The taxonomy of Ficus L., 1753 species is confusing because of the intense morphological variability and the ambiguity of the taxa. This study handled 36 macro-morphological characteristics to clarify the taxonomic identity of the taxa. The study revealed that Ficus is represented in the Egyptian gardens with forty-one taxa; 33 species, 4 subspecies and 4 varieties, and classified into five subgenera: Ficus Corner, 1960; Terega Raf., 1838; Sycomorus Raf., 1838; Synoecia (Miq.) Miq., 1867, and Spherosuke Raf.,1838; out of them seven were misidentified. Amongst, four new Ficus taxa were recently introduced to Egypt namely: F. lingua subsp. lingua Warb. ex De Wild. & T. Durand, 1901; F. pumila L., 1753; F. rumphii Blume, 1825, and F. su

The article discusses political discourse as a communicative space of modern politics in the context of the anthropocentric paradigm. The following components of the political discourse have been outlined: the character of the subject and that of the addressee, genres of oral and written speech, the opposition of monologue and dialogue, the functions, the amount of information among the genres, the aim of speech.

God, may He be glorified and exalted be He, has given every human being the right to life and a dignified life, and has warned against transgression against any of its sanctities without a legitimate right. No one, regardless of his status or authority, can deprive a person of his rights that the Sharia came to preserve, and whoever does that has declared all people to war, as all humanity is in solidarity. In raising the hand that is simplified to harm a person and oppress him unjustly and exalted in the land.
If this is the case, the Sharia came to establish the right of people, groups and individuals, to defend their sanctities, preserve their security, recover their usurped rights, repel the aggression of the aggressors, and oppre

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

       The study aimed to reach the best rating for the views and variables in the totals characterized by qualities and characteristics common within each group and distinguish them from aggregates other for the purpose of distinguishing between Iraqi provinces which suffer from deprivation, for the purpose of identifying the status of those provinces in the early allowing interested parties and regulators to intervene to take appropriate corrective action in a timely manner. Style has been used cluster analysis Cluster analysis to reach the best rating to those totals from the provinces that suffer from problems, where the provinces were classified, based on the variables (Edu

Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar