Search results for: extracurricular English learning

Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Waleed Maqableh, Ahmad Al-Hamad, Manjit Sidhu

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Augmented reality (AR) technology has the capability to provide many benefits in the field of education as a modern technology which aided learning and improved the learning experience. This paper evaluates AR based application with multiple markers interaction technique (touch-to-print) which is designed for analyzing the kinematics of 4BL mechanism in mechanical engineering. The application is termed as AR-4BL-MAST and it allows the users to touch the symbols on a paper in natural way of interaction. The evaluation of this application was performed with mechanical engineering students and human–computer interaction (HCI) experts to test its effectiveness as a tangible user interface application where the statistical results show its ability as an interaction technique, and it gives the users more freedom in interaction with the virtual mechanical objects.

Keywords: augmented reality, multimedia, user interface, engineering, education technology

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Jiao Li

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Language teachers have been expected to play diversified new roles in times of educational changes. Considering the critical role that materials play in teaching and learning, language teachers have been increasingly involved in developing materials. Using identity as an analytic lens, this study aims to explore language teachers’ experiences as materials developers in China, focusing on the challenges they face and responses to them. It will adopt a multimethod approach. At the first stage, about 12 language teachers who have developed or are developing materials will be interviewed to have a broad view of their experiences. At the second stage, three language teachers who are developing materials will be studied by collecting interview data, policy documents, and data obtained from online observation of their group meetings so as to gain a deeper understanding of their experiences in materials development. It is expected that this study would have implications for teacher development, materials development, and curriculum development as well.

Keywords: educational changes, teacher development, teacher identity, teacher learning, materials development

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Authors: Danzhao Cheng, Eugene Ch'ng

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Virtual Reality technologies can reconstruct cultural heritage objects and sites to a level of realism. Concentrating mostly on documenting authentic data and accurate representations of tangible contents, current virtual heritage is limited to accumulating visually presented objects. Such constructions, however, are fragmentary and may not convey the inherent significance of heritage in a meaningful way. In order to contextualise fragmentary historical contents where history can be told, a strategy is to create a guided narrative via role-playing. Such an approach can strengthen the logical connections of cultural elements and facilitate creative synthesis within the virtual world. This project successfully reconstructed the Ningbo Sanjiangkou VR site in Yuan Dynasty combining VR technology and role-play game approach. The results with 80 pairs of participants suggest that VR role-playing can be beneficial in a number of ways. Firstly, it creates thematic interactivity which encourages users to explore the virtual heritage in a more entertaining way with task-oriented goals. Secondly, the experience becomes highly engaging since users can interpret a historical context through the perspective of specific roles that exist in past societies. Thirdly, personalisation allows open-ended sequences of the expedition, reinforcing user’s acquisition of procedural knowledge relative to the cultural domain. To sum up, role-playing in VR poses great potential for experiential learning as it allows users to interpret a historical context in a more entertaining way.

Keywords: experiential learning, maritime silk road, role-playing, virtual heritage, virtual reality

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Authors: Michele Gattullo, Vito M. Manghisi, Alessandro Evangelista, Enricoandrea Laviola

