These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

Many strains of lactic bacteria produce antimicrobial peptides of bacteriocins that are antibiotics used against pathogenic strains. The present work aimed to use a banana peels medium in the fermentation process to replace the commercial MRS medium for decreasing the cost of bacteriocins LAB production. Based on the result, banana peel was a cost-effective and viable alternative carbon source for the production and development of bacteriocin-producing Lactobacilli. The growth of lactobacilli in commercial MRS medium and Banana Peel medium showed no differences, therefore banana peel waste can be used to produce Lactobacilli bacteriocins. Lactobacillus strains grew exceptionally well at 37 C and pH 6.0.

Catalase (EC 1.11.1.6) is a well known enzyme which exists in almost all living creatures exposing to oxygen (such as plants, bacteria, and animals). It is a very necessary enzyme to protect the cell from oxidative detriment by reactive oxygen species (ROS). The aim of this study is the partial purification and characterization of Catalase enzyme from Banana peels. In this study, fresh banana peels are treated with 70 % ethanol ,further separated with chloroform ,water and ethyl acetate respectively .The supernatant of the enzymatic sample which is treated with chloroform is loaded into gel filtration column with Sephadex G-100 (1.0 x 90 cm) equilibrated with pH7 buffer media (phosphate buffer 0.1 M). Kinetic studies of the purified en

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This research aims to removes dyes from waste water by adsorption using banana peels. The conduct experiment done by banana powder and banana gel to compare between them and find out which one is the most efficient in adsorption. Studying the effects different factors on adsorption material and calculate the best removal efficiency to get rid of the methylene blue dye (MB).

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s