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Conventional malware detection approaches have the overhead of feature extraction, the requirement of domain experts, and are time-consuming and resource-intensive. Learning-based approaches are the mainstay of malware detection as they overcome most of these challenges by significantly improving the detection effectiveness and providing a low false positive rate. The exponential growth of malware variants and first-time-appeared malware, which includes polymorphic and zero-day attacks, are some of the significant challenges to learning-based malware detectors. These challenges have catastrophic impacts on the detection effectiveness of these learning-based malware detectors. This paper proposes a novel deep learning-based framework to detect first-time-appeared malware effectively and efficiently by providing better performance than conventional malware detection approaches. First, it translates and visualises each Windows portable executable (PE) file into a coloured image to eliminate the overhead of feature extraction and the need for domain experts to analyse the features. In the subsequent step, a fine-tuned deep learning model is used to extract the deep features from the last fully connected layer. The step has reduced the cost of training required by the deep learning models if used for end-to-end classification. The third step selects the most important and influential features through a powerful feature selection algorithm. The most important features are then fed to a one-class classifier for final detection. With the one-class classifier, an enclosed boundary around the features of benign data is constructed. Anything outside the boundary is declared as an anomaly/malicious. It has enhanced the framework's ability to detect evolving, unseen, polymorphic, and zero-day attacks, as well as reducing the problem of overfitting. The detection effectiveness of the proposed framework is validated with state-of-the-art deep learning models and conventional approaches. The proposed framework has outperformed with an accuracy of 99.30% on the Malimg dataset. The Wilcoxon signed-rank test is used to validate the statistical significance of the proposed framework. It is evident from the results that the proposed framework is effective and can be used in the defence industry, resulting in more powerful and robust solutions against zero-day and polymorphic attacks. [Display omitted] • A novel approach of combining deep learning and machine learning is proposed. First, deep learning is used to extract deep features. The most influential and meticulous features are selected in the subsequent steps to train the machine learning classifier for final detection. The proposed framework eliminates the need for human efforts for reverse engineering tasks. • The proposed framework consists of four steps. In the first step, all PEs are transformed into coloured images. The second step used a deep learning model to extract the deep features. The subsequent step selects the most important features. Finally, the lightweight and most influential features are sent to the final machine learning classifier for final malware detection. • We demonstrate that the proposed framework is lightweight, resilient, efficient and cost-effective. An in-depth analysis is performed to validate the detection effectiveness and generalisation of the proposed framework on multiple datasets. Our results demonstrate that the proposed framework outperformed conventional and state-of-the-art malware detection approaches. [ABSTRACT FROM AUTHOR]

Cancer is considered to be a key cause of substantial fatality and morbidity in the world. A report from the International Agency for Research on Cancer (IARC) states that 27 million new cases of cancer are expected before 2030. 1 in 18 men and 1 in 46 women are estimated to develop lung cancer over a lifetime. This paper discusses an overview of lung cancer, along with publicly available benchmark data sets for research purposes. Recent research performed in medical image analysis of lung cancer using deep learning algorithms is compared using various technical aspects such as efficiency, advantages, and limitations. These discussed approaches provide insight into techniques that can be used to perform the detection and classification of lung cancer. Numerous techniques adapted in the acquisition of the images, extraction of relevant features, segmentation of region affected, selection of optimal features, and classification are also discussed. The paper is concluded by stating the clinical, technical challenges and prominent future directions. [ABSTRACT FROM AUTHOR]

The progress of machine learning (ML) in the past years has opened up new opportunities to the field of gas turbine (GT) modelling. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as multi-mode faults. An important tool for enabling applications are the feature selection and feature learning (FSFL) techniques. In particular, FL techniques have recently facilitated and improved the applicability of ML to GT modelling. This review paper conducts a review on 46 studies that utilised FSFL for GT modelling with ML. The purpose of this review is to investigate how FSFL techniques can help address GT modelling challenges and when researchers should deploy them. Therefore, the theories behind the techniques are illustrated in depth along with practical application examples from the analysed literature. The advantages and limitations of FSFL are discussed, the computational costs of different techniques are compared, and trends in the field are highlighted. Consequently, a novel categorisation framework for FSFL techniques and recommendations regarding when and how to implement them are provided. A new knowledge accumulation, extraction, and transfer concept is proposed to address GT modelling challenges. [ABSTRACT FROM AUTHOR]