Neuro-fuzzy Network Research Articles

Skeleton-based Gait Recognition Based on Deep Neuro-Fuzzy Network

Skeleton-based Gait Recognition Based on Deep Neuro-Fuzzy Network

Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images

This paper introduces a new Neutrosophic Adaptive Neuro-Fuzzy Inference System paired with Explainable Artificial Intelligence to classify oral cancer from clinical photos. The ANFIS model’s interpretability and accuracy have been enhanced in resolving challenging medical images by deploying Neutrosophic logic on a 1000-image dataset to solve the word indeterminacy. A combination of Neutrosophic sets addresses ambiguity, enabling an adaptive neuro-fuzzy network to learn from data to accurately classify oral cancer. This exhibits the benefits of fuzzy logic and neural networks in action. The parameters of this model have been changed meticulously to increase sensitivity, specificity, and accuracy toward diagnostic readiness. These results reflect a substantive enhancement in the model’s ability to distinguish between benign and malignant lesions by delivering accurate and understandable diagnostic decisions existence for clinical adoption. AI medical diagnostic confidence increases the understanding of how the model makes decisions. The ideal objective is to develop a strong, dependable, and easy-to-understand tool to diagnose cancer early. The experimentation on this model can be improved as it may lead to real-time testing, more data for the testing dataset, and using how many types of cancer the model can be applied.

Optimizing Deep Neuro-fuzzy Network for ECG Medical Big Data through Integration of Multiscale Features

Optimizing Deep Neuro-fuzzy Network for ECG Medical Big Data through Integration of Multiscale Features

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture.

In the pandemic of COVID-19 patients approach to the hospital for prescription, yet due to extreme line up the patient gets treatment after waiting for more than one hour. Generally, wearable devices directly measure the preliminary data of the patient stored in capturing mode. In order to store the data, the hospitals require large storage devices that make the progression of data more complex. To bridge this gap, a potent scheme is established for COVID-19 prediction based fog-cloud named Caviar Squirrel Jellyfish Search Optimization (CSJSO). Here, CSJSO is the amalgamation of CAViar Squirrel Search Algorithm (CSSA) and Jellyfish Search Optimization (JSO), where CSSA is blended by the Conditional Autoregressive Value-at-Risk (CAViar) and Squirrel Search Algorithm (SSA). This architecture comprises the healthcare IoT sensor layer, fog layer and cloud layer. In the healthcare IoT sensor layer, the routing process with the collection of patient health condition data is carried out. On the other hand, in the fog layer COVID-19 detection is performed by employing a Deep Neuro Fuzzy Network (DNFN) trained by the proposed Remora Namib Beetle JSO (RNBJSO). Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). Finally, in the cloud layer, the detection of COVID-19 employing Deep Long Short Term Memory (Deep LSTM) trained utilizing proposed CSJSO is performed. The evaluation measures utilized for CSJSO_Deep LSTM in database-1, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) observed 0.062 and 0.252 in confirmed cases. The measures employed in database-2 are accuracy, sensitivity and specificity achieved 0.925, 0.928 and 0.925 in K-set.

VENTILATION CONTROL OF THE NEW SAFE CONFINEMENT OF THE CHORNOBYL NUCLEAR POWER PLANT BASED ON NEURO-FUZZY NETWORKS

The accident at the Chornobyl Nuclear Power Plant (ChNPP) in Ukraine in 1986 became one of the largest technological disasters in human history. During the accident cleanup, a special protective structure called the Shelter Object was built to isolate the destroyed reactor from the environment. However, the planned operational lifespan of the Shelter Object was only 30 years. Therefore, with the assistance of the international community, a new protective structure called the New Safe Confinement (NSC) was constructed and put into operation in 2019. The NSC is a large and complex system that relies on a significant number of various tools and subsystems to function. Due to temperature fluctuations and the influence of wind, hydraulic processes occur within the NSC, which can lead to the release of radioactive aerosols into the environment. The personnel of the NSC prevents these leaks, including through ventilation management. Considering the long planned operational term of the NSC, the development and improvement of information technologies for its process automation is a relevant task. The purpose of this paper is to develop a method for managing the ventilation system of the NSC based on neuro-fuzzy networks. An investigation of the current state of ventilation control in the NSC has been conducted, and automation tools for the process have been proposed. Using an adaptive neuro-fuzzy inference system (ANFIS) and statistical data on the NSC's operation, neuro-fuzzy models have been formed, which allows to calculate the expenses of the ventilation system using the Takagi-Sugeno method. The verification of the proposed approaches on a test data sample demonstrated sufficiently high accuracy of the calculations, confirming the potential practical utility in decision-making regarding NSC’s ventilation management. The results of this paper can be useful in the development of digital twins of the NSC for process management and personnel training.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India.

