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The development of efficient algorithms that perform qualitative operations in the processing and segmentation of graphical images requires good knowledge on both technical and biological aspects of the vision. Due to the subjective nature of the human vision, the evaluation of color identity and closeness, that has been widely used in a variety of image processing problems, is still subjected to research and improvement. This paper analyzes the problem of color tolerance, overviewing past and existing solutions, and suggests a simple Adaptive Neuro-Fuzzy Inference System (ANFIS)-based model as an alternative to the formula-based closeness evaluation. The aim of this research was to develop an alternative to the unified closeness formula, which could be built on expert knowledge or be adapted to fit a particular target. Methods of sample selection, training approaches, and configuration of the ANFIS network were used and a comparative analysis between the ANFIS models and the neural networks was provided. Additionally, the efficiency of regular back-propagation, hybrid, and stochastic learning methods were evaluated along with a critical analysis on the widely used hybrid training method. Experiments were designed and ran on a Java-based platform-independent system, developed as part of our research, to provide flexibility in the modeling and integration for the ANFIS based tasks. [ABSTRACT FROM AUTHOR]

In our current digital life, social networks play a vital role to share the data. A group of hazards that related to the privacy parameters settings of the universal online social network like facebook. The settings corresponding to the user's shared data as well as the data sharing with privacy. A middleware server can split the users with regular social relationships within a same group. The community detection methodology normally request for the full access to the detailed social communications within the users. It is very sensitive to share the personal information like personal images which cause the uncertainty in case of privacy. In this paper, we suggest an Adaptive Framework for privacy preserving in Online Social Networks. It also affords the flexibility in which a third-party server can frequently choose the related sub graph. The emotional detection is constructed with the neuro-fuzzy technique that the fundamental emotions are identified through the singular value decomposition and produces the output of 22 emotions. The experimental results provide the real-time dataset that demonstrate the non-aggregated analysis and practical suggestions highlight the interferences to encourage the secured online social network usage. [ABSTRACT FROM AUTHOR]

Estimation of sediment load in rivers is one of the most important and applicable issues in studies and design of river and water engineering projects. Therefore, identification and suggestion of suitable methods for estimating suspended sediment load should be done based on relevant research. These methods include artificial neural network models, neurophysics, sedimentation rate curve as well as multivariate regression model. In this study, the efficiency of these methods was investigated in predicting discharge rate of suspended sediments of Gharachai River watershed. Daily time series data of measured flow discharge and sedimentation of bridge station of Gharachai River were used. The statistical period used in this model was two years (2016-2017). Independent variables used to enter the network include runoff and suspended load at the Gharachai River Doab Bridge station. The dependent variable, which is the network output, was the suspended load. After modeling with each compound and calculating RMSE and R2 values, the best combination was selected. The results showed that the neurophasic method based on discharge and sediment inputs and artificial neural network models based on discharge inputs were more accurate than multivariate regression and sedimentation rate curve. For values ​​higher the long-run mean of the statistical series, the values ​​simulated by the ANN model and for values less the mean and annual sediment load were also closer to the observed values. While for maximum values, no significant difference was found between ANN models, neurophysics, and linear regression.

Background: In this paper, we have presented a new custom smart CMOS image sensor (CIS) for low power wireless capsule endoscopy. Method: The proposed new smart CIS includes a 256 × 256 current mode pixels array with a new on-chip adaptive neuro-fuzzy inference system that has been used to diagnosing bleeding images. We use a new pinned photodiode to realize the current mode of active pixels in the standard CMOS process. The proposed chip has been implemented in 0.18 μm CMOS 1P6M TSMC RF technology with a die area of 7 mm × 8 mm. Results and Conclusion: A built-in smart bleeding detection system on CIS leads to decrease in the RF transmitter power consumption near zero. The average power dissipation of the proposed smart CIS is 610 μW.

