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The agricultural sector plays an important role in a developing country that covers all economic life because it can become the main source of national income. Efforts that must be avoided are to avoid crop failure, one of the things that cause crop failure is that there is no match with the right planting period at the time of planting. So the purpose of this research is to build a decision-making system in the form of a system for determining planting time based on the traditional Aceh farming system. The process developed with information technology in the decision-making method using Mamdani fuzzy logic based on the Graphic User Interface by involving several criteria including the calculation of the month in the Hijri year, the calculation of days, the calculation of the time of planting, and the level of rainfall. So that when starting the process of planting rice traditionally, farmers with this system can choose the best time to grow crops in rice farming. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%.

In this work a microscopic model on pedestrian flow has been proposed. Observed data is used to calibrate and validate the proposed model. The model developed here uses a fuzzy inference system to represent the rules and a force–field structure to represent the repulsive and attractive impacts of objects and goals, respectively in the flow space. The flow space and time are discretized and viewed as discrete quantities. This microscopic model of pedestrian behavior, which essentially models how each pedestrian behaves over time in the flow space, is embedded in a simulation model which is used to simulate situations similar to the ones for which experiments have been done. The results show that the model performs reasonably well.

The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.

This study aims to present a novel Self-regulating and Intelligence Meta-Heuristic-Fuzzy approach (As Methodological Contribution) for integrated and optimal Human Resource Allocation (HRA) in normal and critical conditions at SMEs (As Conceptual Contribution). In this research, a mathematical model of human resource allocation problem is presented, and then Sugeno Fuzzy Inference (SFI) model is used in the tasks rate adjustment layer. The SFI model is the main part of developing Gray Wolf Optimization (GWO) algorithm to reach the integrated and optimal allocation of available human resources under self-regulating attribute in the novel approach. The novel approach has tested and compared to the best researches using data previous researches and by the top five proposed methods in the researches (Includes: SGA, PRS, SRS, MIP, HM) based on three methods of evaluating the quality of solutions (GA-FSGS, MP-FSGS, GA-SGS). The results showed that increase of Ω from 15,000 to 25,000, and HM and SGA clearly performed better than other previous cases in the larger B100 and B200 datasets. Also, it is verified that the method had better results compare to all previous solving methods, and the quality of the solutions have been the best.

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi-Sugeno (T-S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T-S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70-115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation.

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

The sewage treatment systems manage to reduce pollutants of wastewater to make it reach some requirement. The core of it is to effectively control and determine intermediate aeration amount, which is always challenging. Therefore, a multi‐objective planning mechanism (multi‐objective optimization design combining GRA and fuzzy logic inference) that combines grey relational analysis and fuzzy logic, to find the optimal dissolved oxygen solubility for achieving better outlet water quality, is proposed. First of all, grey correlation coefficient between each optimization target and the reference target is calculated, and are converted into fuzzy inference values through the four steps. After that, it is expected to analyse the average values of process variables to obtain the optimal parameter combinations. A real‐world dataset collected from a realistic sewage treatment plant is utilized as the simulation environment to evaluate the proposed multi‐objective optimization design combining GRA and fuzzy logic inference. Experimental results show that the multi‐objective optimization design combining GRA and fuzzy logic inference makes promotion of 47.34% for fitted outlet water quality compared with the original average annual water quality.

Rice crop is one of the major cultivating crops in India, especially in south India. Fertiliser plays an important role to achieve the highest rice yield. Each crop needs an adequate amount of fert...

The purpose of this article is to optimization of national rice production with fuzzy logic using Mamdani method. Based on the results of the study, it is known that four parameters need to be considered to maintain the price stability of necessities, namely production; availability; demand and distribution. Optimization of production by producers and optimization of the ordering of goods by distributors are important steps to maintain price stability for necessities. Optimization of production and ordering of staple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this optimization is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamdani method, four stages are needed, formation of fuzzy sets; application of implication functions; composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to predict the amount of national rice that must be produced. If it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons, the amount of national rice that must be produced is 14,624,592 tons.

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

Computational Intelligence (CI) is the theory, design, application and development of biologically and linguistically motivated computational paradigms. Machine Learning (ML), Fuzzy Inference Systems (FIS), and Evolutionary Computation (EC) have historically been the three principal pillars of CI. The use of CI has experienced an increasing impact on solving complex problems in antennas and propagation (AP).