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In this work, we explored the use of Augmented Reality (AR) to support students in laboratory lectures in Distance Learning (DL), designing an application that proved to be ready for use next semester. AR could help students in the understanding of complex concepts as well as increase their motivation in the learning process. However, despite many prototypes in the literature, it is still less used in schools and universities. This is mainly due to the perceived limited advantages to the investment costs, especially regarding changes needed in the teaching modalities. However, with the spread of epidemiological emergency due to SARS-CoV-2, schools and universities were forced to a very rapid redefinition of consolidated processes towards forms of Distance Learning. Despite its many advantages, it suffers from the impossibility to carry out practical activities that are of crucial importance in STEM ("Science, Technology, Engineering e Math") didactics. In this context, AR perceived advantages increased a lot since teachers are more prepared for new teaching modalities, exploiting AR that allows students to carry on practical activities on their own instead of being physically present in laboratories. In this work, we designed an AR application for the support of engineering students in the understanding of assembly drawings of complex machines. Traditionally, this skill is acquired in the first years of the bachelor's degree in industrial engineering, through laboratory activities where the teacher shows the corresponding components (e.g., bearings, screws, shafts) in a real machine and their representation in the assembly drawing. This research aims to explore the effectiveness of AR to allow students to acquire this skill on their own without physically being in the laboratory. In a preliminary phase, we interviewed students to understand the main issues in the learning of this subject. This survey revealed that students had difficulty identifying machine components in an assembly drawing, matching between the 2D representation of a component and its real shape, and understanding the functionality of a component within the machine. We developed a mobile application using Unity3D, aiming to solve the mentioned issues. We designed the application in collaboration with the course professors. Natural feature tracking was used to associate the 2D printed assembly drawing with the corresponding 3D virtual model. The application can be displayed on students’ tablets or smartphones. Users could interact with selecting a component from a part list on the device. Then, 3D representations of components appear on the printed drawing, coupled with 3D virtual labels for their location and identification. Users could also interact with watching a 3D animation to learn how components are assembled. Students evaluated the application through a questionnaire based on the System Usability Scale (SUS). The survey was provided to 15 students selected among those we participated in the preliminary interview. The mean SUS score was 83 (SD 12.9) over a maximum of 100, allowing teachers to use the AR application in their courses. Another important finding is that almost all the students revealed that this application would provide significant power for comprehension on their own.

Keywords: augmented reality, distance learning, STEM didactics, technology in education

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Authors: Kunyu Yan

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Early childhood music education plays a significant role in an individual’s growth. Music can help children understand themselves and relate to others, and make connections between family, school, and society. In recent years, with the development of early childhood education in China, an increasing number of kindergartens have been established, and many of them pay more attention to music education. This research has two main aims. One is to discover how and why music is used in both public and private kindergartens. The second aim is to make recommendations for widening the use of music in kindergartens. In order to achieve these aims, the research uses two main methods. Firstly, it considers the historical background and cultural context of early childhood education in China; and secondly, it uses an approach that compares public and private kindergartens. In this research, six kindergartens were chosen from Qingdao city in Shandong Province as case studies, including 3 public kindergartens and 3 private kindergartens. This research was based on using three types of data collection methods: observation, semi-structured interviews with teachers, and questionnaires with parents. Participant and non-participant observational methods were used and included in daily routines at the kindergartens in order to experience the situation of music education first-hand. Interviews were associated with teachers’ views of teaching and learning music, the perceptions of the music context, and their strategies of using music. Lastly, the questionnaire was designed to obtain the views of current music education from the children’s parents in the respective kindergartens. The results are shown with three main themes: (1) distinct characteristics of public kindergartens (e.g., similar equipment, low tuition fee, qualified teachers, etc); (2) distinct characteristics of private kindergartens (e.g., various tuition fees, own teaching system, trained teachers, etc); and (3) differences between public and private kindergartens (e.g., funding, requirements for teachers, parents’ demands, etc). According to the results, we can see that the main purpose of using music in China is to develop the musical ability of children, and teachers focus on musical learning, such as singing in tune and playing instruments. However, as revealed in this research, there are many other uses and functions of music in these educational settings, including music used for non-musical learning (e.g., counting, learning language, etc.) or in supporting social routines.

