Volume 21 Issue 2
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Cloud computing has proven its efficiency, especially after the increasing number of cloud services offered by a wide range of cloud providers, from different domains. Despite, these cloud services are mostly heterogeneous. Consequently, and due to the rising interest of cloud consumers to adhere to a multi-cloud environment instead of being locked-in to one cloud provider, the need for semantically interconnecting different cloud services from different cloud providers is a crucial and important task to ensure. In addition, considerable research efforts proposed interoperability solutions leading to different representation models of cloud services. In this work, we present our solution to overcome this limitation, precisely in the IAAS service model. This solution is a framework permitting the semantic interoperability of different IAAS resources in a multi-cloud environment, in order to assist cloud consumers to retrieve the cloud resource that meets specific requirements.
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Type 2 diabetes mellitus (T2D) is a complex diabetes disease that is caused by high blood sugar, insulin resistance, and a relative lack of insulin. Many studies are trying to predict variant genes that causes this disease by using a sample disease model. In this paper we predict diabetic and normal persons by using fisher score feature selection, chi-2 feature selection and Logistic Regression supervised learning algorithm with best accuracy of 90.23%.
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Zozuliak-Sluchyk, Roksoliana;Tytova, Nataliia;Kozliuk, Oleksandr;Salata, Halyna;Ridei, Nataliia;Yashnyk, Svitlana;Litvinchuk, Svitlana 14
The method of analysis and research is applied in the work methods of managing student activities. Effective forms and methods of student quality management are determined. The model of management of educational process of students is offered. The model is to activate the student's potential, while maintaining the classical educational paradigm. The analysis of features of management of activity of students in modern model of education is carried out. -
The acceptance of smartphone applications in the learning field is one of the most significant challenges for higher education institutions in Saudi Arabia. These institutions serve large and varied sectors of society and have a tremendous impact on the knowledge gained by student segments at various ages. M-learning is of great importance because it provides access to learning through a wide range of mobile networks and allows students to learn at any time and in any place. There is a lack of quality requirements for M-learning applications in Saudi societies partly because of mandates for high levels of privacy and gender segregation in education (Garg, 2013; Sarrab et al., 2014). According to the Saudi Arabian education ministry policy, gender segregation in education reflects the country's religious and traditional values (Ministry of Education, 2013, No. 155). The opportunity of many applications would help the Saudi target audience more easily accept M-learning applications and expand their knowledge while maintaining government policy related to religious values and gender segregation in the educational environment. In addition, students can share information through the online framework without breaking religious restrictions. This study uses a quantitative perspective to focus on defining the technical aspects and learning requirements for distributing knowledge among students within the digital environment. Additionally, the framework of the unified theory of acceptance and use of technology (UTAUT) is used to modify new constructs, called application quality requirements, that consist of quality requirements for systems, information, and interfaces.