Converging ogee spillway is employed as an emergency spillway constructed to control natural disasters such as instant floods. This paper presents an experimental study and artificial intelligence-based modeling of the discharge coefficient (Cd) of the converging ogee spillway (with a curve axis). The ogee spillway structure was tested in both symmetrical and asymmetrical convergence of training walls ranging from 0° to 120°. Through the visual observation, it was found that the tailwater submergence, the depth of approach, the crest and spillway geometry, the head different from design head, and downstream floor position are the important factors that alter the Cd of the converging ogee spillway. Moreover, two different methods (e.g. gene expression programming (GEP) and adaptive neuro fuzzy inference systems (ANFIS)) were assessed for modeling the Cd using original experimental data. Based on the obtained results, both the GEP and ANFIS models have reliable performance in simulating the Cd. Moreover, the performance of GEP model was found to be slightly better than the ANFIS technique. According to the outcome of sensitivity analysis, the ratio of the total upstream head on the spillway to design head (H/Hd) is the most important parameter for the optimum modeling of the Cd of the converging ogee spillway. [ABSTRACT FROM AUTHOR]

In this study, a neuro-fuzzy (NF) analysis method is suggested for the estimation of the operating frequency of triangular ring patch antennas (TRPAs) that operate at ultra high band applications. Although the analysis of regular-shaped patch antennas (PAs) such as rectangular, triangle, and circle is easy, analysis of irregularly shaped patch antennas is difficult and time consuming. Here, this great effort and time has been eliminated by using an artificial intelligence technique such as NF. To create a data set for NF, 100 TRPAs with different physical and electrical properties (L, l, h, and er) are simulated by using an electromagnetic simulator program. The currency and accuracy of the proposed approach is then confirmed on the measurement results of a prototype TRPA fabricated in this study. The results of NF model are compared with the simulation/measurement results and previously the method published in the literature. [ABSTRACT FROM AUTHOR]

In this paper, short-term wind speed forecasting models have been developed using neuro-fuzzy systems. The optimal neuro-fuzzy model has been investigated in detail. In addition, meta-heuristic algorithms, such as artificial bee colony differential evolution genetic algorithm and particle swarm optimization for training adaptive neuro-fuzzy inference systems parameters have been used in this study. This is a novel study, as four different metaheuristic approaches are used to determine the appropriate adaptive neuro-fuzzy inference systems model parameters for short-term wind speed estimation, and analyzed comparatively. To validate the effectiveness of the proposed approach, wind speed series collected from a wind observation station located in Turkey are used in the short-term wind speed forecasting. In the first step, the results of analysis for finding the accurate model revealed that the optimal model that is proposed is adaptive neuro-fuzzy inference systems7-1 architecture. The meta-heuristic algorithms used in the optimization of adaptive neuro-fuzzy inference systems model parameters are then independently run 10 times, and the performance results are calculated statistically for the training and test phases of the adaptive neuro-fuzzy inference systems model. The results of the study clearly show that the adaptive neuro-fuzzy inference systems-particle swarm optimization hybrid model has the best performance in the training aspect, but it is observed that the ANFIS-differential evolution hybrid model gives better results than the others in the test step. [ABSTRACT FROM AUTHOR]

This paper proposes a method to determine urban travel time using imprecise variables. The model calculates values for each edge of the network utilize base a Fuzzy Logic scheme and an adaptive architecture with neural networks. For the uncertainty, the effect of three variables for each street considers; the State of the streets, Traffic zones, and Rainfall (intensity of rain) with an adaptable neural network's architecture. The membership functions experimentally are calculated, getting times closer to the real. The model evaluates the uncertainty for each of the network's edges (streets), intending to adjust the set route travel time; finally, the model updates and modifies the membership functions, making it adaptable to new scenarios. The validation is compared to the MANFIS travel time model with the Fuzzy Logic model, probabilistic model, and desired travel time. The mean absolute percentage error of the three models and the travel time compared, obtaining the ANFIS model a lower result. [ABSTRACT FROM AUTHOR]

