The presence of White Blood Cells (WBCs) in the body of human has a great role in the protection of the body against many pathogens. The recognition of the WBC is the first important step to diagnose some particular diseases. The pathologists usually use an optical microscope to recognize WBCs, but, this process is a quite tedious, time-consuming, error prone, very slow, and expensive. In addition, it needs experts with long practice in this field. For these reasons, a computer assisted diagnostic system that helps pathologists in the process of diagnosis can be effective, easy and safe. This research is devoted to develop a system based on digital image processing methods to localize WBCs nuclei. The proposed system involved a collectio

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.

<span>One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected regi

Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm