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Mobile phones are considered extremely crucial for their daily usage due to their unique features such as mobility, availability, and compatibility. Moreover, the need for mobile apps periodically increases in terms of the variety of end-users as well as mobile platforms. Iraq is an emerging country concerning the development and requirements of mobile apps. There are numerous challenges faced by this trend on mobile phone app requirements, development, and usage. Consequently, the major consideration is how to build a suitable mobile app that can be adopted, adapted, and customized according to Iraqi market requirements. An essential fact worth mentioning is the increase in unemployed computer departments alumni in Iraq. Those who can develop mobile apps and software need to recruit suitable candidates. The purpose of this article is to develop a customized flight booking mobile application to partially reduce and solve the problem of unemployed computer alumni. The target of this application is to reduce time and effort for passengers and offer unique features. The developed application is the adoption of previous applications and the improvement of their inadequacies. By following a six-phase methodology to set a procedural technique for optimizing the use of the mobile application as a source of revenue for unemployed alumni as well as being beneficial for the customer. The validation and verification of the proposed application of this current research are done by evaluating and executing the mobile application. The results of this paper meet the main objective, where, the special features have been implemented and tested. In addition, the application has been installed on mobile phones and tested. Furthermore, the income from this application is obtained through purchasing it or subscribing to it for a specific duration. We encourage researchers as well as alumni who are targeted in this field to be attentive to cutting-edge technology and advancement and the concept of this paper.

The importance of big data implementations is increased due to large amount of gathered data via the online gates. The businesses and organizations would benefit from the big data analysis i.e. analyze the political, market, and social interests of the people. The Internet of Things (IoT) presents many facilities that support the big data transfer between various Internet objects. The integration between the big data and IoT offer a lot of implementations in the daily life like GPS, Satellites, and airplanes tracking. There are many challenges face the integration between big data transfer and IoT technology. The main challenges are the transfer architecture, transfer protocols, and the transfer security. The main aim of this paper is to review the useful architecture of IoT for the purpose of big data processing with the consideration of the various requirements such as the transfer protocol. This paper also reviews other important issues such as the security requirements and the multiple IoT applications. In addition, the future directions of the IoT-Big data are explained in this paper.

Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling TEchnique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (logistic model trees (LMT), simple logistic, and random forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, expectations maximization (EM), and farthest first) are enhanced after applying feature selection approaches and SMOTE filter.

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW.

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.

Prediction in data mining is a sophisticated task that is conducted in various disciplines. Given that the overall success of educational institutions can be measured by their students’ success, many studies are dedicated to predicting it. This paper provides a model of student’s success prediction based on Bayes algorithms and suggests the best algorithm based on performance details. Two built Bayes Algorithms (naive Bayes and Bayes network) were used in this model with students’ questionnaire answers. The questionnaire consists of 62 questions that cover the fields affecting students’ performance the most. The questions refer to health, social activity, relationships and academic performance. The questionnaire is constructed based on a Google form and open-source applications (LimeSurvey); the total number of student answers is 161. To build this model, the tool Weka 3.8 is used. The overall model design process can be divided into two stages. The first stage is finding the most correlated questions to the final class, and the second is applying algorithms and finding the optimal algorithm. A comparison is made between these two Bayes algorithms based on performance details. Finally, the naive Bayes algorithm is selected as an optimal choice for students’ success prediction.

The improvements in educational data mining (EDM) and machine learning motivated the academic staff to implement educational models to predict the performance of students and find the factors that increase their success. EDM faced many approaches for classifying, analyzing and predicting a student’s academic performance. This paper presents a model of prediction based on an artificial neural network (ANN) by implementing feature selection (FS). A questionnaire is built to collect students’ answers using LimeSurvey and google forms. The questionnaire holds a combination of 61 questions that cover many fields such as sports, health, residence, academic activities, social and managerial information. 161 students participated in the survey from two departments (Computer Science Department and Computer Information Systems Department), college of Computer Science and Information Technology, University of Basra. The data set is combined from two sources applications and is pre-processed by removing the uncompleted answers to produce 151 answers used in the model. Apart from the model, the FS approach is implemented to find the top correlated questions that affect the final class (Grade). The aim of FS is to eliminate the unimportant questions and find those which are important, besides improving the accuracy of the model. A combination of Four FS methods (Info Gain, Correlation, SVM and PCA) are tested and the average rank of these algorithms is obtained to find the top 30 questions out of 61 questions of the questionnaire. Artificial Neural Network is implemented to predict the grade (Pass (P) or Failed (F)). The model performance is compared with three previous models to prove its optimality.

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

Hepatitis is considered a liver disease that is difficult to diagnose at an early stage. The number of infected people exceeds two billion, with one million deaths and more than four million infected people registered per year. Therefore, there is a great need for a system to diagnose this disease. Hepatitis is a critical inflammatory liver disease with different causes, including viral infection, alcohol, and the autoimmune system. Several systems were proposed to diagnose and classify this disease, using numerical, rigid, and low level methods such as color histogram, standard deviation, and entropy. In our research, we leveraged these to linguistic, flexible, and high level by applying Fuzzy Logic theory using a Fuzzy Inference System (FIS). In this paper, a model is implemented through many stages where 3D-Discrete Wavelet is applied to remove noise from liver biopsy images. Then the Normalized Mean Color Histogram (NMCH) is extracted as a visual feature, and a FIS is built for diagnosing the class of hepatitis using 45 fuzzy IF-THEN rules. The system is evaluated by calculating precision and accuracy, and the results were both very accurate and interesting. Diagnosis accuracy reaches 96%, with the corresponding approximated time ranging between 0.10 – 0.15 seconds.

Internet of things (IOT) is an emerging field in Iraq that has gained research interest. One of its most important aspects concerns how to bind IOT to enhance healthcare in Iraq. This study was conducted at the College of Computer Science and Information Technology. It discussed the effect of not using IOT to manage critical life-threatening situations in Iraqi healthcare. In this study, we considered IOT technology, particularly the DRONE ambulance, using real data collected via a random questionnaire study in Basra. The data was analyzed using SPSS version 0.23. At the end of the study, we identified the factors that could have affected the use of IOT technology: awareness, security, cost, governmental support, collaboration, and professional behavior. The results showed that collaboration affected the use of IOT technology.

Higher education institutions aim to forecast student success which is an important research subject. Forecasting student success can enable teachers to prevent students from dropping out before final examinations, identify those who need additional help and boost institution ranking and prestige. Machine learning techniques in educational data mining aim to develop a model for discovering meaningful hidden patterns and exploring useful information from educational settings. The key traditional characteristics of students (demographic, academic background and behavioural features) are the main essential factors that can represent the training dataset for supervised machine learning algorithms. In this study, we compared the performances of several supervised machine learning algorithms, such as Decision Tree, Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Sequential Minimal Optimisation and Neural Network. We trained a model by using datasets provided by courses in the bachelor study programmes of the College of Computer Science and Information Technology, University of Basra, for academic years 2017–2018 and 2018–2019 to predict student performance on final examinations. Results indicated that logistic regression classifier is the most accurate in predicting the exact final grades of students (68.7% for passed and 88.8% for failed).

One of the best ways to enhance the performance of all companies and manage Customer Satisfaction is to get the consumers’ complaints and analyze them in order to fix them. These complaints represent the consumers’ behavior to the companies and how these company’s response to them. Besides, customers’ satisfaction is the main goal of all companies and this goal cannot achieve if they do not handle the customers’ complaints. The paper represents a framework of complaint data mart construction where the source data are thousands of complaints about services and financial products of companies. The data mart represents the first step to implement an enterprise data warehouse (DW) to support strategic decisions. Reports are constructed to help analysts and decision-makers to support their decisions related to consumers’ complaints and how to improve service quality. Two different categories of on-line analytical processing (OLAP) reports are used, offline and web OLAP reports. The two types of reports provide a deep view of the data and present the analysts with flexible charts that can be used in supporting strategic decisions. SQL Server Management Studio (SSMS), SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS) 2014 beside SQL Server Data Tools (SSDT) 2013 is used to build the data mart staging table, schema, cube, and OLAP reports. MS Excel Pivot table 2010 is used also to import the cube and build offline reports and implementing OLAP processes. This data mart can be utilized by consumers themselves besides decision-makers and analysts. The data mart can measure how the companies fix complaints issues and prevent them from occurring again and identify the factors that influence financial customers’ satisfaction.

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees’ data of Basra Oil Company for over 15 years. A human resource data mart can provide a base platform to perform a different analysis operation to support the right decisions. Different OLAP reports are implemented to help analysts and decision-makers to get the answers for their questions as OLAP queries. Two categories of reports are implemented offline reports using Microsoft Excel Pivot Table 2010 and web OLAP reports using SQL Server Reporting Service 2014 (SSRS). The tools used to implement data mart vary from SQL Server Management Services (SSMS) 2014, SQL Server Integration Service 2014 (SSIS), SQL Server Analytical Service 2014 (SSAS), SQL Server Reporting Service 2014 (SSRS), SQL Server Data Tools 2013 (SSDT), and Microsoft Excel Pivot Table 2010.

