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CLUSTER PREDICTION MODEL FOR MARKET BASKET ANALYSIS: QUEST FOR BETTER ALTERNATIVES TO ASSOCIATIVE RULE MINING APPROACH Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i3.pp%p

Market basket analysis seeks to apply association rule mining on the massive sales transaction data. It yields an outcome that either aims to suppress product stock-up unnecessarily and/or product being stock-out. Such decision support system seeks to avoid the unnecessary demurrage and help businesses to keep their customers via better decision and improved service. Market data are time-bound on supply-demand value chain. With customer behavior varying in time, we seek to predict purchase of commonly combined itemset for a next period ? so that businesses can better support their decisions via adequate provisions of the required inventory. We use 3-KDD dataset and Delta Mall dataset ? adapting a time-clustering algorithm that examines buying behavior of customers, their preferences and frequency with which goods are purchased in common as a basket. Model yields average 162-rules for four-dataset from dataset. Result shows that previous basket items by random customers allow the selection purchase of items of similar value as best combined due to its shelf-placement using the concept of feature drift.

Extending Campus Network Via Intranet and IP-Telephony For Better Performance and Service Delivery: Meeting Organizational Goals Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
Journal of Applied Science, Engineering, Technology, and Education Vol. 1 No. 2 (2019)
Publisher : Yayasan Ahmar Cendekia Indonesia

The adoption of information and communication technology (ICT) tools and medium in todayâ€™s business, is geared to advance data processing tasks, timely exchange of data, easy access to the Internet at increased speed, extended memory to house large volumes of data, and better communications, etc. Businesses grow in lieu of advancing the services they offer; But, they require as a matter of urgency, a corresponding need for effective communication to grow exponentially. The Intranet provides an option to advance such via its many features (not limited to) collaborative communication channel, ease in business processes, etc. We posit that many businesses lack a clear strategy to implement an effective Intranet design. This often leads to investment profit loss, loss of time, unproductivity, and complete failure in achieving its set goals. Extending Ojugo and Eboka (2020) via a multi-service intranet, the study outcomes an infrastructure that allows the effective integration of data solutions via an open-source protocol, application, hardware, and software. Three common issues observed therein includes: packet loss, jitters, and latency. Jitters and packet loss can be resolved via increased bandwidth allocation; while, latency is minimized via upgrade in the infrastructure. Thus, our proposed solution seeks to provide users with mobility, resilience, economy, flexibility, and productivity with improved service delivery and performance. The study recommends that to harness the full benefits of Intranet and improve communication within businesses and organizations today, there is the need for a constant knowledge update is imperative, which will in turn improve effective communication in the implemented infrastructure.

Extending Campus Network Via Intranet and IP-Telephony For Better Performance and Service Delivery: Meeting Organizational Goals Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
Journal of Applied Science, Engineering, Technology, and Education Vol. 1 No. 2 (2019)
Publisher : Yayasan Ahmar Cendekia Indonesia

The adoption of information and communication technology (ICT) tools and medium in todayâ€™s business, is geared to advance data processing tasks, timely exchange of data, easy access to the Internet at increased speed, extended memory to house large volumes of data, and better communications, etc. Businesses grow in lieu of advancing the services they offer; But, they require as a matter of urgency, a corresponding need for effective communication to grow exponentially. The Intranet provides an option to advance such via its many features (not limited to) collaborative communication channel, ease in business processes, etc. We posit that many businesses lack a clear strategy to implement an effective Intranet design. This often leads to investment profit loss, loss of time, unproductivity, and complete failure in achieving its set goals. Extending Ojugo and Eboka (2020) via a multi-service intranet, the study outcomes an infrastructure that allows the effective integration of data solutions via an open-source protocol, application, hardware, and software. Three common issues observed therein includes: packet loss, jitters, and latency. Jitters and packet loss can be resolved via increased bandwidth allocation; while, latency is minimized via upgrade in the infrastructure. Thus, our proposed solution seeks to provide users with mobility, resilience, economy, flexibility, and productivity with improved service delivery and performance. The study recommends that to harness the full benefits of Intranet and improve communication within businesses and organizations today, there is the need for a constant knowledge update is imperative, which will in turn improve effective communication in the implemented infrastructure.

Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at â€˜agedâ€™ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%.

Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria Ojugo, Arnold Adimabua; Eboka, Andrew Okonji
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at â€˜agedâ€™ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%.

Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network Ojugo, Arnold Adimabua; Nwankwo, Obinna
JINAV: Journal of Information and Visualization Vol. 2 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav274

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.

