Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Background: Abdominal symptoms are possibly the most frequent of all symptoms encountered in surgical practice.  Pain is the most common of all abdominal symptoms.  Causes of acute abdominal pain include both medical and surgical. Most symptoms arise from intra-abdominal organs or systems while some may originate extra abdominally and are then referred to the abdomen. Medical causes of abdominal pain are encountered more frequently.

Objective: To study the causes of acute abdominal pain in patients attending emergency department in Al- Imamain Al- Kadhimain Medical City.

Type of the study: A prospective cross sectional study

Meth

The cyanobacterial neurotoxinβ-N-methylamino-L-alanine (BMAA) is considered to be an “excitotoxin,” and its suggested mechanism of action is killing neurons. Long-term exposure to L-BMAA is believed to lead to neurodegenerative diseases including Parkinson’s and Alzheimer’s diseases and amyotrophic lateral sclerosis (Lou Gehrig’s disease). Objectives of this study were to determine the presumptive median lethal dose (LD₅₀), the Lowest-Observed-Adverse-Effect Level (LOAEL), and histopathologic lesions caused by the naturally occurring BMAA isomer, L-BMAA, in mice. Seventy NIH Swiss Outbred mice (35 male and 35 female) were used. Treatment group mice

Acute lymphoblastic leukemia (ALL) is one of the most common diseases , so in this study the serum level of malondialdehyde and its relationship with metanephrine was investigated in acute lymphoblastic leukemia patients over one month of treatment. Some biochemical parameters (serum glucose , total serum protein , malondialdehyde ,vitamin C, and metanephrine) changed as well as white blood cell count and blood hemoglobinlevelswere analyzed in sixty patients diagnosed with acute lymphoblastic leukemia over one month of treatment compared to healthy control group.Statistically significant increases (p<0.01) in white blood cell (WBC) count, mean concentrations of malondialdehyde (MDA) (p< 0.05) and metanephrine (p< 0.001) were observed in

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod

A study carried out for study effect of furfural that extracted from corn cobs by using specialized reaction system laboratory on phytopathogenic fungi: Pythium aphanidermatum, Rhizoctonia solani, Macrophomina phaseolina and Fusarium solani in addition to biocontrol fungus Trichoderma viride were isolated from infected plants and from their rhizosphere . The preparation results of different concentrations from stock solution in concentration 1% of furflural showed that The concentration was 100 ppm of furfural was inhibited the growth of P. aphanidermatum46.7 % and the was in concentration 400 ppm. while the concentration 500 ppm caused inhibition 50% and 41.1% of R. solani and F. solani respectively. Whereas the concentration 500 pp