Browsing by Author "Janet O. Jooda"

Now showing 1 - 16 of 16
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    An Automatic Door Lock Security System Based on Convolutional Neural Network
    (Dutse Journal of Pure and Applied Sciences (DUJOPAS),, 2023) Janet O. Jooda
    Door lock security is important because it prevents intruders or unauthorized individuals from entering our homes or offices. Previous door lock security systems based on password, radio frequency identification and facial recognition are unreliable. Therefore, this project developed an automatic door lock security system using Convolutional Neural Network. Fifty faces of home owners were captured and trained using CNN. The system was tested at different distances and lightning condition and evaluated using accuracy, precision and f1 score. Results show that the developed system is 91.67% accurate. However, it is recommended that future work considers increasing dataset for model training to obtain more accurate results.
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    Automatic Plagiarism Detection Using Fuzzy-Logic
    (Dutse Journal of Pure and Applied Sciences (DUJOPAS),, 2023) Janet O. Jooda
    Plagiarism occurs when a researcher copies fellow researcher’s work verbatim without acknowledging the author. This work developed an automatic plagiarism detector using fuzzy logic. The system developed was tested with 4 different text documents and evaluated using portability, efficiency, functionality, ease of use and accuracy metrics. Results show that the developed plagiarism detector is very easy to use with high functionality and accuracy as well as moderate efficiency and portability based on user’s assessment. However, future work can increase the data size for model training and consider machine learning techniques to improve accuracy.
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    Comparative Analysis of Feature Level Fusion Bimodal Biometrics for Access Control
    (International Journal of Progressive Sciences and Technologies,, 2021) Janet O. Jooda
    The increasing quest for dependable, robust and secure recognition systems led to combining two or more biometric modalities for improved performance of a biometric system. Bimodal biometric systems have proven to achieve obvious advantages over unimodal systems in various applications such as access control, surveillance, forensics, deduplication and border control etc. In this study, a comparative analysis of combination of three biometric trait at feature level of fusion was carried out. Face, fingerprint and iris images were acquired from LAUTECH biometric database. The bimodal setup consists of face-iris, face-fingerprint and iris-finger modalities. Principal Component Analysis (PCA) was employed for feature extraction, weighted sum technique was used to fuse the images at feature level while Support Vector Machine (SVM) was used for classification. Experimental result revealed that the bimodal biometric achieved an improved performance than the unimodal biometric. The performances of the bimodal systems indicated that combination of face and iris features achieved the best performance with FAR, FRR and accuracy of 0.00%, 1.42% and 99.00% at 38.15 seconds. Hence, a bimodal face-iris recognition system would produce a more reliable security surveillance system for access control than other combination compared in this study.
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    Development of a Modified Simulated Annealing to School Timetabling Problem
    (International Journal of Applied information Systems, 2015) Janet O. Jooda
    This work presents a modified simulated annealing applied to the process of solving a typical high school timetabling problem. Preparation of a high school timetable consists basically of fixing a sequence of meetings between teachers and students in a prefixed period of time in such a way that a certain set of constraints of various types is satisfied. The approach presented in the paper has been successfully used to schedule the first time school timetable of Fakunle Comprehensive High School, Osogbo Nigeria during the 2012/2013 session and it was capable of generating timetables for complex problem instances. A task involving 18 Classes, 45 Teachers and 15 Subjects for Junior Secondary School (JSS) with 3 Levels (JSS 1 to JSS 3), and 6 arms each; and 24 Classes, 77 Teachers and 19 Subjects for Senior Secondary School (SSS), with 3 Levels (SSS 1 to SSS 3), and 8 arms (3 for Science Group, 3 for Commercial Group, and 2 for Art Group), for 6 hours, 5 days respectively. The use of the implemented model resulted in significant time saving in the scheduling of the timetables, and a well spread lessons for the teachers. Also none of the teachers and classes was double booked. It was clearly evident that the developed modified simulated annealing reduces the major weakness of slow convergence (convergence at excessive time) associated with the classical simulated annealing.
