New Method of Measuring TCP Performance of IP Network using Bio-computing

The measurement of performance of Internet Protocol IP network can be done by Transmission Control Protocol TCP because it guarantees send data from one end of the connection actually gets to the other end and in the same order it was send, otherwise an error is reported. There are several methods to measure the performance of TCP among these methods genetic algorithms, neural network, data mining etc, all these methods have weakness and can't reach to correct measure of TCP performance. This paper proposed a new method of measuring TCP performance for real time IP network using Biocomputing, especially molecular calculation because it provides wisdom results and it can exploit all facilities of phylogentic analysis. Applying the new method at real time on Biological Kurdish Messenger BIOKM model designed to measure the TCP performance in two types of protocols File Transfer Protocol FTP and Internet Relay Chat Daemon IRCD. This application gives very close result of TCP performance comparing with TCP performance which obtains from Little's law using same model (BIOKM), i.e. the different percentage of utilization (Busy or traffic industry) and the idle time which are obtained from a new method base on Bio-computing comparing with Little's law was (nearly) 0.13%. KEYWORDS Bio-computing, TCP performance, Phylogenetic tree, Hybridized Model (Normalized), FTP, IRCDComment: 17 Pages,10 Figures,5 Table

Experimental and theoretical calculation of efficiency for flat plate solar collectors in Erbil City

Solar plate collectors are utilized to heat up water or a mixture of water and glycol by capturing solar radiation and transfer this heat to the collector fluid. In this study, the efficiency of solar plate collector during 19th, 20th and 21st of February, was investigated experimentally. The time of day, plate collector mean temperature, solar intensity and external air temperature can effect on the efficiency of solar collector. And the effect of incidence angle on solar irradiation has been studied; as a result, the solar irradiance will be decreased as the angle of incidence increased. A method presented can be used to calculate hour angle, diffuse solar radiation and total solar radiation at various temperatures in this paper its MATLAB programs

Mining method for cancer and pre-cancer detection caused by mutant codon 248 in TP53

Process of prediction has a substantial function in detecting and efficient protection of cancer. The tumor suppressor P53 is approximately near 50% of all human beings tumors due to the mutations which is appear in the TP53 gene to the cells within updated UMD TP53 Mutation Database Oct. 2017 [1], it is so difficult working with prime data (in excel) to predict and diagnosis cancers. In this research a functional model of mining approach and Artificial Neural Network which is proposed to predict cancer and pre-cancer caused by specific codon mutation (each codon has hundreds mutations cause cancers) of tumor protein P53, and applied this approach on mutability of hotspot codon 248 (exon 7), CGG. The Quick Propagation mechanism has been used for training and testing the Neural Network structure to determine the accuracy of the proposed architecture. This research procedure demonstrates that Neural Network based prediction of Cancer and Premalignant Disease (pre-cancer) of mutated codon 248 and manifests perfect performance in the prognosis of the mutation situation to pre-cancer or cancer in general. Using of data mining preprocessing steps and pattern extraction to construct the prediction model by selecting (8) out of (132) new TP53 gene database fields in order to classify the cases to the target class pathology (Cancer, Pre-cancer) using these fields. A high professional Neural Network software simulation (Alyuda NeuroIntellegence) is used to build the classifier and Neural Network, the testing and experimental results from the proposed architecture shows that using Quick Propagation algorithm is very accurate in term of accuracy and minimum error rates showing the results of accuracy (99.97%, 100%, 99.85%) for (Train, Validation and Test) phases respectively with error rate of (0.0003, 0, 0.0015) for (Train, Validation and Test) phases respectively