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Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build a surrogate model. This work makes a comparative analysis of the local explanations provided by two rule-based explanation methods on RETIPROGRAM, a system based on a fuzzy random forest that analyses the health record of a diabetic person to assess his/her degree of risk of developing diabetic retinopathy. The analysed explanation methods are C-LORE-F (a variant of LORE that builds a decision tree) and DRSA (a method based on rough sets that builds a set of rules). The explored methods gave good results in several metrics, although there is room for improvement in the generation of counterfactual examples.

In this paper, a new approach to face description is proposed. The linguistic description of human faces in digital pictures is generated within a framework of fuzzy granulation. Fuzzy relations and fuzzy relational rules are applied in order to create the image description. By use of type-2 fuzzy sets, fuzzy relations, and fuzzy IF-THEN rules, an image recognition system can infer and explain its decision. Such a system can retrieve an image, recognize, and classify – especially a human face – based on the linguistic description. © 2021, Springer Nature Switzerland AG.

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter. © 2020 Marcin Korytkowski et al., published by Sciendo.

In this paper, a new approach to face recognition is proposed. The knowledge represented by fuzzy IF-THEN rules, with type-1 and type-2 fuzzy sets, are employed in order to generate the linguistic description of human faces in digital pictures. Then, an image recognition system can recognize and retrieve a picture (image of a face) or classify face images based on the linguistic description. Such a system is explainable – it can explain its decision based on the fuzzy rules. © 2020, Springer Nature Switzerland AG.

© 2020 J. Adv. Inf. Technol. Having strategies capable of automatically generating classification rules is highly useful in any decision-making process. In this article, we propose a method that can operate on nominal and numeric attributes to obtain fuzzy classification rules by combining a competitive neural network with an optimization technique based on variable population particle swarms. The fitness function that controls swarm movement uses a voting criterion that weights, in a fuzzy manner, numeric attribute participation. The efficiency and efficacy of this method are strongly conditioned by how membership functions to each of the fuzzy sets are established. In previous works, this was done by partitioning the range of each numeric attribute at equal-length intervals, centering a triangular function with appropriate overlap in each of them. In this case, an improvement to the fuzzy set generation process is proposed using the Fuzzy C-Means methods. The results obtained were compared to those yielded by the previous version using 11 databases from the UCI repository and three databases from the Ecuadorian financial system – one from a credit and savings cooperative and two from banks that grant productive and non-productive credits as well as microcredits. The results obtained were satisfactory. At the end of the article, our conclusions are discussed and future research lines are suggested.

Algorithmic music nowadays performs domain specific tasks for which classical algorithms do not offer optimal solutions or require user's expertise. Among these tasks is the extraction of models from data that offer an understanding of the underlying behavior, providing a quick and easy to use way to explore the data for first (sometimes on-the-fly) insights. Learning rules from examples is an approach often used to achieve this goal. However, together with the aforementioned requirements algorithmic composition needs to create new material so that it is perceived as consistent with the material of the data. In addition, the input data sets are usually small because the human is the bottleneck when generating them. In this contribution we present a fuzzy rule induction algorithm focused on generalizing a set of data, complying with the previous requirements, that offers good results for small data sets. For its evaluation -in a field where there are no benchmarks available - data sets obtained during user tests were used. The visual representation offered by the fuzzy chart helps to reduce the cognitive complexity of the devices used in algorithmic music. The results obtained show that this approach is promising for future developments., Peer Reviewed, Postprint (author's final draft)

The performance of distributed adaptive clutter map constant false alarm rate (CMAP-CFAR) detection system using fuzzy fusion rules with homogeneous and non-homogeneous background is considered in this paper. We assume that the sensors are identical and the target is fluctuating according to Swerling I model embedded in a white Gaussian noise with unknown variance. Each detector computes the value of the membership function to the false alarm space from the previous samples of the cell under test and transmits it to the fusion center. These values are combined according to fuzzy fusion rules to produce a global membership function to the false alarm space. The obtained results showed that the best performance was obtained while using the “algebraic product” fuzzy rule and the probability of detection increases significantly with the number of detectors.

