Hinders of Cloud Computing Usage in Higher Education in Iraq: A Model Development

Cloud computing (CC) is a trendy technology that is being used in business and daily life. However, limited studies is found on higher education usage. The barriers and obstacles that confront the usage is not clear and in particular in developing countries. The purpose of this study is to examine the barriers and obstacle that confront the usage CC services in Barash University in Iraq. Using the technology organization environment framework and the internal external factor (IE-TOE), the study proposed the conceptual framework. The data was collected from academic, non-academic staff and students using convivence sampling technique. The data was analyzed using Smart PLS. The findings showed that organizational obstacle followed by technological, internal and external factors, and environmental factors are the most severe obstacles that confront the university in using CC services. Decision makers can benefit from the developed model to ease the implementation of CC

Characterizations of Synthesized Nano Hybrid Octyl Gallate with ZnO LHS and Determined its Anti Microbial Activities

ZnO nanostructures were synthesized by sol-gel reaction between zinc oxide and (Octyl gallate), Octyl gallate Evolution was characterized by UV-VIS spectroscopy,), Fourier  transform  infrared  (FT-IR) spectroscopy technique, X-ray  diffraction  (XRD), Scanning electron microscopy (SEM) and  Atomic Force Microscope (AFM). A well crystalline fiber-like structure was formed. Controlled release was fitted with different modules. Antimicrobial activity tests were done against eight bacterial and three fungal isolates, results showed that nano hybrid composite was efficient in inhibiting both gram positive and gram negative bacteria as well as the fungal isolates used in this study. Keywords: ZnO; nano materials; antimicrobial activity; intercalations

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms

A REVIEW ON INTERNET OF THINGS ARCHITECTURE FOR BIG DATA PROCESSING

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

Investigating the relationship between knowledge management practices and organizational learning practices in the universities’ environment

The concept of knowledge management (KM) and organizational learning (OL) has been embraced by organizations to complement each other. Higher education institutions have embraced KM and OL as a means to improve organizational efficiency. This research explores the link between KM and OL. The target population included all the 432 academicians and administrators from 35 public universities in Iraq. The sampling was selected using a stratified random sampling technique. The correlation among the components of KM and OL was tested as well as the effect of KM components on OL. The findings were derived using smart partial least square. The findings showed that there is significant correlation between components of KM and components of OL. The regression analysis showed also that the effect of KM and its components; knowledge creation, knowledge sharing, knowledge storage, knowledge application and knowledge acquisition on OL are significant. These findings provide insights to universities management on strategies to implement KM practices that can align with OL practices to assure dynamic lifelong mechanisms for the basic daily activities such as teaching, learning, researching, and supervision

A Review of Various Steganography Techniques in Cloud Computing

One of the latest trends in IT sector is cloud computing. It develops the capabilities of organizations dynamically without training new employees, obtaining new software licenses or investing in infrastructure. At present, user keeps and share a high amount of data on cloud, and hence, the security of cloud computing is necessary so that there is no threat to any of the use

A Review of Various Steganography Techniques in Cloud Computing

One of the latest trends in IT sector is cloud computing. It develops the capabilities of organizations dynamically without training new employees, obtaining new software licenses or investing in infrastructure. At present, user keeps and share a high amount of data on cloud, and hence, the security of cloud computing is necessary so that there is no threat to any of the user’s data. Steganography is becoming a standard practice for both cloud users and cloud service providers as a mechanism against unauthorized surveillance. Steganography refers to writing hidden messages in a way that only the sender and receiver have the ability to safely know and transfer the hidden information in the means of communications. The aim of this paper is to provide an overview of steganography in cloud computing and compare various studies on the basis of technique selection, carrier formats, payload capacity and embedding algorithm to open important research directions

CLINICAL DATA WAREHOUSE: A REVIEW

- Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers in the clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is an important solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a central repository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinical decisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools that view 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 Scholar are used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. These papers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to find insights related to aspects of CDW. This review can contribute answers to questions related to CDW and provide recommendations for implementing a successful CDW

epcAware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing

The Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud for analysis, due to longer distances and delays. Fog/edge computing is a new model for analyzing and acting on time-sensitive data (real-time applications) at the network edge, adjacent to where it is produced. The model sends only selected data to the cloud for analysis and long-term storage. Furthermore, cloud services provided by large companies such as Google, can also be localized to minimize the response time and increase service agility. This could be accomplished through deploying small-scale datacenters (reffered to by name as cloudlets) where essential, closer to customers (IoT devices) and connected to a centrealised cloud through networks - which form a multi-access edge cloud (MEC). The MEC setup involves three different parties, i.e. service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management a defficult job. In the literature, various resource management techniques have been suggested in the context of what kind of services should they host and how the available resources should be allocated to customers’ applications, particularly, if mobility is involved. However, the existing literature considers the resource management problem with respect to a single party. In this paper, we assume resource management with respect to all three parties i.e. IaaS, SaaS, NaaS; and suggest a game theoritic resource management technique that minimises infrastructure energy consumption and costs while ensuring applications performance. Our empirical evaluation, using real workload traces from Google’s cluster, suggests that our approach could reduce up to 11.95% energy consumption, and approximately 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27% energy bills and NaaS can increase their costs savings up to 18.52% as compared to other methods

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented