29 research outputs found
Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
No full textExploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points
Microarray cancer feature selection: Review, challenges and research directions
No full textMicroarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed
Development of an inventory management system using association rule
No full textStores today still make use of manual approaches to keeping inventory which could be cumbersome. Having a computerized inventory system would make inventory management more efficient and effective. In this chapter, an Inventory Management System using Association Rule was developed which will ensure proper record keeping and keep items in stocks updated. ANGULARJS, a JavaScript framework, was used for the implementation of the system, PHP (hypertext pre-processor) was used for the backend of the system development as well as the database management, HTML was used alongside CSS for the system interface design and NoSQL database was the database used for this research. In conclusion, a computerized inventory system that had been improved using the association rule method was the resulting product useful for creating transactions, updating items in stock, record keeping, generating reports for decision making, and lastly, the system will make the stores more effective
An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A Population-Based Study
No full textToday, Osteoarthritis remains the most prevalent chronic joint disease and a potentially incapacitating joint illness. It is an enduring health problem which cannot be cure though it can be managed. Osteoarthritis remains a serious public health problem because its burden is high, people who live with it have a greater risk of developing anxiety / or depression and if it is not properly managed, it can bring about disability as well as impairing quality of life. This paper presents a statistical correlation between the reported risk factors of Osteoarthritis and its prevalence in Nigeria. Statistical tests were performed to investigate if there is enough evidence for inferring that the risk factors for Osteoarthritis are true for the whole of Nigerian populatio
Ogundokun Roseline Oluwaseun, Adebiyi Marion Oluwabunmi, Abikoye Oluwakemi C., Oladele Tinuke O.,Dataset on the academic performance of students in 12 programmes from a private university
No full textThe dataset on the academic performance of students in 12 programs from a private university. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programs which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy
No full textDiabetes Retinopathy is a disease which results from a prolonged
case of diabetes mellitus and it is the most common cause of loss of vision in
man. Data mining algorithms are used in medical and computer fields to find
effective ways of forecasting a particular disease. This research was aimed at
determining the effect of using feature selection in predicting Diabetes
Retinopathy. The dataset used for this study was gotten from diabetes retinopathy
Debrecen dataset from the University of California in a form suitable for mining.
Feature selection was executed on diabetes retinopathy data then the Imple�mentation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron
(MLP) and Support Vector Machines was conducted on diabetes retinopathy data
with and without feature selection. There was access to the algorithms in terms of
accuracy and sensitivity. It is observed from the results that, making use of
feature selection on algorithms increases the accuracy as well as the sensitivity of
the algorithms considered and it is mostly reflected in the support vector machine
algorithm. Making use of feature selection for classification also increases the
time taken for the prediction of diabetes retinopathy
Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
No full textPerformance Evaluation: Dataset on the scholastic performance of students in 12 programmes from a private university in the south-west geopolitical zone in Nigeria
No full textDataset of educational performances of college students in 12 programmes in a private university in Nigeria. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programmes which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).</p
A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation
No full textOver the last decades, various methods have been employed in
medical images analysis. Some state-of-the-arts techniques such as deep learn�ing have been recently applied to medical images analysis. This research pro�poses the application of deep learning technique in performing segmentation of
retinal blood vessels. Analyzing and segmentation of retina vessels has assisted
in diagnosis and monitoring of some diseases. Diseases such as age-related
fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arterioscle�rosis and choroidal neovascularization can be effectively managed by the
analysis of retinal vessels images. In this work, a Deep Convolutional Encoder�Decoder Architecture for the segmentation of retinal vessels images is proposed.
The proposed method is a deep learning system composed of an encoder and
decoder mechanism allows a low resolution image set of retinal vessels to be
analyzed by set of convolutional layers in the encoder unit before been sent into
a decoder unit for final segmented output. The proposed system was evaluated
using some evaluation metrics such as dice coefficient, jaccard index and mean
of intersection. The review of the existing works was also carried out. It could be
shown that the proposed system outperforms many existing methods in the
segmentation of retinal vessels images