This paper investigates the possibility of increasing the interpretability of fuzzy rules and reducing the complexity when designing fuzzy rules. To achieve this, three novel fuzzy logic concepts (i.e., relative linguistic labels, high-level rules and linguistic variable weights) were conceived and implemented in a fuzzy logic system for reshoring decision-making. The introduced concepts increase the interpretability of fuzzy rules and reduce the complexity when designing fuzzy rules while still providing accurate results.

© 2019, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved. In the assembly industry, there is a high degree of uncertainty when identifying operational problems, due to limited resources and inefficient production control. Focusing on the assembly of bicycles, this article presents a model that combines linear programming and fuzzy logic to obtain standard times associated with the production lines of bicycles. The tool used for minimizing the objective function was Excel “Solver”, and its formulation involved the identification of variables, restrictions, constant parameters, working conditions and production rates. The times obtained from the linear programming model entered as variables in the fuzzy logic model, to yield standard times estimates. This study allows an identification of the current state of the productive process, obtaining the maximum benefit in operative resources and working conditions. In addition, the model improves decision making through uncertainty control in production planning.

Anomaly detection is used to monitor and capture traffic anomalies in network systems. Many anomalies manifest in changes in the intensity of network events. Because of the ability of EWMA control chart to monitor the rate of occurrences of events based on their intensity, this statistic is appropriate for implementation in control limits based algorithms. The performance of standard EWMA algorithm can be made more effective combining the logic of adaptive threshold algorithm and adequate application of fuzzy theory. This paper analyzes the theoretical possibility of applying EWMA statistics and fuzzy logic to detect network anomalies. Different aspects of fuzzy rules are discussed as well as different membership functions, trying to find the most adequate choice. It is shown that the introduction of fuzzy logic in standard EWMA algorithm for anomaly detection opens the possibility of previous warning from a network attack. Besides, fuzzy logic enables precise determination of degree of the risk.

In this work, a new method for Takagi-Sugeno (T-S) fuzzy modelling based on multidimensional membership functions (MDMFs) is proposed. It is verified that the fuzzy inference method of one-dimensional membership functions (lDMFs) may place the fuzzy rules in inappropriate locations for modelling of nonlinear multivariable systems, while the application of MDMFs allows a better identification through a smaller number of fuzzy rules. The proposed method uses a genetic algorithm (GA) for the adjustment of the MDMFs and the T-S method for modelling and identification of the nonlinear system. As a validation example, a nonlinear multivariable system, a coupled tanks system, is chosen. The results show that the proposed method presents less identification error than the T-S method, with less number of fuzzy rules.

Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature., Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11144)This work is supported by Vetenskapsrådet project: “Disclosure risk and transparency in big data privacy” (VR 2016–03346, 2017–2020).DRIAT

There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador ¿ one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.

In this paper, they considered the method for the development of linguistic variables to control the operating conditions of a diesel engine. Based on the terms, created linguistic variables and the results of the conducted experiments, they develop fuzzy rules of management. Using the Sudzen output and the obtained knowledge base of fuzzy rules, the control values are calculated. According to the proposed methodology, the analysis of the mathematical model of a diesel engine control is performed and realized as a software product.

In this paper, the Fuzzy Expert System (FES) for evaluating contemporary user authentication methods intended for mobile devices is designed and applied. The parameters used as input for this FES are user's priorities such as security, usability, accessibility, pricing, complexity and privacy (SUAPCP) and the output parameter is evaluation (grade) of mobile solutions. The results obtained from developed fuzzy expert system indicate that proposed system can be effectively used for evaluation of contemporary user authentication methods intended for mobile devices. The strength of presented FES is assignment of a concrete numeric value to a specific mobile authentication solution. This FES should have profound positive impact not only on the better quantification of mobile authentication solutions but also on aspect of filling gaps in the current researches such as creating strong mobile authentication in regard to user's priorities. Finally, it is necessary to be noted that the designed FES would not be limited only to mobile context but could be applied to all authentication methods.

This paper introduces a procedural method based on the fuzzy logic and set theory, which analyzes the risk of an IT-System in a facility and its surrounding area. The method analyzes the susceptibility of an electronic system with respect to intentional electromagnetic interferences and classifies the intentional electromagnetic environment (IEME). It extends the well-known statistical-based models fault tree analysis, electromagnetic topology and Bayesian networks (BN) with imprecise data, uncertainness with linguistic terms, and opinions of experts. In a final step, the critical scenarios and the elements and the location that contribute most to the risk are identified, which can be used to enhance the protection level.

