In This paper, we proposed new clustering algorithm depend on other clustering algorithm ideas. The proposed algorithm idea is based on getting distance matrix, then the exclusion of the matrix points which will be clustered by saving the location (row, column) of these points and determine the minimum distance of these points which will be belongs the group (class) and keep the other points which are not clustering yet. The propose algorithm is applied to image data base of the human face with different environment (direction, angles... etc.). These data are collected from different resource (ORL site and real images collected from random sample of Thi_Qar city population in lraq). Our algorithm has been implemented on three types of distance to calculate the minimum distance between points (Euclidean, Correlation and Minkowski distance) .The efficiency ratio of proposed algorithm has varied according to the data base and threshold, the efficiency of our algorithm is exceeded (96%). Matlab (2014) has been used in this work.

The fast development and the huge number of existing web services have raised the problem of the urgent need for matchmaking mechanisms. However state-of-the-art matchmakers are unsuitable for locating web services that use different ontologies. This aspect is important since it is not realistic to assume that Web services will always be defined by the same ontology, as the Web service requester and provider operate independently, each defines their own ontologies to describe their services. This is an emergent research issue that has not been well addressed. This work is a contribution to achieve semantic interoperability in a multi-ontology environment. This paper describes a Web service multi-ontology matchmaker for SAWSDL services, called SAWSDL-MOM which locates web services that use different ontologies. The matchmaker engine incorporates a novel partial ontology alignment algorithm with syntax, linguistic and original structural matchers. In determining the 1:1 mappings the Hungarian algorithm is used. Finally a matchmaking strategy is utilized in finding the score of each service. Experimental evaluation and comparison provide strong evidence that SAWSDL-MOM can significantly improve results, achieve better interoperability and scalability.

The Uplink Synchronous Transmission Scheme [1], is a technique used by operators, that exploit the uplink orthogonality, to reduce multiple access interferences in uplink direction and consequently to increase uplink capacity. The USTS gives better performances when we have an ideal case presented by no channelization code restrictions per scrambling code. In reality, channelization codes are limited. To resolve this problem, several scrambling codes are used to admit more users in the cell. However, the use of different scrambling codes increases the multiple access interference and consequently decreases uplink capacity gain, since signals transmitted by users under different scrambling codes are non-orthogonal. To obtain more performances and therefore to increase the uplink capacity gain, we will study the introduction of a multi-user detector for interferences cancellation, in uplink synchronous mode. For that, two values of interference cancellation efficiency of the multi-user detector are considered. In this study, only the multiple access interference is reduced. To show the effect of other-cell interferences on uplink synchronous mode capacity, two scenarios are considered: an isolated cell and a multiple cell network.

This paper the development of Myanmar-English bidirectional machine translation system is implemented applying Rule based machine translation approach. Stanford and ML2KR parsers are used for preprocessing step. From this step, parsers generate corresponding parse tree structures. Used parsers generate corresponding CFG rules which are collected and created as synchronous context free grammar SCFG rules. Myanmar language can be written free order style, but it must be verb final structure. Therefore, CFG rules are required for reordering the structure of the two languages. After that tree to tree transformation is carried on the source tree structure which corresponds with used parser (Stanford parser or ML2KR's parser). When source parse tree is transformed as target parse tree, it is changed according to the SCFG rules. And then system carries out the morphological synthesis. In this stage, we need to solve only for English to Myanmar machine translation because Myanmar language is morphologically rich language. Therefore, particles for Myanmar language can be solved in this system by proposed algorithm. After finishing morphological synthesis, this system generates meaningful and appropriate smoothing sentences.

The use of wireless devices is increasing tremendously in our day-to-day life because of their portability and ease of deployment. The augmented practices of using these technologies have put the user security at risk. The Stealth Man-In-The-Middle (SMITM) is one of the attacks that has arisen out of the flaw in the wireless technology itself. This attack aims at stealing the data of the network users by redirecting the traffic aimed at a legitimate user towards itself. Moreover the access point or any other detection device connected to the wired media fails to detect this attack. The objective of this work is to develop a technique that would be able to detect SMITM attack efficiently. In this work we present a SMITM detection approach. Our approach detects the SIMTM attack by deploying multiple coordinated sensors. The simulation results witnessed that the proposed scheme is capable of detecting SMITM attack even in case of a mobile attacker.

Airport offers an ecosystem where passengers, airlines, airport, and merchants meet under one roof during travel. During the journey, there is a good amount of time spent by passengers on waiting or transit before boarding the flight. Passengers look for spending that time by shopping, dining, and entertainment. Airport merchants and airlines look for merchandising opportunities to acquire new customers, up-sell, and cross-sell their products and services. Highly personalized merchandising offers can be generated on the fly by combining contextual information from passenger profile, likes and interests, merchant offers, and location specific events, seasons, and festivals. To achieve this, a strong airport merchandising platform is needed. The goal of the airport merchandising platform is to exchange information in a seamless manner across travel systems. The platform is designed on microservices design principles that use information from airlines, airport, social media, and merchant systems. Microservices can promote quick development, deployment, and realization of services. Microservices also improve the time to market capabilities. Mobile and desktop applications consume them to offer a personalized shopping experience to the passengers.

Monitoring of Cloud services is vital for both service providing organizations and consumers. The service providers need to maintain the quality of service to comply their services with the QoS parameters defined in SLA's such as response time, throughput, delay through continuous monitoring of services. The dynamic monitoring involves prediction of SLA violations and subsequent adaptation of the service compositions. The task of adaptation is in fact the task of discovering another plausible composition in the face of services recorded to have generated QoS violations. QoS- Driven Utility based service composition approach considers the individual user's priorities for QoS parameters and determines the overall utility measure of the service composition for the end user. In this work we present the problem of service composition adaptation as a multi-objective assignment optimization problem, which in turn is a NP-hard problem. The evolutionary algorithm GA with Tabu has been formulated as a Memetic and Pareto optimal approach for the adaptation problem and analyzed for efficiency in solving the problem.

A Fatigue Detection system has been developed using non-intrusive vision based approach. The system uses a Logitech USB camera which points towards driver's face and monitors face and eyes to detect driver fatigue. The system is developed on Linux operating system and used DM3730 processor as hardware. The algorithm is developed to estimate whether eyes are open or closed and fatigue is estimated using PERCLOS method. Normal human blinks eyes 12 times in a minute. If the eyes are closed for 15 consecutive frames in a minute or if PERCLOS > 80% than system issues warning to stop the vehicle. The algorithm is tested on 45 different persons i.e., 15 women, 15 men and 15 persons wearing spectacles and the detection rate is 99.2%. The system takes <5ms of time to detect whether eyes are open or closed and the hardware used is small in size and easily implementable.

In healthcare industries, the demand for maintaining large amount of patients' data is steadily growing due to rising population which has resulted in the increase of details about clinical and laboratory tests, imaging, prescription and medication. These data can be called "Big Data", because of their size, complexity and diversity. Big data analytics aims at improving patient care and identifying preventive measures proactively. To save lives and recommend life style changes for a peaceful and healthier life at low costs. The proposed predictive analytics framework is a combination of Decision Tree, Support Vector Machine and Artificial Neural Network which is used to gain insights from patients. Parkinson's disease voice dataset from UCI Machine learning repository is used as input. The experimental results show that early detection of disease will facilitate clinical monitoring of elderly people and increase the chances of their life span and improved lifestyle to lead peaceful life.