The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.

  In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.

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This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

This study aimed to evaluate the sustainable efficiency and calculate the sustainable value, opportunity cost and revenue to cost of potato production farms in Baghdad Governorate. Data were collected from a random sample of 102 spring potato producers in Baghdad governorate for the year 2022, the value-added function was estimated using three levels: economic (seeds and space), and social (education level), while environmental represented by (residual and volatile of nitrogen). Effective resources were calculated to extract the sustainability value- SV appeared on an average of 18023.15 thousand Iraqi dinars. Positive relationship with residual nitrogen and productivity, and Inverse relationship to the amount of volatile nitrogen.

Background Depression is one of the most common medical illnesses worldwide. Medical students during their training will be subjected to many stressors, both mentally and emotionally. Objectives Assess the prevalence of depressive symptoms among medical students and to identify any association between depressive symptoms and some socio-demographic factors.

The current study was conducted to demonstrate the effects of air pollution on different biochemical parameters inCitrus aurantium plant and calculation the Air Pollution Tolerance Index (APTI). Five sites were selected tocollect plant leaves, four of them within the city of Baghdad, namely Al-Jadriya, Al-Andlous, Al-Doura and closeto the private generators to represent the urban areas and Abu Ghraib site to represent the rural area. Seasonalsamples were collected for the period from October 2016 to June 2017. Eleven biochemical parameters totalchlorophyll, ascorbic acid, leaf extract pH, relative water content, total nitrogen, total protein content, total sugarcontent, proline, electrical conductivity, cadmium and lead. The results of Citr

Adult of dipterous flies were collected monthly from exposed animals carcasses during the period from February 2006 to January 2007 in Baghdad city. The results obtained showed that flies could be collected all over the year with variation of their population density in different seasons. The majority of the collected species were abundant during Spring and late Autumn (at lowest numbers) . In this investigation, nineteen species confined to four families were collected; these families are: Calliphoridae, Muscidae, Sarcophagidae and Fanniidae. The species Musca domestica Linn .was the most abundant followed by Chrysomya megacephala ( Fabricius ) , while Pollenia sp . and Fannia sp . were the least abundant species.

Specialized Escherichia coli (E. coli) isolates, called uropathogenic E. coli (UPEC), cause most of urinary tract infections (UITs). Once bacteria reached the urinary tract of the host, they have to adhere to the host cell for the colonization. For this purpose, bacteria have different structures including fimbrial adhesins. Most of the UPECs contain type 1 fimbriae encoded by fim operon (fimB, E, A, I, C, D, F, G, H) which is responsible for the adhesive ability in these isolates. Ninety-four isolates of UPEC were obtained from UTI patients in Baghdad hospitals and their diagnosis were confirmed by the PCR method using 16srDNA as a housekeeping gene. The UPEC isolates were tested for their ability of adherence to the urothelial cells obtai