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. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

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 (222Rn), 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 222Rn gas increases the risk of lung and stomach cancer. In this study, commercially packaged FJs from the seventeen most popular brands consumed in Turkey were analyzed for physicochemical properties and 222Rn activity concentrations to evaluate the radiological health risk. The values of pH, brix and 222Rn activity concentrations in FJ samples varied from 2.68 to 4.28, 2.50 to 14.30%, 9.6 ± 1.1 to 25.2 ± 2.5 mBq/L, respectively. The radiological health risk caused by internal exposure was evaluated for children and adults by estimating the ingestion and inhalation annual effective dose. The average values of the total annual effective dose for children and adults were found as 0.039 µSv and 0.056 µSv, respectively, which are much lower than the recommended dose of 100 µSv for drinking water.

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. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method's performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.

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. The concentration of heavy metals (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Zr, Cd, Hg, and Pb) in lignite coal, slag, and fly ash samples were analyzed using an energy dispersive X-ray fluorescence technique. The concentration of Fe is highest while Hg concentration is lowest in the samples. The concentrations of heavy metals are higher in slag and fly ash samples than in lignite coal. Average values of EF (related to Earth's crust average) revealed that extreme enrichment has been shown by arsenic and mercury in lignite coal and fly ash samples while very high enrichment has been shown in slag samples.

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. As a result, ascending concentration of boron atoms can enhance neutron shielding property of samples.