The compressive capacity of the column is one of the key parameters in the design. The importance of such structural members and their performance under load conditions are very effective in the overall behavior of the structure, and its failure can lead to the collapse of the entire structure. Therefore, determining the capacity of columns is considered an important issue in structural problems. Thus, this article presents an applicable computational framework to predict the compression capacity of stirrups-confined concrete. A machine learning model based on neuro-fuzzy systems was considered to formulate the proposed model. For this purpose, some experimental datasets were gathered from the literature to tune the unknown parameters of the model and evaluate its accuracy. The target, the ratio of the ultimate axial capacity to bearing area, was predicted with consideration of the column properties, including the compressive strength of concrete, stirrups section area, dimension of the stirrups, and the column section. The results showed that the proposed framework could be used as an applicable technique to determine the compressive capacity of the stirrups-confined concrete columns.

Software quality has become a key aspect of any electronic system. In this respect, software reliability is an important quality characteristic and there are many models that aim to estimate the reliability from different perspectives. However, there are no industry established reliability models. There is need to estimate which reliability model has the best performance. In this study several reliability models are analyzed by a soft computing approach, called adaptive neuro-fuzzy inference system (neuro-fuzzy), in order to estimate the models' capability based on root mean square errors (RMSE). Various aspects of accuracy of some of the well-known software reliability models have been used in this work. According to the results Non-Homogeneous Poisson Process Model (NHPP) is the best software reliability model. A combination of Linear Littlewood-Verall (LV) and NHPP is the optimal combination of two software reliability models. In other words, the best results could be achieved if one combines the LV and NHPP models. [ABSTRACT FROM AUTHOR]

Traditional spacecraft attitude determination algorithms suffer from accuracy versus computational load dilemma. As algorithm complexity increases, its computational load increases and also its accuracy. This limits algorithm applicability in real-time (In-Orbit) environment. In this article, a solution based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed. The number of inputs to ANFIS is 12, which implies an extremely large number of rules of the membership functions to be utilized. Consequently, the proposed solution blows up. Hence, a clustering technique should be utilized in order to minimize the number of rules. Design simulations of the former Egyptian spacecraft EGYPTSAT-1 have been utilized as test case. During the standby operational mode, the spacecraft is assumed to have attitude errors within ±10°. ANFIS is a multi-input single-output algorithm. Therefore, a separate ANFIS is developed for each one of the attitude angles. Each one of them is trained based on two data sets, provided by TRIAD and Q-method simulators. Thus, six ANFISs are developed. The results have shown that the developed ANFISs can work in the stand-by operational mode and provide attitude estimates of ±10°. Moreover, the average execution time for ANFIS is about 40% of the average execution time for the analogous Cascade-Forward Neural Network algorithms (CFNN) and 25% of the Extended Kalman Filter average execution time. The computational overhead of the proposed ANFIS algorithms is constant regardless of the attitude determination or estimation algorithm to be replaced by ANFIS. Consequently, very accurate and sophisticated models could be built in order to provide training data set and then replaced by ANFIS without increasing the ANFIS computational load. [ABSTRACT FROM AUTHOR]

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities. [ABSTRACT FROM AUTHOR]

The accuracy-transparency trade-off is one of the most notable challenges when applying machine learning tools in the medical domain. Nefclass is a popular neuro-fuzzy classifier in medical diagnosis systems. Nefclass performs increasingly poorly as the data skewness increases. This paper presents a combined approach to improve the classification accuracy and interpretability of the Nefclass classifier, when feature values of the training and testing datasets exhibit positive skewness. The proposed model consists of two steps. Firstly, a modified Nefclass classifier embedded with a choice of two alternative discretization methods, MME and CAIM is implemented. Secondly, we devised a new rule pruning method based on the Habermans' adjusted residual to reduce the size of the resulting ruleset. This rule-pruning method improves the interpretability of Nefclass without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches provides a considerable improvement in classification accuracy and transparency of Nefclass on skewed data. [ABSTRACT FROM AUTHOR]

