Governments, researchers, and humanitarian agencies have increasingly focused on reducing disaster impacts and enhancing the resilience of individuals, households, and communities, as the human and economic costs of natural disaster events have dramatically increased over the past century. Achieving resilience in a disaster context means the ability to survive future natural disasters with minimum loss of life and property as well as the ability to create a greater sense of place among residents, a stronger, more diverse economy, and a more economically integrated and diverse population. However, less attention has been paid to the significance of social capital in a post-disaster context and its contribution in building community resilience. It is very obvious that the contribution of social capital to post-disaster resilience in a Middle Eastern/Saudi Arabian context is virtually unknown. With a focus on the Saudi Arabian context, this research paper develops a social capital framework centered on resilience and post-disaster recovery. To conduct this study, a holistic approach to data collection is followed through questionnaire surveys, structured and non-structured interviews with citizens, and informal discussions with government and major stakeholders related to flash flood disaster management in the City of Jeddah. It is interesting to note that several religious institutions have played important roles in evacuating people and providing help for a quick recovery. In addition, government organizations are taking the recovery process seriously by providing necessary help in the flood-stricken areas. Within the scope of the given framework, the research explores and evaluates the role of social capital in post-disaster recovery efforts through a case study of the 2009 and 2011 Jeddah flash floods.

On the basis of decision-theoretical rough sets (DTRSs), the three-way decisions give new model of decision approach for deal with the problem of decision. This proposed model of decision method is based on the loss function of DTRSs. First, the concept of fractional orthotriple fuzzy $\beta$ -covering (FOF $\beta$ -covering) and fractional orthotriple fuzzy $\beta$ -neighborhood (FOF $\beta$ -neighborhood) was introduced. We combined loss feature of DTRSs with covering-based fractional orthotriple fuzzy rough sets (CFOFSs) under the fractional orthotriple fuzzy condition. Secondly, we proposed a new FOF-covering decision-theoretical rough sets model (FOFCDTRSs) and developed related properties. Then, based on the grade of positive, neutral and negative membership of fractional orthotriple fuzzy numbers (FOFNs), five methods are established for addressing the expected loss expressed in the form of FOFNs and the corresponding three-way decisions are also derived. Based on this, we presented a FOFCDTRS-based algorithm for multi-criteria decision making (MCDM). Then, an example verifies the feasibility of the five methods for solving the MCDM problem. Finally, by comparing the results of the decisions of five methods with different loss functions.

Spherical hesitant fuzzy sets have recently become more popular in various fields. It was proposed as a generalization of picture hesitant fuzzy sets and Pythagorean hesitant fuzzy sets in order to deal with uncertainty and fuzziness information. Technique of Aggregation is one of the beneficial tools to aggregate the information. It has many crucial application areas such as decision-making, data mining, medical diagnosis, and pattern recognition. Keeping in view the importance of logarithmic function and aggregation operators, we proposed a novel algorithm to tackle the multi-attribute decision-making (MADM) problems. First, novel logarithmic operational laws are developed based on the logarithmic, t-norm, and t-conorm functions. Using these operational laws, we developed a list of logarithmic spherical hesitant fuzzy weighted averaging/geometric aggregation operators to aggregate the spherical hesitant fuzzy information. Furthermore, we developed the spherical hesitant fuzzy entropy to determine the unknown attribute weight information. Finally, the design principles for the spherical hesitant fuzzy decision-making have been developed, and a practical case study of hotel recommendation based on the online consumer reviews has been taken to illustrate the validity and superiority of presented approach. Besides this, a validity test is conducted to reveal the advantages and effectiveness of developed approach. Results indicate that the proposed method is suitable and effective for the decision process to evaluate their best alternative.

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem caused by heavy-duty in daily life. We need some future and optimal classifier selection by using different techniques for depression data extraction using EEG. Intelligent decision support is a decision-making process that is automated based on some input information. The primary goal of this proposed work is to create an artificial intelligence-based fuzzy decision support system (AI-FDSS). Based on the given criteria, the AI-FDSS is considered for classifier selection for EEG under depression information. The proposed intelligent decision technique examines classifier alternatives such as Gaussian mixture models (GMM), k-nearest neighbor algorithm (k-NN), Decision tree (DT), Nave Bayes classification (NBC), and Probabilistic neural network (PNN). For analyzing optimal classifiers selection for EEG in depression patients, the proposed technique is criterion-based. First, we develop a general algorithm for intelligent decision systems based on non-linear Diophantine fuzzy numbers to examine the classifier selection technique using various criteria. We use classifier methods to obtain data from depression patients in normal and abnormal situations based on the given criteria. The proposed technique is criterion-based for analyzing optimal classifier selection for EEG in patients suffering from depression. The proposed model for analyzing classifier selection in EEG is compared to existing models.