Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management. In this study, we have applied and evaluated the efficacy of six deep convolutional neural network (DCNN) models using transfer learning, for identifying pre-cancerous tongue lesions directly using a small dataset of clinically annotated photographic images to diagnose early signs of OCC. DCNN models were able to differentiate between benign and pre-cancerous tongue lesions and were also able to distinguish between five types of tongue lesions, i.e. hairy tongue, fissured tongue, geographic tongue, strawberry tongue and oral hairy leukoplakia with high classification performances. Preliminary results using an (AI + Physician) ensemble model demonstrate that an automated pre-screening process of oral tongue lesions using DCNNs can achieve ‘near-human’ level classification performance for diagnosing early signs of OCC in patients.

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.

This article investigates the strength and limitation of t-test and

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Cloud computing connotes the systematic delivery of computing resources as services to a wide range of users via the Internet. One form of Cloud computing, Infrastructure as a Service (IaaS), ensures the availability of the resources in the form of Virtual Machines (VMs). Such services are leased to users based on demand and are paid for on a pay-per-use basis. This helps to reduce the cost of running the computing needs of the users. Usually, the VMs are ran on datacenters comprise several computing resources that consume lots of energy, and causing hazardous levels of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods of reducing the energy consumption of the datacenters. Nature has been a cause of inspiration and had a solution to all problem. Therefore, this paper presents a comprehensive review of the state-of-the-art, Nature-Inspired algorithms that have been used in solving the energy issues in the Cloud datacenters. We have categorized all the methods considered into three main techniques; virtualization, consolidation, and energy-awareness. Moreover, we reviewed the different methods in terms their goals, methods, advantages, and limitations. We then compared the nature-inspired algorithms based on their features to indicate their utilization of resources and their levels of energy-efficiency. Finally, we have suggested the potential research directions in this research field. We believe that this review work will be of interest to researchers and professional in Cloud computing datacenters in their quest to providing better energy-efficient methods to address the energy consumption issues of the Cloud datacenters..