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Individuals with mobility disabilities can experience numerous health advantages when connecting with nature in various ways, such as passive enjoyment, active participation, or rehabilitative programs. These benefits encompass physical and mental benefits, as well as social gains. However, a range of concerns related to making natural environments accessible to and usable by people with mobility impairments demand the attention of various professionals, including caregivers, landscape architects, rehabilitation therapists, and policymakers. Efforts to promote inclusivity and accessibility aim to remove barriers and create environments where individuals with disabilities can participate fully in education, employment, public life, and social activities. This may involve adapting physical spaces, promoting awareness, providing assistive technology, offering support services, and understanding of disability issues. To enhance the accessibility of public places for disabled people, we must consider multiple criteria and risks. In this article, to address such issues we develop three multicriteria decision-making (MCDM) approaches based on picture cubic fuzzy information. Since aggregation operators (AOPs) play a crucial role in decision-making, we present the aggregation proficiency for picture cubic fuzzy information and develop a series of AOPs, such as picture cubic fuzzy Hamacher order weighted averaging (PCFHOWA), picture cubic fuzzy Hamacher weighted averaging (PCFHWA), picture cubic fuzzy Hamacher hybrid averaging (PCFHHA) operators and present some essential properties of these Opts. After studying their fundamental operations and properties, we utilize these operators to develop multicriteria decision making (MCDM) model with picture cubic fuzzy information. We present the extended TOPSIS method and extended VIKOR mothod for MCDM problems. We present a numerical example related to improving accessibility for disabled people in a public park. The results explore the effectiveness of our proposed methodologies and provide accurate measures to address the uncertainty related to the accessibility of disabled people to public places. [ABSTRACT FROM AUTHOR]

To promote a healthy lifestyle, it is essential for individuals to maintain a well-balanced diet and engage in customized workouts tailored to their specific body conditions and health concerns. In this study, we present a framework that assesses an individual's existing health conditions, enabling people to evaluate their well-being conveniently without the need for a doctor's consultation. The framework includes a kit that measures various health indicators, such as body temperature, pulse rate, blood oxygen level, and body mass index (BMI), requiring minimal effort from nurses. To analyze the health parameters, we collected data from a diverse group of individuals aged 17–24, including both men and women. The dataset consists of pulse rate (BPM), blood oxygen level (SpO2), BMI, and body temperature, obtained through an integrated Internet of Things (IoT) unit. Prior to analysis, the data was augmented and balanced using machine learning algorithms. Our framework employs a two-stage classifier system to recommend a balanced diet and exercise based on the analyzed data. In this work, machine learning models are utilized to analyze specifically designed datasets for adult healthcare frameworks. Various techniques, including Random Forest, CatBoost classifier, Logistic Regression, and MLP classifier, are employed for this analysis. The algorithm demonstrates its highest accuracy when the training and testing datasets are divided in a 70:30 ratio, resulting in an average accuracy rate of approximately 99% for the mentioned algorithms. Through experimental analysis, we discovered that the CatBoost algorithm outperforms other approaches in terms of achieving maximum prediction accuracy. Additionally, we have developed an interactive web platform that facilitates easy interaction with the implemented framework, enhancing the user experience and accessibility. [ABSTRACT FROM AUTHOR]

In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID-19), caused by a virus identified as a novel coronavirus (SARS-CoV-2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still a complicated and uncertain decision. This study's key objective is to develop a novel approach called q-rung orthopair probabilistic hesitant fuzzy rough set (q-ROPHFRS), which incorporates the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q-ROPHFR aggregation operators have been established: the q-ROPHFR Einstein weighted averaging (q-ROPHFREWA) operator and the q-ROPHFR Einstein weighted geometric (q-ROPHFREWG) operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision-making approach in real-world applications, a case study related to selecting drugs for COVID-19 pandemic is addressed. Furthermore, a comprehensive comparison with the q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real-world decision-making. [ABSTRACT FROM AUTHOR]