Keywords: differences between private and public school, early childhood education, music education, uses and functions of music

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Authors: Insook Lee, Heeseung Choi, Kyung-Sook Bang, Sungjae Kim, Minkyung Song, Buhyun Lee

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Backgrounds: A clearer and comprehensive understanding of the effects of forest therapy on depression is needed for further refinements of forest therapy programs. The purpose of this study was to review the literature on forest therapy programs designed to decrease the level of depression among adults to evaluate current forest therapy programs. Methods: This literature review was conducted using various databases including PubMed, EMBASE, CINAHL, PsycArticle, KISS, RISS, and DBpia to identify relevant studies published up to January 2016. The two authors independently screened the full text articles using the following criteria: 1) intervention studies assessing the effects of forest therapy on depression among healthy adults ages 18 and over; 2) including at least one control group or condition; 3) being peer-reviewed; and 4) being published either in English. The Scottish Intercollegiate Guideline Network (SIGN) measurement tool was used to assess the risk of bias in each trial. Results: After screening current literature, a total of 14 articles (English: 6, Korean: 8) were included in the present review. None of the studies used randomized controlled (RCT) study design and the sample size ranged from 11 to 300. Walking in the forest and experiencing the forest using the five senses was the key component of the forest therapy that was included in all studies. The majority of studies used one-time intervention that usually lasted a few hours or half-day. The most widely used measure for depression was Profile of Mood States (POMS). Most studies used self-reported, paper-and-pencil tests, and only 5 studies used both paper-and-pencil tests and physiological measures. Regarding the quality assessment based on the SIGN criteria, only 3 articles were rated ‘acceptable’ and the rest of the 14 articles were rated ‘low quality.’ Regardless of the diversity in format and contents of forest therapies, most studies showed a significant effect of forest therapy in curing depression. Discussions: This systematic review showed that forest therapy is one of the emerging and effective intervention approaches for decreasing the level of depression among adults. Limitations of the current programs identified from the review were as follows; 1) small sample size; 2) a lack of objective and comprehensive measures for depression; and 3) inadequate information about research process. Futures studies assessing the long-term effect of forest therapy on depression using rigorous study designs are needed.

Keywords: forest therapy, systematic review, depression, adult

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Authors: Naci Büyükkaracığan, Yıldırım Akyol

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Information technology is called the technology which provides transmission of information elsewhere regardless of time, location, distance. Today, information technology is providing the occurrence of ground breaking changes in all areas of our daily lives. Information can be reached quickly to millions of people with help of information technology. In this Study, effects of information technology on students for educations and their learning preferences were demonstrated with using data obtained from questionnaires administered to students of 2015-2016 academic year at Selcuk University Kadınhanı Faik İçil Vocational School Construction Department. The data was obtained by questionnaire consisting of 30 questions that was prepared by the researchers. SPSS 21.00 package programme was used for statistical analysis of data. Chi-square tests, Mann-Whitney U test, Kruskal-Wallis and Kolmogorov-Smirnov tests were used in the data analysis for Descriptiving statistics. In a study conducted with the participation of 61 students, 93.4% of students' reputation of their own information communication device (computer, smart phone, etc.) That have been shown to be at the same rate and to the internet. These are just a computer of itself, then 45.90% of the students. The main reasons for the students' use of the Internet, social networking sites are 85.24%, 13.11% following the news of the site, as seen. All student assignments in information technology, have stated that they use in the preparation of the project. When students acquire scientific knowledge in the profession regarding their preferred sources evaluated were seen exactly when their preferred internet. Male students showed that daily use of information technology while compared to female students was statistically significantly less. Construction Package program where students are eager to learn about the reputation of 72.13% and 91.80% identified in the well which they agreed that an indispensable element in the professional advancement of information technology.

Keywords: information technologies, computer, construction, internet, learning systems

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Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

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Authors: Lawrence Meda, Christopher Dumas, Moses Moyo, Zayd Waghid

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As a result of some schools embracing technology to enhance students’ learning experiences in the digital era, the Faculty of Education at a University of Technology in South Africa has mandated lecturers to scale up their utilisation of technology in their teaching. Lecturers have been challenged to utilise the institution’s Learning Management System - Blackboard among other technologies - to adequately prepare trainee teachers to be able to teach competently in schools. The purpose of this study is to investigate the extent to which lecturers are utilising Blackboard to enhance their teaching. The study will be conducted using a quantitative approach, and its paradigmatic position will be positivist. The study will be done as a case study of the university’s Faculty of Education. Data will be extracted from all 100 lecturers’ Blackboard sites according to their respective modules, and it will be analysed using the four pillars of Blackboard as a conceptual framework. It is presumed that there is an imbalance on the lecturers’ utilisation of the four pillars of Blackboard as the majority use it as a content dumping site.

