Browsing by Author "Adewole, K.S."

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    Data Mining for Knowledge Discovery from Financial Institution Database
    (Society for Science and Nature - International Journal of Engineering and Management Sciences. 3(4), 409 – 415, 2012) Abdulsalam, S.O.; Adewole, K.S.; Bashir, S.A.; Jimoh, R.G.; Olagunju, M.
    Due to its importance for the investment decision-making and risk management, describing and predicting stock represents a key topic in stock analysis. Stock or shares are valued and analyzed by stock investors using fundamental analysis and technical analysis. Stock investors describe and predict stock manually based on individual experiences. Employing manual procedure in analyzing stock, most especially technical analysis is always very cumbersome and inefficient; because of much time that is consumed in examining the past records of a respective firm, an investor is willing to invest in. This paper presents a better way of describing and predicting stock, especially technical analysis, by employing data mining techniques. A database was developed employing 360 records of daily activity summary (equities) and 78 records of weekly activity summary (equities) spanning through 18 months that is, from January 2007 to June 2008. These data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange being the financial institution that runs Nigerian stock market; using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc. A data mining software tool was developed and employed in identifying patterns and relationships from the database to generate new knowledge about the data set in the database through the use of data mining techniques that employ regression analysis. KEYWORDS: Data Mining, Stock Exchange, Financial Institution, Decision-Making, Risk Management, Regression Analysis
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    Development of an Intrusion Detection System in a Computer Network
    (Council for Innovative Research - International Journal of Computers and Technology, 12(5), 3479 – 3485, 2014) Babatunde, R.S.; Adewole, K.S.; Abdulsalam, S.O.; Isiaka, R.M.
    The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using Microsoft Visual Basic. Indexing terms: Intrusion detection system, illegitimate, misuse, network.
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    Frequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithm
    (Institute of Electrical & Electronics Engineers (IEEE) - African Journal of Computing and ICT, A Journal of the Institute of Electrical & Electronics Engineers (IEEE) Computer Chapter Nigeria Section. 7(3), 35 – 42, 2014) Adewole, K.S.; Akintola, A.G.; Ajiboye, A.R.; Abdulsalam, S.O.
    Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules. Keywords: Apriori Algorithm, data mining, database, strong rules & inventory