Search

Patients with Parkinson's disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results' explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. [ABSTRACT FROM AUTHOR]

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules' firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes. [ABSTRACT FROM AUTHOR]

An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer's existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert's satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide. [ABSTRACT FROM AUTHOR]

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm. [ABSTRACT FROM AUTHOR]

The current electricity default rates in continental countries, such as Brazil, pose risks to the economic stability and investment capabilities of distribution utilities. This situation results in higher electricity tariffs for regular customers. From a regulatory perspective, the key issue regarding this challenge is devising incentive mechanisms that reward distribution utilities for their operational and investment choices, aiming to mitigate or decrease electricity non-payment rates and avoid tariff increases for regular customers. Despite adhering to the principles of incentive regulation, the Brazilian Electricity Regulatory Agency (ANEEL) uses a methodological approach to define regulatory targets for electricity defaults tied to econometric models developed to determine targets to combat electricity non-technical losses (NTLs). This methodology has been widely criticized by electricity distribution utilities and academics because it includes many ad hoc steps and fails to consider the components that capture the specificities and heterogeneity of distribution utilities. This study proposes a fuzzy inference-based model for defining regulatory default targets built independently of the current methodological approach adopted by ANEEL and aligned with the principles of incentive regulation. An empirical study focusing on the residential class of electricity consumption demonstrated that it is possible to adopt a specific methodology for determining regulatory default targets and that the fuzzy inference approach can meet the necessary premises to ensure that the principles of incentive regulation and the establishment of regulatory targets are consistent with the reality of each electricity distribution utility. [ABSTRACT FROM AUTHOR]

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components. [ABSTRACT FROM AUTHOR]

Image denoising is a fundamental research topic in colour image processing, analysis, and transmission. Noise is an inevitable byproduct of image acquisition and transmission, and its nature is intimately linked to the underlying processes that produce it. Gaussian noise is a particularly prevalent type of noise that necessitates effective removal while ensuring the preservation of the original image's quality. This paper presents a colour image denoising framework that integrates fuzzy inference systems (FISs) with eigenvector analysis. This framework employs eigenvector analysis to extract relevant information from local image neighbourhoods. This information is subsequently fed into the FIS system which dynamically adjusts the intensity of the denoising process based on local characteristics. This approach recognizes that homogeneous areas may require less aggressive smoothing than detailed image regions. Images are converted from the RGB domain to an eigenvector-based space for smoothing and then converted back to the RGB domain. The effectiveness of the proposed methods is established through the application of various image quality metrics and visual comparisons against established state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework's efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for diagnosing failures within a turbogenerator mineral lube oil system, employing a fuzzy inference system (FIS) model. The study leverages real operational data collected from supervisory monitoring sensors across four turbogenerators over a three-year operational span, resulting in a dataset comprising 40,456,663 input parameters. The failure modes were established through expert knowledge, using the Failure Mode, Effect, and Criticality Analysis (FMECA) documentation as the basis. Initially, the model's universe variables were constructed using the sensor calibration range, and then the fuzzy membership functions were formulated based on the operational thresholds inherent to each measured parameter. The fault identification mechanism is underpinned by an inference system employing predefined rules, extrapolated from expert judgments encapsulating failure typologies specific to the turbogenerators' mineral lube oil system, as delineated in the FMECA. The FIS model demonstrates notable efficacy in failure diagnosis with an overall performance evaluation of the system yielding satisfactory outcomes, having a 98.35% true positive rate for failure classification, coupled with a 99.99% true negative rate for accurate classification during normal system operation. These results highlight the visibility of the FIS model in diagnosing failures within the turbogenerator mineral lube oil system, thereby showcasing its potential for enhancing operational reliability and maintenance efficiency. [ABSTRACT FROM AUTHOR]

