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This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values \"C12 = Laughing for no reason\" and \"C16 = Notice the sound of the bell\" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

This paper examines a collection of finger vein enhancement stages that have not only low computational complexity but also high distinguishing capacity. This proposed series of enhancement stages is based on the equalization of fuzzy histograms. A mixture of Hierarchical Centroid and Gradient Histograms was used to extract features. Both the enhancement stages were evaluated using 6 fold stratified cross validation with K Nearest Neighbor and Support Vector Machine (SVM). Experimental results show that the (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm which can be used to solve problems of classification and regression. Calculations of KNN in the test data are highly accurate. Using stratified 6-fold analyzes on all fingers of all hands in the collected database, when selecting the right and middle fingers based on the analysis of the 106 people in the data set. Compared with SVM and related works, the algorithm proposed has optimum performance.

This paper examines a collection of finger vein enhancement stages that have not only low computational complexity but also high distinguishing capacity. This proposed series of enhancement stages is based on the equalization of fuzzy histograms. A mixture of Hierarchical Centroid and Gradient Histograms was used to extract features. Both the enhancement stages were evaluated using 6 fold stratified cross validation with K Nearest Neighbor and Support Vector Machine (SVM). Experimental results show that the (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm which can be used to solve problems of classification and regression. Calculations of KNN in the test data are highly accurate. Using stratified 6-fold analyzes on all fingers of all hands in the collected database, when selecting the right and middle fingers based on the analysis of the 106 people in the data set. Compared with SVM and related works, the algorithm proposed has optimum performance.

Infectious diseases weigh down the communities in the world and scientists to spend more effort via keeping tracking of evolving treatment and detecting methods. These diseases may lead to harm life of people. Early diagnosis could significantly support healthcare specialists to save more lives. Additionally, the pandemic leads to maximizing hospitals visits, medical clinics and healthcare centres. The international health organizations also have shown that there has been a rapid growth of infected cases. Therefore, correct diagnosis has become a pressing problem. Consequently, automated diagnosis becomes a sensible solution to the problem of these diagnosis challenges. This study was conducted to identify the most common infectious diseases in the Iraqi society using a well-designed questionnaire and a proposed automated diagnostic technique. Firstly, the top diseases questionnaire is distributed around the city of Baghdad to different medical clinics. The results from the preliminary analysis of the collected responses (115 responses) showed that the most common widespread diseases in the Iraqi community are diabetes, flu, and typhoid. This was followed by another questionnaire for the identification of symptoms and blood test variables for these diseases. It is worth pointing out that there are not sufficient and updated studies dealing with the diseases that attack the Iraqi community. Toward the automated diagnosis, both infectious diseases (flu and typhoid), identified symptoms are employed as feature space with one of the machine learning techniques. For the results evaluation different measures, such as accuracy, confusion matrix, and efficient verification via ROC, have been used to indicate the system performance. The result shows that typhoid disease has significant diagnosis accuracy of 98% compared to the others. While three machine learning systems named (Native Bayes, Linear discriminant, and Ensemble (subspace discriminant)) were used to diagnose flu disease. The resulting accuracy of all three models are 92% which shows good performing. Therefore, the proposed method shows precise accuracy and systematic manner for analyzing infectious diseases.