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In this study, a new parametric method is proposed to rank intuitionistic fuzzy numbers in a general form. One of the advantages of the proposed method is that the decision maker’s idea is taken into account by selecting appropriate amounts of decision level and hesitation degree parameters. In some illustrative examples, the superiority of the proposed method over some other approaches is demonstrated. Furthermore, to show the ability of the method to solve intuitionistic fuzzy optimization problems, the proposed method is applied to solve intuitionistic fuzzy network data envelopment analysis (IFNDEA) problems. Also, in three appropriate examples, the validity of the suggested method and its capacity to solve real-world problems are illustrated.

Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.