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016., Postprint (author's final draft)

The intention of humans and autonomous robots to interact in shared spatial areas is a challenging field of research regarding human safety, system stability and performance of the system's behavior. In this paper the intention recognition between human and robot from the control point of view are addressed and the time schedule of the exchanged signals is discussed. After a description of the kinematic and geometric relations between human and robot a so-called 'compass dial' with the relative velocities is presented from which suitable fuzzy control rules are derived. The computation of the collision times at intersections and possible avoidance strategies are further discussed. Computations based on simulated and experimental data show the applicability of the methods presented., Funding Agency:AIR-project Action and Intention Recognition in Human Interaction with Autonomous Systems, Action and Intention Recognition in Human Interaction with Autonomous Systems

The issue of environmental quality improvement has been receiving much attention in the developed countries in recent years. Due to that, the role of assessment of ecological risks associated both with natural events and technogene activity of humans is increasing. Previous approaches to the assessment of ecological risks were fully based on statistical data and expert evaluation of potential losses and probabilities of unfavourable consequences. When this kind of assessment is carried out, it is assumed explicitly that experts are able to evaluate point probabilities. However, such assumptions are far from being true. As a result, fuzzy approaches to ecological risk assessment became popular lately. This paper focuses on two practical approaches of that kind. The paper is aimed at attracting practical attention to new up-to-date techniques that could be successfully applied to assess ecological risks in Latvia.

This paper proposes a global fingerprint feature named QFingerMap that provides fuzzy information about a fingerprint image. A fuzzy rule that combines information from several QFingerMaps is employed to register an individual in a database. Error and penetration rates of a fuzzy retrieval system based on those rules are similar to other systems reported in the literature that are also based on global features. However, the proposed system can be implemented in hardware platforms of very much lower computational resources, offering even lower processing time.

Останнім часом досить популярним серед науковців є дослідження адаптивних методів, що мають в основі нечітку логіку або нейромережний апарат в поєднанні з класичними алгоритмами керування. Метою статті є розкриття необхідності та можливості підвищення якості системи автоматичного регулювання (САР) за використання адаптивних алгоритмів. Для ґрунтовного дослідження можливості використання адаптивних регуляторів, зокрема ПІД-нейро та ПІД-нечіткого, для технологічних об’єктів харчової промисловості було зроблено ряд експериментів. Адаптивні алгоритми були реалізовані в середовищі MatLab. Проведений експеримент засвідчує перевагу ПІД-нечіткого адаптивного алгоритму над неадаптивним за рахунок меншої динамічної похибки та часу виходу на завдання (часу регулювання). Проте, якщо розглядати його в порівнянні з ПІД-нейро алгоритмом, то відносні показники є гіршими. Задача створення адаптивних систем є досить актуальною, що об’єктивно пояснюється наведеними прикладами і необхідністю покращення ефективності функціонування технологічних об’єктів харчової промисловості. Recently, quite popular among scientists is investigation of adaptive methods that are based on fuzzy logic and neuro apparatus combined with classical control algorithms. The aim of the article is checking the necessity and possibility of improving the quality of automatic control system (ACS) by using adaptive algorithms. The experiments confirms superiority in -fuzzy adaptive algorithm of nonadaptive due to less dynamic error and control time. However, when viewed in comparison with neuro - PID algorithm , the ratios are worse. Problem of creating adaptive systems is quite relevant that objectively explained above examples and needs to improve the efficiency of the food industry technological objects. The proposed method of correction of transient guaranteed to give positive results, but its implementation using the optimal switching applications sensitive to even small changes in the parameters of the object a

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This paper aims to develop a new approach to case-based reasoning without similarity constraint. The key to this is the case relation model which enables identification of relevant cases from a global perspective. Fuzzy linguistic rules are adopted as powerful means to represent knowledge about relevance between cases in the case relation model. The construction of fuzzy relevance rules can be realized by learning from pairs of cases in the case library. The empirical studies have demonstrated that our CBR system using fuzzy relation model can work with an extremely small number of cases while still yielding competent performance.

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

Presented at the 2011 NDIA Ground Vehicles Systems Engineering and Technology Symposium 9-11 August 2011, Dearborn, Michigan, USA. The original document contains color images.

