Early diagnosis of cancers is a major requirement for patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. The recognition of cancer diseases mostly operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system (FES) and its implementation for the detection of prostate cancer. Specifically, prostate-specific antigen (PSA), prostate volume (PV), age, and percentage free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while PCR serves as an output parameter. Mamdani fuzzy inference method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to determine whether their patients need a biopsy. This system is fast as it requires minimum calculation and hence comparatively less time which reduces mortality and morbidity and is more reliable than other economic systems and can be frequently used by doctors.

Researchers are focusing on improving time series forecasting methods to address real-world problems like COVID-19. Current methods show decreased accuracy due to unpredictable seasonality, and enhancing models to handle long-term dependencies is crucial for better forecasting accuracy. This paper presents a Transformer self-attention based novel approach for infectious disease time series forecasting, specifically for COVID-19. The proposed method utilizes the Ensemble Empirical Mode Decomposition (EEMD) and Local Outlier Factor (LOF) methods for data pre-processing and to detect outliers. Next, a modified self-attention model based on Transformer neural network is introduced for predicting COVID-19 time series forecasting for the first time. The research specifically investigates the application of encoder/decoder networks with an enhanced Positional Encoding approach. This involves using a novel time encoding technique on the input pattern to achieve more precise intended output. Consequently, the parameters in the Transformer model are adjusted using the Arithmetic Optimization Algorithm (AOA) to enhance the accuracy of the prediction. The model generates more accurate predictions over broader time intervals, with the lowest MAE of 371.92 and RMSE of 674.61, indicating superior predictive accuracy by approximately 30% compared to other state-of-the-art methods. The proposed Transformer model has demonstrated significant improvements in robustness and forecasting accuracy compared to standard approaches such as LSTM, RNN, Exponential Smoothing, AutoARIMA, and TBATS for the COVID-19 time series of India, USA, and Brazil. The suggested model, due to its superior predictive accuracy, is applicable in diverse time series forecasting domains such as stock market trends, sales, and industrial consumption forecasting etc.