Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions.

Digital networks and systems are susceptible to malicious software (malware) attacks. Deep learning (DL) models have recently emerged as effective methods to classify and detect malware. However, DL models often relies on gradient descent optimization in learning, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from several limitations, such as high computational cost and local suboptimal solutions. On the other hand, ensemble methods overcome the shortcomings of individual models by consolidating their strengths to increase performance. In this paper, we propose an ensemble-based parallel DL classifier for malware detection. In particular, a stacked ensemble learning method is developed, which leverages five DL base models and a neural network as a meta model. The DL models are trained and optimized with a hybrid optimization method based on BP and Particle Swarm Optimization (PSO) algorithms. To improve scalability and efficiency of the ensemble method, a parallel computing framework is exploited. The proposed ensemble method is evaluated using five malware datasets (namely, Drebin, NTAM, TOP-PE, DikeDataset, and ML_Android), and high accuracy rates of 99.2%, 99.3%, 98.7%, 100%, and 100% have been achieved, respectively. Its parallel implementation also significantly enhances the computational speed by a factor up to 6.75 times. These results ascertain that the proposed ensemble method is effective, efficient, and scalable, outperforming many other compared methods in malware detection.

Attentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants' EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler's fading experiment and use an EEG device to collect data on participants' attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants' attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator.