In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traff

In the current research work, a method to reduce the color levels of the pixels within digital images was proposed. The recent strategy was based on self organization map neural network method (SOM). The efficiency of recent method was compared with the well known logarithmic methods like Floyd-Steinberg (Halftone) dithering and Octtrees (Quadtrees) methods. Experimental results have shown that by adjusting the sampling factor can produce higher-quality images with no much longer run times, or some better quality with shorter running times than existing methods. This observation refutes the repeated neural networks is necessarily slow but have best results. The generated quantization map can be exploited for color image compression, clas

In the present work, classification of radioactive wastes based on Annual Intake (AI) values is studied. Where the characterization of radionuclides was done by hand held GeLi detector with an overall efficiency better than 42%. It was noted the most predominant contaminant are Cs-137, Co-60 and Pa-234.The radioactive waste in disposal silo has been divided into five categories according to the harmful effect of radionuclides.For the purpose of storageradioactive wastein a safe manner, it wassuggesteda new method by shielding radioactive waste in each category with concrete;where the thickness of shielding is the time required to reduce the annual dose to 10%.

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

Purpose: to review in detail various aspects of odontogenic keratocyst, emphasizing recent nomenclature, clinical, histopathological, recurrence, and management of odontogenic keratocyst.

Methods: To achieve the objective of this review, a manual search was done in hard copy books of oral and maxillofacial pathology, and an electronic search was done in the google website, oral and maxillofacial pathology E-books, virtual database sites, such as PubMed, Research Gate, Academia, and Google scholar using the descriptors: odontogenic cyst, kerato odontogenic tumor, odontogenic keratocyst, and jaws cystic lesion. The eligibility criteria for selecting articles were: to be in the English language, stu

      In current generation of technology, a robust security system is required based on biometric trait such as human gait, which is a smooth biometric feature to understand humans via their taking walks pattern. In this paper, a person is recognized based on his gait's style that is captured from a video motion previously recorded with a digital camera. The video package is handled via more than one phase after splitting it into a successive image (called frames), which are passes through a preprocessing step earlier than classification procedure operation. The pre-processing steps encompass converting each image into a gray image, cast off all undesirable components and ridding it from noise, discover differen

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took th

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature