A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy

Hypertensive heart disease in Africa

Hypertension has become an important public health problem in Africa. Currently an epidemiologic transition from infectious diseases is going on in the continent and the prevalence of chronic diseases like hypertension is increasing. The response of the heart to the stress/afterload imposed on the left ventricle by the progressively increasing arterial blood pressure is described as hypertensive heart disease. Hypertensive heart disease and failure are the commonest cardiovascular diseases of Africans. Since hypertension is a treatable cardiovascular risk factor, there is need to create more awareness about the disease and educate our patients concerning drug compliance. There is also a need for longitudinal multicentre study in Africa, in order to assess the severity and burden of the disease

Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

COUNSELLING INTERVENTIONS FOR THE SPECIAL NEEDS STUDENTS IN SECONDARY SCHOOLS IN EKITI STATE

The purpose of this study was to find the counselling interventions for the Special Needs Students in some Secondary Schools in Ekiti State. Three research questions and three hypotheses were formulated for the study. A self constructed instrument is titled “Counselling Interventions for the Special Needs Students” was used for the study. A sample of fifty two respondents was selected using purposive sampling technique. Data were analysed using descriptive and inferential statistics. Results showed that there is no significant difference in terms of class and age but there is difference on the basis of gender on counselling interventions for the special needs students in Ekiti State. Recommendations were made to government, corporate bodies, teachers, counsellors, parents and individual which would enable them develop in order to achieve a high level of self sufficiency and not to be at the mercy of inadequate support services

Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa

Echocardiographic partition values and prevalence of left ventricular hypertrophy in hypertensive Nigerians

BACKGROUND: Left ventricular hypertrophy (LVH) is a well known independent risk factor for cardiovascular events. It has been shown that combination of left ventricular mass (LVM) and relative wall thickness (RWT) can be used to identify different forms of left ventricular (LV) geometry. Prospective studies have shown that LV geometric patterns have prognostic implications, with the worst prognosis associated with concentric hypertrophy. The methods for the normalization or indexation of LVM have also recently been shown to confer some prognostic value especially in obese population. We sought to determine the prevalence of echocardiographic lLVH using eight different and published cut-off or threshold values in hypertensive subjects seen in a developing country's tertiary centre. METHODS: Echocardiography was performed in four hundred and eighty consecutive hypertensive subjects attending the cardiology clinic of the University college Hospital Ibadan, Nigeria over a two-year period. RESULTS: Complete data was obtained in 457 (95.2%) of the 480 subjects (48.6% women). The prevalence of LVH ranged between 30.9–56.0%. The highest prevalence was when LVM was indexed to the power of 2.7 with a partition value of 49.2 g/ht(2.7 )in men and 46.7 g/ht(2.7 )in women. The lowest prevalence was observed when LVM was indexed to body surface area (BSA) and a partition value of 125 g/m(2 )was used for both sexes. Abnormal LV geometry was present in 61.1%–74.0% of our subjects and commoner in women. CONCLUSION: The prevalence of LVH hypertensive patients is strongly dependent on the cut-off value used to define it. Large-scale prospective study will be needed to determine the prognostic implications of the different LV geometry in native Africans

Characterisation of heart failure with normal ejection fraction in a tertiary hospital in Nigeria

<p>Abstract</p> <p>Background</p> <p>The study aimed to determine the frequency and characteristics of heart failure with normal EF in a native African population with heart failure.</p> <p>Methods</p> <p>It was a hospital cohort study. Subjects were 177 consecutive individuals with heart failure and ninety apparently normal control subjects. All the subjects underwent transthoracic echocardiography. The group with heart failure was further subdivided into heart failure with normal EF (EF ≥ 50) (HFNEF) and heart failure with low EF(EF <50)(HFLEF).</p> <p>Results</p> <p>The subjects with heart failure have a mean age of 52.3 ± 16.64 years vs 52.1 ± 11.84 years in the control subjects; p = 0.914. Other baseline characteristics except blood pressure parameters and height were comparable between the group with heart failure and the control subjects. The frequency of HFNEF was 39.5%. Compared with the HFLEF group, the HFNEF group have a smaller left ventricular diameter (in diastole and systole): (5.2 ± 1.22 cm vs 6.2 ± 1.39 cm; p < 0.0001 and 3.6 ± 1.24 cm vs 5.4 ± 1.35 cm;p < 0.0001) respectively, a higher relative wall thickness and deceleration time of the early mitral inflow velocity: (0.4 ± 0.12 vs 0.3 ± 0.14 p < 0.0001 and 149.6 ± 72.35 vs 110.9 ± 63.40 p = 0.001) respectively.</p> <p>The two groups with heart failure differed significantly from the control subjects in virtually all echocardiographic measurements except aortic root diameter, LV posterior wall thickness(HFLEF), and late mitral inflow velocity(HFNEF). HFNEF accounted for 70(39.5%) of cases of heart failure in this study.</p> <p>Hypertension is the underlying cardiovascular disease in 134(75.7%) of the combined heart failure population, 58 (82.9%) of the subjects with HFNEF group and 76(71%) of the HFLEF group. Females accounted for 44 (62.9%) of the subjects with HFNEF against 42(39.3%) in the HFLEF group (p = 0.002).</p> <p>Conclusion</p> <p>The frequency of heart failure with normal EF in this native African cohort with heart failure is comparable with the frequency in other populations. These groups of patients are more likely female, hypertensive with concentric pattern of left ventricular hypertrophy.</p