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Strembitska, Oksana;Tymoshenko, Roman;Mozhaiev, Mykhailo;Buslov, Pavlo;Kashyna, Ganna;Baranenko, Roman V.;Makiievskyi, Oleksii 40
In the article of instability on the peak power level, duration and repetition period of a multifrequency space-time signal, we calculated the maximum values of the errors of the parameters of the laws of spatial-phase-frequency control. Requirements for the accuracy of the location of the phase centers of the emitters in a cylindrical phased array antenna with pyramidal horns; it is advisable to calculate the radiation field using single-stage and multi-stage distribution laws. The phase centers of individual radiation sources of a cylindrical phased array antenna have been studied; they have almost no effect on the duration and period of recurrence. -
Morska, Nataliia;Fedorenko, Olena;Davydova, Olha;Andreev, Vitaly;Bohatyryova, Galina;Shcherbakova, Nataliia 44
The paper proposes to consider information technologies and their application in the educational process as a preparation of presentation material for students of higher educational institutions. The definition and place of information technologies in the educational space are considered. The object of research of this work is the pedagogical technology of presentation of educational information, which substantiates the pedagogical technology of visualization of educational information in higher education, as well as determine its composition and structure. The practical side of pedagogical technology of educational information presentation is considered. -
The movement control order and shutting down educational institution in Saudi Arabia has jeopardized the teaching and learning process. Education was shifted to distance learning in order to avoid any academic loss. In the middle of the Covid-19 crisis, there is a need to assess the full image of e-learning in Saudi Arabia. To investigate student and teachers' perception and acceptance, parents' attitudes and believes about distance education are the main goals of the study. The mix-method research design was employed to collect data. Three surveys were distributed to 100 students and 50 teachers and 50 parents from different educational institutions in Saudi Arabia, while semi-structured interviews were conducted with 10 parents. Random stratified and convenient sampling methods were adopted. Both descriptive and content analysis was conducted using SPSS25.0 and NVIVO software for quantitative and qualitative data accordingly. The findings showed that students are comfortable with remote education and are receiving enough support from schools and instructors but they think online education can't replace conventional face-to-face learning. Moreover, the results showed that teachers are having challenges in preparing online classes because of the development of conducting online classes and the lack of training. However, parents showed negative attitudes regarding the benefits and values of remote education and preferred conventional learning styles in elementary schools. Parents tended to reject and resist distance learning for several reasons: professional knowledge and lack of time to support their young kids in online classes, the shortcomings of e-learning, young children's inadequate self-regulation. Saudi parents are neither trained nor ready to use e-learning. The study provided suggestion and implications for teacher education and policymakers.
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The quality of the E-learning education in Saudi Arabia has been a major concern by many academicians, especially, and people in general as this platform has not been a priority for education. Not until recently, the world has been impacted by the Covid-19 pandemic, which makes every education institution shifted to the online platform to continue the education for the students. Thus, many studies on the perceptions on the online learning have been carried out, and though many are focusing on the perceptions by the education institutions' faculty and administration, there is a lack in the amount of study performed to analyse the students' perceptions of online learning during the pandemic time. The current study is conducted by utilising qualitative methods in order to collect information and investigate the students' perception regarding online learning during the pandemic Covid-19, based on their individual experiences. A number of fifteen (15) students were selected as respondents for the study, in which structured interviews were conducted by using a convenient sampling technique for data collection. Through the discussion, all of the positive and negative perceptions of online learning, as well as the factors contributing to those perceptions were identified. The results of the study found that the positive perceptions were contributed based on the flexibility, cost-effectiveness, availability of the electronic research databases, and well-designed online classroom interfaces. For the negative perceptions from using online learning platforms, the respondents informed that they were contributed by the lecturer's delayed feedback, lack of technical support by lecturers, low in self-esteem and self-motivation, feel isolated, one-way of educational methods, and poorly-designed class materials. Through the findings, the school's administration and lecturers would be able to know the struggles experienced by the students, and eventually come out with better solutions to improve their teaching methods.
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Farhat, Arwa Ben;Chandel, Shyam.Singh;Woo, Wai Lok;Adnene, Cherif 77
In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system. -
Piskunov, Stanislav;Yuriy, Rayisa;Shabelnyk, Tetiana;Kozyr, Anton;Bashynskyi, Kyrylo;Kovalev, Leonid;Piskunov, Mykola 88
A method and a mathematical algorithm for finding a quasi-optimal assignment plan with rectangular efficiency matrices are proposed. The developed algorithm can significantly reduce the time and computer memory consumption for its implementation in comparison with optimal methods. -
This paper aims to study the influence of environmental attitudes on the choice of non-professional investors. It highlights the role of environmental performance assurance on investment judgments. This choice is due to the motivation and importance that investors place on the disclosure of environmental information. The main purpose of the research is focused on the empirical approach justified by the use of a questionnaire addressed to 200 non-professional investors. The results show that attitudes towards the environment do not correlate with the importance that gives this category of investors to the environmental information.. Subsequently, the results prove that the disclosure of an environmental assurance report has a positive impact on investment judgments independently of their appreciation of the environmental information concerning that of financial order.
