The growth of data traffic on the web, the virtualization of services, and the changes in the pattern of traffic between users and data centers have led to a reassessment of the current methods of doing network administration. Software Defined Networks (SDNs) propose a paradigm that delegate the control of packets and flows to applications, developed according to specific requirements, where the OpenFlow protocol can be used for communications. The development of this type of applications, as in any other development area, requires tests and measurement tools to facilitate a performance evaluation. However, the current open-source performance measurement applications for SDN networks cover only very basic characteristics, while there is a wide range of SDN controllers with support to many versions of OpenFlow, making the selection of the controller a difficult point to address. In this paper, we propose a distributed performance evaluation tool for SDN controllers, that can assess the throughput, latency, percentage of memory consumption, percentage of CPU utilization, and consumption in kB for input/output interfaces, using OpenFlow version 1.3. Our tool is based on Cbench, and adds new functionalities such as the graphical representation of results to analyze the outcomes. To validate our tool, we make a performance evaluation of well-known SDN controllers such as Ryu, OpenDaylight, OpenMUL, and Floodlight, in environments under great stress of requests.

Parkinson’s Disease (PD) is one of the leading causes of death around the world. However, there is no cure for this disease yet; only treatments after early diagnosis may help to relieve the symptoms. This study aims to analyze the impact of feature selection techniques on the performance of diagnosing PD by incorporating different data mining techniques. To accomplish this task, identifying the best feature selection approach was the primary focus. In this paper, the authors had applied five feature selection techniques namely: Gain Ratio, Kruskal-Wallis Test, Random Forest Variable Importance, RELIEF and Symmetrical Uncertainty along with four classification algorithms (K-Nearest Neighbor, Logistic Regression, Random forest, and Support Vector machine) on the PD dataset collected from the UCI Machine Learning repository. The result of this study was obtained by taking the four different subsets (Top 5, 10, 15, and 20 features) from each feature selection approach and applying the classifiers. The obtained result showed that in terms of accuracy, Random Forest Variable Importance, Gain Ratio, and Kruskal-Wallis Test techniques generated the highest 89% score. On the other hand, in terms of sensitivity, Gain Ratio and Kruskal-Walis Test approaches produced the highest 97% score. The findings of this research clearly indicated the impact of feature selection techniques on predicting PD and our applied methods outperformed the state-of-the-art performance.

This study examines the functional requirements (FR) and non-functional requirements (NFR) for development of an integrated mobile application and a web-based system for enhancement of HIV/AIDS healthcare information delivery in Tanzania. The study was conducted in Dar es Salaam city in Tanzania. The unstructured interview was carried-out involving 45 people, among them, there were selected relevant users of the proposed system, Information Technologists, System Administrators and HIV/AIDS healthcare practitioners from the HIV/AIDS Care and Treatment Centers (CTCs) in district referral hospitals in Dar es Salaam. The captured requirements were classified into functional and non-functional requirements, the functional requirements were then graphically analyzed using the use case diagram, which was done by using starUML computer software. These findings can be used as the foundation’s building block for the development of a mobile application and web-based system for HIV/AIDS healthcare information delivery services.

Due to the lack of particular algorithms for automatic detection and tracking of person face(s), we have developed a new algorithm to achieve detection and single/multiple face tracking in different background video sequence. To detect faces, skin sections are segmented from the frame by means of YCbCr color model; and facial features are used to agree whether these sections contain person face or not. This procedure is challenging, because face color is unique and some objects may have similar color. Further, color and Eigen features are extracted from detected faces. Based on the points detected in facial region, point tracker tracks the user specified number of faces throughout the video sequence. The developed algorithm was tested on challenging dataset videos; and measured for performance using standard metrics. Test results obtained ensure the efficiency of proposed algorithm at the end.

Landmines detection and removal are one of the biggest problems that faced many countries throughout the world. The procedures of landmines detection and removal are slow, dangerous and labor intensive. Some countries are currently involved in peacekeeping forces, where troops are in constant danger from landmines placed along roads and tracks. Accordingly, such traps are considered as an effective weapon in threatening troop’s lives, and preventing their movements. From this perspective, to meet the need for a fast way to locate landmines, and to offer the highest level of safety for military forces without the risk of triggering them during any mission; a lightweight aerial system that implements a heuristic optimization technique is proposed in this paper. The system is structured with five units: Hexacopter unmanned aerial vehicle (UAV), landmine detector, hands free flight controller, emergency flight controller, and the main on-board flight controller. Drone is equipped with a landmine detector, emergency flight controller, and the main on-board flight controller. Based on the feedback from the landmine detector, Drone will guide the leader of the troop through the communication channel established between the hands free flight controller and the emergency flight controller. The system has been simulated using the MATLAB and the overall concept shows promise. Additionally, experiments are carried out successfully on the real hardware.

Among the speech synthesis approach, concatenative method is one of the most popular method which can produce more natural sounding speech output. The most important challenge in this method is choosing an appropriate unit for creating a database. The present used speech units are word, syllable, di-phone, tri-phone and phoneme. The speech quality may be trade-off between the selected speech units. This paper presents the three speech synthesis system of Myanmar language, respectively based on syllable, di-phone and phoneme speech units by using concatenation method. Then, we compare the speech quality of the three systems, using the subjective tests.

The application of modern information technologies in the oil and gas sector is constantly developing, which facilitates the acceleration of exploration and detection of oil, the increase in oil production and reduction in risks relates to health, human safety, and the environment. The Internet of things in the oil and gas sector, like in all sectors of industry, has great prospects from an economic point of view. The article is devoted to the study of the current state and avenues of solving key problems of effective and reliable functioning of the oil and gas industry as a cyber-physical system using the Internet of things in the Azerbaijani oil company SOCAR. The main technological processes and existing opportunities for the application of information technologies in the oil and gas complex are analyzed. New approaches are proposed to solve the problems in the oil and gas complex as cyber-physical system based on the smart sensors, the Internet of things, wireless networks and cloud technologies. The implementation of the proposed model is aimed at increasing the effectiveness, resource storage, exploration reliability and durability of the oil and gas complex.

Parental diagnosis is required during mid-pregnancy period from 18-22 weeks in order to know the well-being of the fetus. This diagnosis is usually done through ultrasound scanning. Ultrasound scanning which is also called as sonogram, is an ultrasound based medical imagining technique used to envision the fetus and its development during the gestation period. If there is an abnormality in the diagnosed fetus then the parents and the doctors can do emergency parental care. Anomalies in Fetus occur before birth. Detecting fetal anomalies is a difficult task since it needs expertise and also requires a considerable amount of time, which will not be convenient at an emergency situation. In order to improve the diagnosis accuracy and to reduce the diagnosis time, it has become a demanding issue to develop an efficient and reliable medical decision support system. In this paper we present machine learning approach, such as convolution neural network which is most commonly applied to examine visual pretense. The main motive behind using CNN is due to their accuracy, fewer memory requirements and better training of images. This approach have shown great potential to be applied in the development of medical decision support system for Fetal anomalies which need immediate care.