Abstract: Most recent studies have focused on using modern intelligent techniques especially those developed In-Door surveillance systems. Such techniques have been built depending on modern Artificial intelligence-based modules. Those modules act like a human brain, they learn and recognize what they learned. The importance of developing such systems came after the requests of customers and establishments to defend their properties and avoid Intruders' damages. This would be provided by an intelligent module that ensure the correct alarm for correct non-secured state, Thus, an Indoor surveillance module depending on Multi-Connect Architecture Associative Memory (MMCA) has been proposed. This proposed system can be trained for more than to shoot. Thus the module can recognize more than one true state that might be secured or non-secured states in real-time. The current study found an accepted accuracy level between (62.778.8%) at first training cycle with two images. While the final result were between (97-100%) at the fifth training cycle with (10) images. It considered a high performance and very excellent results.

Abstract: In the field of image processing, there is an urgent need to adopt image transformations. In this paper, work is done on image coefficients after decomposing them through Curvelet transformations obtained through the fan filter. The research deals with two stages: the first is to study the effect of the Fan filter by adopting angles (8, 16, 32) on the image (Lina.jpg) of size (256*256) after being analyzed using Curvelet transformations at scales (2,3,4)  through comparing a set of measurements (Contrast, Energy, Correlation, MSE, and PSNR) for both the original and reconstructed images. It can be found that Contrast and Energy criteria remain the same for the original and reconstructed images according to different levels of analysis or directions, so the value of the Correlation measure is 1. The value of the MSE criterion is very small and is almost not affected by the change of the number of angles in one scale, but it is slightly affected by increasing the scale analysis. What was mentioned above applies to the PSNR criterion as well. As for the second stage of the research, which included decomposing the image to its coefficients, canceling the effect of one of these coefficients, and then reconstructing it. The results proved that the two criteria (Contrast and Energy) were not affected with falling Correlation criteria from 1 to values ​​ranging from (0.9987_0.9997) depending on the number of scales used in the Curvelet analysis and the number of angles used in  Fan filter (8,16,32). The results also showed an increase in the MSE value when dropping some frequencies, and a corresponding decrease in the PSNR value. Whereas, the decrease in the MSE scale was demonstrated at a specific scale with the increase in the number of angles in the Fan filter, in contrast to the PSNR scale.

Abstract: In Bresenham's line drawing algorithm, the points of an n-dimensional raster that have to be selected are determined forming a close approximation to a straight line existed between two points. It is widely used for drawing line primitives in a bitmap image (for example: on a computer screen), since only integer addition, subtraction and bit shifting are used. These three operations are cheap concerning standard computer architectures. In addition, it is an incremental error algorithm. It is among the oldest algorithms that have been developed in computer graphics. An extension to the original algorithm may lead to draw circles. This research deals with the Bresenham’s line and circle drawing algorithm based on FPGA hardware platform.  The shapes on the VGA screen are displayed via internal VGA port that is built in the device.

Abstract: By applying Ruscheweyh - type harmonic function on the class ASH(λ,α,k,γ), a new subclass ℋRq(m, α, k, γ) for harmonic univalent function in the unit disk D is introduced, Furthermore, some geometric properties are obtained such as distortion theorem, sufficient coefficient bounds ,extreme points and convex combination conditions for aforementioned subclass.

Abstract: In this paper, we determine sufficient conditions, distortion properties and radii of starlikeness and convexity for functions The hypergeometric meromorphic functions have certain formula in the punctured unit disk which contains in new subclass.

Abstract: Digital images are often obtained with contrast distortions due to different factors that cannot be avoided on many occasions. Various research works have been introduced on this topic, yet no conclusive findings have been made. Therefore, a low-intricacy multi-step algorithm is developed in this study for rapid contrast enhancement of color images. The developed algorithm consists of four steps, in that the first two steps include separate processing of the input image by the probability density function of the standard normal distribution and the softplus function. In the third step, the output of these two approaches is combined using a modified logarithmic image processing approach. In the fourth step, a gamma-controlled normalization function is applied to fully stretch the image intensities to the standard interval and correct its gamma. The results obtained by the developed algorithm have an improved contrast with preserved brightness and natural colors. The developed algorithm is evaluated with a dataset of various natural contrast degraded color images, compared against six different techniques, and assessed using three specialized image evaluation methods, in that the proposed algorithm performed the best among the comparators according to the used image evaluation methods, processing speed and perceived quality.

Abstract: With the rapid growth of computer usage to extract the required knowledge from a huge amount of information, such as a video file, significant attention has been brought towards multi-object detection and tracking. Artificial Neural Networks (ANNs) have shown outstanding performance in multi-object detection, especially the Faster R-CNN network. In this study, a new method is proposed for multi-object tracking based on descriptors generated by a neural network that is embedded in the Faster R-CNN. This embedding allows the proposed method to directly output a descriptor for each object detected by the Faster R-CNN, based on the features detected by the Faster R-CNN to detect the object. The use of these features allows the proposed method to output accurate values rapidly, as these features are already computed for the detection and have been able to provide outstanding performance in the detection stage. The descriptors that are collected from the proposed method are then clustered into a number of clusters equal to the number of objects detected in the first frame of the video. Then, for further frames, the number of clusters is increased until the distance between the centroid of the newly created cluster and the nearest centroid is less than the average distance among the centroids. Newly added clusters are considered for new objects, whereas older ones are kept in case the object reappears in the video. The proposed method is evaluated using the UA-DETRAC (University at Albany Detection and Tracking) dataset and has been able to achieve 64.8% MOTA and 83.6% MOTP, with a processing speed of 127.3 frames per second.

Abstract: Nowadays, the smartphone device has become the most used device for the convenience of the user, smart parking is one such application that helps the consumer to find car parking space in an urban area. Mosul University, in particular, is one of these places. Common problems are the lack of information about vacant parking spaces and there is no way to search for them online. The goal of this work is to produce an Android and iOS app that uses ultrasonic sensors connected to the Arduino MEGA 2560 microcontroller to send parking occupancy values ​​to cloud, in an online database executed using Google Firebase. Finally, this application can book and pay online.

Abstract: This paper presents some games via G-g-open sets by using the concept of grill topological space which is G(Ti, G), where i={0, 1, 2}. By many figures and proposition, the relationships between these types of games have been studied with explaining some examples.

Abstract: The research deals with the intelligent irrigation system using the Internet of Things (IoT) via Low cost and low power system on chip microcontrollers including integrated Wi-Fi with dual-mode Bluetooth ESP32. The objectives of this project are to investigate the concept of an intelligent irrigation system using the Internet of Things, to develop a system using the aforementioned controller that processes data from the soil sensor that automatically irrigates the plant and analyzes the soil status of the plants. In real-time via the smartphone connected to the Internet. The study scope focuses on cropping and horticulture. Sensors had to be installed for each plant as it was necessary to know the condition of the soil. A water pump must also be added to each plant to save water. This project requires the Blynk application which is a platform with IOS and Android apps to control Arduino, Raspberry Pi and the likes over the Internet. It’s a digital dashboard where you can build a graphic interface for your project by simply dragging and dropping widgets.  Software on smartphone and hardware implementation which can detect environment condition using (DHT22: Temperature and Humidity sensor) sensor and soil moisture sensor. The results of this paper are based on the experiments performed.

Abstract: In this paper, we studied and applied a modern numerical method, which is combining Sumudu transform with Adomian decomposition Method to obtain approximate solutions of the nonlinear the Coupled Drinfeld– Sokolov–Wilson (DSW) system. Positive and negative values of the variable x and various values of the variable t were taken with the initial conditions of the system as well as the values of the parameters . The efficiency of the method was verified, as the results obtained were compared with the accurate solution of the system. We noticed that the results are very accurate and the effectiveness of the method was confirmed.

Abstract: The interest in coding was very high because it is widely relied on in the security of correspondence and in the security of information in addition to the need to rely on it in the storage of data because it leads to a pressure in the volume of information when storing it. In this research, image transformation was used to encode gray or color images by adopting parameters elected from contourlet transformations for image. The color images are acquired into the algorithm, to be converted into three slices (the main colors of the image), to be disassembled into their coefficients through contourlet transformations and then some high frequencies in addition to the low frequency are elected in order to reconstruct the image again. The election of low frequencies with a small portion of the high frequencies has led to bury some unnecessary information from the image components. The performance efficiency of the proposed method was measured by MSE and PSNR criteria to see the extent of the discrepancy between the original image and the recovered image when adopting different degrees of disassembly level, in addition, the extent to which the image type affects the performance efficiency of the approved method has been studied. When the practical application of the method show that the level of disassembly is directly proportional to the amount of the error square MSE and also has a great effect on the extent of correlation where the recovered image away from the original image in direct proportional with the increased degree of disassembly of the image. It also shows the extent to which it is affected by the image of different types and varieties, where was the highest value of the PSNR (58.0393) in the natural images and the less valuable in x-ray images (56.9295) as shown in table 4.

Abstract: This paper investigated a modified integral transform method used to solve heat equation in cylindrical coordinate, this modification method has been obtained based on  integral transform (x-coordinate), we expand  integral transform (x-coordinate) to  integral transform (x,y,z,t-coordiantes) and convert it to cylindrical coordinate denoted by  integral transform (r,θ,z,t-coordinates). Finally we used  integral transform to solve heat equation in cylindrical coordinate.

Abstract: Data mining (DM) is an incredible innovation with extraordinary potential to help organizations centre around the main data in the information they have gathered about the conduct of their clients and likely clients. It finds data inside the information that inquiries and reports can't viably uncover. Overall, DM (to a great extent called information or data revelation) is the route toward analysing data according to substitute perspectives and summarizing it into significant information - information that can be used to assemble pay, diminishes costs, or both. DM writing computer programs is one of different logical gadgets for separating data. It grants customers to separate data from a wide scope of estimations or focuses, organize it, and summarize the associations perceived. In reality, DM is the path toward finding associations or models among numerous fields in enormous social datasets. Procedures used in DM measure come from a mix of computational strategies including Artificial Intelligence (AI), Statistics, Machine Learning (ML), and Database (DB) Systems. Aside from the centre techniques used to do the investigation, the cycle of DM can include different pre-handling ventures preceding executing the mining method. Also, a post-preparing stage is normally utilized to picture the aftereffects of the investigation (for example perceived examples or recovered data) in an instinctive and simple to-impart way. From a wide perspective, there are two significant standards of methods: expectation and information disclosure. It includes four sub-groups: a) Classification, Prediction and Regression, b) Clustering, c) Association Rule and Sequence Pattern Mining, and d) Outliers and Anomaly Detection. What's more, there are some generally new and energizing zones of information investigation, for example, spatial DM and graph DM that have been made conceivable through the structure squares of DM techniques. This survey not just advantages analyst to create solid examination subjects and distinguish gaps in the research areas yet additionally helps experts for data mining and Big Data (BD) software framework advancement.

Abstract: Classification is widely and largely used in data analysis, and pattern recognition. The data analysis aims to discover similarities between them and group them based on similarity into multiple classes. Artificial intelligence techniques are characterized by their great ability to classify objects and classify images. In this research, some artificial intelligence algorithms, represented by swarm optimization algorithms, were used to detect and classify plant diseases to healthy and unhealthy through images of different leaves of plants. Where plants are considered one of the most important organisms on this planet because of their important and fundamental role in the continuation of life and in achieving environmental balance, as well as in the economic side in many countries, and other benefits of high importance. These plants are apt to many different diseases. As a result of the technological development that the world witnessed in various areas of life, it was necessary to make use of it in the field of plant disease diagnosis, as many artificial intelligence techniques were employed in the discovery and diagnosis of plant diseases. In this paper, a new method is proposed to classify and distinguish a group of eight different plants to healthy and unhealthy based on the leaf images of these plants They are apples, cherries, grapes, peaches, peppers, potatoes, strawberries, and tomatoes using a hybrid optimization algorithm. In the first stage, the plant leaf images were collected and pre-processed to remove noise and improve contrast. In the second stage, the features were extracted based on the statistical feature extraction method, while in the third stage, the particle swarm (PSO) and chicken swarm optimization(CSO) algorithms were used to diagnose and classify plant diseases. Then these two algorithms were combined to produce a proposed hybrid algorithm called (PSO-CSO) hybrid method. The results obtained from these three algorithms were compared and the proposed method (PSO-CSO) obtained the best results compared to the two methods. Where the proposed method obtained in the first and second tests a diagnostic rate of (96.9%) and (98.18%), respectively.  

Abstract: Mobile malware has become a very hot research topic in the last few years, and this was due to the widespread usage of mobile devices all over the world. Like other systems, mobile devices are prune to different attacks that might invade user’s privacy and lead to private data leakage. Millions of Mobile application have been developed and used Worldwide, most of them are requiring permissions to work properly. The permission management problem is more apparent on Android systems rather than other mobile systems such as iOS. Some of these permissions might lead to successful security attacks on Android systems and hence lead to privacy leakage. To reduce the possibility of such attacks, many researchers have proposed mobile applications that help users to manage access permissions for their mobile applications. Most of the proposed systems lack the ability to profile users according to their preferences and do not provide automatic follow up with temporary granted permissions. In this research, we propose a User Centric Android Application Permission Manager tool called (UCAAPM), that provides an efficient and flexible way for managing permissions and profiling these permissions for each user, these profiles can be used on any Android device. UCAAPM will automatically follow up users permissions and grant/deny the permission on a scheduling basis defined by the user’s profile and according to his preferences. Experimental results showed that the tool works efficiently in terms of CPU, RAM, and power consumption, furthermore users are highly satisfied with using it.

Abstract: Big data is considered a remarkable aspect of development communication and information systems. In terms of delivery, retrieval, and storage, a vast volume of complex data exceeds the capability of conventional software and device capabilities. As a result, advanced alternative solutions that allow their control flow is becoming more prevalent. One of the most difficult fields of information management and technology is the real-time management, analysis, and processing of big data. These issues can be seen in a large amount of everyday produced data in a variety of places, including online social networks and the method of logging cell phone data. This survey looks at several studies that use a range of methods to handle, interpret, and process big data in real-time. Hence, the objective of this survey is to provide a comprehensive overview of the integration between real-time and big data fields of study with the field of E-learning. Finally, this survey also presents the colorful aspects of big data and their relationship to E-learning domains such that e-learning platforms, big data frameworks, and datasets used.