This study was conducted at the poultry farm of the Department of Animal Production/College of Agriculture/University of Baghdad/Abu Ghraib, on 252 birds (180 females and 72 males). This study aims to observe the effect of melatonin implantation and exposure to different light colors and their interaction on characteristics of fertility and hatching of local Iraqi chickens. The birds were divided into three sections (white, red and green) each section contains two lines, one of which has been planted melatonin under the skin of the neck of birds and the other has not been planted hormones. The results showed that melatonin implantation and exposure to different light colors did not significantly affect the hatching rate of fertilized eggs a

Everywhere carriers incur a measure of liability for the safety of the goods. Carriers are liable for any damage or for the loss of the goods that are in their possession as carriers unless they prove that the damage or loss is attributable to certain excepted causes. Damaged and lost items can unfortunately be a common problem when shipping freight. Legal responsibilities arise due to loss or damage during transit while cargo is in their care. This study intends to investigate the nature of the liability of the maritime carrier when this liability is realized, and the extent to which it can be paid or disposed of given the risks realized from the transportation process, which may result in damage or loss of the goods, and the damag

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class