions caused by poor marketing recommendations inhibit enterprises from making as much profit as they should. The purpose of this research was to derive a more optimized association rule learning algorithm that can be used in a web-based recommender system for small-scale enterprises. The method used was a case study approach on a small-scale enterprise called Makewa Hardware located in Ruiru, Kenya. Having access to the enterprise supported the use of the agile methodology, more specifically, extreme programming in the development of the system that applied the algorithm. A sample of training data consisting of transactions made in the past was obtained from the enterprise in order to create the machine learning aspects of the algorithm. The results howed that the derived association rule learning algorithm was able to learn and generate its own frequent-item-set and use this to give appropriate recommendations to customers. The results revealed the system’s ability to make more accurate recommendations.This was based on the pattern of purchases made from the hardware store by various customers. The recommendations were given on a weekly basis. The implication of the results on the subjects showed that more business owners are open to having intelligent systems help make and predict their sales. The findings can be applied not only in hardware stores but also in other retail stores. Future research can ensure that a normal dataset can be transformed into a market basket without it losing important information.
  • Item
    Techniques for evaluation and selection of partners for construction projects
    (Strathmore University, 2017) Musumba, George Wamamu; Nyongesa, Henry O.; Kanyi, Patrick W.; Wario, Ruth D.
    Inter-enterprise collaborations require careful evaluations of partner enterprises and their attributes. Evaluation of partners for a project is a multi-criteria decision making process. The project initiator defines multiple criteria to be used in the selection of suitable partners. This study compares three different multicriteria decision making techniques. Analytical Hierarchy Process (AHP) uses pairwise comparisons of crisp numerical values to derive weights of importance of partners. Fuzzy AHP (FAHP) uses pairwise comparisons of fuzzy values to derive weights of importance. Reduced Group Fuzzy AHP (RGFAHP) computes geometric mean of lower and upper bound fuzzy values to derive weights of importance. Eighty persons evaluated five companies to do structural engineering works for a large building. Their evaluation values were subjected to these algorithms. Total mean relative weights of partners were 0.9936, 0.9968 and 0.9866 with errors of 0.0064, 0.0032 and 0.0134 with time complexities of n(n+6), n(n-1)/2 and n(n-1) for AHP, FAHP and RGFAHP respectively. AHP is effective when dealing with crisp evaluation values while FAHP is effective for fuzzy evaluation values. RGFAHP combines fuzzy approximate reasoning with conventional AHP, reduces the number of comparisons when a large number of attributes are used and deals with imprecise evaluators' judgement.
  • Item
    Algorithmic prediction of internet technology utilization in learning
    (Strathmore University, 2017) Khakata, Esther Nyokabi; Msanjila, Simon Samuel; Omwenga, Vincent Oteke
    Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.
  • Item
    Embedded system for vehicle speed monitoring
    (Strathmore University, 2017) Murakaru, Anne Wamuyu; Orero, Joseph Onderi
    This paper investigated the impact of current approaches taken to curb speeding of public service vehicles in Kenya. A qualitative research pointed out that the existing systems are inefficient and ineffective in monitoring speeding and reporting speeding offenses to the relevant authorities. In addition, public service vehicle drivers are not aware of the current speed limit zones in various locations given that the National Transport Service Authority (NTSA) periodically changes speed limit regulations along particular roads. An embedded system for vehicle speed monitoring was proposed and tested. The objective was to design a real time microcontroller based system for mapping speed limit zones and reporting cases of speeding violations to the relevant authorities through an android mobile application. An LCD Screen was integrated to the microcontroller to provide a visual display of the vehicle location and speed limit within the location. In the event of speeding, an audio alert is triggered to notify the driver and an SMS is sent from the GSM module to a central server. Through this system, public service drivers are aware of the speed limit zones on various roads and are alerted once the speed limit is exceeded. The real time reporting system enables the transport agencies and other regulatory bodies to equally monitor speeding vehicles on the roads.