An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Computational Analysis of Anopheles Gambiae Metabolism to Facilitate Insecticidal Target and Complex Resistance Mechanism Discovery

Insecticide resistance is a genetic characteristic involving changes in one or more insect genes. It is also a major public health challenge combating world efforts on malaria control and strategies. The Malaria vector, Anopheles gambiae (A. gambiae) has formed resistance to the existing classes of insecticides, especially pyrethroid, the only class approved for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Net (LLITNs). Identification of novel insecticidal targets for the development of more effective insecticides is therefore urgent. However, deciding which gene products are ideal insecticidal targets remains a difficult task in the search. To this end, it has been shown that the dissection and comprehensive studies of biochemical metabolic networks has great potential to effectively and specifically identify and extract essential enzymes as potential insecticidal targets. Using the PathoLogic programme, AnoCyc, a pathway/genome database (PGDB) for A. gambiae AgamP3 was constructed, using its annotated genomic sequence and other annotated information from ANOBASE, VECTORBASE, UNIPROT and KEGG databases. Furthermore, additional annotations to proteins annotated as “hypothetical” was gathered using specifically two annotation tools from the DKFZ HUSAR open servers, namely GOPET and DomainSweep and present a more comprehensive annotated PGDB for A. gambiae AgamP3. The resulting PGDB for A. gambiae AgamP3 has been deployed under the www.bioCyc.org databases. Next, a graph based model that analyzed the topology of the metabolic network of Anopheles gambiae was developed to determine the essential enzymatic reactions in the networks. A refined list of 61 new potential insecticidal candidate targets was obtained, which include one clinically validated insecticidal target and host of others with biological evidence in the literature. Finally, the biochemical network of A. gambiae was overlaid with two gene expression data obtained from the treatment of A. gambiae with pyrethroid (permethrin) to elucidate some tightly linked resistance genes and deduce computationally, for the first time, its resistance mechanism(s) toward this insecticid

Development of Electronic Government Procurement (e-GP) System for Nigeria Public Sector.

Business-to-business electronic procurement success in business organizations (private sectors) has been a major driving force for government organizations (public sectors) in developed nations to adopt electronic government procurement in order to reduce cost and improve administrative efficiency. E-procurement within the government is recognized to be the main area of government-to-business that needs to be exploited by government of developing nations. In this paper, we examined the drawbacks of existing procurement process in Nigeria with a view to offering an improved approach. A prototype e-GP system was designed and developed to eliminate the associated bottlenecks with existing system and showcase the attendant benefits of the proposed system which can lead to an improved procurement cycle process flow

Stock Price Prediction using Neural Network with Hybridized Market Indicators

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative decisions

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