Conventional irrigation methods frequently generate excessive or inadequate watering, resulting in the wastage of water and energy and diminished agricultural yields. This study presents a novel intelligent irrigation system that incorporates fuzzy logic and the Internet of Things (IoT) to automate the control of water pumps, thereby eliminating the requirement for human intervention. This novel method enables users to effectively preserve water and electricity by mitigating the issues of excessive and insufficient irrigation of crops. The system utilizes climate sensors that are combined with electrical circuits and connected to an Arduino and a fuzzy inference system (FIS) model to consider climate conditions and soil moisture levels. The sensors are responsible for collecting data that are utilized by the FIS model to control the speed of the water pump effectively. The FIS model integrates fuzzy logic to analyse the data obtained by the Arduino. This analysis enables the Arduino to adjust the pump speed by considering a wide range of sensor inputs. The implementation of this autonomous system eliminates the requirement for human intervention and enhances agricultural productivity by accurately dispensing the optimal quantity of water at the proper intervals. The cessation of water supply occurs when the soil moisture levels reach a sufficient state and resumes when the moisture levels fall below predetermined limits, regulated by various environmental circumstances. A comparative analysis examines the suggested technology, drip irrigation, and manual flooding. The comparison results demonstrate that the intelligent irrigation system accomplishes water and energy conservation. [ABSTRACT FROM AUTHOR]

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems. [ABSTRACT FROM AUTHOR]

To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle's vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F 1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for individual design perception modeling using the YUKI algorithm-trained Fuzzy Inference System. The study focuses on understanding how individuals perceive design based on personality traits, particularly openness to experience, using the YUKI algorithm and Fuzzy C-means clustering algorithm. The approach generates several Sugeno-type Fuzzy Inference System models to predict design perception, to minimize the Root Mean Squared Error between the model prediction and the actual design perception of participants. The results demonstrate that the suggested method offers more accurate predictions compared to the traditional Fuzzy C-means Fuzzy Inference System and Deep Artificial Neural Networks, and the Root Mean Square deviation for individual design perceptions falls within a satisfactory range of 0.84 to 1.32. The YUKI algorithm-trained Fuzzy Inference System proves effective in clustering individuals based on their level of openness, providing insights into how personality traits influence design perception. [ABSTRACT FROM AUTHOR]

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality. [ABSTRACT FROM AUTHOR]

This work introduces a methodology for the automatic unmineable inclusions detection and Bucket Wheel Excavator (BWE) collision prevention, using electromagnetic (EM) inspection and a fuzzy inference system. EM data are collected continuously ahead from the bucket wheel of a BWE and subjected to processing. Two distinct methodologies for data processing were developed and integrated into the MATLAB programming environment. The first approach, named "Simple Mode", utilizes statistical process control to generate real-time alerts in the event of a potential collision involving the excavator's bucket and hard rock inclusions. The advanced processing flow ("Advanced Mode") requires accurate instrument positioning and data from successive EM scans. It incorporates techniques of local resistivity maxima detection (Position Prominence Index) as well as Neural Network-based Pattern Recognition (NNPR). A decision support process based on a Fuzzy Inference System (FIS) has been developed to assist BWE operators in avoiding collision when digging hard rock inclusions. The proposed methodology was extensively tested using synthetic EM data. Limited real data, acquired with a CMD2 (GF Instruments) EM instrument equipped with GPS, were used to control its efficiency. Increased accuracy in the automatic detection of unmineable inclusions was observed using the Advanced Mode. On the other hand, the Simple Mode processing technique offers the advantage of being independent of instrument positioning as well as it provides real-time inspection of the excavated mine slope. This work introduces a methodology for hard rock inclusion detection and can contribute to the optimization of mine operations by improving resource efficiency, safety, cost savings, and environmental sustainability. [ABSTRACT FROM AUTHOR]