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The study aimed to analyze the current situation of the electronic portal of the Northern Border University, in terms of content and components, the extent of quality of use, service assurance and integrity, linguistic coverage of objective content, in addition to assessing the efficiency of the Blackboard e-learning platform and measuring the degree of safety of the portal, in addition to measuring the extent of satisfaction, through a sample that included 135 faculty members, as the researcher was keen to apply the case study methodology with the use of the questionnaire as the main tool for measurement, and the study found that there is an average trend among faculty members in the degree of content for the components of the portal and electronic security While it rose to good use, and very good at using the Blackboard platform.
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Allehaibi, Khalid Hamid Salman;Basori, Ahmad Hoirul;Albaqami, Nasser Nammas 110
The Coronavirus or COVID-19 is contagiousness virus that infected almost every single part of the world. This pandemic forced a major country did lockdown and stay at a home policy to reduce virus spread and the number of victims. Interactions between humans and robots form a popular subject of research worldwide. In medical robotics, the primary challenge is to implement natural interactions between robots and human users. Human communication consists of dynamic processes that involve joint attention and attracting each other. Coordinated care involves sharing among agents of behaviours, events, interests, and contexts in the world from time to time. The robotics arm is an expensive and complicated system because robot simulators are widely used instead of for rehabilitation purposes in medicine. Interaction in natural ways is necessary for disabled persons to work with the robot simulator. This article proposes a low-cost rehabilitation system by building an arm gesture tracking system based on a depth camera that can capture and interpret human gestures and use them as interactive commands for a robot simulator to perform specific tasks on the 3D block. The results show that the proposed system can help patients control the rotation and movement of the 3D arm using their hands. The pilot testing with healthy subjects yielded encouraging results. They could synchronize their actions with a 3D robotic arm to perform several repetitive tasks and exerting 19920 J of energy (kg.m2.S-2). The average of consumed energy mentioned before is in medium scale. Therefore, we relate this energy with rehabilitation performance as an initial stage and can be improved further with extra repetitive exercise to speed up the recovery process. -
Saeed, Soobia;Abdullah, Afnizanfaizal;Jhanjhi, NZ 120
The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively. -
This paper is in the intersection of software engineering and system engineering, two intimately intertwined disciplines. A dominating theme in this paper is the integral conceptualization of systems at large, as well as an underlying concern with software systems. In the software development life cycle, challenges still exist in translating requirements into a design artifact and then into an implementation (e.g., coding), then validating the results. From our perspective, software engineering requires an integrating paradigm toward a unified modeling orientation. Many methodologies, languages, and tools exist for facilitating system development processes. This paper is a venture into project development. To focus the materials, we concentrate on Harel's novel (and classic) development environment, which integrates a scenario-based engineering object orientation and statecharts through developing a railcar system. The railcar system is used as a detailed sample of translating requirements into a design artifact and then into an implementation, then validating the result. The project is re-cased as a single integrated modeling endeavor to be contrasted with the scenario and statecharts' development. The result of this scheme is an enriched understanding through experimenting with and contrasting various development methods of software projects.
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Bakhmat, Nataliia;Ridei, Nataliia;Liubarets, Vladyslava;Ivashchenko, Victoria;Petrovska, Olga;Averina, Kateryna 142
The main idea of the work is to address issues related to the activation of incentives in students of educational institutions, as an integral part of motivation in pedagogy, which is one of the priorities of higher education, in turn, the correct and timely application of motivational factors allows qualitative analysis, on the activation of cognitive interest in training. The purpose of the article is to study and qualitatively assess the methods and ways to stimulate students while studying in higher education. In solving the set tasks, a qualitative analysis of the known literature on the definition and application of motivation in the educational process. Definitely a stimulating process as a factor of psychological motivation. It is concluded that neither in understanding the essence of the stimulus, its role in the regulation of behavior, nor in understanding the relationship between stimulation and stimulus does not exist. -
Koffi, Dagou Dangui Augustin Sylvain Legrand;Ouattara, Nouho;Mambe, Digrais Moise;Oumtanaga, Souleymane;ADJE, Assohoun 148
The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training. -
Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.
