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The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. [ABSTRACT FROM AUTHOR]

In the shape analysis community, decomposing a 3D shape intomeaningful parts has become a topic of interest. 3D model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction, shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which are usually focused on a particular attribute of 3Dobjects and so are difficult to generalize. In this paper, we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes. The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views. Contrasting viewpoints, on the other hand, might not have been associated, and a 3D region could correlate into totally distinct outcomes depending on the viewpoint. To address this, we ran each view through (shared weights) CNN and Bolster block in order to create a probability boundary map. The Bolster block simulates the area relationships between different views, which helps to improve and refine the data. In stage two, the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view. Finally, a layer that is fully connected is used to return coherent edges, which are then back project to 3D objects to produce the final segmentation. Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks. [ABSTRACT FROM AUTHOR]

The development of Grey Wolf Optimisation (GWO) Algorithm was motivated by the biological behaviours of swarm of wolves hunting for prey. This paper presents recent progress on Grey Wolf Optimization (GWO) algorithm, its variants and their applications, issues, and likely prospects. The review revealed that opportunities still exists for development of more robust and stable variants of GWO that will overcome the shortcomings of existing variants. This review has the potential to stimulate researchers in the area of nature-inspired algorithms to further advance the effectiveness of the GWO and its ability to solve problems. Such problems can be real-life, complicated and nonlinear optimization problems in different domain of human endeavour. Suggestions for new research directions that have the capacity to increase the performance of GWO are presented. It is expected that this paper will serve as reading material for beginners whereas experienced researchers can also use it as an article yardstick for further development of GWO algorithms. [ABSTRACT FROM AUTHOR]