The article provides an overview of the existing structures of fuzzy model systems for arc length control and optimal control of arc steel-melting furnaces (ASFs) electrical mode. The analysis of known adaptive optimal control strategies of alternating current steel-melting arc furnaces modes according to complex criteria of energy efficiency, energy saving and reliability was performed. Expedient system engineering solutions are proposed for the comprehensive improvement of energy efficiency and electromagnetic compatibility of arc furnaces and the power grid, which are implemented by improving automatic power and arc length control systems. Control principles, indicators of arc length control dynamics, energy efficiency and electromagnetic compatibility of various systems are analyzed. The expediency of using fuzzy control models for comprehensive improvement of dynamics, energy efficiency and electromagnetic compatibility indicators is substantiated. The expediency of implementing simultaneous phase-independent control of arc lengths and fast-acting arc currents control is substantiated. A variant of the double-circuit structure of fuzzy arc lengths control system and implementation of the multi-criteria optimal control strategy of electrical modes (EM) is proposed. The design of fuzzy inference systems for the synthesis of the electrode movement control signal was carried out in order to improve the dynamic accuracy of the ASF electrical mode coordinates stabilization. A computer Simulink model of the developed double-circuit system was created, and studies of the dynamics and energy efficiency indicators when handling deterministic and random perturbations were performed. The obtained results of model studies showed an improvement of the control dynamics quality indicators under the influence of deterministic and random perturbations and an increase in the energy efficiency of ASF EM at various technological stages of melting. The expediency of the practical use of the proposed solutions for operating arc furnaces is substantiated. [ABSTRACT FROM AUTHOR]

A Fuzzy Inference System (FIS) is a system that represents human reasoning based on fuzzy if-then rules to extract valid results from imprecise or uncertain information. These systems have been applied to various engineering fields, such as control and image recognition, and have encouraged the search for development tools that provide facilities for the design, simulation, and implementation of FISs. Most of the available software tools for the design of FISs have been developed by the scientific community, and very few are commercial tools. According to the state of the technology, the fuzzy systems implemented in hardware have shown higher performance than software implementations. For this reason, different strategies for automatic hardware synthesis of FISs have been proposed in the literature. The available tools for FIS implementation, including MATLAB, Simulink, and Xfuzzy environment, require advanced hardware knowledge, and it is necessary to synthesize and implement the program on FPGA each time a change is made to the code. This paper presents a novel approach for the implementation of fuzzy systems on hardware devices that enables the direct implementation of FISs through a graphical user interface. The VHDL code is pre-synthesized and integrated with the interface, allowing users to experiment with different input parameters, such as membership functions and fuzzy rules, without resynthesizing the code on an FPGA. To verify the effectiveness of the proposed approach, experiments were conducted on two similar System-on-Chip (SoC) devices, VEEK-MT and VEEK-MT2. The results showed that the proposed solution represents a significant advancement in the study of fuzzy systems on hardware devices, providing a flexible and user-friendly approach to their design and implementation. [ABSTRACT FROM AUTHOR]

Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods. [ABSTRACT FROM AUTHOR]

Anxiety is a common mental health issue that affects a significant portion of the global population and can lead to severe physical and psychological consequences. The proposed system aims to provide an objective and reliable method for the early detection of anxiety levels by using patients' physical symptoms as input variables. This paper introduces an expert system utilizing a fuzzy inference system (FIS) to predict anxiety levels. The system is designed to address anxiety's complex and uncertain nature by utilizing a comprehensive set of input variables and fuzzy logic techniques. It is based on a set of rules that represent medical knowledge of anxiety disorders, making it a valuable tool for clinicians in diagnosing and treating these disorders. The system was tested on real datasets, demonstrating high accuracy in the prediction of anxiety levels. The FIS-based expert system offers a powerful approach to cope with imprecision and uncertainty and can potentially assist in addressing the lack of effective remedies for anxiety disorders. The research primarily focused on Asian countries, such as Pakistan, and the system achieved an accuracy of 87%, which is noteworthy. [ABSTRACT FROM AUTHOR]

The software development process (SDP) is complex because it uses specific knowledge, skills, tools, and techniques to deliver value to people, and the user's needs are always growing and becoming more complex. Practitioners and academics have proposed different approaches for improving the successful implementation of the SDP. This remains a non-trivial task, and the number of successful software development projects increases only slightly each year. This paper proposes a new approach for adapting the classical SDP to a dynamic, knowledge-intensive, and context-dependent model by applying the case-handling and fuzzy inference approaches for SDP modeling and simulation. The advantages and novelties of this approach consist in: SDP dynamicity modeling using the case-handling approach; the inclusion of human factor uncertainty into the execution of SDP activities through fuzzification; and agent-based simulation, which allows for the inclusion of human actors in SDP simulations. Modeling and simulation of Agile SDPs were carried out using this prototype. Experiments show the correspondence of the proposed approach to the needs of dynamically changing and human-factor-dependent SDPs, as well as the possibilities for modeling and simulating the SDP, predicting SDP execution, and observing possible risks. [ABSTRACT FROM AUTHOR]

On 6 February 2023, a powerful earthquake at the border between Turkey and Syria caused catastrophic consequences and was, unfortunately, one of the deadliest earthquakes of the recent decades. The moment magnitude of the earthquake was estimated to be 7.8, and it was localized in the Kahramanmaraş region of Turkey. This article aims to investigate the behavior of more than 50 different lithosphere–atmosphere–ionosphere (LAI) anomalies obtained from satellite data and different data services in a time period of about six months before the earthquake to discuss the possibility of predicting the mentioned earthquake by an early warning system based on various geophysical parameters. In this study, 52 time series covering six months of data were acquired with: (i) three identical satellites of the Swarm constellation (Alpha (A), Bravo (B) and Charlie (C); and the analyzed parameters: electron density (Ne) and temperature (Te), magnetic field scalar (F) and vector (X, Y and Z) components); (ii) the Google Earth Engine (GEE) platform service data (including ozone, water vapor and surface temperature), (iii) the Giovanni data service (including the aerosol optical depth (AOD), methane, carbon monoxide and ozone); and (iv) the USGS earthquake catalogue (including the daily seismic rate and maximum magnitude for each day), around the location of the seismic event from 1 September 2022 to 17 February 2023, and these were analyzed. The results show that the number of seismic anomalies increased since about 33 days before the earthquake and reached a peak, i.e., the highest number, one day before. The findings of implementing the proposed predictor based on the Mamdani fuzzy inference system (FIS) emphasize that the occurrence of a powerful earthquake could be predicted from about nine days to one day before the earthquake due to the clear increase in the number of seismo-LAI anomalies. However, this study has still conducted a posteriori, knowing the earthquake's epicenter and magnitude. Therefore, based on the results of this article and similar research, we emphasize the urgency of the creation of early earthquake warning systems in seismic-prone areas by investigating the data of different services, such as GEE, Giovanni and various other global satellite platforms services, such as Swarm. Finally, the path toward earthquake prediction is still long, and the goal is far, but the present results support the idea that this challenging goal could be achieved in the future. [ABSTRACT FROM AUTHOR]

Developing countries all over the world face numerous difficulties with regard to food security. The purpose of this research is to develop a new approach for evaluating wheat's suitability for cultivation. To this end, geographical information systems (GIS) and fuzzy inference systems (FIS) are used as the most appropriate artificial intelligence (AI) tools. Outcomes of investigations carried out in the western Nile Delta, Egypt. The fuzzy inference system used was Mamdani type. The membership functions used in this work are sigmoidal, Gaussian, and zmf membership. The inputs in this research are chemical, physical, and fertility soil indices. To predict the final soil suitability using FIS, it is required to implement 81 IF-THEN rules that were written by some experts. The obtained results show the effectiveness of FIS in predicting the wheat crop's suitability compared to conventional methods. The research region is split into four classes: around 241.3 km2 is highly suitable for wheat growth, and 224 km2 is defined as having moderate suitability. The third soil suitability class (low), which comprises 252.73 km2, is larger than the unsuitable class, which comprises 40 km2. The method given here can be easily applied again in an arid region. Decision-makers may benefit from the research's quantitative findings. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data. Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path where these points will stay fixed during the whole network lifetime, we propose an adaptive path construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are: the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value for each sensor node generated based on the previous three parameters and, thus, each RP is updated to its new location accordingly. Simulation results have shown that the proposed approach extends the network lifetime compared with another existing approach that uses fixed RPs. For example, in terms of using the first dead node as a metric for the network lifetime, when the number of deployed sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been achieved compared with another related approach that uses fixed RPs. [ABSTRACT FROM AUTHOR]

Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research. [ABSTRACT FROM AUTHOR]

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory. [ABSTRACT FROM AUTHOR]