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    DEVELOPMENT OF A PREDICTIVE FUZZY LOGIC MODEL FOR MONITORING THE RISK OF SEXUALLY TRANSMITTED DISEASES (STD) IN FEMALE HUMAN
    (International Research Journal of Engineering and Technology, 2020) Janet O. Jooda
    The purpose of this study is to develop a classification model for monitoring the risk of sexually transmitted diseases (STDs) among females using information about non-invasive risk factors. The specific research objectives are to identify the risk factors that are associated with the risk of STDs; formulate the classification model; and simulate the model. Structured interview with expert physicians was done in order to identify the risk factors that are associated with the risk of STDs Nigeria following which relevant data was collected. Fuzzy Triangular Membership functions was used to map labels of the input risk factors and output STDs risk of the classification model identified to associated linguistic variables. The inference engine of the classification model was formulated using IF-THEN rules to associate the labels of the input risk factors to their respective risk of still birth. The model was simulated using the fuzzy logic toolbox available in the MATLAB® R2015a Simulation Software. The results showed that 9 non-invasive risk factors were associated with the risk of STDs among female patients in Nigeria. The risk factors identified were marital status, socio- economic status, toilet facility used, age at first sexual intercourse, practice sex protection, sexual activity (in last 2 weeks), lifetime partners, practice casual sex and history of STDs. 2, 3 and 4 triangular membership functions were appropriate for the formulation of the linguistic variables of the factors while the target risk was formulated using four triangular membership functions for the linguistic variables no risk, low risk, moderate risk and high risk. The 2304 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the STDs as consequent part of each rule. This study concluded that using information about the risk factors that are associated with the risk of STDs, fuzzy logic modeling was adopted for predicting the risk of STD based on knowledge about the risk factors.
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    Development of an Enhanced Convolutional Neural Network for Self-proctoring in Online Examination Systems
    (IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON)2024, 2024) Janet O. Jooda
    Self- proctoring systems leverage on artificial intelligence algorithms to detect abnormal behaviour during examination, and the Convolutional Neural Network (CNN) has been proven to be one of the most preferred deep learning algorithms to serve this purpose. Convolutional neural networks (CNN) are good for human activity recognition, detection and classification purposes, however, they require a lot of training data which can slow down execution and increase computational time during classification. Therefore, this research addressed the aforementioned problem by developing an enhanced convolutional neural network for online examination self-proctoring systems. The technique was achieved by selecting optimal weight values in the Convolutional network with a Gravitational Search Algorithm optimizer which ultimately improved the model’s computation time. The result of the CNN-GSA technique achieved an F1-Score of 91%, Accuracy of 83.33%, Precision of 83% and a recall of 99.98% at 0.657 seconds.
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    Facial Emotion Recognition And Detection Using Convolutional Neural Network
    (Journal of Computing, Science &Technology, 2024) Janet O. Jooda
    Human emotions have important role in communication especially to understand the emotions of those with speech problems. Various facial emotion recognition and detection systems have been developed but most of these systems have difficulty in performing a muti-class classification and yielded lower accuracy. Therefore, this research employed convolutional neural network for recognition and detection of four basic emotions: happy, sad, angry, neutral. The dataset training the convolutional neural network model was obtained human emotion, accuracy, machine locally and it include about 133 images. Results show that the system developed performed well with an learning, communication, detection accuracy of 0.9533, precision of 0.97, F1-score of 0.94 and recall of 0.93. The approach used showed a significant improvement over traditional machine learning methods and be a useful tool for those with speech problems and visually to predict human emotion.
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    Feature Fusion Using GSA for Multi- Instance Authentication System
    (Asian Research Journal of Current Science, 2023) Janet O. Jooda
    Multi-instance fusion of fingerprint authentication system at score level overcomes a few of the shortcomings of a Unimodal Biometric System (UBS) and enhanced the efficiency of the system. However, due to loss of information at higher levels, the features fused at the score level are confined in comparison to feature level fusion and could lead to poor performance. In this study, multi-instance fusion of fingerprints was done at feature level using Gravitational Search Algorithm (GSA) to select and combine minimal relevant informative texture features subsets from multi- instances of fingerprint and considerably improves the performance of the system. The approach was validated by creation of multi-instances of fingerprint database acquired locally from 150 subjects in an uncontrolled environment and texture based feature extraction was considered and classification of fused texture feature was done using back propagation neural network. The results show that the presented technique was effective in subject authentication with accuracy of 97.09%, indicating that it can successfully secure fingerprint authentication systems from unauthorized attacks.