Keywords: blackboard, digital, education, technology

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

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Authors: Ambeck Celyne Tebid, Harry S. Rampa

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The South African Education system recognises the need for all learners including those experiencing learning difficulties, to have access to a single unified system of education. For teachers to be pedagogically responsive to an increasingly diverse learner population without appropriate support has been proven to be unrealistic. As such, this has considerably hampered interest amongst teachers, especially those at the foundation phase to work within an Inclusive Education (IE) and training system. This qualitative study aimed at investigating foundation phase teachers’ experiences of school-based support teams (SBSTs) in two Full-Service (inclusive schools) and one Mainstream public primary school in the Gauteng province of South Africa; with particular emphasis on finding ways to supporting them, since teachers claimed they were not empowered in their initial training to teach learners experiencing learning difficulties. Hence, SBSTs were created at school levels to fill this gap thereby, supporting teaching and learning by identifying and addressing learners’, teachers’ and schools’ needs. With the notion that IE may be failing because of systemic reasons, this study uses Bronfenbrenner’s (1979) ecosystemic as well as Piaget’s (1980) maturational theory to examine the nature of support and experiences amongst teachers taking individual and systemic factors into consideration. Data was collected using in-depth, face-to-face interviews, document analysis and observation with 6 foundation phase teachers drawn from 3 different schools, 3 SBST coordinators, and 3 school principals. Data was analysed using the phenomenological data analysis method. Amongst the findings of the study is that South African full- service and mainstream schools have functional SBSTs which render formal and informal support to the teachers; this support varies in quality depending on the socio-economic status of the relevant community where the schools are situated. This paper, however, argues that what foundation phase teachers settled for as ‘support’ is flawed; as well as how they perceive the SBST and its role is problematic. The paper conclude by recommending that, the SBST should consider other approaches at foundation phase teacher support such as, empowering teachers with continuous practical experiences on how to deal with real classroom scenarios, as well as ensuring that all support, be it on academic or non-academic issues should be provided within a learning community framework where the teacher, family, SBST and where necessary, community organisations should harness their skills towards a common goal.

Keywords: foundation phase, full- service schools, inclusive education, learning difficulties, school-based support teams, teacher support

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Rosilah Hassan, Karam Dhafer Mayoof, Norngainy Mohd Tawil, Shamshubaridah Ramlee

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Application of adaptive online quiz system and a design are performed in this paper. The purpose of adaptive quiz system is to establish different questions automatically for each student and measure their competence on a definite area of discipline. This model determines students competencies in cases like distant-learning which experience challenges frequently. Questions are specialized to allow clear deductions about student gains; they are able to identify student competencies more effectively. Also, negative effects of questions requiring higher knowledge than competency over student’s morale and self-confidence are dismissed. The advantage of the system in the quiz management requires less total time for measuring and is more flexible. Self sufficiency of the system in terms of repeating, planning and assessment of the measurement process allows itself to be used in the individual education sets. Adaptive quiz technique prevents students from distraction and motivation loss, which is led by the questions with quite lower hardness level than student’s competency.