• Biological control (BC) technologies are necessary to address the significant constraint that Fall armyworm (FAW) poses in cereal production in Africa. • The study employs a step-by-step modeling approach to map suitable sites of BC technologies, specifically parasitoids (Cotesia icipe). • Pest infestation levels are estimated using an evolutionary adaptive Neuro-Fuzzy inference system with a high coefficient of determination (R2) > 0.89. • We utilize fuzzy inference theory to accurately map the suitability of different regions in Kenya for the adoption of augmentative biocontrol using the parasitoids C. icipe in maize farms. • Artificial intelligence-based methods provide an effective advisory tool for guiding the deployment of biological control agents, such as parasitoids, for sustainable FAW management. Fall armyworm (FAW) Spodoptera frugiperda (J. E. Smith) is a major pest affecting cereal production in Africa. Biological control (BC) technologies are being promoted as a sustainable alternative to chemical control, which can lead to health risks and environmental hazards. However, effective deployment of these technologies requires site-specific recommendations. In this study, we use a step-by-step modelling approach to map suitable sites for BC technologies, focusing on the parasitoid Cotesia icipe using the FAW level of infestation, the ecological niche of the parasitoid, and the FAW host crop. The level of pest infestation was estimated using an evolutionary adaptive Neuro-Fuzzy inference system (R2 > 0.89) while the pest ecological niche was obtained using the maximum entropy algorithm (area under curve, AUC > 0.9). A fuzzy operator was used to combine all fuzzified variables into a single layer that represents the landscape's overall suitability for C. icipe in maize farms. Our computational findings indicate that C. icipe holds substantial promise as a BC agent in maize farms, with suitability levels consistently surpassing 90% throughout maize cropping seasons. The findings demonstrate that the utilization of artificial intelligence, combined with data science and knowledge representation, serves as an effective advisory tool for guiding the deployment of BC agents, such as parasitoids, for the sustainable management of FAW. This approach enables informed decision-making and enhances the efficacy of FAW management strategies by providing valuable insights and recommendations based on data-driven and computer intelligence analyses. [ABSTRACT FROM AUTHOR]

Cloud computing provides tool-based IT services to customers worldwide. It is built on a fee-for-service model and enables the hosting of large programs from consumer, scientific, and commercial domains. However, cloud application hosting data centers have enormous energy consumption, which leads to high operating expenses and a carbon footprint. Cloud centers get a lot of requests from different users every day. The centers need strong servers that consume a lot of energy and peripherals in order to run in order to meet these needs. It should go without saying that reducing cloud center energy usage requires efficient resource utilization. We have taken a novel hybrid method in this study. Neural fuzzy systems have been employed for the dynamic resource allocation in the cloud with the goal of minimizing energy consumption and load prediction, and the ant colony optimization method has been utilized for the virtual machine's movement. The experimental results show that the proposed strategy works optimally, as compared to other methods in the literature that have the same objective. When compared to other comparable approaches, the suggested solution has an average resource loss of 23.1 % in 720 time periods and an average request denial rate that is 6.5 % lower. Moreover, extensive testing with the SPECpower benchmark has confirmed the proposed methodology, yielding a forecast accuracy of over 97 % for the proposed energy consumption model. [ABSTRACT FROM AUTHOR]

Abstract Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

Studying the current level of energy consumed by the transportation sector in Jordan is a top priority and an important variable when it comes to modelling accurate projections of future consumption in order to monitor Jordan's sustainable development. This study compares two methods for modelling energy consumption within the Jordanian transportation sector: a multivariate linear regression model and a Neuro-fuzzy model. Within these two paradigms, energy consumption is modelled as a function of a number of factors such as: vehicles number, level of income and ownership, and fuel prices. A parallelism between the two models is highlighted providing a precise simulation for the energy consumption in the Jordanian transportation sector. The comparison proposes that when it comes to forecasting, the performance of the neuro-fuzzy model exceeds that of the multivariate linear regression model.

