A Model for Estimation of Malaria Prediction in North-East Zone, Nigeria (Published)
This study examined Autogressive Integrated Moving Average (ARIMA) model for malaria prediction in the North-Eastern geo-political zone, Nigeria. Cross-sectional research design was adopted in this study as data were collected at a specific period of time. The datasets were collected from Federal Medical Centre, Azare and Federal Teaching Hospital, Gombe, spans through five years (2018 – 2022). The datasets were divided into 80% training set and 20% testing set. ARIMA model was used for estimation and best model was found to be ARIMA(2,2,2). The experiment was conducted in R-Studio. The model was diagnosed and cross-checked for the accuracy using Box-Ljung Statistic, normality curve, ACF, and PACF plots. ARIMA(2,2,2) was used to predict three-year future malaria incidents. The results showed that malaria cases were high in January 2023 with 305 cases (LCI=288 & UCI=898 cases). Also, in year 2024, cases of malaria would be high in December with 38 cases (LCI=653 & UCI=781 cases). Observing year 2025, malaria cases will toll high in December with 53 cases (LCI=714 & UCI=808 cases). It was also discovered that as months of the year increase, the cases of malaria increase. Mean Absolute Percentage Error (MAPE) of the ARIMA(2,2,2) was estimated and yielded 12.43% which implies that the model has 87.57% accuracy. Based on the findings, it is recommended that more treated mosquitoes net and medications should be provided by governments and NGOs to reduce malaria infections in the zone.
Keywords: ARIMA, Estimation, MAPE, Malaria, Model, Prediction, r-studio
Development of Models for Ticketing in Public Transportation System in Nigeria (Published)
Transportation is a system that humans use to carrying out their day-to-day activities. In the bid to do activities, transportation by any means has to be used. In the public transportation system, they use the manual or traditional method to process ticketing. This causes many flaws such as long queues, communication gap, miscalculation of tickets, improper records of commuters’ information etc. For commuters to gain a convenient platform for bus reservations, real-time information updates, and a smoother experience; and for the companies to benefit from automated reservations, improved communication, and data-driven insights for better decision- making, there is need to develop a model for ticketing in public transportation system. The models for ticketing in urban transportation system have methods that involve pricing algorithm and route algorithm where scaling factors and user capacity planning models are calculated. With the introduction of the ticketing model in transportation system, existing challenges in urban mobility are revolutionized. The intersection of technology and transportation are positioned in such a way that the model seeks to enhance the overall commuter experience seamlessly by integrating reservation processes, real-time updates, amount payable, time and date of travels and communication between branches. The study envisions a shift towards a more accessible, efficient, and user-centred urban transportation system. It represents a transformative catalyst in transportation system especially by land/roads which are being used by automobiles (buses, cars, tricycle etc).
Keywords: Model, Transportation, average time, route optimization
Information Overload: A Conceptual Model (Published)
This age of massive production and usage of information ranging from online resources to print has constantly created the need to educate individuals on Information overload, which happens when one is saddled with the task of processing and accessing excessive information at work and in life generally. Information overload is the abundance of information with limited cognitive processing capacity to the receiver. Despite its widespread discussion, a universally accepted definition or explanation remains elusive due to the diverse terminology employed. This variation in terminology implies differing levels of information overload. There is a dire need to develop a variety of models that assist information designers in understanding, measuring, and determining when an individual becomes overloaded with information. Drawing on Dubin’s theory, which provides a systematic framework for conceptual model development, this study utilizes the initial stages of theory building to create a Conceptual Model of information overload and its Primary Components together with their Sub-components. This model serves as a foundation for generating testable hypotheses and operationalizing the concept of information overload for further empirical investigations.
Keywords: Information, Model, cognitive overload, information processing, overload
A Model for Malicious Website Detection Using Feed Forward Neural Network (Published)
Malicious websites are the most unsafe criminal exercises in cyberspace. Since a large number of users go online to access the services offered by the government and financial establishments, there has been a notable increase in malicious websites attacks for the past few years. This paper presents a model for Malicious website URL detection using Feed Forward Neural Network. The design methodology used here is Object-Oriented Analysis and Design. The model uses a Malicious website URLs dataset, which comprises 48,006 legitimate website URLs and 48,006 Malicious website URLs making 98,012 website URLs. The dataset was pre-processed by removing all duplicate and Nan values, therefore making it fit for better training performance. The dataset was segmented into X_train and y_train, X_test and y_test which holds 60% training data and 40% testing data. The X_train contains the dataset of malicious and benign websites, while the y_train holds the label which indicates if the dataset is malicious or not. For the testing dataset the X_test contains both the malicious and non-malicious websites, while the y_test holds label which indicates if the dataset is malicious or not. The model was trained using Feed Forward Neural Network, which had an optimal accuracy 97%.
Keywords: Model, Neural network, detection, feed, malicious website
A COMPUTERIZED IDENTIFICATION SYSTEM FOR VERB SORTING AND ARRANGEMENT IN A NATURAL LANGUAGE: CASE STUDY OF THE NIGERIAN YORUBA LANGUAGE (Published)
The context of Understanding has continued to be a major attraction to researchers in Natural Language Processing. This is built on the theory that language can be used effectively if it is understood and can be analyzed and as such, most Natural Language Processing research tend towards the belief that the human brain has a section dedicated for language analysis and understanding therefore, human ambiguity which, remains the major difference between natural and computer languages, can be modeled using appropriate man machine modeling tools since programming languages are designed to be unambiguous, that is, they can be defined by a grammar that produces a unique parse for each sentence in the language. The paper evaluates the classification process for a Natural language ‘the Yoruba language’ and presents a new method by which the language can be transformed into a computer understandable language using its morphological identification framework. Result shows that the approach is admissibly in line with known benchmarks. The paper recommends that non tonal language can also be experimented using the defined approach.
Keywords: Model, Morphology, Natural language, Tonal Language, Yoruba