Computer Engineering

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Computer Engineering

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    Intelligent Railway Cross Level Gates and Signaling System using Fuzzy Logic Control Technique
    (Convenant University, 2016-05-09) Olaniyi, O. M., Abdullahi, I. M., Maliki, D., & Lasore, T. M.
    Current manually operated gates at the railway cross levels of developing countries are stressful and time wasting. This has exposed pedestrians to high rate of accident resulting to loss of lives and drastic reduction of the country’s economy. Different systems have been developed to prevent rail accidents at the level crossing but they are not effective and in most cases are too expensive to implement. This study presents a prototype model of an intelligent railway cross level gates and signaling system using Mamdani fuzzy logic control technique. The intelligent system has the ability to detect the arrival/departure of a train and close/open the cross level gates respectively. The system response was evaluated with respect to time. The results after the evaluation of the developed system showed that the system with fuzzy intelligent control technique has a high response with respect to time compared to a system without an intelligent technique. The large scale implementation of the developed intelligent railway cross-level gate and signaling system can be used to prevent avoidable accident occurrence at the level crossings and thus, reduces loss of lives as well as improvement of the nation’s economy through efficient delivery of goods and services in Africa.
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    Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I. (2017). Development of an Artificial Neural Network Model for Daily Electrical Energy Management. Proceedings of the 2nd International Engineering Conference, (IEC 2017), Federal University of Technology Minna, Nigeria, pp 120-125.
    (Federal University of Technology Minna, 2017-05-07) Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I
    Efficient monitoring and control of electrical energy do not only prevent fire out-breaks caused by electrical appliances, but can also reduce excessive billings and prevent electrical installations. Most Energy Management Systems (EMS) for remote controlling of electrical appliances rely mostly on sensors, data and GSM networks which are un-reliable or even un-available in most part of developing world, this makes them less reliable. Therefore, there is need for an intelligent system that can manage electrical consumption intelligently using user-appliance interactive pattern over a period of time for intelligent control of users’ appliances in his/her absence. The model parameters (number of neurons and training algorithms) that affects its performance were first investigated and adopted. The performance of the developed model was evaluated using Regression analysis (R) and Mean Square Error (MSE) using ANN and Simulink tool boxes in Matlab R2015b. A good model can be used for real time control when deployed. Also, Scale Conjugate Gradient (SCG) training algorithm should also be used because of its high performance for pattern recognition problems. This work will go a long way in efficiently controlling household electrical appliances in the absence of the users thereby preventing fire disasters caused by electrical appliances, reducing the tariffs of consumers while increasing lifespan of electrical installations.
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    Fingerprint Based Driver’s Identification System.
    (Federal Univesity of Technology Minna, 2018-09-06) Inalegwu, O. C., Maliki, D., Agajo J., Ajawo, L. A., & Abu, A. D
    This design work presents a proposed replacement to the current system used by the Federal Road Safety Commission (FRSC) for checking licensed/unlicensed drivers. It gives a faster and less tedious way of identifying registered and licensed road users using biometric captures. The system employs the use of an Arduino board to control and process the functioning of other peripherals: the fingerprint scanner and the Organic Light Emitting Diode (OLED) screen connected to it to achieve its purpose. The prototype system developed was able to displays driver’s information on the OLED screen (Age, Name, Sex and License ID); the average response time of the system was also calculated to be 1.41 seconds, which is a good response time considering the system in question. The false Accept rate and false reject rate were relatively low (after a sample test with 25 individuals); at 4% and 8% respectively. Also, for its implementation, the components are readily available, relatively cheap and the system is on that can be easily adopted by the FRSC if access to their already existing database is granted. Consequently, it is safe to say that the developed system measured up to the design expectations; it meets the aim of a proposed replacement for the present analogue and easy to beat system employed by the FRSC.
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    Parameter Investigation and Analysis for Elite Opposition Bacterial Foraging Optimization Algorithm
    (Federal University of Technology Minna, 2019-04-22) Maliki, D., Muazu, M. B., Kolo, J.G., & Olaniyi, O. M
    The investigation and analysis of algorithm parameters is an important task in most of the global optimization techniques. However, finding the best set of parameter value for the optimum performance of an algorithm still remain a challenging task in a modified Bacteria Foraging Optimization Algorithm (BFOA) since most toe the existing research focuses on the application o the algorithm and likewise it benchmarking with the global test function. The Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) is a modified nature inspired optimization algorithm from BFOA which focuses on the generation of an elite solution from the opposition solution for an optimization process. This research is focuses on the investigation of such parameters population size, probability of elimination dispersal, step size and number of chemotaxis so as to determine the extent to which they affect the optimal solution from the EOBFOA with respect to global minimum or least minimum standard deviation. From the results obtained, it was observed that the global minimum in EOBFOA depend on the exploitation ability of the bacteria in the search space.
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    Deploying A Standalone Facial and Emotion Recognition Classroom Management System on Resource-Constrained.
    (Izmir Turkiye, 2024-12-20) Abdullahi, I.M., Maliki, D., Abdulqudus, A., Abraham, S.A, & Ibrahim, M.
