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In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.

Avatar is a system that leverages cloud resources to support fast, scalable, reliable, and energy efficient distributed computing over mobile devices. An avatar is a per-user software entity in the cloud that runs apps on behalf of the user's mobile devices. The avatars are instantiated as virtual machines in the cloud that run the same operating system with the mobile devices. In this way, avatars provide resource isolation and execute unmodified app components, which simplifies technology adoption. Avatar apps execute over distributed and synchronized (mobile device, avatar) pairs to achieve a global goal. The three main challenges that must be overcome by the Avatar system are: creating a high-level programming model and a middleware that enable effective execution of distributed applications on a combination of mobile devices and avatars; re-designing the cloud architecture and protocols to support billions of mobile users and mobile apps with very different characteristics from the current cloud workloads; and explore new approaches that balance privacy guarantees with app efficiency/usability. We have built a basic Avatar prototype on Android devices and Android x86 virtual machines. An application that searches for a lost child by analyzing the photos taken by people at a crowded public event runs on top of this prototype.

MobiStore is a P2P data store for decentralized mobile computing, designed to achieve high availability and load balance. MobiStore uses redundant peers to compensate for churn and high link variability specific to mobile wireless networks. It structures the P2P network into clusters of mobile peers that replicate stored content, thus achieving high availability. Load balance is achieved through consistent hashing, randomization of request distribution, and load adaptive cluster management. Furthermore, MobiStore can route lookup requests in O(1) hops. Simulation results show MobiStore achieves an availability, i.e., lookup success rate, between 1.2 and 5 times higher than a baseline system built over the well-known Chord P2P protocol; it also reduces the latency up to 5 times compared with the baseline.

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