The possibility of improving the capability-based planning process is being investigated in order to minimize the need for weapons and military equipment and to maximize the acquisition of capabilities during their life cycle. Using the theory of fuzzy sets, the assessment of alternative samples of weapons and military equipment is carried out. A fuzzy inference model for determining the usefulness of weapons and military equipment in acquiring opportunities has been developed, based on fuzzy logic. The modeling of alternative models of weapons and military equipment was carried out in terms of costs at the stages of the life cycle “Use” and “Support” using the modern software environment MATLAB. The simulation results provided an opportunity at the planning stage to improve the efficiency of defense resource management using the value of the usefulness of weapons and military equipment. The introduction of the developed model into defense management makes it possible to automatically determine weapons and military equipment without the participation of experts, taking into account the cost of their life cycle and to bring the planning process closer to Euro-Atlantic approaches.

The rapid increase in the power demand and the capacity shortage of transmission and distribution system drives the integration of Distributed Generation (DG) units in electrical power distribution networks. The integration of DG resources with distribution network can cause significant impacts in protection due to the bidirectional flow of current, particularly the changes in magnitude and direction of short circuit currents. It may also lead to false tripping or fail to trip the over current protection relays in the power system. The relay parameters have to adapt to the changes in the system to avoid unnecessary trippings. The proposed adaptive over current protection scheme, sets the parameters of the relays according to the changes in the network. This method determines the plug multiplier settings (PMS) and the time multiplier settings (TMS) using Sugeno Fuzzy Inference System (SFIS). The proposed methodology is tested for IEEE 13 bus system and 33 bus system and with the obtained adaptive relay settings, the validation for relay coordination is done using ETAP.

The application of non-invasive devices in personal health care is becoming more and more widespread, especially sleep quality, which is a critical part of personal health because it is often associated with many diseases. Using some sensor devices such as pressure sensors, photoplethysmography and heart rate devices, the authors can collect a lot of physiological signals. In this work, they provide a method of fuzzy inference to evaluate the sleep phase, which uses the values of heart rate, heart rate variation, and body movement as input parameters that collected by the sensor devices. This method has been applied to the actual product. The results show that the measurement results of this method are consistent with polysomnography, which is recognised as the best method for measuring sleep quality currently. At the same time, the device can make some additional contributions to monitoring personal health. Combining personal activity information collected by GPS with heart rate information collected by heart rate sensors and using process mining to analyse those data, they can provide good recommendations for personal health care.

Publication of scientific article is now getting bigger, but the process of the scientific article acceptance takes a long time. The longest stage in the process, especially, is in the review process. The process about article assessment consists of many criteria which cause a very high level of subjectivity. Computerized system on the assessment of the scientific article acceptance apply a reasoning scheme using Fuzzy Inference Tsukamoto Logic; therefore, by using the logic, the duration issue in the assessment process can be handled fast.

The article describes a method for making redundant decisions with subsequent associative choice between them. The method is intended for use in an automated system for predicting coronary heart disease. It can be implemented in mathematical models for determining coronary risks based on three groups of informative features; algorithmic support for the synthesis of neural network models and for fuzzy inference models; an associative decision-making algorithm for synthesizing redundant channel classifiers; a redundant channel classifier providing associative choice between redundant decisions. Experimental studies have shown that the diagnostic efficiency of models with associative choice exceeds that of currently used models by 10-16%, while the use of associative choice and redundant channels makes it possible to increase diagnostic efficiency by 8% compared to the diagnostic efficiency of individual channels.

The rehabilitation of armless or footless patients is of great importance. One choice for such issue is using the electroencephalograph (EEG) brain computer interface to help the patients communicate with outside. Classifying the EEG signals generated from mental activity is one of the most important technologies. However, existing classification methods often suffer the overfitting problem caused by the small training data sets while big dimensionality of feature space. Fuzzy inference can imitate the human judgement, effectively dealing with uncertainty and small-sample learning problems. Besides, biclustering has shown excellent performance in constructing rule base. This paper proposes a novel biclustering based fuzzy inference method for EEG classification. It can be divided into five steps. The first step is generating features with common spatial pattern. The second step is searching local coherent patterns with column nearly constant biclustering. The third step is to transform the patterns to if-then rules with column averaging and majority voting strategy. Subsequent step is to employ Mamdani fuzzy inference to map the input feature vector into decimals. Finally, particle swarm optimization is utilized to generate optimal threshold for linear classification. Experiments on several commonly used data sets show that the proposed method has advantages over competitors in terms of classification accuracy.

In a company, salary is a salary for employees who have been working for a month. However, in order to provide fair salary to all employees, the company must determine the criteria for providing salary. By using fuzzy logic, wages can be determined by going through the following stages: Fuzzification, Formation of the knowledge base, Fuzzy Inference, and Defuzzification. One of the fuzzy logic methods that can be used is the Tsukamoto method, where this method has an output in the form of firm values. To determine the salary, the data is collected from the Central Statistics Agency website in accordance with the criteria to be examined. With this research, employers can use the calculations from this research to determine salary salaries for their employees quickly, well, and precisely. So that the problem of determining the wages of their employees' salaries can be resolved properly.