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UNGUREANU, Ovidiu Costica;POPESCU, Marius-Constantin;CIOBANU, Daniela;UNGUREANU, Elena;SARLA, Calin Gabriel;CIOBANU, Alina-Elena;TODINCA, Paul 171
Currently, hospitals and medical practices have a large amount of unstructured information, gathered in time at each ward or practice by physicians in a wide range of medical branches. The data requires processing in order to be able to extract relevant information, which can be used to improve the medical system. It is useful for a physician to have access to a patient's entire medical history when he or she is in an emergency situation, as relevant information can be found about the patient's problems such as: allergies to various medications, personal history, or hereditary collateral conditions etc. If the information exists in a structured form, the detection of diseases based on specific symptoms is much easier, faster and with a higher degree of accuracy. Thus, physicians may investigate certain pathological profiles and conduct cohort clinical trials, including comparing the profile of a particular patient with other similar profiles that already have a confirmed diagnosis. Involving information technology in this field will change so the time which the physicians should spend in front of the computer into a much more beneficial one, providing them with the possibility for more interaction with the patient while listening to the patient's needs. The expert system, described in the paper, is an application for medical diagnostic of the most frequently met conditions, based on logical programming and on the theory of probabilities. The system rationale is a search item in the field basic knowledge on the condition. The web application described in the paper is implemented for the ward of pathological anatomy of a hospital in Romania. It aims to ease the healthcare staff's work, to create a connection of communication at one click between the necessary wards and to reduce the time lost with bureaucratic proceedings. The software (made in PHP programming language, by writing directly in the source code) is developed in order to ease the healthcare staff's activity, being created in a simpler and as elegant way as possible. -
The purpose of this study is to examine empirically the short- and long-run determinants of entrepreneurial activity in a sample of 15 the Middle East and North African economies between 2006 and 2018. More specifically, four groups of determinants are considered in the analysis, namely economic, demographic, business environment, and institutional. Given the autoregressive feature of the entrepreneurial activity process, a dynamic panel data model is estimated using the system GMM estimator. Findings reveal that unemployment, trade openness, population density, and economic freedom are the main drivers of new business creation in the short-run, while the cost and number of procedures to start a new business negatively affect entrepreneurship. In the long-run, the same findings hold true. Moreover, education and political stability and the absence of violence/terrorism positively affect entrepreneurial activity. Policy recommendations are accordingly designed.
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In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.
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Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.
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Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.
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TREY, Zacrada Francoise;GOORE, Bi Tra;BAGUI, K. Olivier;TIEBRE, Marie Solange 205
Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems. -
Yamani, Hanaa;AlHarthi, Ahmed;Elsigini, Waleed 212
The objectives of this research were to identify the digital competencies required for information science specialists at Saudi universities and to examine whether there existed conspicuous differences in the standpoint of these specialists due to years of work experience with regard to the importance of these competencies. A descriptive analytical method was used to accomplish these objectives while extracting the required digital competency list and ascertaining its importance. The research sample comprised 24 experts in the field of information science from several universities in the Kingdom of Saudi Arabia. The participants in the sample were asked to complete a questionnaire prepared to acquire the pertinent data in the period between January 5, 2021 and January 20, 2021. The results reveal that the digital competencies required for information science specialists at Saudi universities encompass general features such as the ability to use computer, Internet, Web2, Web3, and smartphone applications, digital learning resource development, data processing (big data) and its sharing via the Internet, system analysis, dealing with multiple electronic indexing applications and learning management systems and its features, using electronic bibliographic control tools, artificial intelligence tools, cybersecurity system maintenance, ability to comprehend and use different programming languages, simulation, and augmented reality applications, and knowledge and skills for 3D printing. Furthermore, no statistically significant differences were observed between the mean ranks of scores of specialists with less than 10 years of practical experience and those with practical experience of 10 years or more with regard to conferring importance to digital competencies. -
Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.
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Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.