Keywords: e-learning, adaptive system, security, quiz database

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Authors: Heikki Ruohomaa, Vesa Salminen

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Regions are facing huge competition to attract companies, businesses, inhabitants, students, etc. This way to improve living and business environment, which is rapidly changing due to digitalization. On the other hand, from the industry's point of view, the availability of a skilled labor force and an innovative environment are crucial factors. In this context, qualified staff has been seen to utilize the opportunities of digitalization and respond to the needs of future skills. World Manufacturing Forum has stated in the year 2019- report that in next five years, 40% of workers have to change their core competencies. Through digital transformation, new technologies like cloud, mobile, big data, 5G- infrastructure, platform- technology, data- analysis, and social networks with increasing intelligence and automation, enterprises can capitalize on new opportunities and optimize existing operations to achieve significant business improvement. Digitalization will be an important part of the everyday life of citizens and present in the working day of the average citizen and employee in the future. For that reason, the education system and education programs on all levels of education from diaper age to doctorate have been directed to fulfill this ecosystem strategy. Goal: The Fourth Industrial Revolution will bring unprecedented change to societies, education organizations and business environments. This article aims to identify how education, education content, the way education has proceeded, and overall whole the education business is changing. Most important is how we should respond to this inevitable co- evolution. Methodology: The study aims to verify how the learning process is boosted by new digital content, new learning software and tools, and customer-oriented learning environments. The change of education programs and individual education modules can be supported by applied research projects. You can use them in making proof- of- the concept of new technology, new ways to teach and train, and through the experiences gathered change education content, way to educate and finally education business as a whole. Major findings: Applied research projects can prove the concept- phases on real environment field labs to test technology opportunities and new tools for training purposes. Customer-oriented applied research projects are also excellent for students to make assignments and use new knowledge and content and teachers to test new tools and create new ways to educate. New content and problem-based learning are used in future education modules. This article introduces some case study experiences on customer-oriented digital transformation projects and how gathered knowledge on new digital content and a new way to educate has influenced education. The case study is related to experiences of research projects, customer-oriented field labs/learning environments and education programs of Häme University of Applied Sciences.

Keywords: education process, digitalization content, digital tools for education, learning environments, transdisciplinary co-operation

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Nigel P. Coutts, Stellina Z. Sim

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Research suggests that the pedagogy we utilize when teaching mathematics contributes to a negative attitude towards the discipline. Worried by this, we have explored teaching mathematics for understanding, fluency, and confidence. We investigated strategies to engage students with the beauty of mathematics, moving them beyond mimicry and memorization. The result is an integrated pedagogy and curriculum arrangement which combines concept-based mathematics with Number Talks, Visible Thinking Routines, and Teaching for Understanding. Our qualitative research shows that students self-report greater self-confidence and heightened engagement with mathematical thinking. Teacher reflections on student learning echo this finding. As a result of this, we advocate for teacher training in the implementation of a concept-based curriculum supplemented with Number Talk strategies.

Keywords: mathematical thinking, teaching for understanding, student confidence, concept-based learning, engagement

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Authors: Samabia Tehsin, Sumaira Kausar

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Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results.

Keywords: caption text, content-based image retrieval, document analysis, text extraction

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Authors: Mamadou Dione, Eric Matzner-lober, Philippe Alexandre

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The Energy Transition Act defined by the French State has precise implications on Renewable Energies, in particular on its remuneration mechanism. Until then, a purchase obligation contract permitted the sale of wind-generated electricity at a fixed rate. Tomorrow, it will be necessary to sell this electricity on the Market (at variable rates) before obtaining additional compensation intended to reduce the risk. This sale on the market requires to announce in advance (about 48 hours before) the production that will be delivered on the network, so to be able to predict (in the short term) this production. The fundamental problem remains the variability of the Wind accentuated by the geographical situation. The objective of the project is to provide, every day, short-term forecasts (48-hour horizon) of wind production using weather data. The predictions of the GFS model and those of the ECMWF model are used as explanatory variables. The variable to be predicted is the production of a wind farm. We do two approaches: a direct approach that predicts wind generation directly from weather data, and an integrated approach that estimâtes wind from weather data and converts it into wind power by power curves. We used machine learning techniques to predict this production. The models tested are random forests, CART + Bagging, CART + Boosting, SVM (Support Vector Machine). The application is made on a wind farm of 22MW (11 wind turbines) of the Compagnie du Vent (that became Engie Green France). Our results are very conclusive compared to the literature.

