Evaluating efficiency in water and sewerage services: An integrated DEA approach with DOE and PCA.

Evaluating the performance of service organizations like Water and Sewerage companies is essential for optimal operations, high-quality service, and cost efficiency. This paper introduces a model using data envelopment analysis (DEA) to assess the efficiency of operational units within such companies. The selection of key performance indicators is complicated by the numerous inputs and outputs, each affecting systems and activities differently.

Data-driven modeling using system dynamics simulation to provide relief in earthquake based on different scenarios.

Effective relief reduces damages and protects people during natural disasters, such as earthquakes. This research proposes a data-driven model based on sustainability, taking into account the pre and post-crisis simultaneously. Real data was used to validate the model in various earthquake scenarios.

A medical decision support system for predicting the severity level of COVID-19.

The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process.

An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays.

Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning.

Analysis and decision based on specialist self-assessment for prognosis factors of acute leukemia integrating data-driven Bayesian network and fuzzy cognitive map.

The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist's self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool.

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning.

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test.

Road map for progress and attractiveness of Iranian hospitals by integrating self-organizing map and context-dependent DEA.

Hospitals play an important role in healthcare systems and usually stay on the end node of the healthcare chain. Thus, determining their road map to get close to the desired efficiency frontier and developing short-term and long-term plans could help to manage costs and resources, efficiently. As the efficiency frontier depends on the size of the hospital and the complexity of its structure, the homogeneity in benchmarking must be considered.

Do Geographical Locations Affect in Hospitals Performance? A Multi-group Data Envelopment Analysis.

Hospitals are the main and the last component of the health care systems. Therefore, evaluating the performance of hospitals is vital. Several studies are done in hospitals evaluation, but almost none of them consider the geographical features.

Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game.

This paper introduces a new integrated approach to measure unified efficiency of the healthcare systems. Health centers as an important part of the healthcare systems are considered for evaluation. For this purpose, we define two categories of inputs to measure performance of health centers based on medical human resources and characteristics of spatial information by using geographic information system (GIS).