Shear connectors in steel-concrete composite frames are essential elements to transfer the shear between steel and concrete. Several parameters must be considered in predicting the strength of these connectors. This research aims to estimate the performed rib shear strength of connectors in composite frames. To this end, four variables including the compressive strength of concrete, area of dowels, the transverse area in rib holes, and also connector height, are applied to a neuro-fuzzy model and the shear strength is selected as the target of the system. The model is trained using an experimental database and validated with an acceptable error. The estimated shear strength of connectors were satisfactorily similar to the measurements reported by the laboratories.

O câncer de mama apresenta elevado índice de mortalidade em todo o mundo, sendo o mais incidente em mulheres. Seu diagnóstico tendo sido realizado por meio de rastreamento, ecografias mamárias e mamografias. Este trabalho tem como objetivo desenvolver um classificador para identificar o câncer de mama utilizando dados antropométricos e parâmetros de exame sanguíneo de rotina que são os biomarcadores. Redes-Neurais do tipo Perceptron Multi-Camadas(MLP) e as redes Neuro-Fuzzy (ANFIS) empregados a um comitê de decisão, trazendo como resultado uma classificação do câncer de mama, com acurácia de 97\% , um valor superior apresentado comparado aos trabalhos dos últimos anos que utilizaram biomarcadores semelhantes no período de 2013 ao início do ano de 2018.

In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated.

Rainfall- Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall- Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall- Runoff prediction.

This work proposes sentiment analysis for low-resource languages like Hindi using Neuro-Fuzzy Technique. Low-resource languages suffer from the scarcity of resources; consequently, we propose a method that can be implemented for any language. We use information theory for establishing a relation between terms that exists in a sentence. This work proposes a novel approach for calculating feature values using Kullback-Leibler (KL) divergence method. The feature values are employed to calculate the membership values associated with the Fuzzy logic in Neuro-Fuzzy Technique. The novelty of this method lies in its predictive nature that can mitigate the impact generated from un-labeled, unknown data or multi-domain data. We have seen the results for multi-domain data in our experiments. We evaluate our results using Accuracy, Precision, Recall and F1-Score. Our experiments show the efficacy of the proposed approach. It achieved 93.01% accuracy for English dataset and 91.18% accuracy for Hindi dataset which is more than the other state-of-art techniques like Naïve Bayes and SVM. Additionally, we found that our approach provides satisfactory results with multi-domain data as both the datasets were of different domains. [ABSTRACT FROM AUTHOR]

The design process of a novel adaptive critic based neuro-fuzzy controller is described. The concept is based on separating state variables into groups, assigning the groups to a multi-layer structure, and employing individual neuro-fuzzy controllers for each layer. The correlation procedure between layers is then defined and a proportional–derivative critic is generalized to be used with this structure. The controller's structure leads to a high reduction in the number of tunable parameters. For easier tuning, different gains are considered in the structure and an approach for the synthesis of the networks' initial parameters is given. The effectiveness of the controller is then verified by investigating two case studies: 1. Ball and beam regulation 2. Stabilizing the chaotic spinning disk's lateral vibrations. Simulations proved the feasibility and robustness of the proposed controller. • A novel multi-critic neuro-fuzzy controller is designed based on the concept of reinforcement learning. • Proportional–derivative critic is generalized to be used with dynamic systems of any form. • High reduction in the algorithm complexity is resulted using the proposed control scheme. • Stabilization of the lateral vibrations of the chaotic spinning disk is done successfully. [ABSTRACT FROM AUTHOR]

In the data security context, anomaly detection is a branch of intrusion detection that can detect emerging intrusions and security attacks. A number of anomaly detection systems (ADSs) have been proposed in the literature that using various algorithms and techniques try to detect the intrusions and anomalies. This paper focuses on the ADS schemes which have applied fuzzy logic in combination with other machine learning and data mining techniques to deal with the inherent uncertainty in the intrusion detection process. For this purpose, it first presents the key knowledge about intrusion detection systems and then classifies the fuzzy ADS approaches regarding their utilized fuzzy algorithm. Afterward, it summarizes their major contributions and illuminates their advantages and limitations. Finally, concluding issues and directions for future researches in the fuzzy ADS context are highlighted. [ABSTRACT FROM AUTHOR]

