Neuro-fuzzy Network Research Articles

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

SummaryThe earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak‐based UNet (BLSNet), multilayer maxout, resistance spot welding‐based deep recurrent neural network (RSW‐based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

Underwater Target Tracking in Radar Images Using Exponential Competitive Swarm-based Deep Neuro Fuzzy Network

The tracking of underwater targets represents a basic unit in underwater surveillance for offering effective search and rescue operations. However, the tracking of the target is a major constraint in finding the underwater target. This paper devises a model for underwater target tracking considering radar signals. Radar signals are subjected to the image reconstruction process to make them suitable for further processing. The gridding is the next step, which is utilized for generating the grids to focus on each region in a precise manner. The features like Ridgelet transform and Local Gabor XOR Pattern (LGXP) are produced. Data augmentation is the next process, which helps to reduce the size of data by performing resizing. At last, the tracking of targets is performed from each grid with a Deep neuro fuzzy network (DNFN). Here, DNFN training is performed with Exponential-Competitive Swarm Optimization (E-CSO), which is derived by integrating Exponential Weighted Moving Average (EWMA) and Competitive Swarm Optimization (CSO). The proposed E-CSO-based DNFN offered high efficiency with a maximal detection rate of 98.8%, minimal Mean square error of 0.012, and Mean Absolute Percentage Error (MAPE) of 17.883.

Hybrid optimization based deep neuro fuzzy network for skin cancer detection

SummaryMelanoma is the most prevalent type of skin cancer, affecting skin surface cells. The risk features of the disease are minimized by finding the disease at time or in the initial stage. Even through, various skin cancer detection methods are developed, detecting the disease using discriminative features still results a challenging task. An efficient detection strategy based on the proposed Lion Cat Swarm Optimization‐based Deep Neuro Fuzzy Network (LCSO‐based DNFN) is created to identify skin cancer in its early stages. The LCSO is the integration of the lion optimization algorithm (LOA) and cat swarm optimization (CSO) algorithm. To make treatment measure at right time increases the probability of survival and so detecting the disease using deep learning classifier is performed with hybrid optimization algorithm. The segmentation result is effective through the fusion model and it ensures to achieve accurate detection process. Rather than extracting the features, the proposed method generates optimal result through data augmentation. The performance of the proposed LCSO‐based DNFN is determined using skin disease dataset, and the introduced approach obtained outstanding performance with the measures of accuracy, sensitivity, and specificity with the values of 93.10%, 92.95%, and 92.87%, respectively.

ChSO-DNFNet: Spam detection in Twitter using feature fusion and optimized Deep Neuro Fuzzy Network

The usage of social networking platforms for interaction and meeting has grown significantly. However, as social networks continue to expand, Twitter has emerged as a vital social media for real life and has become a major source of spam. To address the aforementioned problems, spam identification on social media becomes an increasingly crucial task. The features that are present in the high dimension data of social networks cannot be used effectively by the existing approaches. In order to filter the spam information in social media, a Chimp Sailfish Optimization-based Deep Neuro Fuzzy Network (ChSO-based DNFN) is proposed. The proposed method effectively performs well under high dimensional data in real platform environment using deep learning classifier. It is more robust and generates optimal result and also reduces the computational complexity problems. Additionally, the proposed approach demonstrated improved performance in terms of metrics like precision, recall, and F-measure, which were measured using a 5k continuous dataset and yielded values of 0.894, 0.903, and 0.898, respectively.

FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing

Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack.

An Efficient and Secure Authentication Protocol with Deep Learning Based Key Generation toward Securing Healthcare Data in IoT

Due to the emergence of technologies in the growing world, the IoT is termed as a novel concept. The combination of internet with smart devices provides convenient user-related services. The attack toward confidential data may leak data and can cause serious security issues. Meanwhile, the classical security techniques cannot be utilized in a direct manner. Thus, novel techniques are needed for safeguarding the IoT from security issues. This paper designs secure authentication model for IoT in the healthcare. Several entities, such as gateway node, sensor node and medical professional are included for performing the secure authentication. The proposed protocol is devised by providing a mathematical model that makes usage of hashing function, encryption algorithm, XOR, Chebyshev polynomial, passwords, secret keys, and other security operations. Here, Secret key is generated with the deep neuro fuzzy network (DNFN) using Extreme Learning Machine (ELM) based sub-key generation. The proposed technique outperformed with less computation time of 33,484,154,235 ns, smallest memory of 51.750 MB, and highest detection rate of 94.9%.