    In recent times, it has been proven in most industries that deep learning can play a huge part in the development and automation of processes otherwise performed manually by humans alone. The trend however has encountered more of a shift and tend towards transfer learning where standalone systems can be built on weights that have been extensively trained to be use-case agnostic. This project seeks to address the problem of student truancy. The methodology applied is a combination of a deep learning use-case agnostic weight embedding obtained from a popular network called Face net. Recognition is performed by computing facial distances using the weight embedding. Also addressed is the common reliance on internet for functionality present in most modern-day systems by deploying all the resources necessary on a resource-constrained development board. Emotions during class are also analyzed to improve classroom experience which will be displayed on a web application dashboard powered by artificial intelligence back-end. The results obtained show an above average recognition rate of 0.63 with emotional recognition accuracy of 0.72. The implications of these results are that accurate attendance can be taken in an organization with minor increments to the system such as increased computational capabilities.
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    Towards The Development of An Intelligent Evaporative Cooling System for Post-Harvest Storage of Selected Fruits
    (Federal University of Technology, Minna, 2024-12-03) Isah, O.R., Adebayo S.E., Nuhu, B.K., Umar, B.U., Maliki, D
    Poor management of post-harvest storage of fruits and vegetables has led to enormous food wastage and economic loss globally. Refrigerating systems have been adopted over the years to avert these losses; however, installing them is expensive and can cause chilling injury and moisture loss to the fruits and vegetables when they go below 20℃ temperature. An evaporative cooling system has recently been widely used to preserve fruits and vegetables because it’s cheap to implement, especially for small-scale farmers. This system reduces the temperature and increases the air humidity in their chamber by removing latent heat from the evaporated water when exposed to sunlight. The existing evaporative system has been efficient in preserving the quality of fruits and vegetables as well as extending their shelf-life; however, they lacked automated operation and control mechanisms, intelligent mechanisms capable of identifying the physical state of the fruits, adaptive control techniques for the storage and remote monitoring, feedback scheme of the system for use by the farmers. The abovementioned limitations have prevented the system from achieving optimal performance in preserving fruits. Hence, this research aims to develop a multi-chamber evaporative cooling preservative system for post-harvest storage of fruits. In the first step, Tomato images were collected and trained with the MobileNetV2 model, achieving accuracy, precision and recall of 88%, 89% and 88% respectively. Overall, the model performs well, however, fine- tuning the model or using more training data could help improve its performance further
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    A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)
    (International Conference of the Faculty of Science, 2025-01-14) Ahmed, Y.E., Abdullahi, I.M., Maliki, D., & Akogbe, M. A
    One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
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    Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching
    (Faculty of Science Lafiya, 2025-01-12) Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, D
    One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
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    Web-Based Decision Support System for Diagnosis of Ebola Virus Disease Using Bayesian Networks
    (2016) Dogo, E. M.; Kolo, J. G.; Aror, O.; Rahman, A. T.
    The recent epidemic of the Ebola Virus Disease (EVD) left many dead in West Africa and in other parts of the world. A major problem faced was late diagnosis or diagnostic error of the disease; this was due to largely unavailability of medical professionals familiar with the disease and low doctor to patient ratio. An accessible method for reliable diagnosis is required to offset the low ratio of doctors to population. This paper presents the application of Bayesian networks for diagnosis of EVD. A general procedure for implementing a Bayesian network model is proposed; thereafter we demonstrate how the resulting Bayesian network can be applied in a medical diagnostic decision support system. The system uses the questionnaire method to elicit symptoms and is accessible through web browsers over the internet and mobile phones to potential patients and medical practitioners. The system developed is able to provide diagnosis in the form of probabilities, for the presence or absence of EVD in an individual. The probability of an individual infected by the disease depends on present or absent of particular symptoms according to the gathered disease pathology. The system was successfully developed, and had a diagnostic accuracy of 77% when compared to the World Health Organization (WHO) algorithm, but refinements of the conditional probability distribution would provide the most accurate sensitivity to symptoms and also improve the accuracy of diagnosis. Finally, web functionality, performance and usability test on the developed web application is carried out by simulating various load patterns and the result was generally acceptable.
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    Development of Time Controlled Based Solar Radiation Tracking System
    (Journal of Science Technology Mathematics and Education (JOSTMED), 2017-06-17) Maliki, D., Ibrahim I., Nuhu B. K., Abdullahi I. M., & Ajao, L. A
    The increase in daily use of electricity with limited in the amount of fossil fuel necessitated researches to explore other methods of producing energy. Many renewable sources of electricity are in existence, one of the cheapest, free and the most abundant renewable source of energy is the electricity generated from the sun. Electricity from solar radiation is environmentally friendly as it poses no harmful hazard to the surrounding. Today, radiation from the sun can be harnessed with the use of the photovoltaic material like the solar panel. It was observed that the sun direction keeps changing during the day as a result of the rotation of the earth and obtaining maximum amount of solar energy from a fixed solar panel cannot be totally achieved throughout the day. To this extend, a time controlled based solar radiation tracking system was developed. The developed system is capable of continuously changing the direction of sun module as the sun transverse the sky with the use of an intelligent fuzzy rules base on input variations. The performance of the tracking system when compared with the fixed solar device gave an output of 19.54% increase in voltage output.