Identification of significant features and data mining techniques in predicting heart disease

This paper provides a brief overview on the recent advances of small-scale unmanned aerial vehicles (UAVs) from the perspective of platforms, key elements, and scientific research. The survey starts with an introduction of the recent advances of small-scale UAV platforms, based on the information summarized from 132 models available worldwide. Next, the evolvement of the key elements, including onboard processing units, navigation sensors, mission-oriented sensors, communication modules, and ground control station, is presented and analyzed. Third, achievements of small-scale UAV research, particularly on platform design and construction, dynamics modeling, and flight control, are introduced. Finally, the future of small-scale UAVs' research, civil applications, and military applications are forecasted.

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A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.

UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD

with the rampant increase in the heart stroke rates at juvenile ages, we need to put a system in place to be able to detect the symptoms of a heart stroke at an early stage and thus prevent it. It is impractical for a common man to frequently undergo costly tests like the ECG and thus there needs to be a system in place which is handy and at the same time reliable, in predicting the chances of a heart disease. Thus we propose to develop an application which can predict the vulnerability of a heart disease given basic symptoms like age, sex, pulse rate etc. The machine learning algorithm neural networks has proven to be the most accurate and reliable algorithm and hence used in the proposed system.

Prediction of Heart Disease Using Machine Learning

Software-defined networking (SDN) is a communication paradigm that brings cost efficiency and flexibility through software-defined functions resident on centralized controllers Although SDN applications are introduced in a limited scope with related technologies still under development, operational SDN networks already face major security threats Therefore, comprehensive and efficient solutions are crucial Especially, large-scale security threats such as distributed-denial-of-service (DDoS) attacks are jeopardizing safety and availability of data and services in these systems A DDoS attack is aimed at making resources unavailable to legitimate users via overloading systems with excessive superfluous traffic from distributed sources In this paper, we describe and evaluate a joint entropy-based security scheme (JESS) to enhance the SDN security with the aim of a reinforced SDN architecture against DDoS attacks In particular, our proposed model devises a statistical solution to detect and mitigate these hazards To the best of our knowledge, JESS is the first model that utilizes joint entropy for DDoS detection and mitigation in the SDN environment Since it relies on a statistical model, it mitigates not only known attacks but also unfamiliar types in an efficient manner

JESS: Joint Entropy-Based DDoS Defense Scheme in SDN

Significant developments and technical trends in the area of navigation systems are reviewed. In particular, the integration of the Global Positioning System (GPS) and Inertial Navigation System (INS) has been an important development in modern navigation. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing integrated navigation
systems of accuracy, performance, low cost and all the issues that aid in optimizing their operating efficiency. The integration of GPS and INS has been successfully used in practice during the past decades. However, much of the work has focused on the use of a high accuracy Inertial Measurement Unit (IMU), which is an inertial sensors block without navigation solution output, and hence, this research area is also reviewed in this paper.

A Review of Navigation Systems (Integration and Algorithms)

One of the most important applications of machine learning systems is the diagnosis of heart disease which affect the lives of millions of people Patients suffering from heart disease have lot of independent factors such as age, sex, serum cholesterol, blood sugar, etc in common which can be used very effectively for diagnosis In this paper an Extreme Learning Machine (ELM) algorithm is used to model these factors The proposed system can replace a costly medical checkups with a warning system for patients of the probable presence of heart disease The system is implemented on real data collected by the Cleveland Clinic Foundation where around 300 patients information has been collected Simulation results show this architecture has about 80% accuracy in determining heart disease

Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis

Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

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Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Data centre prediction models can be used to forecast future loads for a given centre in terms of CPU, memory, VM requests, and other parameters. An effective and efficient model can not only be used to optimize resource allocation, but can also be used as part of a strategy to conserve energy, improve performance and increase profits for both clients and service providers. In this paper, we have developed a prediction model, which combines k-means clustering techniques and Extreme Learning Machines (ELMs). We have shown the effectiveness of our proposed model by using it to estimate future VM requests in a data centre based on its historical usage. We have tested our model on real Google traces that feature over 25 million tasks collected over a 29-day time period. Experimental results presented show that our proposed system outperforms other models reported in the literature.

Using ELM Techniques to Predict Data Centre VM Requests

Software Defined Networks (SDNs) are gaining success in modern IT. While using SDN, enterprises and the research sector have encountered many challenges related to security and exposed weaknesses in heavy load management systems. In this paper, three architectures are proposed which are designed to work with all existing OpenFlow, OpenStack and OpenDaylight platforms. These architectures improve the controller framework without any interruption to network operation. At the same time, some of the key issues related to load balancing and security in a nSDN framework will be addressed. This work will support the IT community not only by reducing the cost of SDN network architecture but also by reducing the cost of load balancing and security that are very important in the networking world.

Salam Ismaeel

Papers

A Review of Navigation Systems (Integration and Algorithms)

Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Using ELM Techniques to Predict Data Centre VM Requests

Role-based multiple controllers for load balancing and security in SDN