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy person's sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. With constant rise in the COVID-19 cases, there has been a constant rise in the need of efficient and safe ways to detect an infected individual. With the cases multiplying constantly, the current detecting devices like RT-PCR and fast testing kits have become short in supply. An effectual Covid-19 detection model using devised hybrid Honey Badger Optimization-based Deep Neuro Fuzzy Network (HBO-DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed hybrid HBO algorithm. Accordingly, the developed Hybrid HBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed Hybrid HBO-based DNFN is outpaced than other existing approaches in terms of testing accuracy, sensitivity and specificity of “0.9176, 0.9218 and 0. 9219”. All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. When k-fold value is 9, sensitivity of existing techniques and developed JHBO-based DNFN is 0.8982, 0.8816, 0.8938, and 0.9207. The sensitivity of developed approach is improved by means of gaussian filtering model. The specificity of DCNN is 0.9125, BI-AT-GRU is 0.8926, and XGBoost is 0.9014, while developed JHBO-based DNFN is 0.9219 in k-fold value 9.

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review.

The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

Hybrid optimization enabled deep learning technique for multi-level intrusion detection

The intrusion detection system identifies the attack through the reputation and progression of network methodology and the Internet. Moreover, conventional intrusion recognition techniques usually utilize mining association rules for identifying intrusion behaviors. However, the intrusion detection model failed to extract typical information of user behaviors completely and experienced several issues, including poor generalization capability, high False Alarm Rate (FAR), and poor timeliness. This paper uses a hybrid optimization-based Deep learning technique for the multi-level intrusion detection process. First, the fisher score scheme is applied to extract the important features. Then, in the data augmentation the data size is increased. In this model, Rider Optimization Algorithm-Based Neural Network (RideNN) is employed for first level detection, where the data is categorized as normal and attacker. Besides, the RideNN classifier is trained by devised Rider Social Optimization Algorithm (RideSOA). Additionally, the Deep Neuro Fuzzy network (DNFN) is utilized for the second level classification process in which attack types are categorized. Besides, the DNFN classifier is trained through devised Social Squirrel Search Algorithm (SSSA). The introduced intrusion detection algorithm outperformed with maximum precision of 0.9254, recall of 0.8362, and F-measure 0.8718.

Intelligent decision support system for cardiovascular risk prediction using hybrid loss deep joint segmentation and optimized deep learning

Cardiovascular disease (CVD) represents an emerging death reason worldwide. CVD is based on the capability to discover the high-risk individuals before designing overt events. An effective technique for CVD risk prediction is developed using retinal fundus images. Initially, the retinal fundus images are subjected to pre-processing using grayscale conversion. The optic disc is detected with binarization and circle fixing. Then, the blood vessel segmentation uses deep joint segmentation, wherein dice coefficient and binary cross-entropy are integrated. After that, the feature extraction is done for mining convenient features that include several statistical features. Meanwhile, features like Local Directional Texture Pattern (LDTP) and Local Gabor Binary Pattern (LGBP) are mined from the inputted image. Then, the cardiovascular risk prediction is made by a Deep neuro-fuzzy network (DNFN), such that the risks are classified into normal and hypertensive. Finally, the DNFN is trained using the developed Fractional Calculus-Horse Herd Optimization Algorithm (FCHOA), which is devised by combining Fractional Calculus (FC) and the Horse Herd Optimization algorithm (HOA). The proposed FCHOA-based DNFN offered enhanced efficiency with the highest accuracy, sensitivity and specificity of 91.6, 92.3 and 91.9%.