Investigating The Unexpected Price Plummet And Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches Ojugo, Arnold Adimabua; Otakore, Oghenevwede Debby
Quantitative Economics and Management Studies Vol. 1 No. 3 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The energy market aims to manage risks associated with prices and volatility of oil asset. It is a capital-intensive market that is rippled with chaos and complex interactions among its demand-supply derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of price direction. Our study sought to investigate the reasons for the unexpected plummet in price of the energy market using evolutionary modeling â€“ which seeks to analyze input data and yield an optimal, complete solution that are tractable, robust and low-cost with tolerance of ambiguity, uncertainty and noise. We adopt the Gabillonâ€™s model to: (a) predict spots/futures prices, (b) investigate why previous predictions failed as to why price plummet, and (c) seek to critically evaluate values reached by both proposed deep learning model and the memetic algorithm by Ojugo and Allenotor (2017).

Predicting Futures Price And Contract Portfolios Using The ARIMA Model: A Case of Nigeriaâ€™s Bonny Light and Forcados Ojugo, Arnold Adimabua; Yoro, Rume Elizabeth
Quantitative Economics and Management Studies Vol. 1 No. 4 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

Market prediction has been the goal of many study as investors sought traded assets since the inception of the capital market. With each asset exchanged for money, investors seek to stay ahead the market trend in the hope of amassing profits. Businessesâ€™ growth (rise/fall) is evident upon their response to market behaviour. Thus, accurate prediction of the market often offers as its reward, enlarged financial portfolio. Market participants thus, seek to manage the risks associated with asset prices and its volatility, which can be rippled with chaos and complex tasks arising from a demand-supply curve. We seek to model the Oil market and forecast its price direction supported with empirical evidence using ARIMA model to analyze inputs in search of an optimal solution. We adopt the OPEC model to: (a) predict spot/futures-prices, (b) investigate why previous prediction was poor and price plummeted, and (c) compares value(s) from Ojugo and Yoro (2020) and Ojugo and Allenotor (2017). Results shows demand-supply curve rise (and a price rise) even though the policies and trend in real life scenario is currently experiencing a price plummet.

Intelligent Peer-To-Peer Banking Framework: Advancing The Frontiers of Agent Banking For Financial Inclusion In Nigeria Via Smartphones Ojugo, Arnold Adimabua; Otakore, Oghenevwede Debby
Quantitative Economics and Management Studies Vol. 1 No. 5 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The advent of the retail point of sale (POS) system as a critical component of the traditional retail infrastructure seeks to advance client payment-ease for goods and services rendered by vendors as well as the effective collection of funds by the vendor. It also aids the vendor to collect in advance monies that the client may wish to spend later on goods and services. Thus, the POS has since become a necessity in modern retail stores as its increased usage has seen a transformation from a single machine to a cloud and smart platforms. Our study seeks to model a conceptual framework for decentralized POS as adapted to smartphones. This will enhance cashless transaction irrespective of a customerâ€™s location globally and locally. Built around the block-chain technology, it seeks to minimize challenge(s) of time, installation requirements incurred with the adoption of automatic teller machine (ATM), location and citing of agent-banking in a rural area with low tele- and tech-penetration.

Multi-Agent Bayesian Framework For Parametric Selection In The Detection And Diagnosis of Tuberculosis Contagion In Nigeria Ojugo, Arnold Adimabua; Nwankwo, Obinna
JINAV: Journal of Information and Visualization Vol. 2 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav375

Decision making has become quite a critical factor in our everyday living. The provision of data alongside its consequent processing has further sought to extend and expand our reasoning faculties as well as effectively aid proper decision making. But data is daily, produced at an exponential rapid rate and the volume in amount of data churned out to be processed even more so that we now require data storage optimization techniques to process such humongous volume of data. These have today, necessitated the need for advancement in data mining process. With the tremendous advances made in data mining, machine learning, storage virtualization and optimization â€“ amongst other fields of computing â€“ researchers now seek a new paradigm and platform called data science. This field today has become quite imperative as it seeks to provide beneficial support in constructing models and algorithms that can effectively assist domain experts and practitioners to make comprehensive and sound decisions regarding potential problematic cases. We focus on modeling social graph using implicit suggest algorithm in medical diagnosis to effectively respond to problematic cases of Tuberculosis (TB) in Nigeria. We introduce spectral clustering and Bayesian Network, construct algorithms cum models for predicting potential problematic cases in Tuberculosis as well as compare the algorithms based on data samples collected from an Epidemiology laboratory at the Federal Medical Center Asaba in Delta State of Nigeria. The volume of data was collated and divided into two data sets which are the training dataset and the investigation dataset. The model constructed by this study has shown a high predictive capability strength compared to other models presented on similar studies.

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