Keywords: forecast aggregation, machine learning, spatio-temporal dynamics modeling, wind power forcast

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Authors: Bichi Muktar Sani

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The philosophy of Physical and Health Education is to develop academic and professional competency which will enable individuals earn a living and render unique services to the society and also provide good basis of knowledge and experience that characterize an educated and fully developed person through physical activities. With the increase of sedentary activities such as watching television, playing videogames, increased computer technology, automation and reduction of high school Physical and Health Education schedules, young people are most likely to become overweight, and less fit. Physical Education is a systematic instruction in sports, training, practice, gymnastics, exercises, and hygiene given as part of a school or college program. Physical and Health Education is the study, practice, and appreciation of the art and science of human movement. Physical and Health Education is course in the curricula that utilizes the learning in the cognitive, affective, and psychomotor domains in a lay or movement exploration setting. The paper made some recommendations on the way forward.

Keywords: issues, challenges, physical education, school

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Authors: Hualter Oliveira Barbosa, Raquel Feitosa do Vale Cunha, Erika Muniz dos Santos, Fernanda Belmira Souza, Fabio Sousa, Luis Henrique Pascareli, Franciney de Oliveira Lima, Ana Cláudia Reis da Silva, Christiane Moreira de Almeida

Abstract:

Software development is intrinsic human-based knowledge-intensive. Due to globalization, software development has become a complex challenge and we usually face barriers related to knowledge management, team building, costly testing processes, especially in distributed settings. For this reason, several approaches have been proposed to minimize barriers caused by geographic distance. In this paper, we present as we use experimental studies to improve our knowledge management process using the Wiki system. According to the results, it was possible to identify learning preferences from our software projects leader team, organize and improve the learning experience of our Wiki and; facilitate collaboration by newcomers to improve Wiki with new contents available in the Wiki.

Keywords: mobile product, knowledge transfer, knowledge management process, wiki, GSD

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Authors: Dachuan Shi, M. Hecht, Y. Ye

Abstract:

With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

Procedia PDF Downloads 117

Authors: Natcha Wattanaprapa, Alongkorn Taengtong, Phachaya Chaiwchan

Abstract:

This study investigates and promotes the distribution of community enterprise products of Samut Songkhram province by using e-commerce web technology to help distribute the products. This study also aims to develop the information system to be able to operate on multiple platforms and promote the easy usability on smartphones to increase the efficiency and promote the distribution of community enterprise products of Samut Songkhram province in three areas including Baan Saraphi learning center, the learning center of Bang Noi Floating market as well as Bang Nang Li learning center. The main structure consists of spreading the knowledge regarding the tourist attraction in the area of community enterprise, e-commerce system of community enterprise products, and Chatbot. The researcher developed the system into an application form using the software package to create and manage the content on the internet. Connect management system (CMS) word press was used for managing web pages. Add-on CMS word press was used for creating the system of Chatbot, and the database of PHP My Admin was used as the database management system. The evaluation by the experts and users in 5 aspects, including the system efficiency, the accuracy in the operation of the system, the convenience and ease of use of the system, the design, and the promotion of product distribution in Samut Songkhram province by using questionnaires revealed that the result of evaluation in the promotion of product distribution in Samut Songkhram province was the highest with the mean of 4.20. When evaluating the efficiency of the developed system, it was found that the result of system efficiency was the highest level with a mean of 4.10.

Keywords: community enterprise, digital commerce, promotion of product distribution, Samut Songkhram province

Procedia PDF Downloads 134

Authors: Azusa Katsumata

Abstract:

Several universities in Japan have introduced activities aimed at the mutual enlightenment of a diversity of people in career education. However, several programs emphasize on delivering results, and on practicing the prepared materials as planned. Few programs focus on unexpected failures and setbacks. This way of learning is important in career education so that classmates can help each other, overcome difficulties, draw out each other’s strengths, and learn from them. Seijo University in Tokyo offered excursion focusing Various Problems of Society, as second year career education course, Students will learn about contraception, infertility, homeless people, LGBT, and they will discuss based on the excursion. This paper aims to study the ‘learning platform’ created by a series of processes such as the excursion, the discussion, and the presentation. In this course, students looked back on their lives and imagined the future in concrete terms, performing tasks in groups. The students came across a range of values through lectures and conversations, thereby developing feelings of self-efficacy. We conducted a questionnaire to measure the development of career in class. From the results of the questionnaire, we can see, in the example of this class, that students respected diversity and understood the importance of uncertainty and discontinuity. Whereas the students developed career awareness, they actually did not come across that scene and would do so only in the future when it became necessary. In this class, students consciously considered social problems, but did not develop the practical skills necessary to deal with these. This is appropriate for one of project, but we need to consider how this can be incorporated into future courses. University constitutes only a single period in life-long career formation. Thus, further research may be indicated to determine whether the positive effects of career education at university continue to contribute to individual careers going forward.

Keywords: career education of university, excursion, learning platform, problems of society

Procedia PDF Downloads 253

Authors: E. Maoz

Abstract:

Background: As a teaching method, project-based learning is strongly linked to developing students’ critical thinking skills. It combines creative independent thinking, team work, and disciplinary subject-field integration. In the 'Introduction to Nursing Research Methods' course (year 3, Generic Track), project based learning is used to teach the topic of 'Evidence-Based Nursing'. This topic examines a clinical care issue encountered by students in the field. At the end of their project, students present proposals for managing the said issue. Proposals are the product of independent integrative thinking integrating a wide range of factors influencing the issue’s management. Method: Papers by 27 groups of students (165 students) were content analyzed to identify which themes emerged from the students' recommendations for managing the clinical issue. Findings: Five main themes emerged—current management approach; adapting procedures in line with current recent research recommendations; training for change (veteran nursing staff, beginner students, patients, significant others); analysis of 'economic benefit vs. patient benefit'; multidisciplinary team engagement in implementing change in practice. Two surprising themes also emerged: advertising and marketing using new technologies, which reflects how the new generation thinks. Summary and Recommendations: Among the main challenges in nursing education is training nursing graduates to think independently, integratively, and critically. Combining PBL with classical teaching methods stimulates students cognitively while opening new vistas with implications on all levels of the profession: management, research, education, and practice. Advanced students can successfully grasp and interpret the current state of clinical practice. They are competent and open to leading change and able to consider the diverse factors and interconnections that characterize the nurse's work.

Keywords: evidence based nursing, critical thinking skills, project based learning, students education

Procedia PDF Downloads 79

Authors: Roslina Ibrahim, Azizah Jaafar, Khalili Khalil

Abstract:

Educational games (EG) are regarded as a promising teaching and learning tool for the new generation. Growing number of studies and literatures can be found in EG studies. Both academic researchers and commercial developers come out with various educational games prototypes and titles. Despite that, acceptance of educational games still lacks among the students. It is important to understanding students’ perceptions of EG, since they are the main stakeholder of the technology. Thus, this study seeks to understand perceptions of undergraduates’ students using a framework originated from user acceptance theory. The framework consists of six constructs with twenty-eight items. Data collection was done on 180 undergraduate students of Universiti Teknologi Malaysia, Kuala Lumpur using self-developed online EG called ROBO-C. Data analysis was done using descriptive, factor analysis and correlations. Performance expectancy, effort expectancy, attitude, and enjoyment factors were found significantly correlated with the intention to use EG. This study provides more understanding towards the use of educational games among students.

Keywords: educational games, perceptions, acceptance, UTAUT

Procedia PDF Downloads 398

Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 69