The Software Effort Estimation (SEE) process is used to predict the effort involved in developing a software project inaccuracy. This process permeates the development stages of the software project. Although there are many models for... more
The Software Effort Estimation (SEE) process is used to predict the effort involved in developing a software project inaccuracy. This process permeates the development stages of the software project. Although there are many models for estimating the effort, we will do our best to follow the best model for estimating the effort. The main objective of this research is to apply the Long Short-Term Memory (LSTM) algorithm and discover its accuracy in estimating the software effort. Then, compare the results with other models taken from previous work and find the best one among the models. The LSTM algorithm has been used with two data sets: China, which contains 499 projects, and Kitchenham, 145 projects. The metrics adopted to measure the accuracy of the models are Root Mean Squared Error (RMSE), respectively, Mean Absolute Error (MAE), and R_Squared. The results of RMSE, MAE, and R_Squared are 0.016, 0.019, and 0.972, respectively, in the china dataset. At the same time, the RMSE, MAE, and R_Squared results are also 0.017, 0.058, and 0.896, respectively, in the Kitchenham dataset. Therefore, the experiments' results showed the LSTM algorithm's superiority over the other algorithms in both data sets.
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Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem.... more
Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the Gray Wolf Optimization algorithm (GWO) and Artificial Bee Colony algorithm (ABC), which can effectively recognize Hancitor in networks.
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Detecting anomalous traffic on the internet has remained an issue of concern for the community of security researchers over the years. The advances in the area of computing performance, in terms of processing power and storage, have... more
Detecting anomalous traffic on the internet has remained an issue of concern for the community of security researchers over the years. The advances in the area of computing performance, in terms of processing power and storage, have fostered their ability to host resource-intensive intelligent algorithms, to detect intrusive activity, in a timely manner. As part of this project, we study and analyse the performance of Self Organization Map (SOM) Artificial Neural Network, when implemented as part of an Intrusion Detection System, to detect anomalies on acknowledge Discovery in Databases KDD 99 and NSL-KDD datasets of internet traffic activity simulation. Results obtained are compared and analysed based on several performance metrics, where the detection rate for KDD 99 dataset is 92.37%, while detection rate for NSL-KDD dataset is 75.49%.
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The field of Human Activity Recognition (HAR) is an active research field in which methods are being developed to understand human behavior by interpreting features obtained from various sources, these activities can be recognized using... more
The field of Human Activity Recognition (HAR) is an active research field in which methods are being developed to understand human behavior by interpreting features obtained from various sources, these activities can be recognized using interactive sensors that are affected by human movement. Sensor can embed elements within Smartphones or Personal Digital Assistants (PDAs). The great increase in smart phone users and the increase in the sensor ability of these smart phones, and users usually carry their smartphones with them. This fact makes HAR more important and accepted. In this survey, A number of previous studies were studied and analyzed, where we prepared a comparison of the research works conducted over the period 2010-2020 in human activity recognition using Smartphone sensors. Comparison charts highlight their most important aspects such as a type of sensor used, activities, sensor placement, HARsystem type (offline, online), computing device, classifier (type of algorith...
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Machine printed Arabic Character Recognition System (MACRS] is concerned with recognition of machine printed alphanumeric Arabic characters. In the present work, characters have been represented (extracted) by using geometric moment... more
Machine printed Arabic Character Recognition System (MACRS] is concerned with recognition of machine printed alphanumeric Arabic characters. In the present work, characters have been represented (extracted) by using geometric moment invariant (3 order). The technique used in this research can be divided into three major steps. The first step is digitization and preprocessing to create connected component, detect the skew of a character image and correct it. The second is feature extraction, where geometric moment invariant features of the input, Arabic character is used to extract features. Finally, we describe an advanced system of classification using probabilistic neural networks structure which yields significant speed improvements. MACRS is tested using 2961 patterns for a total 141 classes with roughly 21 patterns in each class. It is important to note here that the system performs extremely well with recognition rates ranging between 84% and 88% on different folds and the ove...
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In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type... more
In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and ...
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... in 1987, many research efforts have been focused on how to effectively and accurately construct detection models. ... have good generalization capabilities and can be efficiently used for approximation task, classification and... more
... in 1987, many research efforts have been focused on how to effectively and accurately construct detection models. ... have good generalization capabilities and can be efficiently used for approximation task, classification and processing of noisy and incomplete data, what is ...
