The goal of this study is to evaluate and compare the prediction performance of three different models, Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting dementia. The models were trained and tested using a dataset of 149 participants and nine input variables, with a $70 \% / 30 \%$ data split. Performance indicators for evaluation were $\mathrm{R}^{2}$, RMSE, and MSE. The results show that all models correctly predicted dementia, with ANN and ANFIS marginally exceeding MLR in terms of prediction accuracy. The training and testing phases yielded a perfect $\mathrm{R}^{2}$ score of 1, suggesting a strong fit to the dataset. This study contributes to dementia prediction research by providing a detailed review of various models.

Adrenocortical Carcinoma (ACC) can be detected and diagnosed using CT scans with the help of deep learning models, such as the novel MSHA model. The dataset used in the study consists of 53 patients with verified ACC and their contrast-enhanced CT scans. The MSHA model combines various approaches based on attention-based contextual information with mixed-scale dense convolution, a self-attention mechanism, and hierarchical feature fusion. Evaluation criteria are utilized to assess the model’s performance, demonstrating outstanding precision, sensitivity, specificity, and an F1 score of $\mathbf{9 6. 0 \%}$, along with an accuracy of 96.65%. This performance surpasses well-established models such as ResNet50, VGG16, VGG19, and InceptionV3 and proves valuable for detecting and diagnosing ACC, enhancing patient care and outcomes.

Bone cancer is a serious morbidity and mortality factor that occurs in rare cases with less than ${1 \%}$ prevalence across all cancers. Despite being rare, bone cancer has a mortality rate, accounting for less than ${1 \%}$ of deaths worldwide and claiming about ${2, 1 0 0}$ people in the United States in 2022. This includes cancers in both adults and children. Most mortality cases are traced to the late detection of tumors and wrong systematic diagnostic and therapeutic decisional approaches deployed either by the concerned physicians, patients, or close associates. To circumvent the hurdles associated with poor therapeutic decision-making, relating to bone cancers and to eliminate scenarios associated with uncertainties in any decision-making platform, this study proposed the multicriteria decision-making (MCDM) method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE) to compare, evaluate, and rank conventional and modern treatment approaches for bone cancer. Results from this study showed that, with a net flow of 0.1737, Radiofrequency was determined as the most preferred and most favorable treatment approach for bone cancer. Surgery, Cryotherapy, Immunotherapy, and Radiation therapy came second, third, fourth, and fifth with net flows of $0.1309,-0.0154,-0.0797$, and −0.0902 respectively. Chemotherapy with a net flow of $-{0 . 1 1 9 3}$ was the least preferred treatment alternative. However, the outranking results may differ if different weight preference functions are assigned to each selected criterion

The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: $\mathbf{R}^{2}$, MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.

Genetic algorithms (GA) are search engines that either optimize or reduce predefined functions. The technique of selection is an important phase in GA. This research study aims to evaluate, compare, and rank the selection techniques in GA. The evaluated selection techniques are; roulette wheel selection, elitist selection, rank selection, tournament selection, truncation selection, Boltzmann selection, and stochastic Universal Sampling selection. The comparison was based on the following selected criteria; (performance accuracy, fitness value accuracy, execution time, preservation of diversity, computational cost, ease of use, and bias levels. The paper incorporates the aforementioned criteria into the fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE). This decision-making tool was used to determine the most preferred selection techniques in GA. The results from this research study showed that with an outranking net flow of 0.0690 elitism was determined as the most effective selection technique for GA based on the given criteria and their importance levels. Followed by the tournament selection technique with a positive net flow of 0.0343 and then, the rank selection technique, roulette wheel selection technique, truncation selection technique, and stochastic universal sampling selection technique occupied the third, fourth, fifth, and sixth positions with a net flow of 0.0078, -0.0102, -0.0116, -0.0156. Boltzmann selection technique ranked least among the considered selection techniques with a net flow of -0.0738 due to its features with all the criteria. With this study, we have provided a supportive tool for the decision-makers in the selection of the genetic algorithm techniques, and we have shown the applicability of the fuzzy PROMETHEE approach in this case by providing the advantages and disadvantages of each decision point.