Compound Facial Expression Recognition and Pain Intensity Measurement Using Optimized Deep Neuro Fuzzy Network

The automatic measurement of pain intensity from facial expressions, mainly from face images describes the patient’s health. Hence, a robust technique, named Water Cycle Henry Gas Solubility Optimization-based Deep Neuro Fuzzy Network (WCHGSO-DNFN) is designed for compound FER and pain intensity measurement. However, the proposed WCHGSO is the incorporation of Water Cycle Algorithm (WCA) with Henry Gas Solubility Optimization (HGSO). Here, Compound Facial Expressions of Emotion Database (dataset-2) is made to perform compound FER, whereas the input image from UNBC pain intensity dataset (dataset-1) is utilized to measure the pain intensity, and the processes are performed separately. The developed technique achieved better performance with respect to testing accuracy, sensitivity, and specificity with the highest values of 0.814, 0.819, and 0.806 using dataset-1, whereas maximum values of 0.815, 0.758 and 0.848 is achieved using dataset-2.

Intrusion detection using optimized ensemble classification in fog computing paradigm

Fog computing is a framework, which expands services of the cloud to network for addressing inherent issues of the cloud. Intrusion Detection Systems (IDSs) represent a major unit of the security model for fog networks for facilitating service quality. This paper devises a hybrid optimization-driven ensemble classifier in a fog computing platform. In fog computing, the complete processing is done utilizing trio layers that include the cloud layer, end point layer, and fog layer. In the cloud layer, three procedures, like data transformation, selection of features, and classification processes were carried out. A data transformation is conducted with log transformation. A feature is chosen using Kolmogorov–Smirnov correlation-based filter. Then, the classification is done utilizing ensemble classifiers, named RideNN, Deep Neuro-Fuzzy Network (DNFN), and Shepard convolutional neural network (ShCNN). The tuning of the ensemble classifier is conducted utilizing the developed Rider Sea Lion Optimization (RSLO) algorithm. RSLO algorithm is conceived by amalgamating the Rider optimization algorithm (ROA) and Sea Lion Optimization Algorithm (SLnOA). At an end point layer, a physical process is done. In the fog layer, intrusion detection is done based on a trained ensemble classifier. The proposed RSLO-based ensemble approach provides enhanced performance with higher precision of 88%, recall of 88.4%, F-measure of 88.2%, and accuracy of 97.2%.

Classification of pathological disorders using optimization enabled deep neuro fuzzy network

The discovery of voice pathology is an emerging domain in biomedical engineering. The assessment helps to offer information for effective diagnosis. An optimization-enabled deep model for pathological disorder classification is devised in this paper. The speech signal is employed as the input of the pre-processor which subsequently helps to eliminate noise considering the Hanning window. The initial improvement is done by exploiting multi-band spectral subtraction for generating frames. The enhancement of speech is performed using Deep Residual Network (DRN) classifier. The training of DRN is performed by exploiting adopted Feedback Artificial Tree Firefly algorithm (FATFA) that is attained by combining Feedback Artificial Tree Algorithm (FAT), and Firefly algorithm (FA). The features, like Amplitude modulation spectrogram (AMS), and frequency-domain features namely tonal power ratio, pitch chroma, spectral spread, spectral flux, as well as spectral autocorrelation is mined from the improved speech. The classification is done with Deep Neuro-Fuzzy Network (DNFN), which is tuned with the devised Verse FATFA. The proposed Verse FATFA is conceived by merging FATFA and Multiverse Optimizer (MVO). Therefore, the devised Verse FATFA-based DNFN categories voice disorders into Laryngitis, Dysphonie, spasmodischedysphonie, Leukoplakie, Cyste, Dish-Syndrom, and Monochorditis. The proposed Verse FATFA-based DNFN provides enhanced performance with maximum values of 94.93% accuracy, 95.94% sensitivity with 91.93% specificity.

