Search results for: blended and integrated learning

Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Chirine Dannaoui, Maya Antoun

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This paper explores the intricate dynamics of play-based early childhood education (ECE) and the impact of professional development on teachers implementing play-based pedagogy, particularly in the context of vulnerable Syrian refugee children in Lebanon. By utilizing qualitative methodologies, including classroom observations and in-depth interviews with five early childhood educators and a field manager, this study delves into the challenges and transformations experienced by teachers in adopting play-based learning strategies. The research unveils the critical role of continuous and context-specific professional development in empowering teachers to implement play-based pedagogies effectively. When appropriately supported, it emphasizes how such educational approaches significantly enhance children's cognitive, social, and emotional development in crisis-affected environments. Key findings indicate that despite diverse educational backgrounds, teachers show considerable growth in their pedagogical skills through targeted professional development. This growth is vital for fostering a learning environment where vulnerable children can thrive, particularly in humanitarian settings. The paper also addresses educators' challenges, including adapting to play-based methodologies, resource limitations, and balancing curricular requirements with the need for holistic child development. This study contributes to the discourse on early childhood education in crisis contexts, emphasizing the need for sustainable, well-structured professional development programs. It underscores the potential of play-based learning to bridge educational gaps and contribute to the healing process of children facing calamity. The study highlights significant implications for policymakers, educators, schools, and not-for-profit organizations engaged in early childhood education in humanitarian contexts, stressing the importance of investing in teacher capacity and curriculum reform to enhance the quality of education for children in general and vulnerable ones in particular.

Keywords: play-based learning, professional development, vulnerable children, early childhood education

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: Fiona Jensen, Barb Goodwin, Nancy Kleiman, Rhonda Usunier

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Background: Clinical simulation is now a common component in many health profession curricula in preparation for clinical practice. In the Rady Faculty of Health Sciences (RFHS) college leads in simulation and interprofessional (IP) education, planned an eight hour simulated hospital day shift, where seventy students from six health professions across two campuses, learned with each other in a safe, realistic environment. Learning about interprofessional collaboration, an expected competency for many health professions upon graduation, was a primary focus of the simulation event. Method: Faculty representatives from the Colleges of Nursing, Medicine, Pharmacy and Rehabilitation Sciences (Physical Therapy, Occupation Therapy, Respiratory Therapy) and Pharmacy worked together to plan the IP event in a simulation facility in the College of Nursing. Each college provided a faculty mentor to guide the same profession students. Students were placed in interprofessional teams consisting of a nurse, physician, pharmacist, and then sharing respiratory, occupational, and physical therapists across the team depending on the needs of the patients. Eight patient scenarios were role played by health profession students, who had been provided with their patient’s story shortly before the event. Each team was guided by a facilitator. Results and Outcomes: On the morning of the event, all students gathered in a large group to meet mentors and facilitators and have a brief overview of the six competencies for effective collaboration and the session objectives. The students assuming their same profession roles were provided with their patient’s chart at the beginning of the shift, met with their team, and then completed professional specific assessments. Shortly into the shift, IP team rounds began, facilitated by the team facilitator. During the shift, each patient role-played a spontaneous health incident, which required collaboration between the IP team members for assessment and management. The afternoon concluded with team rounds, a collaborative management plan, and a facilitated de-brief. Conclusions: During the de-brief sessions, students responded to set questions related to the session learning objectives and expressed many positive learning moments. We believe that we have a sustainable simulation IP collaborative learning opportunity, which can be embedded into curricula, and has the capacity to grow to include more health profession faculties and students. Opportunities are being explored in the RFHS at the administrative level, to offer this event more frequently in the academic year to reach more students. In addition, a formally structured event evaluation tool would provide important feedback and inform the qualitative feedback to event organizers and the colleges about the significance of the simulation event to student learning.

Keywords: simulation, collaboration, teams, interprofessional

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Authors: Nina Dzamashvili Fogelström

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This paper examines concepts of Game-Based Learning and Gamification. Conducted literature survey found an increased interest in the academia in these concepts, limited evidence of a positive effect on student motivation and academic performance, but also certain scepticism for adding games to traditional educational activities. A small-scale empirical study presented in this paper aims to evaluate student experience and usefulness of GameBased Learning and Gamification for a better understanding of the threshold concepts in software engineering project courses. The participants of the study were 22 second year students from bachelor’s program in software engineering at Blekinge Institute of Technology. As a part of the course instruction, the students were introduced to a digital game specifically designed to simulate agile software project. The game mechanics were designed as to allow manipulation of the agile concept of team velocity. After the application of the game, the students were surveyed to measure the degree of a perceived increase in understanding of the studied threshold concept. The students were also asked whether they would like to have games included in their education. The results show that majority of the students found the game helpful in increasing their understanding of the threshold concept. Most of the students have indicated that they would like to see games included in their education. These results are encouraging. Since the study was of small scale and based on convenience sampling, more studies in the area are recommended.

Keywords: agile development, gamification, game based learning, digital games, software engineering, threshold concepts

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Authors: Paula Escudeiro, Nuno Escudeiro, Márcia Campos, Francisca Escudeiro

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In today's world, technical and scientific knowledge plays a vital role in education, research, and employment. Deaf students face unique challenges in educational settings, particularly when it comes to understanding technical and scientific terminology. The reliance on written and spoken languages can create barriers for deaf individuals who primarily communicate using sign language. This lack of accessibility can hinder their learning experience and compromise equity in education. To address this issue, the TechWhiz project has been developed as a comprehensive glossary of scientific and technical concepts explained in sign language. By providing deaf students with access to education in their first language, TechWhiz aims to enhance their learning achievements and promote inclusivity while also fostering equity in education for all students.

Keywords: deaf students, technical and scientific knowledge, automatic sign language, inclusive education

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Authors: Danah Al-Selahi, Sulaiman Al-Ghunaim, Bashayer Sadiq, Fatma Al-Otaibi, Ali Ameen

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In light of recent changes affecting the Oil & Gas Industry, optimization measures have become imperative for all companies globally, including Kuwait Oil Company (KOC). To keep abreast of the dynamic market, a detailed Integrated Asset Plan (IAP) was developed to drive optimization across the organization, which was facilitated through the in-house developed software “Well Inventory Data Entry” (WIDE). This comprehensive and integrated approach enabled centralization of all planned asset components for better well planning, enhancement of performance, and to facilitate continuous improvement through performance tracking and midterm forecasting. Traditionally, this was hard to achieve as, in the past, various legacy methods were used. This paper briefly describes the methods successfully adopted to meet the company’s objective. IAPs were initially designed using computerized spreadsheets. However, as data captured became more complex and the number of stakeholders requiring and updating this information grew, the need to automate the conventional spreadsheets became apparent. WIDE, existing in other aspects of the company (namely, the Workover Optimization project), was utilized to meet the dynamic requirements of the IAP cycle. With the growth of extensive features to enhance the planning process, the tool evolved into a centralized data-hub for all asset-groups and technical support functions to analyze and infer from, leading WIDE to become the reference two-year operational plan for the entire company. To achieve WIDE’s goal of operational efficiency, asset-groups continuously add their parameters in a series of predefined workflows that enable the creation of a structured process which allows risk factors to be flagged and helps mitigation of the same. This tool dictates assigned responsibilities for all stakeholders in a method that enables continuous updates for daily performance measures and operational use. The reliable availability of WIDE, combined with its user-friendliness and easy accessibility, created a platform of cross-functionality amongst all asset-groups and technical support groups to update contents of their respective planning parameters. The home-grown entity was implemented across the entire company and tailored to feed in internal processes of several stakeholders across the company. Furthermore, the implementation of change management and root cause analysis techniques captured the dysfunctionality of previous plans, which in turn resulted in the improvement of already existing mechanisms of planning within the IAP. The detailed elucidation of the 2 year plan flagged any upcoming risks and shortfalls foreseen in the plan. All results were translated into a series of developments that propelled the tool’s capabilities beyond planning and into operations (such as Asset Production Forecasts, setting KPIs, and estimating operational needs). This process exemplifies the ability and reach of applying advanced development techniques to seamlessly integrated the planning parameters of various assets and technical support groups. These techniques enables the enhancement of integrating planning data workflows that ultimately lay the founding plans towards an epoch of accuracy and reliability. As such, benchmarks of establishing a set of standard goals are created to ensure the constant improvement of the efficiency of the entire planning and operational structure.

Keywords: automation, integration, value, communication

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Chantana Insra

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The research objective focuses on creating a prototype media of the tactile texture of muscles for educational institutes to help visually impaired students learn massage extra learning materials further than the ordinary curriculum. This media is designed as an extra learning material. The population in this study was 30 blinded students between 4th - 6th grades who were able to read Braille language. The research was conducted during the second semester in 2012 at The Bangkok School for the Blind. The method in choosing the population in the study was purposive sampling. The methodology of the research includes collecting data related to visually impaired people, the production of the tactile texture media, human anatomy and Thai traditional massage from literature reviews and field studies. This information was used for analyzing and designing 14 tactile texture pictures presented to experts to evaluate and test the media.