Fuzzy information granulation transfers the time series analysis from the numerical platform to the granular platform, which enables us to study the time series at a different granularity. In previous studies, each fuzzy information granule in a granular time series can reflect the average, range, and linear trend characteristics of the data in the corresponding time window. In order to get a more general information granule, this paper proposes polynomial fuzzy information granules, each of which can reflect both the linear trend and the nonlinear trend of the data in a time window. The distance metric of the proposed information granules is given theoretically. After studying the distance measure of the polynomial fuzzy information granule and its geometric interpretation, we design a time series prediction method based on the polynomial fuzzy information granules and fuzzy inference system. The experimental results show that the proposed prediction method can achieve a good long-term prediction. [ABSTRACT FROM AUTHOR]

Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability ( P r k ) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed P r k value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal P r k value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable P r k . The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively. [ABSTRACT FROM AUTHOR]

The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-min data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system. [ABSTRACT FROM AUTHOR]

Although the International Regulations for Preventing Collision at Sea (COLREGs) provide guidelines for determining the encounter relations between vessels and assessing collision risk, most collision accidents occur in crossing situations. Accordingly, prior studies have investigated methods to identify the relation between the give-way and stand-on vessels in crossing situations to allow the stand-on vessel to make the optimal collision-avoidance decision. However, these studies were hindered by several limitations. For example, the collision risk at the current time (t) was evaluated as an input variable obtained at the current time (t), and collision-avoidance decisions were made based on the evaluated collision risk. To address these limitations, a collision risk prediction system was developed for stand-on vessels using a fuzzy inference system based on near-collision (FIS-NC) and a sequence model to facilitate quicker collision avoidance decision making. This was achieved by predicting the future time point (t + i) collision risk index (CRI) of the stand-on vessel at the current time point (t) when the own-ship is determined to be the stand-on vessel in different encounter relations. According to the performance verification results, navigators who use the developed system to predict the CRI are expected to avoid collisions with greater clearance distance and time. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) are complex systems interacting with the physical world where instant external changes and uncertain events exist. The Internet of Things is a paradigm that can interoperate with a CPS to increase the CPS's network and communication capabilities. In the literature, software agents, particularly belief–desire–intention (BDI) agents, are considered options to program these heterogeneous and complex systems in various domains. Moreover, fuzzy logic is a method for handling uncertainties. Therefore, the enhancement of BDI with fuzzy logic can also be employed to improve the abilities, such that autonomy, pro-activity, and reasoning, which are essentials for intelligent systems. These features can be applied in CPSs and IoT interoperable systems. This study extends the CPSs and IoT interoperable systems using fuzzy logic and intelligent agents as symmetric paradigms that equally leverage these domains as well as benefit the agent & artifact approach. In this regard, the main contribution of this study is the integration approach, used to combine the CPS and IoT augmented with fuzzy logic using BDI agents. The study begins with constructing the design primitives from scratch and shows how Jason BDI agents can control the distributed CPS. The study then performs the artifact approach by encapsulating a fuzzy inference system, utilizing time-based reasoning, and benefiting from symmetric fuzzy functions. Lastly, the study applies the self-adaptiveness method and flexibility plan selection, considering the run-time MAPE-K model to tackle run-time uncertainty. [ABSTRACT FROM AUTHOR]

Autonomous surface vehicles need to be at least as safe as conventional vessels, if not safer, when navigating on waters. With a great deal of navigation algorithms for surface vessels out there, the safety of their produced paths is questionable, and, in most cases, complicated to assess and compare. Hence, this paper proposes a method for extended collision risk assessment for paths generated by autonomous navigation algorithms as follows: (1) static, dynamic, and historic risk factors are calculated; (2) individual risk value is determined using a fuzzy inference system; (3) the extended collision risk assessment (ECRA) score is acquired using a root-mean-square method. Finally, a comparison of the ECRA score of each path determines the path with the lowest risk. The validation results show that the proposed method is able to detect lower/higher risk scenarios and assign an adequate risk value in most cases. Risk reduction for cautious paths varies up to 8.43%, while risk increases for incautious paths—up to 57.98%. The results indicate that the method could be used for navigation algorithm evaluation and comparison with some improvements. This research also reveals several promising future directions and applications of the method. [ABSTRACT FROM AUTHOR]

Optimal power allocation (PA) is a decisive part of the power domain non-orthogonal multiple access (PD-NOMA) technique. In PD-NOMA, users are served at the same time and using the same frequency band, but at differing power levels. In this paper, the optimization problem for PA is formulated with distance (d), signal-to-noise ratio ( S N R ), and foliage depth ( d f ) constraints. A fuzzy inference system (FIS) addresses the optimization problem by allocating the optimal power factors (power levels) to each user in the vicinity of a 5G base-station (gNodeB). The proposed system incorporates a cooperative relaying technique at the near-user to assist the far-user facing signal degradation and greater path losses. A realistic 5G micro-cell is analyzed for downlink PD-NOMA where superposition coding (SC) is used at the transmitter side, a successive interference cancellation (SIC) scheme at the near-user, and a maximum ratio combining (MRC) technique at the far-user's receiver, respectively. For both simple PD-NOMA and cooperative relaying PD-NOMA, the presented technique's bit-error-rate (BER) performance is evaluated against various SNR values, and it is concluded that cooperative PD-NOMA outperforms simple PD-NOMA. By combining the presented FIS system with cooperation relaying, the proposed FIS method guarantees user fairness in PD-NOMA systems while also significantly improving performance. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs' applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people's lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air. [ABSTRACT FROM AUTHOR]

Oil is one of the most important sources of energy, about 25 percent of which comes from offshore sources. As a result, the transportation of oil tankers, and the construction of offshore oil platforms and subsea pipelines have increased, to facilitate offshore oil exploitation. However, most oil spill risk assessments analyze the impact of one risk source, and rarely consider multiple risk sources in the study area. This paper focuses on three risk sources that may cause oil spills in a certain area, and establishes an oil spill risk assessment model through a fuzzy inference system. Oil spill probabilities for different risk sources are calculated through the model. According to the definition of oil spill risk, the risk probability of multiple risk sources in the study area is obtained, which can provide technical support for regional oil spill emergency capacity and emergency resource allocation. [ABSTRACT FROM AUTHOR]

The big hospitals' electricity supply system's reliability is discussed in this article through Petri nets and Fuzzy Inference System (FIS). To simulate and analyse an electric power system, the FIS Mamdani in MATLAB is implemented. The advantage of FIS is that it uses human experience to provide a faster solution than conventional techniques. The elements involved are the Main Electrical Power, the Generator sets, the Automatic Transfer Switches (ATS), and the Uninterrupted Power Supply (UPS), which are analysed to characterize the system behaviour. To evaluate the system and identified the lower reliability modules being proposed, a new reliable design model through the Petri Nets and Fuzzy Inference System approach. The resulting approach contributes to increasing the reliability of complex electrical systems, aiming to reduce their faults and increase their availability. [ABSTRACT FROM AUTHOR]

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size "±5%" (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments. [ABSTRACT FROM AUTHOR]

This paper designs a bidding and supplier evaluation model focused on strategic product procurement, and develops their respective evaluation knowledge bases. The model is built using the most relevant variables cited in the reviewed procurement literature and allows to compare two evaluation methods: a factor weighting method (WM) and a fuzzy inference system (FIS). By consulting an expert panel and using a two-tuples symbolic translation system, strong fuzzy partitions for all model variables are built. The method, based on central symmetry, permits to obtain the fuzzy label borders from their cores, which have been previously agreed among experts. The system also allows to agree the fuzzy rules to embed in the FIS. The results show the FIS method's superiority as it allows to better manage the non-linear behavior and the uncertainty inherent to the supplier evaluation process. [ABSTRACT FROM AUTHOR]

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However,when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods [ABSTRACT FROM AUTHOR]

Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%~94.0% accuracy, outperforming other methods by 26.9%~38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types. [ABSTRACT FROM AUTHOR]

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. [ABSTRACT FROM AUTHOR]

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset. [ABSTRACT FROM AUTHOR]

This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increase the system's reliability based on the results achieved. [ABSTRACT FROM AUTHOR]