Application of genetic optimized artificial immune system and neural networks in spam detection

Enhancement of email spam detection using improved deep learning algorithms for cyber security

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

Mechanical behaviors of tension and relaxation of tongue and soft palate: Experimental and analytical modeling

This study is to characterize mechanical properties of uniaxial tension and stress relaxation responses of muscle tissues of tongue and soft palate. Uniaxial tension test and stress relaxation test on 39 fresh tissue samples from four five-month-old boars (65 ± 15 kg) are conducted. Firstly, the rationality of the samples' dimension design and experimenal data measurement is validated by one-way ANOVA F-type test. Mechanical properties, including stress-strain relationship and stress relaxation characteristic, are then investigated in details to show the nonlinear behaviors of the tissue samples clearly. Finally, a constitutive model of representing the mechanical properties is formulated within the nonlinear integral representation framework proposed by Pinkin and Rogers, and corresponding material parameters are fitted to the experimental data based on the Levenberg-Marquardt minimization algorithm. The results of the fitting comparison prove that the formulated constitutive model can capture the observed nonlinear behaviors of the muscle tissue samples in both the axial tension and stress relaxation experiments.

Sleep scoring using artificial neural networks

Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.