Fuzzy logic: A "simple" solution for complexities in neurosciences?

Godil, Saniya S.; Shamim, Muhammad Shahzad; Enam, Syed Ather; Qidwai, Uvais

doi:10.4103/2152-7806.77177

Cited by 51 publications

(8 citation statements)

References 101 publications

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“…Likewise, fuzzy outputs can be subject to various interpretation facts that makes analysis difficult. In addition, it requires a lot of data and expertise to develop a fuzzy system [118].…”

Section: The Use Of Fuzzy Logic For Business Performance Assessment Bmentioning

confidence: 99%

Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic

et al. 2019

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Companies constantly thrive to enhance their performance in order to achieve their environmental, social, and economic goals. One way to achieve these goals is to integrate some effective management tools, such as an environmental management system (EMS), that can support sustainable development and value creation. Based on the literature, in this study some EMS integration dimensions were identified that can be used to assess its influence on business performance. The study was conducted on SMEs listed on the AeRO-Bucharest Stock Exchange that voluntary apply the ISO 14001 EMS, using a quantitative approach. To assess the EMS integration score, principal component analysis was used. Using logistic regression analysis, business performance probability (quantified by return on assets) was estimated for each company included in the final sample. Fuzzy logic was further used for modelling the influence of EMS integration on business performance probability. The research results showed that EMS integration has different effects on business performance probability, considering the influence of activity field and environmental management experience. This research assertion could be extended for all the listed Romanian companies or for major companies at the EU level.

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“…Likewise, fuzzy outputs can be subject to various interpretation facts that makes analysis difficult. In addition, it requires a lot of data and expertise to develop a fuzzy system [118].…”

Section: The Use Of Fuzzy Logic For Business Performance Assessment Bmentioning

confidence: 99%

Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic

et al. 2019

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“…Due to the lack of information on how the models from previous studies were built, we cannot provide an explanation of this result. However, we are aware that the fuzzy-logic model's performance depends on the domain expertise of humans to design fuzzy rules and membership functions, as well as to correctly interpret the fuzzy outputs for a particular classification task [77].…”

Section: Performance and Statistical Evaluationmentioning

confidence: 99%

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Silva

Naranjo

2020

Sensors

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Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.Sensors 2020, 20, 1692 2 of 21 and ultimately minimizing traffic incidents [8]. Many potential applications that utilize driving-style recognition are being developed [9]. For example, insurance companies are developing automatic ways to ascertain driving styles of their clients in order to offer the right policy [10]. In addition, in the area of advanced driver-assistance systems (ADAS), researchers are interested in the development of intelligent tools to provide tailored feedback according to the driver's behavior [11].Current studies have reported different methodologies to classify driving styles using in-vehicle data. These methodologies mostly depend on (i) the inputs that can be extracted and derived from the data gathered (e.g., acceleration, deceleration, brake) [12,13]; (ii) the computational models to classify driving styles [14,15]; (iii) driving-styles outputs (e.g., calm, normal, aggressive) [16,17]; and (iv) the performance metrics that evaluate these models [18,19]. Although these studies show a growing interest in classifying driving styles empirically, few studies have systematically evaluated which inputs and models are better predictors of a particular driving style's output.The existing body of research has attempted to evaluate several computational models to identify driving styles usin...

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“…Worth noting is the limitation with current statistical methods used in medicine over the last century based on traditional binary “Boolean logic” (such as yes/no, present/absent, a disease exists or not, and so forth). In reality, diseases occur in a continuum or spectrum in what has been recently described in fuzzy statistics 41-43. Fuzzy logic or mathematics accounts for the fact that for example someone can have mild, moderate, or severe intracranial hypertension.…”

Section: Concept Of Exposure (Intervention) and Disease (Outcome)mentioning

confidence: 99%

Essentials of research methods in neurosurgery and allied sciences for research, appraisal and application of scientific information to patient care (Part I)

El-Shehaby

Baeesa

2016

NSJ

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Every neurosurgeon ought to be acquainted with the basics of research methods to enhance the comprehension of the research process and critical appraisal procedures of a scientific write-up. This in turn will ensure the appropriate application of scientific knowledge to patient care. Recent publications reveal that a significant proportion of articles published in neurosurgery are mislabeled with dire consequences on the sorting and indexing of evidence. Furthermore, many clinicians report that they feel unqualified to read the medical literature critically hence, it is for this reason that we conducted this review. Herein, we present a simple algorithm to facilitate the comprehension of research methods, as well as elucidate on the anatomy of common study designs in neurosurgery. Illustrative examples are provided when necessary. Understanding research methods and the critical analysis of published reports of clinical investigation is a fundamental skill of the physician to enable the incorporation of new clinical knowledge to practice

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Fuzzy logic: A "simple" solution for complexities in neurosciences?

Cited by 51 publications

References 101 publications

Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic

Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Essentials of research methods in neurosurgery and allied sciences for research, appraisal and application of scientific information to patient care (Part I)

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