Network-Computation in Neural Systems最新文献

We, the Editors and Publisher of Network: Computation in Neural Systems, have retracted the following article:Barve, A., & Patheja, P. S. (2023). A clustering approach for attack detection and data transmission in vehicular ad-hoc networks. Network: Computation in Neural Systems, 1-26. https://doi.org/10.1080/0954898X.2023.2279973Since publication, significant concerns have been raised about the fact that this article has substantial overlaps with the following article:Barve, A. & Patheja, P. S. (2023). A clustering approach for attack detection and data transmission in vehicular ad-hoc networks. Ad Hoc & Sensor Wireless Networks, 58. 1-2, p. 127-149.DOI: 10.32908/ahswn.v58.10375Further investigations by the Publisher revealed that these overlaps are present in all sections of the article, including the figures and tables without appropriate acknowledgement. Upon query, the authors agree that the article is a duplicate submission. As this is a serious breach of our Editorial Policies, we are retracting the article from the journal. The corresponding author listed in this publication has been informed.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'.

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.

Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential shapes, and image intensities, segmentation of the brain tumour by magnetic resonance imaging (MRI) is challenging. In this research, the deep learning (DL)-enabled brain tumour detection is developed by hybrid optimization method. The pre-processing stage used adaptive Wiener filter for minimizing the noise from input image. After that, the abnormal section of the image is segmented using U-Net. Afterwards, the data augmentation is accomplished to recover the random erasing, brightness, and translation characters. The statistical, shape, and texture features are extracted in feature extraction process. In first-level classification, the abnormal section of the image is sensed as brain tumour or not. Here, the Red Deer Tasmanian Devil Optimization (RDTDO) trained DenseNet is hired for brain tumour detection process. If tumour is identified, then second-level classification provides the brain tumour classification, where deep residual network (DRN)-enabled RDTDO is employed. Furthermore, the system performance is assessed by accuracy, true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with the maximum values of 0.947, 0.926, 0.950, 0.937, and 0.926 are attained.

The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.

This paper proposes a two phases-based training method to design the codewords to map the cluster indices of the input feature vectors to the outputs of the new perceptrons with the multi-pulse type activation functions. Our proposed method is applied to classify two types of the tachycardias. First, the total number of the new perceptrons is initialized as the dimensions of the input feature vectors. Next, a set of new perceptrons with each new perceptron having a single pulse type activation function is designed. Then, the new perceptrons with the multi-pulse type activation function are designed based on those new perceptrons with the single pulse type activation function. After that, the codewords are assigned according to the outputs of the new perceptrons with the multi-pulse type activation functions. Finally, a condition on the codewords is checked. The significance of this work is to guarantee to achieve the no classification error efficiently through using more than one new perceptron with the multi-pulse type activation if the feature space can be linearly partitioned into the multiple clusters. The computer numerical simulation results show that our proposed method outperforms the conventional perceptrons with the sign type activation function.

The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.

In this paper, we propose a Gudermannian neural network scheme to solve optimal control problems of fractional-order system with delays in state and control. The fractional derivative is described in the Caputo sense. The problem is first transformed, using a Padé approximation, to one without a time-delayed argument. We try to approximate the solution of the Hamiltonian conditions based on the Pontryagin minimum principle. For this purpose, we use trial solutions for the states, Lagrange multipliers, and control functions where these trial solutions are constructed by using two-layered perceptron. We then minimize the error function using an unconstrained optimization scheme where weight and biases associated with all neurons are unknown. Some numerical examples are given to illustrate the effectiveness of the proposed method.

The segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles. This impacts the outcome of tumour detection, causing errors. For addressing this issue, a Feature-Map based Transform Model (FMTM) is introduced to focus on heterogeneous features of input picture to map differences and intensity based on transition Fourier. Unchecked machine learning is used for reliable characteristic map recognition in this mapping process. For the determination of severity and variability, the method of identification depends on symmetry and texture. Learning instances are taught to improve precision using predefined data sets, regardless of loss of labels. The process is recurring until the maximum precision of tumour detection is achieved in low convergence. In this research, FMTM has been applied to brain tumour segmentation to automatically extract feature representations and produce accurate and steady performance because of promising performance made by powerful transition Fourier methods. The suggested model's performance is shown by the metrics processing time, precision, accuracy, and F1-Score.

Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90.