In human life, many things require decision making, including in agriculture. One of them is rice farmers who make the decision to determine the selling price of their grain according to the quality of their grain. By using Fuzzy logic, the grain price can be determined by going through the following stages: Fuzzification, Knowledge Base Formation, Fuzzy Inference, and Defuzzification. One of the Fuzzy logic methods that can be used is the Tsukamoto method, where this method has an output in the form of firm values. To be able to determine the price of grain, the data is taken from the Central Statistics Agency website, so that later prices and levels of grain quality can be determined properly. With this research, the farmers can determine the price of their grain exactly according to the quality of the grain. So that the problem of determining their grain prices can be overcome properly.

Increasing electricity demand gives a rise to the necessity of cleaner and safer electric supply. Short-term-related time series prediction in electricity power system with high precision is irrepl...

Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets.

Automatic extracting road intersection points is essential for applications such as data registration between vector data and remote sensing images, aircraft-assisted navigation. However, at a large scale, it is difficult to quickly and accurately extract road intersection points due to the problems caused by complex structures, geometric texture noise interference. In this context, taking OpenStreetMap (OSM) data as priori knowledge, we propose a method for automatic extraction of complex road intersection points based on fuzzy inference. First, OSM data are analyzed to obtain structural information of intersection points. Local search areas are built around the intersection points. Second, within the local search area, the candidate intersection point set are generated. Meanwhile the input image is segmented using multiresolution segmentation; then we establish a fuzzy rule to infer the road area from the segmentation result. The fuzzy indexes and rules are established for the candidate intersection point set to deduce the road intersection area. Finally, based on the results of the previous step, the road intersection points are extracted based on the line segment constraint, structure matching, and linkage equation. Three sets of high-resolution remote sensing images were used to verify the feasibility of the method. We demonstrate that the correctness and positioning accuracy of this method are superior to those of other methods through contrastive analysis.

Solar energy has a key role in producing clean and emissions-free power compare to conventional methods. However, sustainable development also requires a reliable and predictable energy source. It also needs methods to measure and predict predictable supply. The main aim of the study is to improve reliable and precise solar radiation prediction models on monthly mean daily basis using various machine learning techniques. Simple Membership Function and Fuzzy Rule Generating Technique (SMGRT), which does not require error and trial for model adjustment, is the first-choice model in this study. Experience and observations about the model will greatly reduce the volume of processing for the fuzzy SMGRT model. On the other hand, Deep Learning (DL) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have become increasingly popular in understanding nonlinear data structures and solving complex problems. Therefore, DL and ANFIS were also applied to estimate solar radiation. The data set used in the study were created using sunshine duration (s), extra-terrestrial solar radiation (H0), relative humidity (RH), cloudiness (C), air temperature (T) and soil temperature (ST) parameters. Estimation performance of models was evaluated by using several statistical indicators which are Mean Bias Error (MBE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (R2). When the performances of the models were compared, it was seen that all three models obtained remarkable results. In addition, it was shown that the models performed well based on the metrics in the testing phase. The SMGRT model has slightly better performance than DL and ANFIS for different input combinations. SMGRT Model 1 (with inputs H0, s, and T) shows the best statistical performance (MBE = 0.156, MSE = 1.878, RMSE = 1.371, and R2 = 0.960) not only in SMGRT models but also in others.

In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological remission. An untreated AIH can lead to scarring of the liver and ultimately to liver failure. If rapidly diagnosed, AIH can often be controlled by medication. NFCM is a new extension of FCM, which employs a NFIS to determine the causal relationships between concepts. In the proposed approach, weights are calculated using the knowledge and experience of experts as well as the advantages of NFIS. This makes the presented model more accurate. Having a high convergence speed, the proposed NFCM model performs well by achieving an AIH diagnosis accuracy of 89.81%. The superiority of the proposed NFCM model over the conventional FCM is that, it uses the NFIS to determine the link weights which train system parameters.

Copyright © The Author(s) 2019. Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts’ experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area. National Key R&D Program of China (2018YFC0830900); National Natural Science Foundation of China (61873094); Science and Technology Program of Guangzhou, China (201804010246); Natural Science Foundation of Guangdong Province of China (2018A030313338).