Current understanding of nitrate export from catchments indicates that the transport dynamics are mainly driven by hydrological processes characterised by complex nonlinear relationships. The aim of this paper is to develop a hybrid deterministic-fuzzy rule based model capable of simulating catchment scale nitrate transport on the basis of the relationships between driving and resultant variables. The deterministic water balance model WaSiM-ETH is used for the simulation of hydrological flow components. The simulated flow components from the WaSiM-ETH model together with observations are used to develop a fuzzy rule based nitrate transport model. The fuzzy rules are derived using a simulated annealing optimisation procedure supplemented by knowledge about data relationships. The study is undertaken using daily time step data from the Weida catchment in the North-Eastern Germany, which is a 100 km2 subcatchment of the Weisse Elster river in the Elbe river basin. The models show reasonable performance with regards to the magnitude and dynamics of the streamflow, and nitrate-N concentration and load. The superior performance of the fuzzy rule based model in comparison to a multiple linear regression model indicates a complex nonlinear relationship between driving and resultant variables. The assessment of the rules provides explicit insights on the qualitative and quantitative relationships between different variables and their relative importance. The subsurface flow is found to be the most important variable which corresponds to the prevailing understanding that the nitrate transport processes are mainly driven by it. The relative importance of temperature as an input variable indicates the effect of seasonal variability. The hybrid model is valid for present land use characteristics and management practices, which can be extended to include additional variables that affect nitrate entry to subsurface flow.

In this paper, the new fuzzy morphological associative memories (FMAMs) based on fuzzy operations (Lambda, .) and (V, .) are presented. FMAM with (V, .) is extremely robust for dilative noise and FMAM with (Lambda, .) is extremely robust for erosive noise. Autoassociative FMAM has the unlimited storage capability and can converge in one step. The convex autoassociative FMAM can be used to achieve a reasonable tradeoff for the mixed noise. Finally, comparisons between autoassociative FMAM and the famous FAM are discussed. FMAM, as another new encoding way of fuzzy rules, still has a multitude of open problems worthy to explore in the future.

Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifica- tions may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule generation cost-sensitive. Furthermore, it will be shown how interpretability aspects and the costs of feature acquisition can be ac- counted for during classifier design. Natural language text is used to explain the generated fuzzy rules and their design process

This paper provides a comprehensive analysis of the working and requirements of fuzzy systems with the view to devise appropriate visualisation framework and techniques for these systems using a user- and task-oriented approach. We firstly discuss the nature of fuzzy data and the essential components of typical fuzzy systems, then categorise different visualisation requirements from three perspectives: user of fuzzy systems, designer of fuzzy systems and designer of visualisation systems. The visualisation framework also include mechanisms for capturing users’ profiles in order to customise the system to their own needs. We then examine how different visualisation techniques can be adapted to satisfy these requirements. Motivations for an architecture of a visualisation system which is based on a multi-agent approach are also presented.

This paper provides a comprehensive analysis of the working and requirements of fuzzy systems with the view to devise appropriate visualisation framework and techniques for these systems using a user- and task-oriented approach. We firstly discuss the nature of fuzzy data and the essential components of typical fuzzy systems, then categorise different visualisation requirements from three perspectives: user of fuzzy systems, designer of fuzzy systems and designer of visualisation systems. The visualisation framework also include mechanisms for capturing users’ profiles in order to customise the system to their own needs. We then examine how different visualisation techniques can be adapted to satisfy these requirements. Motivations for an architecture of a visualisation system which is based on a multi-agent approach are also presented.

This contribution presents a new algorithm (NORFREA) to select fuzzy rules from a grid partition of the input domain. Besides using an efficiency measure for the rules, this algorithm employs an heuristic technique to reduce the influence of the initial grid structure. Different benchmarks of classification problems are included to illustrate the advantages of this algorithm.

