The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

Mammary tumors (CMT) in dogs in Iraq may be induced by carcinogenic war ordnance. In our study, 10 virgin un- spayed military/pet bitches aged 5-15 years presented with abnormal masses in the abdomen with painful oedema, swelling, anorexia, weight loss, weakness and mild fever. Examination of regional lymph nodes and thoracic radiography confirmed metastasis. Tumors were excised and determined to be mostly adenocarcinomas involving multiple glands, solid in texture, 5-15 cm in size, mostly in the inguinal mammary glands at stage T3: >5 cm. Microscopy confirmed presence of adenocarcinoma in 8 dogs and solid carcinoma in 2 with half of tumors being grade III. Tumors had pleomorphic hyperchromatic cell nuclei in stroma, epithelial cells of duc

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

The important device in the Wireless Sensor Network (WSN) is the Sink Node (SN). That is used to store, collect and analyze data from every sensor node in the network. Thus the main role of SN in WSN makes it a big target for traffic analysis attack. Therefore, securing the SN position is a substantial issue. This study presents Security for Mobile Sink Node location using Dynamic Routing Protocol called (SMSNDRP), in order to increase complexity for adversary trying to discover mobile SN location. In addition to that, it minimizes network energy consumption. The proposed protocol which is applied on WSN framework consists of 50 nodes with static and mobile SN. The results havw shown in each round a dynamic change in the route to reach mobi

Plane cubics curves may be classified up to isomorphism or projective equivalence. In this paper, the inequivalent elliptic cubic curves which are non-singular plane cubic curves have been classified projectively over the finite field of order nineteen, and determined if they are complete or incomplete as arcs of degree three. Also, the maximum size of a complete elliptic curve that can be constructed from each incomplete elliptic curve are given.

This research examines the future of television work in light of the challenges posed by artificial intelligence (AI). The study aims to explore the impact of AI on the form and content of television messages and identify areas where AI can be employed in television production. This study adopts a future-oriented exploratory approach, utilizing survey methodology. As the research focuses on foresight, the researcher gathers the opinions of AI experts and media specialists through in-depth interviews to obtain data and insights. The researcher selected 30 experts, with 15 experts in AI and 15 experts in media. The study reveals several findings, including the potential use of machine learning, deep learning, and na

Breast tumors patients generally have more oxidative stress than normal females. This was clear from a highly significant elevation (P<0.05) in malondialdehyde level in RBCs, serum and tissue of all patients groups with breast cancer as compared with control group. In this study we had found that free radicals in malignant breast tumors were higher than benign tumors, therefore the MDA might be used as a marker for prognosis of the disease.

The amount of protein in the serum depends on the balance between the rate of its synthesis, and that of its catabolism or loss. Abnormal metabolism may result from nutritional deficiency, enzyme deficiency, abnormal secretion of hormones, or the actions of drugs and toxins. Renal cancer is the third most common malignancy of the genitourinary system, and accounts for 3% of adult malignancies globally. Total serum proteins were measured in malignant kidney tumor, benign kidney tumors, and non tumoral kidney diseases patient groups, as well as in healthy individuals. A significant decrease (p< 0.001) of total serum protein levels in patients with malignant kidney tumors when compared with those of benign tumors, non tumoral diseases, and hea

A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique, Omar Jihad Banawi*, Raghad

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d