The optimal fuzzy reasoning (OFR) method proposed by Zhang and Cai (2004) is treated as a control problem and it is dramatically different from other existing fuzzy reasoning methods. The aim of this paper is to make some notes on OFR methods. We give a modification of an OFR method presented by Zhang and Cai. Some advantages and limitations of OFR methods are examined. We introduce several new OFR methods based on the Łukasiewicz implication, the Kleene-Dienes implication and the Zadeh implication. The reasoning consistency of the proposed methods is studied. The relationship between the OFR method and other fuzzy reasoning methods (e.g., the compositional rule of inference method and the pointwise optimizing fuzzy inference method) is examined.

The decision-making model with basic fuzzy rule modus ponens is suggested in this paper to control the hand prosthesis. The hand movements are described by angles of finger and wrist flexion. Electromyogram (EMG) of hand muscles was used as a source of the input data. Software was developed to implement the decision-making model with fuzzy rule modus ponens. In particular, the software receives EMG data, executes calculations and visualises the output data. The key advantage of the model is smoothness of output data changes; this way a maximum approach to natural hand movements is reached.

Poly Cystic Ovarian Syndrome (PCOS) is a hormonal disorder found in women of reproductive age. It includes failure to ovulate, infertility along with mental health complications. There are different methods used for the detection of PCOS, but these methods limitedly support the integration of PCOS and mental health issues. To address these issues, in this paper we present an automated early detection and prediction model which can accurately estimate the likelihood of having PCOS and associated mental health. In real life applications we often see that people are prompted to answer in linguistic terminologies to express their wellbeing for the questions asked by the clinician. In order to model the inherent linguistic nature of the mapping between symptoms and diagnosis of PCOS a fuzzy approach is used. Therefore, in the present study, Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is evaluated for its performance. Using the local yet specific dataset collected on a spectrum of women, the Fuzzy TOPSIS is compared with the widely used Support Vector Machines (SVM) algorithm. Both the methods are evaluated on the same dataset. An accuracy of 98.20% using Fuzzy TOPSIS method and 94.01% using SVM is obtained. Along with improvement in the performance, methodological contribution, the early detection and treatment of PCOS and mental health issues can together aid in taking preventive measures in advance. The psychological wellbeing of the women is also objectively evaluated and can be brought into the PCOS treatment protocol.

Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public Git repository. Thus, it is necessary to develop the ontology service. The ontology service must allow working with ontologies and making fuzzy inferences. The article presents ontology models for decision support, fuzzy inference, and the fuzzy inference algorithm. The article considers examples of DSSs for balancing production capacities and image analysis. The article also describes the architecture of the ontology service. The proposed novel ontology models for decision support make it possible to reduce the time of a knowledge base formation. The ontology service can integrate with external systems with HTTP protocol.

Many citizens nowadays cope with busy and dynamic lifestyles. Adopting or maintaining a healthy lifestyle to prevent chronic diseases or mental disorders is a core societal challenge. The current global pandemic has evidenced more than ever before the critical importance of engaging citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Recommender Systems for personalized health and well-being have lately become a research trend, particularly for food and physical activity recommendation. This paper presents F-EvoRecSys: an extension of an evolutionary algorithm-driven approach for "healthy bundle" well-being recommendations that incorporates a fuzzy inference engine aimed at improving physical activity recommendations based on users’ exercising habits. An experimental study demonstrates how this can lead to more diversified recommendations. The paper also discusses challenges and future directions for personalized well-being recommender systems under different perspectives.

SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 -- 12 October 2021 through 14 October 2021 -- -- 172791, Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one. © 2021, Society of Petroleum Engineers.

The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume factor can be acquired from pressure-volume-temperature (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous correlations and machine learning methods have some limitations, such as the lack of accuracy. Furthermore, most earlier models have not studied the relationships between the inputs and outputs to show the proper physical behaviors. Consequently, this study comes to develop a model to predict the oil formation volume factor at the bubble point (Bo) using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS model was built based on 924 data sets collected from published sources. The ANFIS model and previous 28 models were validated and compared using the trend analysis and statistical error analysis, namely average absolute percent relative error (AAPRE) and correlation coefficient (R). The trend analysis study has shown that the ANFIS model and some previous models follow the correct trend analysis. The ANFIS model is the first rank model and has the lowest AAPRE of 0.71 and the highest (R) of 0.9973. The ANFIS model also has the lowest average percent relative error (APRE), root mean square error (RMSE), and standard deviation (SD) of -0.09, 1.01, 0.0075, respectively. The authors express their appreciation to the Universiti Teknologi PETRONAS for supporting this work under the YUTP-Grant cost center 015LC0-098. They also especially thank COREOR, Petroleum Engineering, Universiti Teknologi PETRONAS, for supporting this work. Scopus

This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.