Goal: This article aims to present a systematic approach to improve the resource allocation and human queues prioritization patterns. Design / Methodology / Approach: To achieve such a purpose, effective criteria using a fuzzy-Delphi method, and subject-related researchers’ views were obtained. Utilizing the Analytic Network Process method, the weights of each criterion was measured. Then, considering the established weights and using a fuzzy-TOPSIS method, a prioritization system via Discrete Event Simulation was developed. Results: Results indicate that the established approach properly improved the performance of the prioritization system in terms of resources and facilities allocation in neurology’s ICUs. Limitations of the investigation: A drawback of this research can be in states of emergency which limits the options at hand and the criteria proposed may set a drawback on the aim of the study. Practical implications: The results show that the proposed model can modify patient entry based on multiple criteria in terms of productivity and social justice in the patient queuing strategy. Originality / Value: The contribution of this research is threefold: the literature has been reviewed to conclude the criteria concerning decisions around ICUs, the concluded criteria filtered through an expert panel which can be relied based on the method, a real application of the steps proposed is presented which allows comparing the accuracy and efficiency of the decisions made in the hospitals.

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.

Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer's disease. Ad 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 (SVM). 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 SVM kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network (DCNN) architecture to identify Alzheimer's-related mental disorders. According to the findings, the SVM approach accurately recognized over 93% of the photos tested. The DCNN approach was one hundred percent accurate during model training, whereas the SVM approach achieved just 93 percent accuracy. In contrast to SVM's accuracy of 89.3%, the DCNN model test's findings were accurate 98.8% of the time. Based on the findings reported here, the proposed DCNN architecture may be used for diagnostic purposes involving the patient's mental state.