Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer.

Evaluation of radiological health risk due to ingestion and inhalation of radon in commercial packaged fruit juices consumed in Turkey.

Fruit juices (FJs) are among the most popular beverages frequently preferred by consumers, believing FJs contain the nutritional values, minerals, phytochemicals, vitamins, and antioxidants necessary for a healthy life. However, FJs may contain natural radionuclides such as radon (Rn), which originates from the fruit and water utilized in their production, at levels that may pose a health risk to people. Inhalation and ingestion of Rn gas increases the risk of lung and stomach cancer.

Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm.

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images' features were extracted based on the convolutional neural network.

Assessment of the Enrichment of Heavy Metals in Coal and Its Combustion Residues.

Coal-fired thermal power plants remain one of the main sources of electricity generation in Turkey. Combustion of coal creates coal ash and slag, which are often stored in landfills located near residential and agricultural fields, increasing the potential for high environmental contamination and health risks. This study investigates the content and enrichment factor (EF) of heavy metals in pulverized lignite coal and its combustion residues from the Kangal lignite coal-fired thermal power plant situated in the Central Anatolian Region of Turkey.

Investigation of neutron shielding properties depending on number of boron atoms for colemanite, ulexite and tincal ores by experiments and FLUKA Monte Carlo simulations.

(241)Am-Be source and three samples including different amounts of boron atoms per unit volume called colemanite, ulexite and tincal were used in total macroscopic cross section experiments. Also FLUKA Monte Carlo code was used to simulate total macroscopic cross sections, absorbed doses and deposited energies by low energy neutron interactions. Besides half value layers of samples were calculated and compared to paraffin.