Cancer has a disproportionately large influence on the death rate of adults. A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness. Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body. Nevertheless, manual diagnosis may be time-consuming and subjective owing to the wide range of interobserver variability induced by the enormous number of medical imaging data. This variability is caused by the fact that medical imaging data are collected. Because of this, the process of accurately diagnosing a patient could become more difficult. To execute jobs that included machine learning and the interpretation of complicated imagery, cutting-edge computer technology was necessary. Since the 1980s, researchers have been working on developing a computer-aided diagnostic system that would help medical professionals in the early diagnosis of various malignancies. According to the most recent projections, prostate cancer will be discovered in the body of one out of every seven men at some time throughout the course of their life. It is unacceptable how many men are being told that they have prostate cancer, and the condition is responsible for the deaths of a rising number of men every year. Because of the high quality and multidimensionality of the MRI pictures, you will also need a powerful diagnosis system in addition to the CAD tools. Since it has been shown that CAD technology is beneficial, researchers are looking at methods to improve the accuracy, precision, and speed of the systems that use it. The effectiveness of CAD technology has been shown. This research proposes a strategy that is both effective and efficient for the processing of images and the extraction of features as well as for machine learning. This work makes use of MRI scans and machine learning in an effort to detect prostate cancer at an early stage. Histogram equalization is used while doing the preliminary processing on photographs. The image’s overall quality is elevated as a result. The fuzzy C means approach is used in order to segment the images. Using a Gray Level Cooccurrence Matrix (GLCM), it is feasible to extract features from a dataset. The KNN, random forest, and AdaBoost classification algorithms are used in the classification process.

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Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m−2·year−1 versus 9.01 kWh·m−2·year−1 for ANFIS-GWO and 11.80 kWh·m−2·year−1 for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings.

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