The present document reports an application process of the Knowledge Discovery in Serial Measurement (KDSM) methodology, the results obtained with it, KDSM is a methodology for the analysis of very short and repeated serial measures with blocking factor in a ill-structured domain pertaining of a labor field. KDSM methodology was applied to obtain knowledge from a training program evolution (PROBECAT), as well as its effectiveness through the information analysis related to: the positioning of enabled people, characteristics of the municipalities where the training program occurred and the necessities of the productive sector. The obtained information by KDSM allowed to know the effect of each training course done in municipalities where the PROBECAT is established. In addition, with this information the operation of PROBECAT can be modified in order to do it more opportune and effective. On the other hand, a particular solution and results to a problem of obtained attributes are not characterizing attributes is introduced., Postprint (published version)

In this report, an application of a methodology of automatic generation of conceptual descriptions for characterizing a given partition in an ill-structured domain is presented. A specific application on a wastewater treatment process (wwtp) illustrates the behaviour of this methodology. The methodology is based on the combination of statistical tools and inductive learning, in such a way that the nature of the data is preserved, avoiding previous transformations of the variables. Thus qualitative and quantitative information can be induced from data. This information is useful for the automatic generation of a system of fuzzy rules, which, in turn, allows the posterior recognition of the obtained classes. In previous works it has been proved that the multiple box-plot is a useful and powerful statistical tool for distinguishing classes by means of numerical variables. It constitutes the basis for the methodology presented here, which permits detection of relevant variables characterized of any classes. In this report, we propose the first version of a formal methodology having as an objective the automatic generation of conceptual class descriptions. The goal is to characterize the various situations that can arise in a day at a wastewater treatment plant (relevant information to facilitate the plant's managing the decision making processes)., Postprint (published version)

We present a new method that integrates intensity features and a local fractal-dimension feature into a region growing algorithm for the segmentation of natural images. A fuzzy rule is used to integrate different types of features into a segmentation algorithm. In the proposed algorithm, intensity features are used to produce an accurate segmentation, while the fractal-dimension feature is used to yield a rough segmentation in a natural image. The effective combination of the different features provides the segmented results similar to the ones by a human visual system. Experimental results demonstrates the capabilities of the proposed method to execute the segmentation of natural images using the fuzzy region-growing algorithm., 特集 : 「産業におけるソフトコンピューティングに関する国際会議'99」発表論文選集

In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the question, first, of the practical determination of membership functions, second of the management of the fuzziness in some optimisation models, and finally of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using of fuzzy rules, a new algorithm easy of implementing in the Model-Base Management System of any oriented Decision Support System is shown.

In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the {\it Backpropagation} algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an aptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the assignement of semantic to the classes obtained in a classification without previous classes process is also included.

Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.

Pazhoumand-Dar, H. (2019). FAME-ADL: a data-driven fuzzy approach for monitoring the ADLs of elderly people using Kinect depth maps. Journal of Ambient Intelligence and Humanized Computing, 10(7), 2781–2803. Available here

Pazhoumand-Dar, H., Armstrong, L. J., & Tripathy, A. K. (2019). Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data. Journal of Ambient Intelligence and Humanized Computing, 11, 1727–1747. https://doi.org/10.1007/s12652-019-01447-3

© 2018, Institute of Advanced Scientific Research, Inc. All rights reserved. The application of an intelligent model for tuning an automated test system for diesel engines is considered. A neuro-fuzzy network has been designed to produce a control effect on the diesel. A technique for designing a knowledge base for controlling the operating modes of a diesel engine during its testing has been developed. In the life cycle of products, including internal combustion engines (ICE), a significant place is occupied by various technological tests of both individual units and the engine as a whole. Modern requirements to constant increase of technical level of let out designs result in that the share of expenses for carrying out of tests of diesel engines at creation of new samples all more increases. Especially large these costs become when the levels of automation of production and scientific research work do not match. In connection with this automation and mechanization of technological trials is one of the main tasks of increasing the technological level of production and the quality of the parts produced.

© Medwell Journals, 2014. This study deals with the method for controlling a test stand of diesel engines based on fuzzy neural network. Structure and training algorithm have been proposed for a fuzzy neural network to control a diesel engine during testing. A knowledge base structure has been proposed. Fuzzy rules have been described to control a diesel engine. Techniques and algorithms have been realized in the form of a computer program. The effectiveness of the proposed automated diesel engine test system has been analyzed.

© Medwell Journals, 2014. This study deals with the method for controlling a test stand of diesel engines based on fuzzy neural network. Structure and training algorithm have been proposed for a fuzzy neural network to control a diesel engine during testing. A knowledge base structure has been proposed. Fuzzy rules have been described to control a diesel engine. Techniques and algorithms have been realized in the form of a computer program. The effectiveness of the proposed automated diesel engine test system has been analyzed.

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.