Resource Search Method of Mobile Intelligent Education System Based on Distributed Hash Table

Mobile Networks and Applications - In order to realize the best matching search of mobile intelligent education system resources, a resource search method of mobile intelligent education system...     

Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI

Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4-class MI-based BCIs. In this study, 4-class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two-phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair-wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non-stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two-stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.     

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naïve Bayes, are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model’s classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.     

An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier

The Journal of Supercomputing - Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and...     

Catalysis of neural activation functions: Adaptive feed-forward training for big data applications

Deep Learning in the field of Big Data has become essential for the analysis and perception of trends. Activation functions play a crucial role in the outcome of these deep learning frameworks. The existing activation functions are hugely focused on data translation from one neural layer to another. Although they have been proven useful and have given consistent results, they are static and mostly non-parametric. In this paper, we propose a new function for modified training of neural networks that is more flexible and adaptable to the data. The proposed catalysis function works over Rectified Linear Unit (ReLU), sigmoid, tanh and all other activation functions to provide adaptive feed-forward training. The function uses vector components of the activation function to provide variational flow of input. The performance of this algorithm is tested on Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR10) datasets against the conventional activation functions. Visual Geometry Group (VGG) blocks and Residual Neural Network (ResNet) architectures are used for experimentation. The proposed function has shown significant improvements in comparison to the traditional functions with a 75 ± 2.5% acuuracy across activation functions. The adaptive nature of training has drastically decreased the probability of under-fitting. The parameterization has helped increase the data learning capacity of models. On performing sensitivity analysis, the catalysis activation show slight or no changes on varying initialization parameters.