Journal of Al-Qadisiyah for Computer Science and Mathematics

Software-defined network (SDN) is based on separating data plane from control plane and decision-making processes to centralize network control. The OpenFlow (OF) protocol is the most common and widely used in software-defined network for communicating and controlling switches. With this protocol, the switch learns the routing information from the controller and then passes data packets based on this information. One of the most important components of the SDN is the controller, which is the smartest component of the network. Many controllers have been developed since the advent of SDN. One of the most important and famous controllers is Ryu. Due to the importance of the Ryu controller in SDN, in this article, the performance of the Ryu controller in terms of latency and throughput are evaluated. Cbench is used for measuring throughput and latency of this controller.

It is known that some dynamical systems have not analytical solution which is impossible or sometime difficult to find. In fact, undertaking a manual simulation or using complicated methods (need to find derivatives such that Newton method) of such systems is a difficult task due to the complexity of the computations. Therefore, a computerized simulation is frequently required to find accurate results in fast execution time especially for solving biological problems like Predator Prey model. This paper aims to find integral representation solutions for Predator Prey model using the new method proposed by Mohammed, J. & Tyukin, I in [16]. These integral representation solutions are periodic and depend on parameters of the Predator Prey model explicitly, then, however, it requires to estimate these parameters. Application of the method to solve parameter estimation problem of predator–prey model is illustrated in details with constructing the solutions for state and parameter estimation.

Multivariate analysis of covariance models are widely used in human societies, principally in health, ecology, environmental science and psychological perspective. As such, the researchers face the task of seeking for ways to improve the performance of these models to respond to the requirements which are treating. This paper proposes an extension of two way MANOVA to two way MANCOVA model under effect one covariate factor for heteroscedastic and unbalanced cell sizes. A type III sums squares of an influence is computed that adjusted for all other influences for suggested two way MANCOVA model, no matter in which order they are included. As classical tests could dangerously biased when homogeneity violates the assumption of cell covariance matrices, the suggested model implemented the modified tests obtained by Zhang & Xiao (2012) to test the hypothesis of model

A solution is proposed for determining and organizing the size of the FIFO-stack in memory, required for load the coordinates of adjoining pixels in an image segmentation algorithm based on region growing. The FIFO-stack, organized on the idea of a ring multi-bit shift register, is considered, and the expression of memory calculations is performed through storing process in parallel registers. The conditions for maximum loading of the memory stack are formulated, for which an expression is obtained that allowing to accurately determining the required size of the FIFO-stack, which provides memory resources savings.

The main purpose of this paper is to express a new class of separation axioms based on open sets, known as Nb open, as well as to analyze and verify several essential ideas linked with this class.

As technology advances worldwide, several governments and health organizations develop or implement mobile health systems and applications to treat and monitor patients. Several health systems based on the Internet of Things (IoT) applications have been built to monitor patients remotely. Different mechanisms and techniques have been used in healthcare applications, such as centralized, decentralized, and hybrid. In addition, many communication technologies were used, such as Bluetooth, GPS, WiFi, and others. This systematic analysis discusses these aspects, focusing on user security, privacy, effective data extraction, and confidential handling. This review investigates all researches published between 2015 and 2021, where four databases (IEEE Xplore, ScienceDirect, Research Gate, and Springer) were considered. A detail of used exclusion criteria and selection procedure to assess the collected publications were presented. Only thirteen papers have matched the criteria thoroughly examined and included in this study. The findings are provided throughout the papers to highlight gaps and concerns in an IoT-based healthcare system that will be constructed to effectively decrease potential dangers during messaging while preserving total user privacy and security.

Medical image segmentation is of large significance in supporting information about human body structures which assist physicians in correct diagnosis to determine doing radiotherapy or surgeries. Therefore, accurate interest region detection in ultrasound images represents a challenging function and hence needs to apply more trusted tools to gain the best segmentation and classification of kidney stones. This challenge in ultrasound images includes many factors like low contrast, occlusions, signal deviations, and noise made it difficult to determine these stone’s boundaries. This paper applied gradient vector flow (GVF) model which has a large capture range to identify the image boundaries of kidney stones region and estimate variation in stone measurement to prepare a suitable treatment diagnosis.

In this paper, we defined and studied the idea of principally -g-supplemented modules as an advanced concept of -g-supplemented modules. Many properties, characterizations and examples of these modules are discussed. Also, a number of relations between these modules and other kinds of modules are examined in this work.

Many numerous genera of bacteria that have a foremost influence on human health. One of them is Enterococcus, which has numerous species excluding Faecium and Faecalis. It reasons many sicknesses such as the bowel inflammation bacteremia, urinary tract infection, wound inflammation and meningitis. In instruction to describe, the fitting antibiotic used to luxury these diseases, where the antibiotics use to inhibit Faecium bacteria different from Faecalis bacteria, it is necessary to determine the exact type of bacteria, especially that the two types Faecium and Faecalis are similar in shape to the extent large.

