Determining suitability of speech-enabled examination result management system

The focus of this study is to determine the suitability of speech-enabled examination result management system as a tool for checking and managing students’ examination results. The theory of task-technology fit was used to identify the factors that significantly influence the use of the system. 374 verified data from students and instructors that responded to the questionnaire were analyzed and reported. The factors investigated in this study were cost, task, mobility, attitude, fitness, performance and utilization. Structural equation modeling was engaged to study the relationship between the variables and also analyze the data. The outcome of the constructed model proved that mobility, task and cost had significant influence on the fitness of the system

Spatial Analysis of Violent Crime Dataset Using Machine Learning

The monster called crime has been living with us from the beginning of human existence and impacts negatively on the general health of a nation. Different approaches were employed in the past studies for predicting occurrence of violent crime to aid predictive policing, which makes conventional policing more efficient and proactive. This paper investigates the accuracy of Machine Learning-based crime prediction approaches, which were used previously by other researchers. This study presents Machine Learning approaches to violent crime prediction. Five years’ historical dataset between July 2014 and July 2019 were collected from Nigerian Police Lagos, analyzed and used for training the models built. Two different Machine Learning predictive models, Decision Tree and K-Nearest Neighbor, were implemented using IBM Watson Studio and violent crime prediction accuracy of 79.65%, and 81.45% were obtained, respectively, with the real-life dataset collected from Nigerian Police Obalende Lagos and online crime reported portal during violent crime prediction in Lagos. This could be used to enhance crime prevention and control strategies in curbing the worrisome crime rate in the country

Enhancing the Low Adoption Rate of M‐commerce in Nigeria Through Yorùbá Voice Technology

There has been claims and reports that 70% of m‐commerce in Nigeria failed due to non‐interactive, non‐responsive and non‐interesting platform. Despite the popularity and growth of m‐commerce globally, developing countries like Nigeria seems to be lagging behind and at the same time, many mcommerce sites have been reported to close down due to unprofitability. The key factors that contribute to this failure are health, literacy e‐literacy language and accessibility barrier. All these key factors are formidable barriers to adoption of m‐commerce in Nigeria and have discouraged most people from fully adopting m‐commerce. Hence, this work explored a Yorùbá voice‐based m‐commerce system to enhance smooth m‐commerce operations and at the same time, enhance the low adoption rate of mcommerce users. This work was able to discover a foundation for advancing the current growing trends of m‐commerce making them sustainable which is also applicable to e‐commerce or any e‐platforms

Engineering and Deploying a Cheap Recognition Security System on a Raspberry Pi Platform for a rural Settlement

Security is one of the most fundamental challenges of mankind, providing affordable devices for apprehending criminals. Using smart technology is on the rise and the ability to have full surveillance records of both authorised and unauthorized entrance to designated facility or important resource in a timely manner is highly desirable in modern society of today. This paper proposes the use of Histogram of Oriented Gradients (HOG) to train a model capable of recognising authorised personnel on a raspberry pi device for the purpose of security and ease of access to vital infrastructure. HOG was the preferred choice because it is not computationally intensive as compared to Convolutional Neural Networks (CNN) and most other relatively comparable computational algorithms. The HOG network detect faces and sends a report to Firebase Database and an image is also sent to Google Cloud Storage (GCS) a package on the Google Cloud Platform (GCP). Both data from Firebase and GCS are sent to a companion android application where the user can view who entered specific locations, at specific time with accompanying pictorial evidence. The recognition system was deployed on a raspberry pi device that’s feeds in visual data via an inexpensive camera. Collectively, the proposed system is a relatively cheap smart technology security system with inherent ability to accomplish real-time surveillance tasks using widely penetrated android phone technology while maintaining low computational overheads