Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.
Food color has a great impact on food consumption and production. Many companies, restaurants and markets use the color perception theory to increase their sales. Recent studies have shown the negative impact of the food colors. So we analyzed the effect of synthetic food colors like orange red, lemon yellow, kesar yellow and apple green on actively dividing root tip cells of Allium cepa. Four different dyes were administered for the treatment of actively dividing root tip cells for 7-day duration along with control. Mitotic analysis clearly revealed the dye induced endpoint deviation like reduction in the frequency of normal divisions in a dose dependent manner. Mitotic divisions in the control sets were found to be normal dye has induced several chromosomal aberrations (genotoxic effect) at various stages of cell cycle such as stickiness of chromosomes, micronuclei formation, precocious migration of chromosome, unorientation, forward movement of chromosome, laggards, and Chromatin Bridge. Among all, stickiness of chromosomes was present in the highest frequency followed by partial genome elimination as micronuclei. The present study suggests that extensive use of synthetic dye should be forbidden due to genotoxic and cytotoxic impacts on living cells. Thus, there is an urgent need to assess potential hazardous effects of these food colors on other test systems like human and nonhuman biota for better scrutiny.
Water is the most important resource of the Earth and is significantly utilized for agriculture, urbanization, industry, and population. This increases the demand for water; meanwhile, the climatic condition decreases the supply of it. A rise in temperature of 1 degree Celsius might dry up 20% of renewable water resources, and to circumvent the water scarcity, it is necessary to reuse, create, and consume less water without wasting it. Water desalination is the process used to reuse the used or saline water by promptly extracting the salt or unwanted minerals and producing fresh consumable water. Based on the International Desalination Association, around 300 million people rely on desalination and the people of the Middle East region rely the most upon it. Around 7% of desalination plants are located in countries such as Saudi Arabia, Bahrain, Kuwait, and the United Arab Emirates. Reverse osmosis (RO) is the relevant desalination process in this type of area however, the conventional methods include more complexities, and hence to address this issue we proposed a novel approach known as Hybrid Capuchin and Rat swarm algorithm (HCRS) for effective water desalination technology using conventional sources and renewable energy in the middle east region. Moreover, a hybrid reverse osmosis plant model is developed for identifying renewable sources such as wind and solar energy. The proposed optimization can be used to mitigate the life cycle cost and enhances the reliability of the hybrid schemes. The experiment is conducted in a MATLAB simulator and compared the results with state-of-art works over the metrics such as relative error, system cost, and reliability. Our proposed method outperforms all the other approaches.
Soil testing is a unique tool for finding the available soil reaction (pH), organic carbon, and nutrients status of the soil. It helps to select the suitable crops concerning available pH and soil nutrients level to increase crop production. In this current approach, the soil test prediction is used to differentiate several soil features like soil fertility indices of available pH, organic carbon, electrical conductivity, macro nutrients, and micro nutrients. The Classification and prediction of the soil parameters lead to reduce the artificial fertilizer inputs, increasing crop yield, improves soil health and crop growth and increase profitability. These problems are solved by using fast learning and classification techniques known as machine learning (ML) classifier techniques such as random forest, Gaussian naïve Bayes, logistic Regression, decision tree, k-nearest neighbour and support vector machine. After the analysis decision tree classifier attains the maximum performance to solve all problems which goes above 80% followed by other classifiers.
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