The target of this research is to classify Enterococcus bacteria into Faecium and Faecalis by using Bag of features technique, which accomplished and tested on database of the two species bacteria. The development method that combines circular shift pixel images, discrete Rigelet transform and Bag of features method which gives a better result on the non-stationary signals, which are executed on the above technique and newly trained classifier functional on blood agar plated images.

In biological methods, the biologists cultivate bacteria in blood agar plates to determine the type of bacteria where examined in the electron microscope by using the eyes to the dependence on the form of bacteria and often they need to re-implant the sample in other mediums in order to know the type of bacteria. This requires the cost of media and chemical materials and time. The method used in this research, the input for the program is blood agar plates images only and the type of bacteria is directly and accurately, this means that the method adopted in the program reduces costs and effort in biological methods, thus, this research helps biologists to be able to diagnose bacteria with less effort, cost and time.
The results exposed that the enriched classification method stretches better classification results on samples used images, when the average accuracy in the training set and validation set by using Bag of feature method are (0.83 and 0.79) respectively, they were increased in the proposed method to (1.00 and 0.92) respectively.

In this article, we introduced and investigated some relations between the concept of strongly generalized -radical supplemented module (for short, -module) and many other types of modules.

Differential geometry, a branch of mathematics that studies the geometry of curves, surfaces, and manifolds (the higher-dimensional analogs of surfaces). Curve is a smoothly flowing line (non sharp changes) or a curve must bend (change direction) but in Mathematics a straight line also a curve. We will present new definitions and theorems.

Electroencephalography (EEG) is a hot topic all around the world. EEG signals, a series of measurements taken using electrodes on the scalp, can indicate brain activity. They are more private, sensitive, and difficult to steal and recreate. EEG data are increasingly commonly employed in diagnosing brain illnesses and the field of Brain-Computer interfaces thanks to advancements in biomedical signal processing techniques (BCI). BCI is a brain-computer interface that uses electrical impulses from the brain to communicate. EEG signals are used to interpret the electrical activity of the brain. The electrical activity of the brain is read using EEG signals. Many studies are being conducted in many fields to benefit from this technology. Studying EEG gives a solid understanding of how brain signals function in various moods and activities. Lie detection is a new technology that is being used to combat crime. Traditionally, this has been accomplished by language analysis, face and body movement recognition, training observation, and voice stress analysis. EEG analysis provides a better understanding of brain activity thanks to advances in cognitive science and neuroscience. Deception identification has become a severe issue as crime has increased. Previous surveys have discussed numerous approaches supported with experimental outcomes and compared them. This paper addressed each direction and offered different sets of characteristics and electrodes, EEG signal preprocessing, feature extraction, feature selection, and classification. Also, it discusses many methods which may need some adjustments at each phase of brain signal processing for lie detection.

The necessity for audio compression continues to be a critical issue., as it is associated with lowering the data capacity of one of the most widely distributed digital files transmitted between remote parties, audio compression remains a critical topic. The effectiveness of the two sound pressure units in this article has been tested. Because of this industry's importance,, which is reflected in storage capacity and transportation needs, Over the last two decades, sound pressure has been the topic of intense investigation. The rapid expansion of the computer industry is leading to a growing demand for high- quality audio data; As a result, the development of sound compression methods, of which there are two types: lossy and lossless, is critical. The purpose of this study is to explore sound compression techniques and to highlight the suitability and applications of each approach.

There are many characteristics of human beings, such as fingerprints, DNA, and retinal pigmentation that are essential. A person's voice is unique to each individual. Humans use speech to communicate their thoughts and feelings. The process of determining one's mental state involves expressing one's basic emotions in words. A person's emotions play a significant part in his or her daily existence. In order to convey one's thoughts and feelings to others, it is essential to use this method. Emotions can be discerned from speech information because humans have a built-in ability to do so. The selection of an emotion recognition body (speech database), the identification of numerous variables connected to speech, and the selection of a suitable classification model are the main hurdles for emotion recognition. To identify emotions, an emotion identification system analyzes auditory structural elements of speech. The analysis is based on multiple research papers and includes an in-depth examination of the methodology and data sets. The study discovered that emotion detection is accomplished using four distinct methodologies: physiological signal recognition, facial expression recognition, a variety of speech signals, and text semantics in both objective and subjective databases such as JAFFE, CK+, Berlin emotional database, and SAVEE. In general, these techniques enable the identification of seven basic emotions. To determine the emotion, the audio expression for eight emotions (happy, angry, sad, depressed, bored, anxious, afraid, and apprehensive), all published research maintain an average level of accuracy. The major goal of this survey is to compare and contrast numerous previous survey methodologies, which are backed up by empirical evidence. This study covered signal collection processing, feature extraction, and signal classification, as well as the pros and downsides of each approach. It also goes over a number of strategies that may need to be tweaked at each step of speech emotion recognition.