Clustering is most common and well-established data mining technique for discovering patterns in data. Besides other types of data, clustering is also widely used for the same purpose for data acquired from educational settings. Among its di erent variants, K-means algorithm is popular in EDM community for its simplicity and ease of use. However, K-means algorithm itself does not impose the number of clusters. The optimal number of clusters in dataset remains a debatable issue. Di erent methods exist which can be used to estimate the number of clusters present in the dataset. In this paper, we present a comparison of di erent methods used for determining the optimal value of K. We use ve datasets and seven methods to nd the optimal number of clusters in these datasets. Two of the datasets have been extracted from educational settings. The other three are open datasets. We compare results obtained from di erent methods using these ve datasets. EDM community is growing rapidly and the researchers are experimenting with more and more methods. The analysis presented in this paper will help EDM practitioners to choose appropriate technique based on objective evaluation measures to determine the optimal value of K.
The process of providing the security to secret information by hiding it into different information is called as the Steganography. The information which is to be hidden in an image is which is carrier is also in an image format. The secret image can be embedded in another image so that it makes the intruder hard to find the data which is hidden. In this method, the carrier image can act as a good source of embedding since the entire image is aligned in the form of pixels. Hence, the data which is embedded inside the carrier image makes it harder to the intruder to access and modify it. Embedding the secret information in these pixels of an image leads to high security. In this paper, selected TEB features such as such as Texture, Edge sensitivity and Brightness sensitivity where calculated are computed for the entire image and then further, the inference rules are formed, and then combined to embed the secret information in the pixels of the carrier image. and combined with the inference system for hiding the information. Experimental results show that this system hides the secret information more effectively with a high information hiding rate.
In this study, the improvement of a corridor consisting of four junctions, located in Atatürk University Campus, was studied. The traffic volumes of this corridor which are expected to arise after 20 years were determined by origin-destination matrix estimation method. The junctions located on the corridor were analyzed by utilizing VISSIM package software and they were evaluated according to the parameters of travel time, average delay, queue length, CO emissions and fuel consumption. However, one of the most important parameters of junction design is traffic safety. Since traffic safety is an abstract parameter, it cannot be used on micro-simulation analyses. Therefore, multi-criteria decision-making models are required to be utilized in order to observe the effects of traffic safety on the determination of a convenient junction type. For this reason, the Analytic Hierarchy Process (AHP), which is one of the multi-criteria decision-making methods, was utilized. Three different scenarios were constituted for the current corridor. The most convenient junction type was determined by comparing the scenarios with the existing condition via both micro-simulation and AHP method. Considering the results, it can be said that scenario 1 (the case that all of the junctions were roundabouts) is the most convenient junction type in terms of the parameters mentioned above and traffic safety.
The increasing demand of network services intensifying the need of more bandwidth for supporting QoS. Optical network provide large bandwidth to overcome the scarcity of bandwidth problem, but it majorly have an issue for assigning optimal bandwidth to manage the traffic condition and minimizing the blocking probability in routing. In this paper, an Optimal Bandwidth Assignment (O-BA) approach tominimize theblocking probability is proposed to improvisation the bandwidth allocation in varying traffic in optical network routing. The main aim of the O-BA is to reducethe blocking probability and improve bandwidth utilization to achieve better throughput. The simulation results in compare to the conventional bandwidth distribution algorithms shows a significant reduction in blocking probability and enhance the bandwidth usages.
This paper addresses the carrier-frequency offset (CFO) estimation in noncircular complex-valued colored multiplicative noise channel. The non-circular multiplicative noise is modelled as non-circular complex-valued first-order autoregressive (NC-AR(1)) model. The primary aim is to characterize the significant gains in terms of CFO estimation that can be provided by exploiting the non-circularity property of the channel model. A high signal-to-noise-ratio (SNR) approximation time domain scheme for joint CFO and fast-fading NC-AR(1) channel parameters estimation is proposed based on the maximum likelihood (ML) principle. Using this approach, the CFO estimate is first obtained by solving a one-dimensional optimization problem. We also derive an approximate high-SNR ML CFO estimation approach under a quasi-static non-circular channel model. To evaluate the performance of these approaches, we derive closed-form expressions of the exact Cramér-Rao lower bound (CRLB) of the CFO estimate for both slow-fading and fast-fading channel models. Analytical sensitivity analysis is performed for NC-AR(1) parameters by deriving high-SNR approximate expressions of the CRLBs. Finally, theoretical analysis and simulation results show that the proposed estimators provide significant performance as compared to the conventional circular CFO estimation schemes.
In today’s competitive business environment, employees’ skills and performance have become an important determinant of survival, and achieving gain competitive advantage. Employee job performance is influenced by many factors, and it is assumed that one of these factors is perceived supervisor support. In this study, it was aimed to reveal the effect of perceived supervisor support on the job performance of hospital employees. The study was performed between 6 November 2018 and 12 December 2018 in Istanbul and Bursa, in Turkey. Data were collected from employees working in private hospitals operating in these cities, and survey method was used to collect data. A total of 203 valid questionnaires were included in the analysis. Correlation and regression analyses were performed to determine the relationships between perceived supervisor support and employee job performance. According to the results of the study, it is concluded that there is a positive and high correlation between perceived supervisor support and employee job performance. In addition, it was concluded that perceived supervisor support significantly and positively influences employee job performance. According to these results, it is recommended that hospital management should focus on activities that increase supervisor support.
the primary function of this system is to monitor the temperature and heart beat of the patient. The Data collected by the sensors are sent to the Microcontroller. The Microcontroller transmits the data over the air by using the GSM modem from the transmitter to recording system in the receiving end. The information is sent as an SMS to the care monitoring system or to the experts to take action. Not only we send the information through GSM module as SMS we also display the readings on LCD. When the conditions go abnormal then sensors sense those values and then alarm around by blowing the alarm and also sending an emergency SMS to the desired destination.