In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performance measures are used as a criterion to decide which classifier is the best one to detect the images with high accuracy. Eventually, the simulation results show that each classifier detect the damage/no damage image with different performance measures and then makes it easy to select the best one.
The present study discusses the problem based learning in Iraqi classroom. This method aims to involve all learners in collaborative activities and it is learner-centered method. To fulfill the aims and verify the hypothesis which reads as follow” It is hypothesized that there is no statistically significant differences between the achievements of Experimental group and control group”. Thirty learners are selected to be the sample of present study.Mann-Whitney Test for two independent samples is used to analysis the results. The analysis shows that experimental group’s members who are taught according to problem based learning gets higher scores than the control group’s members who are taught according to traditional method. This
... Show MoreThe present study discusses the problem based learning in Iraqi classroom. This method aims to involve all learners in collaborative activities and it is learner-centered method. To fulfill the aims and verify the hypothesis which reads as follow” It is hypothesized that there is no statistically significant differences between the achievements of Experimental group and control group”. Thirty learners are selected to be the sample of present study.Mann-Whitney Test for two independent samples is used to analysis the results. The analysis shows that experimental group’s members who are taught according to problem based learning gets higher scores than the control group’s members who are taught according to traditional method. This
... Show MoreThe Arabic Language is the native tongue of more than 400 million people around the world, it is also a language that carries an important religious and international weight. The Arabic language has taken its share of the huge technological explosion that has swept the world, and therefore it needs to be addressed with natural language processing applications and tasks.
This paper aims to survey and gather the most recent research related to Arabic Part of Speech (APoS), pointing to tagger methods used for the Arabic language, which ought to aim to constructing corpus for Arabic tongue. Many AI investigators and researchers have worked and performed POS utilizing various machine-learning methods, such as Hidden-Mark
... Show MoreThis paper presents an IoT smart building platform with fog and cloud computing capable of performing near real-time predictive analytics in fog nodes. The researchers explained thoroughly the internet of things in smart buildings, the big data analytics, and the fog and cloud computing technologies. They then presented the smart platform, its requirements, and its components. The datasets on which the analytics will be run will be displayed. The linear regression and the support vector regression data mining techniques are presented. Those two machine learning models are implemented with the appropriate techniques, starting by cleaning and preparing the data visualization and uncovering hidden information about the behavior of
... Show MoreWhen images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensiona
... Show MoreIn this study, NAC-capped CdTe/CdS/ZnS core/double shell QDs were synthesized in an aqueous medium to investigate their utility in distinguishing normal DNA from mutated DNA extracted from biological samples. Following the interaction between the synthesized QDs with DNA extracted from leukemia cases (represents damaged DNA) and that of healthy donors (represents undamaged DNA), differential fluorescent emission maxima and intensities were observed. It was found that damaged DNA from leukemic cells DNA-QDs conjugates at 585 nm while intact DNA (from healthy subjects) DNA–QDs conjugates at 574 nm. The obtained results from the optical analyses indicate that the prepared QDs could be utilized as probe for detecting disrupted DNA th
... Show MoreThe phenomena of Dust storm take place in barren and dry regions all over the world. It may cause by intense ground winds which excite the dust and sand from soft, arid land surfaces resulting it to rise up in the air. These phenomena may cause harmful influences upon health, climate, infrastructure, and transportation. GIS and remote sensing have played a key role in studying dust detection. This study was conducted in Iraq with the objective of validating dust detection. These techniques have been used to derive dust indices using Normalized Difference Dust Index (NDDI) and Middle East Dust Index (MEDI), which are based on images from MODIS and in-situ observation based on hourly wi
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
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