Department of Computer Science

Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/44

Browse

Author: search.filters.author.Steve AdeshinaEnd date: 2019

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    DEVELOPMENT OF ROAD ANOMALY DATA TRANSMISSION USING ANT COLONY OPTIMIZATION ALGORITHM IN A VEHICLE-TO-VEHICLE COMMUNICATION
    (IEEE, 2019-02-02) Muktar Othman; Steve Adeshina; Moussa Mahamat Boukar
    This study aim is to design a road anomaly transmission Algorithms using Ant Colony Optimization(ACO) based Technique in a Vehicle-to-Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication. The developed VACO also uses the features of VANET to find out the optimal path by considering a minimum number of nodes and cost parameters, which provides information related to accidents, speed of neighbouring vehicle and weather to help users in making informed decisions. Vehicle routing protocol based on ACO (VACO) also ensures to mitigate issues by combining the reactive and proactive approach and considers the parameters affecting the Quality of Service (QoS) such as latency, bandwidth, and delivery ratio in evaluating the Algorithms.
  • No Thumbnail Available
    Item
    A framework for Poultry weather control with IoT in sub-Saharan Africa
    (15th International Conference on Electronics Computer and Computation (ICECCO 2019), 2019-02-02) Nasiru Afeez; Steve Adeshina; Abdullahi Inci; Moussa Mahamat Boukar
    Poultry farming in the sub-Saharan region of Africa is fraught with a lot of challenges among which are high temperature and humidity. In this paper, the authors proposed an Internet of Things (IoT) framework that will help in regulating the various climatic conditions that will help at providing a high yield of poultry products. This framework is aimed at providing proactive and preventive ways to avert or reduce the high mortality rate in a flock of birds as a result of heat stress. IoT which is a connected environment of monitoring sensors with high precision and an accurate decision taken would be presented in managing environmental conditions of poultry house that will gather information, analyze it and effect an action based on the predetermined weather conditions that are suitable for bird’s existence
  • No Thumbnail Available
    Item
    Analysis of Bad Roads Using Smart phone
    (IEEE, 2019-02-02) Moussa Mahamat Boukar; Steve Adeshina
    Developing nations are faced with a lot of bad roads with potholes of different debt ranges, the maintenance and rehabilitation process by government agencies is an ongoing effort that requires periodic bad road inventory to guarantee safety. Bad roads are either identified by government agency’s survey teams or individual who volunteer to report these conditions to the authorities. Our research provided a simple but effective solution to aid in automatically reporting bad roads using smart-phones through measuring the pavement profile based on the vibration of a moving vehicle. In this article, we will explain how we used some a smart-phone in reading the vibration pattern, GPS location, speed and direction of a vehicle that drives through a pothole, these parameters are periodically streamed to a cloud application. We used standard deviation to measure the level of dispersion around a segmented set of streamed vehicle vibration to identify potholes of different sizes, we also used Artificial Intelligence - supervised learning algorithm (classification) to reduce the false positive error rates due to human behaviors. The final results show a distinct vibration levels between small pot-holes, speed bumps and big pot-holes, these values are displayed on map application to visualize the geographical locations of these pot-holes (Google maps)
  • No Thumbnail Available
    Item
    Time Series Analysis and prediction of bitcoin using Long Short Term Memory Neural Network
    (International Conference on Electronics Computer and Computation, 2019-02-02) Temiloluwa I. Adegboruwa; Steve Adeshina; Moussa Mahamat Boukar
    Bitcoin is the first digital currency that uses decentralization to solve the issue of trust in performing the functions of a digital currency successfully. This digital currency has shown extraordinary growth and intermittent plunge in value and market capitalization over time. This makes it important to understand what determines the volatility of bitcoin and to what extent they are predictable. Long Short Term Memory Neural Networks (LSTM-NN) have recently grown popular for time series prediction systems but there has been no consensus on methods to model time series inputs for LSTMs, this paper proposes the need for this problem to be solved by conducting an experimental research on the efficacy of an LSTM-NN given the form of its time-series input features.