Comparative Analysis of Conventional, Artificial Intelligence, and Hybrid-Based MPPT Technique for 852.6-Watt PV System

In this article, Matlab & Simulation software is used for analysis and comparison of 8(Eight) different MPPT. Different MPPT techniques that have been considered in this article are PWM-based, Perturb and Observation (P&O), Incremental Conductance (InC), and Modified InC (MIC) that comes under the Conventional Method. In the Artificial Intelligence, Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN) is chosen and in Hybrid method Neuro-Fuzzy Network (NFN) and Adaptive Neural Fuzzy Inference System (ANFIS) has been considered. PV module of 852.2 Watt is designed with the Boost Converter which can boost the voltage up to 185 Volt for all MPPT. A set of data has been taken for FLC, ANN, NFN, and ANFIS. After implementation, the result has been analyzed for standard test conditions and for the different environmental conditions. In this article, both irradiation and the temperature have been varied together for all MPPT rest are kept constant.

Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process

Additive manufacturing, also known as three-dimensional printing, is a computer-controlled advanced manufacturing process that produces three-dimensional items by depositing materials directly from a computer-aided design model, usually in layers. Due to its capacity to manufacture complicated objects utilizing a wide range of materials with outstanding mechanical qualities, fused deposition modeling is one of the most commonly used additive manufacturing technologies. For printing high-quality components with appropriate mechanical qualities, such as tensile strength and flexural strength, the selection of adequate processing parameters is critical. Experimentally, the influence of process parameters such as the raster angle, printing orientation, air gap, raster width, and layer height on the tensile strength of fused deposition modeling printed items was examined in this work. Through analysis of variance, the impact of each parameter was measured and rated. The system’s response was predicted using an adaptive neuro-fuzzy technique and an artificial neural network. In Minitab software, the Box-Behnken response surface experimental design was used to generate 46 experimental trials, which were then printed using acrylonitrile butadiene styrene polymer materials on a three-dimensional forge dreamer II fused deposition modelling printing machine. The results revealed that the raster angle, air gap, and raster width had significant impacts on the tensile strength. The adaptive neuro-fuzzy approach and artificial neural network predicted tensile strength accurately with an average percentage error of 0.0163 percent and 1.6437 percent, respectively. According to the findings, the model and experimental data are in good agreement.

Development of methodological principles of routing in networks of special communication in the conditions of fire damage and radio electronic flow

The object of research is a system of special communication. Decision making support systems (DMSS) are actively used in all spheres of human life. They are especially common in the processing of large data sets, forecasting processes, providing information support in the decision-making process by decision-makers. Systems of analysis of information transmission in special purpose radio communication systems are no exception. However, there are a number of problems in the transmission of information, namely: the transmission of information takes place in a complex electronic environment against the background of intentional and natural interference; elements of the radio communication system are the objects of primary fire damage due to high radio visibility for radio intelligence. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment, artificial neural networks and the ant algorithm. Their advantage is also the ability to work in real time and quickly adapt to specific situations. Therefore, in this paper the methodological principles of routing in special communication networks in the conditions of fire damage and electronic suppression are developed. Improving the efficiency of information processing (reducing error) evaluation is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. Efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; absence of accumulation of an error of training of artificial neural networks as a result of processing of the information arriving on an input of artificial neural networks. The approbation of the use of the offered technique on the example of the estimation of information transfer in the conditions of influence of destabilizing factors is carried out. This example showed an increase in the efficiency of evaluation at the level of 15–25 % on the efficiency of information processing.

Jaya Honey Badger optimization-based deep neuro-fuzzy network structure for detection of (SARS-CoV) Covid-19 disease by using respiratory sound signals

PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

Position control of a wheel-based miniature magnetic robot using neuro-fuzzy network

AbstractUntethered small-scale robots can accomplish tasks which are not feasible by conventional macro robots. In the current research, we have designed and fabricated a miniature magnetic robot actuated by an external magnetic field. The proposed robot has two coaxial wheels and one magnetic dipole which is capable of rolling and moving on the surface by variation in the direction of magnetic field. To generate the desired magnetic field, a Helmholtz electromagnetic coil is manufactured. To steer the robot to the desired position, at first the robot dynamics is investigated, and subsequently a controller based on a neuro-fuzzy network has been designed. Finally, the proposed controller is implemented experimentally and the performance of the control system is demonstrated.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network

Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network