The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the cone penetration test result. Even though most present classification charts or methods were developed on the basis of data which were compiled over many countries, they should be verified to be feasible for local country. However, unfortunately, revision of those charts is quite difficult or almost impossible even though a chart provides misclassified soil class. In this research, a new method for developing soil classification model is proposed by using soft computing theory—fuzzy C-mean clustering and neuro-fuzzy theory—as a function of 5173 piezocone penetration test (PCPT) results and soil boring logs compiled from 17 local sites around Korea. Feasibility of the proposed soil classification model was verified from the viewpoint of accuracy of the classification result by comparing the classification results not only for data which were used for developing the model but also new data, which were not included in developing the model with real boring logs, other fuzzy computing classification models, and Robertson’s charts. The biggest advantage of the proposed method is that it is easy to make the piezocone soil classification system more accurate by updating new data.

This study aims to establish a fatigue life predictive model based on multifractality of road excitations using neuro-fuzzy method to assess the durability of suspension spring. Traditional durability analysis in time domain is complicated and time-consuming due to the needs of large data amount. Thus, it is an idea to adopt an adaptive neuro-fuzzy inference system (ANFIS) for relating the performance of coil spring to the multifractal properties of road excitations, giving a meaningful fatigue life prediction. Different membership function numbers were tested to obtain the optimum membership function number. During the data training process, the checking data was used to test the trained model each Epoch of training for overfitting detection. As a result, the Morrow-based fatigue life prediction model was found to give the most suitable result with three membership functions. The SWT-based model needed five membership functions due to nonlinear properties in the SWT-based fatigue life data. Training process of Morrow-based-ANFIS was stopped at Epoch 8 given its lowest checking root-mean-square-error of 0.6953. SWT-based model recorded a higher error of 0.7940. The neuro-fuzzy models gave accurate fatigue life predictions with 96 % of the data distributed within the acceptance boundary, hence, contributing to an acceptable assessment of coil spring fatigue life based on load multifractality. This study had shown a nonlinear relationship between road multifractality and durability performance of coil spring. Multifractality had been proven an important feature to characterise various road excitations for durability prediction. [ABSTRACT FROM AUTHOR]

This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase. Bi-spectral Index signal (BIS) as a common approach of controlling DOA generates noise and delays in the initial phase of induction. This may lead to useless information in the process of controlling. Moreover, using the BIS index entails a time-consuming process, high equipping costs, and a lack of accessibility to device accessories. To overcome these problems, we propose a new model of controlling DOA with no need for the use of such an index. Hence, an estimation strategy for DOA is developed applying a feedforward neural network and an adaptive neuro-fuzzy inference estimation model. This model estimates the dose of intravenous anesthetic drugs concerning the patients' needs resulting in optimal drug dose and stable anesthesia depth. The proposed estimations are tested by sensitivity analysis being compared with real data obtained from the classical model (PK-PD) revised approach and BIS approach on 13 patients undergoing surgery. The results show an accuracy of 0.999, indicative of a high-validated model. Compared to BIS, our proposed model not only controls DOA accurately but also achieves outcomes in practice successfully. Some practical implications for future research and clinical practice are also suggested. [ABSTRACT FROM AUTHOR]

Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVMPK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values. [ABSTRACT FROM AUTHOR]

