Browsing by Author "Muhammed, K. J."

Now showing 1 - 4 of 4
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    Effects of Problem Solving Method with Power Point on Achievement of Secondary School Physics Students in Refraction of Light Waves
    (Association for Innovative Technology Integration in Education (AITIE) Conference, 2017) Abdulwaheed, O. I; Muhammed, K. J.
    This study was designed to determine the effect of problem solving method with PowerPoint on SS II physics students' academic achievement in refraction of light waves in Ilorin Kwara State. Two Research questions were raised and two Null hypotheses were tested. The study adopted a pre-test and posttest control and experimental group design. The representatives sample size for the study comprises of one hundred and twelve (112) Physics students selected using purposive sampling techniques from three (3) senior secondary schools in Ilorin, Kwara State. The Researcher taught the experimental group Refraction of light waves using problem solving method with power point, while the control group was exposed to problem solving method using traditional approach. The instrument for data collection was 50-items multiplechoice tagged Achievement Test on Refraction of Light Waves (ATRLW) reflecting the content in the lesson note. A reliability coefficient of 0.785 was obtained using Pearson's moment correlation coefficient(r). The data was analyzed statistically using independent sample t-test and the statistical results accepted the null hypothesis of the study. The study revealed that using problem solving method with power point and problem solving method with traditional approach both enhances SS II physics student academic achievement in refraction of light waves, also there was no discrimination of the academic achievement of male and female students when taught with problem solving method using power point. The use of problem solving method with power point has an essential effect on male and female students. The study also revealed that problem solving method generally enhances students' ability in solving problem in physics. It was concluded that problem solving-ability in students is a critical variable of students' academic achievement in refraction of light waves. Key word: Problem solving, Power Point, Physics , refraction of light waves
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    Fingerprint Biometric-Based Cryptographic System as a Security Approach in Grid Environment
    (Journal of Computations & Modelling (JCoMod), 2014) Abdulraheem, M.; Aremu, D. R.; Adewole, K. S.; Muhammed, K. J.
    Interconnection of computer systems for the purpose of sharing resources in grid is increasing on the daily bases. Resource shared in grid is not limited to files alone but also includes computer resources such as memory, processors, etc. The security challenges resulting from this sharing is enormous including authentication, authorization, integrity, availability. These call for research attentions as evidenced from the reviewed literatures. Several researches have proposed cryptographic approaches as a promising solution to the various security challenges. However, issues surrounding the knowledge-based authentication approach motivate the researchers to be more innovative by proposing biometric-based cryptographic model to secure grid resources. This paper examines and analyses existing efforts to tackle these challenges. It also examines Grid Architecture where various components of a grid play major role in resource sharing and securing of grid. Biometric-based model was proposed that provides security for grid users using fingerprint for authentication and authorization in grid due to its ease of collectability.
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    Hybridization Algorithms for Cancer Disease Diagnosis Using Microarray Data
    (U6 Initiative for Development, 2017) Muhammed, K. J.; Oladele, T. O; Adewole, K. S.
    Cancer is one of the most common deadly diseases in the world. The conventional diagnostic techniques are not always effective as they rely on the physical and morphological appearance of the tumor. The ability of the physicians to effectively treat and cure cancer is directly dependent on their capability to detect cancers at their earliest stages. Early stage prediction and diagnosis is difficult with those conventional techniques such as physical appearance of tumor. However, these techniques are costly, time consuming, requires large laboratory setup and highly skilled persons. There is need for faster, easier, more accurate and effective method, using modern technology to address the challenges. In this dissertation, Hybridized model of Genetic Algorithm and Neural Network was developed and simulated in Weka environment using microarray cancer dataset. Microarray studies are characterized by a low sample number and a large feature space with many features irrelevant to the problem being studied. This makes feature selection a necessary pre-processing step for many analyses, particularly classification. Various stages involved in Genetic Algorithm and Neural Network were studied and simulated. An hybrid model that combines the optimization power of Genetic algorithm for reduction of high dimensional microarray data and Neural network for classification between malignant pleural mesothelioma (MPM) and adenocarcinoma (ADCA) of the lung was proposed. The solution found by the combined Genetic Algorithm and Neural Network performed effectively well. The genetic algorithm reduced 12,533 attributes in the microarray dataset to 748 attributes. The reduced microarray dataset was used to train the multilayer perceptron neural network classifier. The trained classifier achieved 97.5% accuracy when evaluated with the testing microarray dataset. The results presented in this dissertation revealed that the proposed hybrid Genetic Algorithm and Neural Network performs better with over 97% accuracy when used to classify microarray dataset of lung cancer
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    The rise of spam accounts in Microblogging social networks - an experimental case of the features for spammer detection
    (International Journal for the Application of Wireless and Mobile Computing, 2018) Kayode, S. A; Usman-Hamza, F. A.; Ahmed, O. A; Muhammed, K. J.
    Microblogging social network, such as Twitter, has become attractive communication media for social spammers to spread malicious contents. As opposed social networks like Facebook and Renren, content distributed on microblogging social networks is unstructured, noisy and short. This characteristic hinders the performance of the traditional semantic analysis technique to effectively detect microblogging spammers. In addition, existing approaches for spammer detection have faced different evasion tactics. In this paper, a framework for identifying spammers on microblogging networks using Twitter as a test bed is proposed. The framework explored a unified feature learning approach by considering five main categories of features. A set of unique features were introduced to complement the existing features in the literature. Eleven (11) supervised machine learning algorithms were trained and tested based on these features. Experimental results demonstrate that Decorate ensemble classifier achieved the best results with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.973 and F-measure of 0.929 using 10-fold cross-validation. Using percentage split, Decorate achieved AUCROC of 0.975 and F-measure of 0.940. Experiments were also conducted to investigate the contributions of each category of features. The results indicate that the proposed framework based on the features utilized provides a feasible solution for spammer detection on Twitter microblog.