Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

Biodiesel is becoming one of the most attractive alternative biofuels for petroleum-based diesel fuels. The castor plant is one of the abundant non-edible oils found in many countries. This paper aims to study Libyan castor oil and its potential for diesel conversion. Experiments were carried out in the laboratories of the Specific Center for Training in the Oil Industries in Al-Zawiya. The oil was extracted using a Soxhlet extractor and n-hexane solvent at 60 °C. Transesterification reactions were conducted in a batch reactor (a three-neck flask was used, where the middle opening carries a reflux condensation unit) at 65 °C. The methanol-to-castor oil molar ratio was 6:1, with a catalyst concentration of 1 wt.% relative to the ca

This study included the Zakhikhah area in the Al- Anbar desert, which it bounded on the north, east, and west by the Euphrates River and on the south by the Ramadi-Qaim road. Several exploratory field trips were taken to the study area. During this time, a semi-detailed area survey was carried out based on satellite imagery captured by American Land sat-7, topographic maps, and natural vegetation variance. All necessary field tools, including a digital camera and GPS device, were brought to determine the soil type and collect plant samples. All of these visits are planned to cover the entire state of Zakhikhah. All vegetation cover observations, identifying sampling sites and attempting to inventory and collect medicinal plants in t

<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

Introduction: Since the hallmark of gestational trophoblastic disease is trophoblastic proliferation, Ki67 is regarded as the best marker in studying hydatidiform mole.This study was conducted to evaluate the role of this proliferative marker in distinguishing among hydropic abortion, partial and complete hydatidiform mole. Materials and methods: This is a cross sectional study involving the application of Ki67 on a total of 90 histological samples of curetting materials from molar (partial and complete mole) and non molar hydropic abortion belong to Iraqi females, so three study groups were created. Immunohistochemical expression in villous cytotrophoblasts, syncytiotrophoblasts and stromal cells were recorded separately by three i

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