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The process to identify the real painting of an artist is considered a difficult task and needs expert and time to do this operation. This paper presents an automated system to simulate Grey Wolf Optimization Swarm Intelligence Algorithm... more
The process to identify the real painting of an artist is considered a difficult task and needs expert and time to do this operation. This paper presents an automated system to simulate Grey Wolf Optimization Swarm Intelligence Algorithm to distinguish between Modigliani’s paintings and his contemporaries is designed and tested. An automated system to distinguish between Modigliani’s painting and his contemporaries consists of three processing steps. In the first step, the digital paintings for Modigliani and his contemporaries are processed automatically, the second step is feature extraction step, and the last step is the recognition step used Grey Wolf algorithm. An automated system that simulates the Grey Wolf Optimization Algorithm to distinguish between Modigliani’s paintings and his contemporaries has been developed and tested. The testing results show that the rate of the difference is 91.5%.
Loading balance is one of the most important problems in computer cloud environment because it needs to optimize task scheduling. The environment of cloud computing is not sufficient to give good quality for user’s service because the... more
Loading balance is one of the most important problems in computer cloud environment because it needs to optimize task scheduling. The environment of cloud computing is not sufficient to give good quality for user’s service because the tasks schedule and allocation are not of the same loads. In this paper a new strategy is proposed to balance the loads using swarm Bat Algorithm (BA). Bat algorithm behaviour uses echolocation to detect their prey for creating load balancing model. The proposed strategy depends on Naïve-Bayes algorithm to classify virtual machines (VMs) that is used to update the migrated task for the information used by the bats. The migrated tasks assigned heavy loaded VMs to light loaded VM providing numbers of tasks have lowest priority in task’s queue. The tasks from heavy-loaded migrated virtual machines are migrating to lighted-loaded virtual machine, it is found that tasks have high-priority from minimizing of services in VM and avoid tasks have large number of...
This research introduces an automatic ultrasound human pregnancy images classification (male or female) system using artificial neural network to classify the pregnancy images. An automatic ultrasound human pregnancy images system... more
This research introduces an automatic ultrasound human pregnancy images classification (male or female) system using artificial neural network to classify the pregnancy images. An automatic ultrasound human pregnancy images system consists of three Modules: The first is preprocessing ultrasound images (noise removing , image normalization and segmentation), second is feature extraction module then pregnancy classification module. After preprocessing, features extracting by using kernel principal component analysis (kernel PCA) after that, Elman neural network is used to a classify training and testing these pregnancy images. The system produces promising results for pregnancy images classification.
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We report on the development of phase-separated, disordered nanopillars that are integrated as corrugated or as planarized light scattering layers for the outcoupling of waveguide and substrate modes in organic light emitting diodes.
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The advent of the intelligent transport systems has prompted traffic simulation to become one of the most used approaches for traffic analysis in support of the design and evaluation of traffic systems. The capability of traffic... more
The advent of the intelligent transport systems has prompted traffic simulation to become one of the most used approaches for traffic analysis in support of the design and evaluation of traffic systems. The capability of traffic simulation to emulate the time variability of traffic events makes it a matchless facility for capturing the complexity of traffic systems. There are many researches which implement fuzzy but to specific traffic problems and there are many traffic simulation applications but with no support for fuzzy logic, This paper presents a simulation environment, designed for testing and evaluation of any fuzzy logic based traffic management systems. The user can simulate any traffic isolated intersection or intersection network with multiple lanes, specify input parameters , build fuzzy rules that control the traffic flow and simulate the model to monitor the efficiency of model by notice the output parameter. A graphical user interface allows visualization of the simulation, including animation of vehicle movements.
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The advent of the intelligent transport systems has prompted traffic simulation to become one of the most used approaches for traffic analysis in support of the design and evaluation of traffic systems. The capability of traffic... more
The advent of the intelligent transport systems has prompted traffic simulation to become one of the most used approaches for traffic analysis in support of the design and evaluation of traffic systems. The capability of traffic simulation to emulate the time variability of traffic events makes it a matchless facility for capturing the complexity of traffic systems. There are many researches which implement fuzzy but to specific traffic problems and there are many traffic simulation applications but with no support for fuzzy logic, This paper presents a simulation environment, designed for testing and evaluation of any fuzzy logic based traffic management systems. The user can simulate any traffic isolated intersection or intersection network with multiple lanes, specify input parameters , build fuzzy rules that control the traffic flow and simulate the model to monitor the efficiency of model by notice the output parameter. A graphical user interface allows visualization of the simulation, including animation of vehicle movements.