Deep learning methods for RNA sequencing data have exploded in the recent years due to the advent of singlecell RNA sequencing (scRNA-seq), which enables the study of multiple cells per-patient simultaneously. However, in the case of rare cell types, data scarcity continues to exist, posing several challenges, while preventing the exploitation of deep learning models’ full predictive power. Generating realistic synthetic cells to augment the data could allow for more informative subsequent downstream analyses. Herein, we introduce Mask-cscGAN, a conditional generative adversarial network (GAN) that generates realistic synthetic cells with desired characteristics managing also to model genes’ sparsity through learning a mask of zeros. Employed for the augmentation of a glioblastoma multiforme (GBM) malignant cells dataset, Mask-cscGAN generates realistic synthetic cells of desired cancer subtypes. Generating cells of a rare cancer subtype, Mask-cscGAN improves the classification performance of the rare cancer subtype by 12.29%. MaskcscGAN is the first to generate realistic synthetic cells belonging to specified cancer subtypes, and augmentation with MaskcscGAN outperforms state-of-the-art methods in rare cancer subtype classification.

Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time to perform fine tuning of hyper-parameters for object classification. We propose a cloud based video analytics system built upon an optimally tuned deep learning model to classify objects from video streams. The tuning of the hyper-parameters including learning rate, momentum, activation function and optimization algorithm is optimized through a mathematical model for efficient analysis of video streams. The system is capable of enhancing its own training data by performing transformations including rotation, flip and skew on the input dataset making it more robust and self-adaptive. The use of in-memory distributed training mechanism rapidly incorporates large number of distinguishing features from the training dataset - enabling the system to perform object classification with least human assistance and external support. The validation of the system is performed by means of an object classification case-study using a dataset of 100GB in size comprising of 88,432 video frames on an 8 node cloud. The extensive experimentation reveals an accuracy and precision of 0.97 and 0.96 respectively after a training of 6.8 hours. The system is scalable, robust to classification errors and can be customized for any real-life situation.

The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high through-put when tested on a number of video streams using cloud infrastructure.

Online social networks (OSNs) have attracted millions of users worldwide over the last decade. In response to a series of urgent issues faced by existing OSNs, such as information overload, single-point failure, and the privacy issue, this paper introduces a self-organized decentralized OSN (SDOSN) over a social overlay resembling real-life social graph. The social overlay considers social relationship and semantic content of users and focuses on the key OSNs functionality of efficient information dissemination and service discovery. Then a swarm intelligence search method is proposed to facilitate adaptive learning and effective service discovery in decentralized environments. Our evaluation, performed in simulation over a real-world dataset, shows that the proposed approach achieves better performance comparing with the state-of-the-art methods on different network structures.

Similarly to social networks where people are connected by their social relationships, two autonomous peer nodes can be connected in unstructured peer-to-peer (P2P) networks if users in those nodes are interested in each other's data. The similarity between P2P networks and social networks, where peer nodes are people and connections are relationships, leads us to believe that human strategies in social networks are useful for improving the performance of resource discovery by self-organising autonomous peers on unstructured P2P networks. In this paper, we present an efficient social-like peer-to-peer (ESLP) model for resource discovery by mimicking different human behaviours in social networks.