With the tremendous advancement in information and communication techniques, the health care system is being transformed into smart healthcare systems. The large volume of patient data, such as Electronic Health Records, has shown the importance of new technologies like machine learning and artificial intelligence in advanced disease prediction. Diabetes Mellitus is a worldwide health disorder which needs to be addressed seriously, especially for elderly society. This is because it can be the first phase of many other serious diseases like heart attack, kidney disorder, etc. In this article, a smart diabetic monitoring/prediction system is proposed with different machine learning algorithms. To be precise, the risk of diabetes can be predicted well in advance on the basis of the patient health data and genetic conditions. This is where the role of machine learning and artificial intelligence comes in predicting the diseases. Diabetes diagnosis is usually done with patient data, such as blood glucose and random blood glucose from the daily examination. To predict degenerative diseases, machine learning methods such as random forest and support vector machines are used. Accuracy is one of the essential variables that should be examined during the disease diagnosis. In this article, we use a machine learning algorithm that can more accurately diagnose diabetes than the current ones. Also the efficiency of different existing algorithms will be compared with the proposed methodology. [ABSTRACT FROM AUTHOR]

In this article we present the way how to create a program in the Scilab-Xcos environment with which it can be dimensioned, mathematically modeled and simulated a ventilation installation using artificial intelligence for its implementation. The mathematical equations implemented in the program with which the dimensioning and simulation are done are presented. A neuro-fuzzy system was used to generate the fan characteristics. [ABSTRACT FROM AUTHOR]

Virtualization provides the flexibility to distribute the workload among physical servers to reduce overall electrical power consumption, through the consolidation of Virtual Machines (VMs). Many research projects have been done on VM migration to reduce energy consumption in data centers while ensuring a high level of adherence to the Service Level Agreements (SLA). Service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, in this paper, we propose a new intelligent VM migration approach, called CLANFIC, which utilizes modified Cellular Learning Automata based Evolutionary Computing (CLA-EC) and neuro-fuzzy to minimize the number of VM migrations and improve energy consumption. This goal is achieved by utilizing an optimized placement method and delaying migration time based on future resource demand prediction. This algorithm reduces the number of migrations in two steps (i) finding the optimal virtual machine placement and replacement on physical servers by using modified CLA-EC (ii) predicting future resource usage in each host by a neuro-fuzzy algorithm to prevent unnecessary migrations. The experimental results on the real workload traces from PlanetLab show that the proposed method reduces the mean migration number, energy consumption, and SLA violation of the data center by 59.05%, 8.5%, and 70.76%, respectively. [ABSTRACT FROM AUTHOR]

The poor management of hotspots in electrical systems often leads to very devastating consequences; an extremity is fire outbreak which could result in loss of lives and/or properties. Hence, the necessity to effectively manage or handle electrical hotspots cannot be overstated. This paper proposes a model for arresting the occurrence of the destructive activities of electrical hotspots in industries by controlling the temperature, humidity and dust density within electrical components/equipment. To this end, a thermal imaging camera is employed for the detection of various locations and magnitudes of hotspots within electrical systems. Based on the globally approved industrial standards for the prevention of thermally induced electrical systems failure, each of the electrical components and equipment [whose thermal excursion is beyond the allowable temperature rise under measured load values (i.e. ΔTcorr)] is identified and treated by adopting the recommended actions. Additionally, a fuzzy logic control (FLC) system is designed. This is further developed into an adaptive neuro-fuzzy inference system (ANFIS) for the control of the operation of the air handling unit (AHU) and the aspirator suction speed. This arrangement, thus, leads to heat reduction and dust elimination within the electrical components/equipment in industrial space, thus preventing the destructive effects of the occurrence of hotspots. However, for the sake of graphical representations of this scheme, the MATLAB environment is created for the generation of the optimum temperatures at various locations within the electrical systems. From this development, it is established that the framework has very high potential to eliminate the catastrophic effects of hotspots in electrical systems. [ABSTRACT FROM AUTHOR]

