Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Aphid Aphis spp (Hemiptera:Aphididae) and Thrips Thrips spp (Thysanoptera: Thripidae) an economically important pests on several crops in the world and Iraq, that transfer many viruses diseases to it. Field studies were conducted to assessment the population density of these insects and susceptibility of six varieties (Barin, Revera, Divela, Rudlph, Alazata and Pleny) to infestation during 2013 spring season. The results were showed that all Potato varieties were infested by Aphis and Thrips on spring plantation but with different percentage. The Divela variety was higher percentage of infestation and high population density of aphid which averaged 1.47 insect/ leaf while in Alazata was the lower population density which averaged 1.02 in

It is no secret to anyone that studying and investigating books of jurisprudence, despite their suffering, is a pleasure that cannot be compared to pleasure, and it has benefits and importance that cannot be limited and summed up, and it has great fruits that researchers and students of knowledge reap, as well as enriching libraries with jurisprudential material, after dusting them and taking them out to the light of libraries to be seen. Researchers and scholars, generation after generation, so that this nation can benefit from this pioneering intellectual and scientific product that was written for us by our first ancestors of working scholars who enriched human civilization with this scientific material, which has become a beacon guid

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in)