This study investigates data set as satellite images of type multispectral Landsat-7, which are observed for AL_Nasiriya city, it is located in southern of Iraq, and situated along the banks of the Euphrates River. These raw data are thermal bands of satellite images, they are taken as thermal images. These images are processed and examined using ENVI 5.3 program. Consequently, the emitted Hydrocarbon is extracted, and the black body algorithm is employed. As well as, the raster calculations are performed using ArcGIS, where gas and oil features are sorted. The results are estimate and determine the oil and gas fields in the city. This study uncovers, and estimates several unexplored oil and gas fields. Whereas,

     Meerkat Clan Algorithm (MCA) is a nature-based metaheuristic algorithm which imitates the intelligent behavior of the meerkat animal. This paper presents an improvement on the MCA based on a chaotic map and crossover strategy (MCA-CC). These two strategies increase the diversification and intensification of the proposed algorithm and boost the searching ability to find more quality solutions. The 0-1 knapsack problem was solved by the basic MCA and the improved version of this algorithm (MCA-CC). The performance of these algorithms was tested on low and high dimensional problems. The experimental results demonstrate that the proposed algorithm had overcome the basic algorithm in terms of solution quality, speed a

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

      The aim of this article is to present the exact analytical solution for models as system of (2+1) dimensional PDEs by using a reliable manner based on combined LA-transform with decomposition technique and the results have shown a high-precision, smooth and speed convergence to the exact solution compared with other classic methods. The suggested approach does not need any discretization of the domain or presents assumptions or neglect for a small parameter in the problem and does not need to convert the nonlinear terms into linear ones. The convergence of series solution has been shown with two illustrated examples such (2+1)D- Burger's system and (2+1)D- Boiti-Leon-Pempinelli (BLP) system.

A new method based on the Touchard polynomials (TPs) was presented for the numerical solution of the linear Fredholm integro-differential equation (FIDE) of the first order and second kind with condition. The derivative and integration of the (TPs) were simply obtained. The convergence analysis of the presented method was given and the applicability was proved by some numerical examples. The results obtained in this method are compared with other known results.

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the

This paper proves the existence of face antimagic labeling for double duplication of barycentric subdivision of cycle and some other graphs