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75h and 3.0g/L and 3.5g/L, respectively, high CODRE (93%) and lipid concentration (2.8g/L) could be obtained consistently.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging

In the food industry, quality and safety issues are associated with consumers’ health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels’ intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products.

Neuro-fuzzy system of continuous control of density asphalt concrete mixtures

Improving the quality and increasing the service life of asphalt concrete pavements of roads is a national economic problem, which is solved through measures related to improving the regulatory framework, improving the properties of road materials, automating its control and management of the compaction process. Existing automated systems for monitoring and controlling the density of road rollers are based on artificial intelligence methods. A feature of the construction of the upper layers of asphalt concrete road surfaces in the Russian Federation is a significant impact on their quality of the results of the work of asphalt pavers, which provide several technological operations - acceptance, laying and compaction of asphalt concrete mixtures. The use of an automatic density control system for asphalt concrete mixtures in the process of laying them will eliminate many defects in road surfaces during their operation. The aim of the work is to build a system of continuous density control in the process of laying and compacting asphalt concrete mixtures by pavers based on artificial intelligence methods. The article presents the results of the development of a new system for intelligent control of the density of the asphalt concrete mixture by pavers. It is proposed to use the structure of a neuro-fuzzy network of the ANFIS type. Training of a neuro-fuzzy system of the ANFIS type was performed on the basis of a combination of methods of least squares and a decreasing gradient on an array of variables obtained on the basis of the results of experimental studies performed by VNIIStroydormash, SoyuzDorNII, MADI. Automation of density control is aimed at improving the quality of asphalt concrete pavements of roads.

Grape leaf disease prediction using Sine Cosine Butterfly Optimization-based deep neuro fuzzy network

Grape leaf disease prediction using Sine Cosine Butterfly Optimization-based deep neuro fuzzy network

Robust MPC-RG for an autonomous racing vehicle considering obstacles and the battery state of charge

The design of a controller able to deal with uncertainties and physical constraints plays an essential role in fast and complex systems. Then, a reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is proposed. The MPC-RG guarantees constraint satisfaction and recursive feasibility online while including obstacle avoidance capability and energy-aware management by solving a multi-objective optimization problem. In particular, a trade-off between maximizing the longitudinal velocity and the state of charge of the vehicle’s battery, as well as minimizing the variation of control actions is adopted. Moreover, the proposed MPC-RG is combined with a state-feedback linear quadratic regulator (LQR) and a Kalman filter (KF) to compensate for modeling errors and exogenous disturbances, as well as to estimate the unmeasured lateral velocity. In fact, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference network is used. The performance of the developed approach is assessed in simulations using a well-known case study based on a 1/10 scale RC electric car.

Iterative Neuro-Fuzzy Hammerstein Model Based Model Predictive Control for Wind Turbines

Strong nonlinearity and high volatility are the main features of wind turbine systems. Under the conditions of random wind speed changes, how to quickly control the wind turbine output power within the rated range is a major challenge for wind power system control. This paper proposed an iterative neuro-fuzzy Hammerstein model based model predictive control for wind turbines. Initially, an iterative neuro-fuzzy Hammerstein model is used to characterize the feature of wind turbines. The nonlinear static component's aerodynamic property is estimated using neuro-fuzzy networks, and the linear dynamic component is identified using the AutoRegressive with Exogenous Input (ARX) model. Following this, a generalized wind turbine controlled object with linear properties is constructed by computing the inverse of the nonlinear part, converting the wind turbine's nonlinear control challenge into a linear model control problem. This is done by the unique structure of the Hammerstein model, which allows the linear and nonlinear parts to be separated. Furthermore, a globally convergent parameter learning method is proposed and applied to identify the nonlinear parameters of the Hammerstein model. Eventually, the implementation of the Hammerstein-MPC is compared with the MPC, fuzzy MPC, and PI controllers by FAST simulation. These results demonstrate the superiority of the control effect based on the generalized wind turbine system.