Most of the ecological systems have the elements to produce divisions and dynamics behavior, and food chains are ecosystems with familiar structure. Modeling efforts of the dynamics of food chains which are initiated long ago confirm that food chains have very rich dynamics. This work focused on applying biological mathematical model to analyzing predation or competition relationships in the natural environment between predators and preys. We are interesting to consider two species of animals; interdependence might arise because one species (the “prey”) serves as a food source for the other species (the “predator”). Models of this type are thus called predator-prey models. Initially, we exercised the mathematical model of one prey and one predator. Later on, we considered very excited model that dealing with one predator and two preys. The populations of the prey and predator will be modeled by two differential equations for the early case and with three differential equations for a later model. The Matlab command ode45 can be used to solve such systems of differential equations.

The medical state of patients faced a lot of researches in the last decades to enhance the medical treatment and save the lives of patients. Based on that, the automated medical diagnosis became a necessity due to its important role in reducing clinicians’ efforts and providing fast and accurate results. Remote Patients Monitoring (RPM) devices are used for this purpose by making tests and sending the results to the professionals. The proposed system is built based on three medical indicators (blood pressure, heart rate, and body temperature).The system combined both fuzzy inference system and Arduino to collect medical data, process them and make decision. The medical data are collected through sensors related to each indicator. Arduino is used to collect the sensory data and send them to Fuzzy Inference System (FIS) in order to get the infer result information to make decision. FIS gets the medical sensory data, processes them to make the assessment and sends medical state as a numeric result to Arduino to make the decision. FIS used a knowledge base written in IF-THEN rules composed of multiple linguistic variables. The proposed system can be used to help the doctors in determining the initial medical state of patients and make the right decision.

In the last decade, Tumor diagnosis data received a lot of attention and researches due to its importance. The development in the hardware and software technologies led to necessity for tools not to store them but also to analyze these medical records and find results to support decisions. Association rules and decision tree are interested mining algorithms which can be used to find and explore relations between attributes in a data set. In this paper, association rules and decision tree algorithms are applied with tumor data set in order to get analytical results to support medical decisions. The results can be depended in early detection of tumor rather to provide second opinion for clinicians. Data mining algorithms are applied on 7544 medical diagnosis cases from more than 10 medical centers in Basra, Iraq.

Prediction in data mining is a sophisticated task that is conducted in various disciplines. Given that the overall success of educational institutions can be measured by their students’ success, many studies are dedicated to predicting it. This paper provides a model of student’s success prediction based on Bayes algorithms and suggests the best algorithm based on performance details. Two built Bayes Algorithms (naive Bayes and Bayes network) were used in this model with students’ questionnaire answers. The questionnaire consists of 62 questions that cover the fields affecting students’ performance the most. The questions refer to health, social activity, relationships and academic performance. The questionnaire is constructed based on a Google form and open-source applications (LimeSurvey); the total number of student answers is 161. To build this model, the tool Weka 3.8 is used. The overall model design process can be divided into two stages. The first stage is finding the most correlated questions to the final class, and the second is applying algorithms and finding the optimal algorithm. A comparison is made between these two Bayes algorithms based on performance details. Finally, the naive Bayes algorithm is selected as an optimal choice for students’ success prediction.

In the last decade, data warehousing became the most important technology in the field of decision support especially related to critical situations. Most of Decision Support Systems (DSS) related to clinical decisions depend on Data Warehouse (DW) and On-line Analytical Processing (OLAP) in analyzing the related information in order to assist clinicians in making right decisions. In this work, cancer data warehouse (CDW) is designed and implemented based on medical paper records of cancer infections. At first, thousands of medical cancer records were turned from paper based into electronic based by registering them in excel files and access database. These files became the source of the proposed cancer DW. Secondly, the proposed cancer DW is depended as a platform to perform different (OLAP) operations to get analytical results that support decisions maker related to cancer infections.

Diseases registry databases in Iraqi hospitals hold huge information which can be used to support strategic clinical decisions. The clinicians and professionals often need tools to get the valuable information. This information cannot be got with normal functions used with operational databases. Using On-Line Analytical Processing (OLAP) gives insight and clear view on the information from different points. It makes it very easy and fast to view results as reports. The researchers used with clinical data warehouse to give decision makers on-time results to assist them in support their decisions. Two database sources used to construct and build data warehouse in order to use OLAP to view multidimensional data.

Almost all the Decision Makers in medical path need a good platform which providing analytical results to use themin order to support their decisions.Due to vast amount of patient's records in Electronic Health Record(EHR) rather than Electronic Medical Records(EMR),Medical data can be used to get valuable information to support decisions of specialists in clinical path.Clinicaldata warehouse can combine different sources of clinicaldata into single repository and use it to produce analytical information to support decisions. Since Online Analytical Processing (OLAP) can be used in analyzing data and find the relationship between many factors and provide good view of data from many points of view so using data warehousing and OLAP techniques is the good choice for helping professionals and analysts in finding their goals.We will use diseases registry data (Part from EMR) to design and implement data warehouse as the first step that could be used later by OLAP or Data Mining to support Clinical Decisions. In this work we combined different sources of diseases registry dataintosingle repository.