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Cervical cancer (CC) is affecting women predominantly, and early diagnosis could cure this cancer. This study aims to design and develop an effective deep learning-based classification model to detect early CC stages using clinical data. The proposed method is a combination of an unsupervised deep learning and a supervised neural network, i.e. sparse stacked autoencoder (SSAE) and fuzzy adaptive resonance theory MAP (FAM), respectively, and is denoted as SSAE-FAM. Specifically, SSAE is applied to tackle the data sparsity problem. It extracts the representative features from a data set through feature transformation. The transformed features are then classified by FAM. In this study, a CC data set obtained from the University of California Irvine (UCI) machine learning repository is utilised for evaluation. Owing to missing data in the original CC data set, two data sets are generated from the original CC data samples using two data preprocessing techniques. Both generated CC data sets with four target classes (i.e. Schiller, Cytology, Biopsy, and Hinselmann) are evaluated as four independent binary-class problems. We improve the classification performance of FAM by mitigating the data sparsity problem. Based on a series of experimental studies, SSAE-FAM outperforms other state-of-art methods by achieving 99.47%, 99.34%, 99.48%, and 99.81% mean accuracy rates, respectively, with the first CC data set, and 99.74%, 99.86%, 99.77%, and 99.80% mean accuracy rates, respectively, with the second CC data set. The results positively indicate the usefulness of SSAE-FAM for early CC diagnosis. [ABSTRACT FROM AUTHOR]

Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods. [ABSTRACT FROM AUTHOR]

Malicious software (Malware) is a key threat to security of digital networks and systems. While traditional machine learning methods have been widely used for malware detection, deep learning (DL) has recently emerged as a promising methodology to detect and classify different malware variants. As the DL training algorithm is oriented on gradient descent optimization, i.e. the Backpropagation (BP) algorithm, several shortcomings are encountered, e.g., local suboptimal solutions and high computational cost. We develop a new DL-based framework for malware detection. In this regard, we introduce a hybrid DL optimization method by exploiting the integration of BP and Particle Swarm Optimization (PSO) algorithms to provide optimal solutions for malware detection. Many hybrid DL optimization methods in the literature are not implemented under a parallel computing setup. In this paper, we develop an efficient distributed parallel computing framework for implementing the proposed DL-based method to improve efficiency and scalability. The experimental results on several benchmark data sets indicate efficacy of the proposed solution in malware detection, which significantly outperforms other machine learning methods in terms of effectiveness, efficiency and scalability. • Deep learning with BP-PSO yields very high classification performances in malware detection. • PSO-BP combines the merits of both local–global optimization capabilities. • Parallel computing improves efficiency and scalability of the proposed method in malware detection. [ABSTRACT FROM AUTHOR]