Taylor-Gorilla troops optimized deep learning network for surface roughness estimation

ABSTRACT In order to guarantee the desired quality of machined products, a reliable surface roughness assessment is essential. Using a surface profile metre with a contact stylus, which can produce accurate measurements of surface profiles, is the most popular technique for determining the surface roughness of machined items. One of the limitations of this technique is the work piece surface degradation brought on by mechanical contact between the stylus and the surface. Hence, in this paper, a roughness assessment technique based on the suggested Taylor-Gorilla troops optimizer-based Deep Neuro-Fuzzy Network (Taylor-GTO based DNFN) is proposed for estimating the surface roughness. Pre-processing, data augmentation, feature extraction, feature fusion, and roughness estimation are the procedures that the suggested technique uses to complete the roughness estimate procedure. Roughness estimation is performed using DNFN that has been trained using Taylor-GTO, which was created by combining the Taylor series with the Gorilla troop’s optimizer. The created Taylor-GTO based DNFN model has minimum Mean Absolute Error, Mean Square Error, and RMSE of 0.403, 0.416, and 1.149, respectively.

Spoken emotion recognition through human-computer interaction using a novel deep learning technology

The paradigm of textual or display-based control in human-computer interaction (HCI) has changed in favor of more understandable control methods, such as gesture, voice, and imitation. Speech in particular contains a large quantity of information, revealing the speaker's inner state as well as his or her goal and intention. The speaker's request can be understood through language analysis, but additional speech features show the speaker's mood, purpose, and intention. As a consequence, in modern HCI systems, emotion identification from speech has become crucial. Additionally, it is challenging to aggregate the results of the many professionals engaged in emotion identification. There have been several methods for analyzing sound in the past. However, it was impossible to analyses people's emotions during a live speech. Studies on real-time data are now more prominent than ever because of the advancement of artificial intelligence and the great performance of deep learning techniques. This research uses a cutting-edge deep-learning technique to identify emotions in human speech. The research made use of the open-source Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. More than 2000 fragments of data were captured by 24 performers as speeches and songs for the RAVDESS dataset. The actors' responses to eight distinct moods were recorded. It was designed to find various emotion classifications. In this study, a novel neuro-fuzzy swallow swarm-optimized deep convolutional neural networks (NFSO-DCNN) approach for classification was suggested. The performance of the suggested model was compared to that of similar research, and the outcomes were assessed. Employing the suggested example on the RAVDESS dataset, an overall accuracy of 98.5% was attained for categorizing emotions

Reflectance material classification using optimized deep learning and change detection of LANDSAT surface reflectance images

The time-series changes that are affected by a specific alteration cannot be accurately found using change detection techniques. However, there are not many techniques available for identifying seasonal changes. This study develops a new deep model for identifying reflectance materials depending on optimization. At time instances “t” and “t-1” the images are accumulated in this instance. The Type 2 Fuzzy and Cuckoo Search-Based Filter (T2FCS) is used for the pre-processing of two images. Then, Fuzzy Local Information C-Means (FLICM) is used to segment both images. On the image accumulated at time “t” the feature extraction is performed to extract statistical features like mean, variance, kurtosis, energy, skewness, and standard deviation as well as imperative features like Local Optimal Oriented Pattern (LOOP), Local Ternary Pattern (LTP), Local Gabor XOR Pattern (LGXP), and others. The reflectance material is then categorized using a Deep Neuro-Fuzzy Network (DNFN) with segments created from images collected at time instance “t-1” and features collected from “t”. The proposed Jaya Student psychology-based optimization (JSPO), which was created by fusing the Jaya algorithm and the Student psychology-based optimization method(SPBO), is used here to train the DNFN. The segmented output that was collected at times “t” and “t-1” is then used to do the change detection. With the highest accuracy (94.1%) and maximum sensitivity (93.8%), the proposed JSPO-based DNFN demonstrated improved performance. For applications involving optical imagery, the Landsat program is crucial.

Loan eligibility prediction using adaptive hybrid optimization driven-deep neuro fuzzy network

Loan eligibility prediction using adaptive hybrid optimization driven-deep neuro fuzzy network

Improvement of the quality management system of industrial enterprises based on the use of corporate knowledge

This article analyzes the procedures for improving quality management using the example of solving problems of compliance with product quality requirements at industrial enterprises. The quality of the products largely depends on the use of modern methods and tools of quality management, as well as on improving the competence of personnel in the field of quality systems. In this regard, it is proposed to apply the corporate knowledge of the enterprise, including models and algorithms of artificial intelligence. Corporate knowledge is formed on the basis of the formation and processing of documentation, interviewing qualified specialists and intellectual data analysis on the implementation of business processes. During the research, an ontology of quality management was developed, decision-making rules were created and the results of improving the quality management system using a neuro-fuzzy network were predicted. The use of these artificial intelligence tools will make it possible to form a unified terminology to ensure an unambiguous perception of information by all participants in the process and the use of a knowledge base to support decision-making. It is proposed to use quality management tools to improve the efficiency of decisions made by intelligent tools, i.e. production rules and neural networks.

Application of neuro-fuzzy systems technology for control the density of asphalt concrete mixtures in the paving process

The increase in the number of cars and trucks, and the intensity of their use, directly affects the asphalt-concrete road surfaces of roads. This is manifested in the reduction of overhaul terms of operation and large financial costs. Improving the quality and increasing the service life of asphalt concrete pavements of roads is a national economic problem. This problem is solved by improving the regulatory framework, improving the properties of road materials, optimizing and automating technological processes in road construction. Road roller density monitoring and control systems are based on Intelligent Compaction and Continuous Compaction Control technologies. In the Russian Federation, the quality of road surface construction largely depends on the results of the work of pavers, who ensure the acceptance, laying and compaction of mixtures. Many defects in the operation of road surfaces are eliminated by high-quality compaction. Automatic control and control of the sealing process for asphalt pavers have not been developed.The aim of the study is to build a density management system for asphalt pavers based on an intelligent automatic control system with feedback. Real-time prediction of the volume density of the layer, effective manual (operator) and automatic density control is organized to achieve the required quality indicators.The results of the development of a new system for intelligent control of the density asphalt concrete mixtures by pavers is considered. The automatic control system with feedback includes a continuous density control system designed to calculate the seal quality index based on the implementation of the neural network structure algorithm in real time, as well as a neuro-fuzzy PID regulator. The study considers a system with models of control objects of high order – the fourth and sixth orders. The structure of a neuro-fuzzy network of the ANFIS type is proposed. The generation of a fuzzy output system is performed on the basis of the lattice partition method. ANFIS training is performed by a hybrid method on an array of variables obtained as a result of modeling an automatic control system with an analog PID controller. Possible modes of use an intelligent compaction control system: continuous automatic control with manual control of the operating modes of compaction; automatic density control. Automatic measure of density and control compaction modes is aimed at improving the quality of asphalt concrete pavements of roads and improving the quality of asphalt pavements efficiency of technological processes road construction.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Data-Driven Adaptive Modelling and Control for a Class of Discrete-Time Robotic Systems Based on a Generalized Jacobian Matrix Initialization

Data technology advances have increased in recent years, especially for robotic systems, in order to apply data-driven modelling and control computations by only considering the input and output signals’ relationship. For a data-driven modelling and control approach, the system is considered unknown. Thus, the initialization values of the system play an important role to obtain a suitable estimation. This paper presents a methodology to initialize a data-driven model using the pseudo-Jacobian matrix algorithm to estimate the model of a mobile manipulator robot. Once the model is obtained, a control law is proposed for the robot end-effector position tasks. To this end, a novel neuro-fuzzy network is proposed as a control law, which only needs to update one parameter to minimize the control error and avoids the chattering phenomenon. In addition, a general stability analysis guarantees the convergence of the estimation and control errors and the tuning of the closed-loop control design parameters. The simulations results validate the performance of the data-driven model and control.