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    Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning
    (Results in Engineering, 2024) Janet O. Jooda
    Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was pro­ posed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model’s performance was optimised using the AHT tuning technique. The AHT-SEML model’s efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub- Saharan cities, at mmWaves frequencies. The AHT-SEML model’s performance was compared to three empir­ ical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using eval­ uation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML’s t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p- values of 1.91 × 10− 14 and 2.50 × 10− 13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML’s performance.
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    Long-Short-Term Memory Model for Fake News Detection in Nigeria
    (Ianna Journal of Interdisciplinary Studies, 2023) Janet O. Jooda
    Background: The advent of technology allows information to be passed through the Internet at a breakneck speed and enables the involvement of many individuals in the use of different social media platforms. Propagation of fake news through the Internet has become rampant due to digitalisation, and the spread of fake news can cause irreparable damage to the victims. The conventional approach to fake news detection is time-consuming, hence introducing fake news detection systems. Existing fake news detection systems have yielded low accuracy and are unsuitable in Nigeria. Objective: This research aims to design and implement a framework for fake news detection using the Long-Short Term Memory (LSTM) model. Methodology: The dataset for the model was obtained from Nigerian dailies and Kaggle and pre-processed by removing punctuation marks and stop words, stemming, tokenisation and one hot representation. Feature extraction was done on the datasets to remove outliers. The locally acquired dataset from Nigeria was balanced using Synthetic Minority Oversampling Techniques (SMOTE) Long-Short Term Memory (LSTM), a variant of Recurrent Neural Network (RNN)- which solved the problem of losing gained knowledge and information over a long period faced by RNN- was used as the detection model This model was implemented using Python 3.9. The model detected fake news by classifying real and fake news approaches. The dataset was fed into the model, and the model classified them as either fake or real news by processing the dataset through input and hidden layers of varying numbers of neurons. accuracy F1 score and detection time were used as the evaluation metrics. The results were then compared to some selected machine learning models and a hybrid of convolutional neural networks and long short-term memory models (CNN-LSTM). Results: The result shows that the LSTM model on a balanced dataset performed best as the two news classes were accurately classified, giving an average detection accuracy of 92.86%, which took the model 0.42 seconds to detect whether news was real or fake. Also, 87.50% average detection accuracy was obtained from an imbalanced dataset. Compared to other machine learning models, SVM and CNN-LSTM gave 81.25% accuracy for imbalanced datasets and 82.14% and 78.57% for balanced datasets, respectively. Conclusion: The outcome of this research shows that the deep learning approach outperformed some machine learning models for fake news detection in terms of performance accuracy.
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    Multiple Instances Fingerprint Image Data Acquisition
    (International Conference on Electrical, Computer and Energy Technologies, 2023) Janet O. Jooda
    Using public fingerprint databases to validate the multi-instance fusion approach of a Multi-instance Biometric Authentication System (MBAS) for accurate authentication is a means of overcoming some of the limitations of a Unimodal Biometric System (UBS). Nevertheless, a significant portion of the web databases used for MBAS (Machine Learning-Based Automated Systems) were obtained under controlled conditions, with the photos being curated to align with a certain algorithmic objective. The performance of biometric systems exhibits variability when the datasets used in the algorithms undergo modifications as a result of discrepancies in the settings under which the photos were obtained. In this study, a database containing multiple fingerprint instances was developed locally in an uncontrolled environment. The aim was to create a database containing numerous examples of fingerprints for the same person from one or multiple data collection sessions. The database was produced locally by acquiring six samples of ten fingerprint instances of 150 subjects in an uncontrolled environment using a Futronic fingerprint scanner. The created database and results will be helpful to all researchers in the biometrics field as a tool for improving multi-instance fusion methods and enabling an unbiased evaluation of algorithms.
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    Novel Method to Ensure Security in Telecommunications Systems
    (Asian Basic and Applied Research Journal, 2023) Janet O. Jooda
    Data in the cloud and all forms of wireless communication are susceptible to many forms of attack. Forming hybridized cipher with symmetric and asymmetric algorithms allays the fear of security concern having identified the weakness of single-layer encryption. However, the hybrized cipher requires exchange of secret keys between sender and receiver and also paid little attention to throughput. Hence, this research removes the need to share secrete key for a private key by developing a hybrid cipher using the output Elliptic Curve Cryptography (ECC) for key exchange of symmetric key cipher, RC4c.The stages involved in the developed ECCRC4c algorithm are ECC and RC4c encryption. Varying lengths of data in step of 8 bits were used to estimate the performance metrics of the proposed system of this research. The results showed a significant improvement in the throughput and computation time on the existing algorithm. There was an improvement of 94.6% in throughput and 89.9% in computation time which implied power savings. Performance metrics of a hybrid cryptographic algorithm are proportional to performance metrics of the components ciphers as shown from the individual metrics of DES, RSA, ECC, RC4c, RSADES and ECCRC4c.