Self-Excited induction generators (SEIG) display a low voltage and frequency regulation due to variable applied load and input rotation speed. Current work presents a simulation and performance analysis of a three-phase wind-driven, SEIG connect to a three-phase load. In addition, an investigation of the dynamic operation of the induction generator from starting steady state until no-load operation. It is assumed that the input mechanical power is constant where the rotor of the SEIG rotates at a constant speed. The value of the excitation capacitance which is necessary to the operation of the induction generator also computed to ensure a smooth and self-excitation starting. The output voltage of the generator is adjusted by varying the reactive power injected by STATCOM. A 3-phase IGBT voltage source inverter with a fuel cell input supply is connected as STATCOM which is used to compensate for the reduction in the supply voltage and its frequency due to variation occurred in the applied loads. This work includes introducing a neuro-fuzzyy logic controller to enhance the performance of the SEIG by regulation the generated voltage and frequency. The dynamic model of SEIG with STATCOM and loads are implemented using MATLAB/SIMULINK. [ABSTRACT FROM AUTHOR]

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an “expert” fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional.

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

Customer churn has become a significant problem and is one of the prime challenges that many in the services industry are facing. While all kinds of churn lead to incur loss, the loss of low-value customers will be naturally less damaging than the loss of loyal and high-value ones. So companies need to build a churn prediction model for their high-value customers. In this paper, a two-phase framework for prediction of high-value customer churn has been proposed. Phase 1 is the identification phase which takes into account social-network based variables of customers in identifying the high-value ones. The data of an identified high-value customer is used as the input for Phase 2 to prepare the churn prediction model. Data of a major telecommunication company has been used to implement the framework. The customers were clustered by using K-means algorithm. After ranking clusters, the top-cluster was selected according to clusters ratings. The data belonging to the top cluster is used in churn prediction model building phase. In this phase, two neuro-fuzzy techniques, namely the adaptive neuro-fuzzy inference system (ANFIS) and the locally linear neuro-fuzzy (LLNF) have been applied together with locally linear model tree (LoLiMoT) learning algorithm on churn data. A new algorithm has been devised for comparing these methods with the most widely used neural networks such as multi layer perceptron (MLP) and radial basis function (RBF) networks. Results of comparison indicate that the neuro-fuzzy techniques perform better than neural network models and they are a good candidate for churn prediction purposes. [ABSTRACT FROM PUBLISHER]

Research Question: The paper explores the problem of defining the number of required employees and their skills, as well as optimizing the work time. Motivation: Motivation for this paper arises from the fact that an organizational structure of business system has to be conceived and proportioned so as to justify the main company jobs and goals. Almost every job set for the management must be performed properly and reliably in changeable environmental conditions. In order to maintain the efficiency of complex business systems with dynamic and stochastic changeable and unchangeable variables, it is essential that the organizational structure be flexible and constantly adjusted. Idea: The idea of the paper was to develop a model for human resource allocation (HRA) in fuzzy environment. Data: The model is tested in realistic industrial environment - the example of the logistics administrative bodies in the Petroleum Industry of Serbia. Tools: To solve the problem, the following approaches are used: Fuzzy logic, Neural network and Simulated annealing. The model for HRA is based on an adaptive neuro-fuzzy inference system (ANFIS) and fuzzy mathematical model (FMM) for treating uncertainty. ANFIS-FMM model enables the human resource strategy development based on the optimisation of the employees’ work time. In this ANFIS-FMM model the input variables are described using fuzzy sets represented by Gaussian functions. Using expert reasoning, an unique knowledge base is formed which enables the human resource strategy development based on the optimisation of the employees’ work time. Findings: Based on authors’ knowledge there is a lack of the fuzzy logic applications, adaptive neuro-fuzzy models, linear and dynamic programming, as well as heuristic and metaheuristic models related to HRA. In most of the papers, the classical methods of strategic management have been used for HRA. Contribution: In this paper the model for HRA is developed based on adaptive neuro-fuzzy technique application and FMM. The model has four main advantages over other HRA methods: the system possesses adaptability, the model is efficient in conditions of uncertainty, the neuro-fuzzy based HRA model allows dynamic decision making in HRA management through the implementation of a computer-based system, it can be IT-supported tool.