This research including, CO3O4 was prepared by the chemical spry pyrolysis, deposited film acceptable to assess film properties and applications as photodetector devise, studying the optical and optoelectronics properties of Cobalt Oxide and effect of different doping ratios with Br (2, 5, 8)%. the optical energy gap for direct transition were evaluated and it decreases as the percentage Br increase, Hall measurements showed that all the films are p-type, the current–voltage characteristic of Br:CO3O4 /Si Heterojunction show change forward current at dark varies with applied voltage, high spectral response, specific detectivity and quantum efficiency of CO3O4 /Si detector with 8% of Br ,was deliberate, extreme value with 673nm.

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

The current study showed that the plants were collected from 23 geographical locations in Brenaj, Wasit, Iraq. The region was characterized by a great diversity of wild plants spread densely in this region. The results were as follows: 32 families, 149 species. Asteraceae was the most widespread with 29 species from the group of dicotyledons, followed by the Fabaceae family (19) species, but there are 13 plant families, with one plant species recorded for each plant family. in Brenaj, Wasit included: Aizoaceae, Capparaceae, Convolvulaceae, Frankeniaceae, Molluginaceae, Papaveraceae, Phyllanthaceae, Primulaceae, Rutaceae, Rubiaceae, Verbenaceae, Zygophyllaceae, Urticaceae, while the plant family Poaceae was most widespread in genera and spec

With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases