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Entropy-Based Fuzzy TOPSIS Method for Investment Decision Optimization of Large-Scale Projects.

Computational intelligence and neuroscience
Investment of large-scale projects must consider various factors, such as economic conditions and investment environment when making decisions. In large-scale project investment problems, almost 90% of them are completed in a multiobjective context. ...

A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Journal of biomedical informatics
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and ...

Mapping the evidence on identity processes and identity-related interventions in the smoking and physical activity domains: a scoping review protocol.

BMJ open
INTRODUCTION: Smoking and insufficient physical activity (PA), independently but especially in conjunction, often lead to disease and (premature) death. For this reason, there is need for effective smoking cessation and PA-increasing interventions. I...

Comprehensive evaluation of land reclamation schemes in mining areas based on linguistic intuitionistic fuzzy group decision-making.

Environmental science and pollution research international
Schemes to protect the geological environment and reclaim land are core requirements for an application for mining rights and complying with mining regulations. Mining enterprises must be supervised to ensure they fulfill their obligations. To guide ...

Tutorial: Artificial neural networks to analyze single-case experimental designs.

Psychological methods
Since the start of the 21st century, few advances have had as far-reaching impact in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and psychology. In research methods,...

A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm.

Sensors (Basel, Switzerland)
Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different ne...

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.

Computational intelligence and neuroscience
Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-wor...

Social robots in the instruction of social skills in autism: a comprehensive descriptive analysis of single-case experimental designs.

Disability and rehabilitation. Assistive technology
PURPOSE: The rapid technological advances, the traits of individuals with ASD and their interest in technology are promising for the instruction of social skills to individuals with autism spectrum disorder (ASD) using various technological intervent...

Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

Medical physics
PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend t...

A novel tool that allows interactive screening of PubMed citations showed promise for the semi-automation of identification of Biomedical Literature.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Systematic reviews form the basis of evidence-based medicine, but are expensive and time-consuming to produce. To address this burden, we have developed a literature identification system (Pythia) that combines the query fo...