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Traffic congestions is a crucial problem affectingcities around the globe and they are only getting worse as thenumber of vehicles tends to increase significantly. Traffic signalcontrollers are considered as the most important mechanism tocontrol traffic, specifically at intersections, the field of MachineLearning introduces advanced techniques which can be appliedto provide more flexibility and adaptiveness to traffic controltechniques. Efficient traffic controllers can be designed using areinforcement learning (RL) approach but major problems offollowing RL approach are, exponential growth in the state andaction spaces and the need for coordination. We use real trafficdata of 65 intersection of the city of Ottawa to build oursimulations and show that, clustering the network usinghierarchal techniques has a great potential in reducing the stateactionpair significantly and enhance overall trafficperformance.

Traffic jam is a crucial issue affecting cities around the world. They are only getting worse as the population and number of vehicles continues to increase significantly. Traffic signal controllers are considered as the most important mechanism to control the traffic, specifically at intersections, the field of Machine Learning offers more advanced techniques which can be applied to provide more flexibility and make the controllers more adaptive to the traffic state. Efficient and adaptive traffic controllers can be designed using a multi-agent reinforcement learning approach, in which, each controller is considered as an agent and is responsible for controlling traffic lights around a single junction. A major problem of reinforcement learning approach is the need for coordination between agents and exponential growth in the state-action space. This paper proposes using machine learning clustering algorithm, namely, hierarchal clustering, in order to divide the targeted network into smaller sub-networks, using real traffic data of 65 intersection of the city of Ottawa to build our simulations, the paper shows that applying the proposed methodology helped solving the curse of dimensionality problem and improved the overall network performance.

Recently, Power Consumption is one of The Most Important Researches on Cloud Computing Systems such as CPU central Processors, Drives, memory, and temperature that affect on consuming the amount of energy. But also the amount of software approach has more effective on it, such as scheduling, resource allocation, and task consolidation.Task Consolidation is an effective technique that is used to save power consumption which consolidates tasks by virtualization technologies. The goal of this optimizing is not only to minimize energy costs in cloud computing systems but also to respond to government and environmental rules. There are more than techniques to minimize energy consumption here we focus on the task consolidation technique and its important algorithms. In task consolidation, idle resources consume a major amount of energy consumption. Virtualization is an important part of the task consolidation strategy.In this survey, we discuss how efficient task consolidation algorithms affect cloud resource power consumption.

As expectations for the data processing platforms to support processing for healthcare data, new services need to be deployed in healthcare services. One of them is Electronic health records. It raised its importance recently from the data become shareable, connection oriented, high available, and united. Therefore, a first motivation is to help healthcare organizations to define which the appropriate processing platform can support real time processing for huge electronic health records data. Although define different platforms that enhanced analytics and prediction methods of healthcare entities which produces a huge amount of data. A second possible motivation is to go beyond the limitations of traditional processing techniques that used in electronic health records. The big data processing is the cheaper solution that performing real-time analysis on the huge data. This paper identifies and discusses new data processing platforms that can be used in electronic health records.

Big data has become one of the most imperative technologies for collecting, handling and analysing enormous volumes of data in a high-performance environment. Enterprise healthcare organizations needs high compute power for the large volume of sensitive data, as well as large storage for storing both data and results, preferably in the cloud. However, security and privacy of patient data have become a critical issue that restricts many healthcare services from using cloud services to their optimal level. Therefore, this issue has limited healthcare organizations from migrating patient data to a cloud storage, because the cloud operators have chance to access sensitive data without the owner’s permission. This paper proposes an intelligent security system called Intelligent Framework for Healthcare Data Security (IFHDS). IFHDS enables to secure and process large-scale data using column-based approach with less impact on the performance of data processing. The intelligent framework intends masking personal data and to encrypt sensitive data only. The proposed IFHDS splits sensitive data into multiple parts according to sensitivity level, where each part is stored separately over distributed cloud storage. Splitting data based on sensitivity level prevents cloud provider to break complete record of data if succeeds to decrypt part of data. The experimental results confirm that the proposed system secure the sensitive patient data with an acceptable computation time compared to recent security approaches.

The availability of Healthcare Data Center (HDC) from network or machine failures has led to the appearance of new approaches to self healing data centres. Modern data centers looking to ensure the reliability of Distributed Computing Systems (DCS) in the most critical era. Also, modern HDC need to provide highest reliability infrastructure for all distribution critical jobs and data sources including electronic health records, electronic medical records and IoT. Redundancy is the traditional technique that improves the reliability of DCS among HDC. Inherently, redundancy is a very expensive approach in terms of hardware and software resources. In this context, this paper presents an Enhanced MapReduce Agent Mobility (EMRAM) framework to maximize reliability of DCSs among HDC without redundancy. The proposed framework migrating tasks to the available machines in the in HDC when identifying unhampered machine failure. Some practical experiments are performed to evaluate the proposed (EMRAM) framework by considering different scenarios. The obtained results are compared with the most recent frameworks showing a relatively better performance and higher reliability than other recent frameworks.

One of the leading technologies is the Smart Home, where the home itself recognizes certain changes in home environment and provides adequate services to residents. Also, the Smart Home can anticipate the any dangerous state so it can adapt itself for this future case. Our goal is to anticipate states with too large power take-off; it means to eliminate power spikes. Program allows starting of simulation also without anticipation. The results are suitable for comparison with simulations with the block of anticipation. Anticipation based on evolutionary algorithms. So, we can see the quality of anticipation in the best way.