In this paper, (1) one simplified the standard TOPSIS to new Multi-Criteria Decision-Making (MCDM) called Simplified-TOPSIS. Simplified-TOPSIS gives the same results and simplifies the calculation of the classical TOPSIS. An example is presented distinctions between Simplified-TOPSIS and classical TOPSIS are underlined. (2) extend the new Simplified-TOPSIS method to Neutrosophic-simplified-TOPSIS using single valued Neutrosophic information. An example showing the interest of Neutrosophic-simplified-TOPSIS to manipulate the uncertainty linked to information presented in Multi-Criteria Decision-Making.

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Neutrosophic-Simplified-Topsis. Multi-Criteria Decision-Making Using Combined Simplified-Topsis Method and Neutrosophics

Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various input and output datasets. The impact of data aggregation (10-min and 30-min intervals) was examined and compared to original sensor data (5-min intervals) to evaluate the performance of the LSTM model. The results show that 50-neuron LSTM architecture performed better (MAPE = 0.09, RMSE = 0.0008, R2 = 0.8) in predicting discharges for the study area. The study indicates that using the same sequence length for the prior and the forecast can improve the effectiveness of the LSTM model. Based on the results, using a 10-min aggregated discharge dataset reduces energy consumption, transmission bandwidth, and storage capacity. Additionally, it improves prediction performance compared to an original 5-min interval data in Ålesund city.

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Lung diseases represent one of the main causes of death in the world. Making an opportune diagnosis would improve the quality of life of patients. A set of factors intervene in the diagnostic process that significantly impact the evolution of the disease. However, there is uncertainty to determine which factors promote the development of lung disease. The present research aims to propose a method for prioritizing risk factors for chronic obstructive pulmonary disease. The method uses a multi-criteria approach where uncertainty is modeled using neutrosophic numbers. The proposed method is implemented in a case study at Alfredo Noboa Montenegro Hospital. The result is the prioritization of the factors whose mitigation would favor the reduction of lung disease.

Risk Factors Prioritization for Chronic Obstructive Pulmonary Disease

Predicting the condition of sewer pipes plays a vital role in the formulation of predictive maintenance strategies to ensure the efficient renewal of sewer pipes. This study explores the potential application of ten machine learning (ML) algorithms to predict sewer pipe conditions in Ålesund, Norway. Ten physical factors (age, diameter, depth, slope, length, pipe type, material, network type, pipe form, and connection type) and ten environmental factors (rainfall, geology, landslide area, population, land use, building area, groundwater, traffic volume, distance to road, and soil type) were used to develop the ML models. The filter, wrapper, and embedded methods were used to assess the significance of the input factors. A dataset consisting of 1159 inspected sewer pipes was used to construct the sewer condition models, and 290 remaining inspections were used to verify the models. The results showed that sewer material and age are the most significant factors, otherwise the network type is the least contributor affecting the sewer conditions in the study area. Among the considered ML models, the Extra Trees Regression (R2 = 0.90, MAE = 11.37, and RMSE = 40.75) outperformed the other ML models and it is recommended for predicting sewer conditions for the study area. The results of this study can support utilities and relevant agencies in planning predictive maintenance strategies for their sewer networks.

https://www.mdpi.com/2073-4441/14/24/3993/pdf?version=1671445363

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T 2 *‐weighted images of cervical spondylotic myelopathy

In machine learning, it is common when multiple algorithms are applied to different data sets that are complex because of their accelerated growth, a decision problem arises, ie, how to select the algorithm with the best performance? This has generated the need to implement new information analysis techniques to support decision making The technique of multi-criteria decision making is used to select particular alternatives based on different criteria The objective of this article is to present some Machine Learning models applied to a data set in order to select the best alternative according to the criteria using the TOPSIS method The deductive method and the scanning research technique were applied to study a case study on the Wisconsin Breast Cancer dataset, which seeks to evaluate and compare the performance and effectiveness of machine learning models using the TOPSIS

Maikel Yelandi Leyva Vazquezl

Papers

A Framework for Selecting Machine Learning Models Using TOPSIS