Researchers used different methods such as image processing and machine learning techniques in addition to medical instruments such as Placido disc, Keratoscopy, Pentacam;to help diagnosing variety of diseases that affect the eye. Our paper aims to detect one of these diseases that affect the cornea, which is Keratoconus. This is done by using image processing techniques and pattern classification methods. Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection. In this study, sixteen features were extracted from the four refractive maps along with five readings from the Pentacam software. The

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

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

The development issue is considered as one of the most important matters of concern to governments, intellectuals and specialists in the contemporary world, especially in developing countries that are trying hard to move out of the afflicting underdevelopment domain to a brighter future and achieve the aspirations of those countries through organized planning which take advantage of available resources, whether material or humanitarian, in the best way possible.
The development process are taking place at multiple levels ranging from national to regional to local where the community is stimulated and excited for the cooperation, participation and preparation to perform planned change in the methods of thinking, acting and production.<

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

In this study, the results of the uranium concentrations and specific activity in 10 rice samples are described using a solid-state track detector (CR-39). Samples were collected from various local Iraqi markets with different origins (Iraq, India, America, and Thailand). Our findings found that the results of uranium concentration in all studied samples are ranging from (0.55 ± 0.28 to 1.74 ± 0.31) ppm with a weighted average of (1.24 ± 0.99) ppm. Also, results demonstrate that the specific activity values of the studied samples swing between values of (6.88 ± 3.52 and 21.49 ± 3.85) Bq/Kg. The obtained results of the studied rice samples are indicated that it is less than the acceptable limit of those studies established by ma