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

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

Exploring the Use of Biometric Smart Cards for Voters’ Accreditation: A Case Study of Nigeria Electoral Process

Voting remains an integral component of every democratic electoral process. it is an avenue for citizens to exercise their rights in order to elect those who will lead them in various vacant political offices. However, enhancing voters’ trust and confidence in electoral processes are significant factors that could encourage the active participation of citizens in elections. Eligible voters tend to decline to participate in an election when they have a feeling that their votes may not eventually count. Furthermore, electoral processes that lead to the emergence of candidates must be adjudged to be free, fair and credible to a high degree for the result to be widely acceptable. Unacceptable election results could lead to protests and total cancelation of the election thereby resulting in loss of time and resources invested in it. To ensure that only registered voters cast their votes on election days, measures must be put in place to accredit voters on election days effectively. Therefore, this article explores the use of biometric smart cards for voters’ verification and identification. With the Nigerian electoral process in view, the existing Nigerian voting procedure was reviewed, lapses were identified and solutions based on the use of the biometric smart card were proffered. If adopted, the proposed adoption of biometric smart cards for voters’ accreditation will enhance the country’s electoral process thereby ensuring that only registered voters cast their votes. The approach presented could also reduce the number of electoral processes and personnel required during election days, thus reducing voting time and cost

Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree

RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. A feature ranking and earlier experience are used. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques

Deep Neural Trading: Comparative Study With Feed Forward, Recurrent and Autoencoder Networks

Algorithmic trading approaches based on news or social network posts claim to outperform classical methods that use only price time series and other economics values. However combining financial time series with news or posts, requires daily huge amount of relevant text which are impracticable to gather in real time, even because the online sources of news and social networks no longer allow unconditional massive download of data. These difficulties have renewed the interest in simpler methods based on financial time series. This work presents a wide experimental comparisons of the performance of 7 trading protocols applied to 27 component stocks of the Dow Jones Industrial Average (DJIA). The buy/sell trading actions are driven by the stock value predictions performed with 3 types of neural network architectures: feed forward, recurrent and autoencoder. Each architecture types in turn has been experimented with different sizes and hyperparameters over all the multivariate time series. The combinations of trading protocols with variants of the 3 neural network types have been in turn applied to time series, varying the input variables from 4 to 17 and the training period from 8 to 16 years while the test period from 1 to 2 years