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    Optimized gravitational search algorithm for feature fusion in a multimodal biometric system
    (Results in Engineering, 2023) Janet O. Jooda
    In recent years, multimodal biometric systems have gained significant attention due to their capacity to enhance recognition accuracy and robustness. The integration of multiple biometric traits, such as face, fingerprint, iris, and voice, has shown promising results in addressing the limitations of unimodal systems. However, achieving efficient and accurate feature fusion remains a critical challenge in the development of multimodal biometric systems. This study proposes an optimized approach utilizing the Gravitational Search Algorithm (GSA) for feature fusion in a multimodal biometric system. The objective is to enhance recognition performance by effectively combining complementary information from multiple biometric traits. The evaluation performance to determine the effect of the optimization at threshold values of 0.22, 0.35, 0.5, 0.8 and 1.0 was compared with the traditional GSA and presented. This study achieved the highest GWGSA accuracy at 0.8 and 1.0 thresholds to outperform other thresholds as shown in T able 1. The proposed approach is tested with real-world datasets and compared against existing fusion techniques, demonstrating its superiority in terms of recognition accuracy and computational efficiency.
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    OPTIMIZING HYPERPARAMETERS OF A DEEP LEARNING ALGORITHM FOR A REAL TIME FACE RECOGNITION SYSTEM
    (Kongzhi yu Juece/Control and Decision, 2024) Janet O. Jooda
    Convolutional Neural Networks (CNNs) have demonstrated remarkable success in various image recognition and classification tasks. This model cannot perform well in challenging conditions, such as, low light, varying camera angles and occlusions or crowded scenes. Therefore, this research Developed Spider wasp Optimized Convolutional neural network base on real time face recognition system (SWO- CNN). The outcomes of this research thus lend credence to the assertion that the SWO method, when combined with CNN technology, enhanced the detection of faces while also accelerating improved. This research has contributed to knowledge with an empirical proof that the application of SWO based CNN technique achieved an improved performance with low processing time in the detection of faces in videos processing. With the developed technique, face detection in surveillance systems could be greatly improved.
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    Performance Evaluation Of Selected Principal Component Analysis-Based Techniques For Face Image Recognition
    (INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, 2015) Janet O. Jooda
    Principal Component Analysis (PCA) is an eigen-based technique popularly employed in redundancy removal and feature extraction for face image recognition. In this study, performance evaluation of three selected PCA-based techniques was conducted for face recognition. Principal Component Analysis, Binary Principal Component Analysis (BPCA), and Principal Component Analysis – Artificial Neural Network (PCA-ANN) were selected for performance evaluation. A database of 400, 50x50 pixels images consisting of 100 different individuals, each individual having 4 images with different facial expressions was created. Three hundred images were used for training while 100 images were used for testing the three face recognition systems. The systems were subjected to three selected eigenvectors: 75, 150 and 300 to determine the effect of the size of eigenvectors on the recognition rate of the systems. The performances of the techniques were evaluated based on recognition rate and total recognition time.The performance evaluation of the three PCA-based systems showed that PCA – ANN technique gave the best recognition rate of 94% with a trade-off in recognition time. Also, the recognition rates of PCA and B-PCA increased with decreasing number of eigenvectors but PCA-ANN recognition rate was negligible.
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    Selected Soft Computing Algorithms For Solving Travelling Salesman Problem
    (International Journal of Progressive Sciences and Technologies,, 2021) Janet O. Jooda
    Traveling Salesman Problem (often called TSP) is a classic algorithmic problem in the field of computer science and operations research. It is focused on optimization. In this context, better solution often means a solution that is cheaper, shorter, or faster. TSP is a mathematical problem. It is most easily expressed as a graph describing the locations of a set of nodes. Given a set of cities and distance between every pair of cities, the problem of Traveling Salesman Problem is to find the shortest possible route that visits every city exactly once and returns to the starting point. The aim of this project is to adapt Bat, Bee, Firefly, and Flower pollination algorithms, implement and evaluate the selected algorithms for solving Travelling Salesman Problem.