Keywords: blind, tactile texture, muscle, visual arts and design

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Authors: Shivani Singh, Shantanu Nakhare, Kalyani Nair, Rohan Shetty

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As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.

Keywords: machine learning, online social networks, soft text classifier, support vector machine

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Authors: Thomas Chong, Irene Victor

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This paper discusses the history and plausible future of assistive technology and what it means for students with disabilities in Singapore, a country known for its high quality of education in the world. Over more than a century, students with disabilities have benefitted from relatively low-tech assistive technology (like eye-glasses, Braille, magnifiers and wheelchairs) to high-tech assistive technology including electronic mobility switches, alternative keyboards, computer-screen enlargers, text-to-speech readers, electronic sign-language dictionaries and signing avatars for individuals with hearing impairments. Driven by legislation, the use of assistive technology in many countries is becoming so ubiquitous that more and more students with disabilities are able to perform as well as if not better than their counterparts. Yet in many other learning environments where assistive technology is not affordable or mandated, the learning gaps can be quite significant. Without stronger legislation, Singapore may still have a long way to go in levelling the playing field for its students with disabilities.

Keywords: assistive technology, students with disabilities, disability laws in Singapore, inclusiveness

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Authors: Ora Geiger

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Reading is a cognitive process of deciphering visual signs to produce meaning. During the reading process, written information of symbols and signs is received in the person’s eye and processed in the brain. This definition is relevant to both the reading of letters and the reading of musical notation. But while the letters of the alphabet are signs determined arbitrarily, notes are recorded systematically on a staff, with the location of each note on the staff indicating its relative pitch. In this paper, the researcher specifies the characteristics of alphabet reading in comparison to musical notation reading, and discusses the question whether a person diagnosed with dyslexia will necessarily have difficulty in reading musical notes. Dyslexia is a learning disorder that makes it difficult to acquire alphabet-reading skills due to difficulties expressed in the identification of letters, spelling, and other language deciphering skills. In order to read, one must be able to connect a symbol with a sound and to join the sounds into words. A person who has dyslexia finds it difficult to translate a graphic symbol into the sound that it represents. When teaching reading to children diagnosed with dyslexia, the multi-sensory approach, supporting the activation and involvement of most of the senses in the learning process, has been found to be particularly effective. According to this approach, when most senses participate in the reading learning process, it becomes more effective. During years of experience, the researcher, who is a music specialist, has been following the music reading learning process of elementary school age students, some of them diagnosed with Dyslexia, while studying to play soprano (descant) recorder. She argues that learning music reading while studying to play a musical instrument is a multi-sensory experience by its nature. The senses involved are: sight, hearing, touch, and the kinesthetic sense (motion), which provides the brain with information on the relative positions of the body. In this way, the learner experiences simultaneously visual, auditory, tactile, and kinesthetic impressions. The researcher concludes that there should be no contra-indication for teaching standard music reading to children with dyslexia if an appropriate process is offered. This conclusion is based on two main characteristics of music reading: (1) musical notation system is a systematic, logical, relative set of symbols written on a staff; and (2) music reading learning connected with playing a musical instrument is by its nature a multi-sensory activity since it combines sight, hearing, touch, and movement. This paper describes music reading teaching procedures and provides unique teaching methods that have been found to be effective for students who were diagnosed with Dyslexia. It provides theoretical explanations in addition to guidelines for music education practices.

Keywords: alphabet reading, dyslexia, multisensory teaching method, music reading, recorder playing

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Ujjal Chattaraj, P. C. Dasbebartta, S. Bhuyan

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This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification.

Keywords: AdaBoost learning algorithm, face detection, framework, traffic safety

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Authors: Kai Chen, Shuguang Cui, Feng Yin

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Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP.

Keywords: Gaussian process, spectral mixture, non-stationary, convolution

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Authors: Yogesh Kumar Tiwari

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Chhattisgarh has realized the fundamental role of information and communication technology in the globalized world where knowledge is at the top for the growth and intellectual development. They are spreading so widely that one feels lagging behind if not using them. The influence of these radiating and technological tools has encompassed all aspects of the educational, business, and economic sectors of our world. Undeniably the computer has not only established itself globally in all walks of life but has acquired a fundamental role of paramount importance in the educational process also. This role is getting all pervading and more powerful as computers are being manufactured to be cheaper, smaller in size, adaptable and easy to handle. Computers are becoming indispensable to teachers because of their enormous capabilities and extensive competence. This study aims at observing the effect of using computer based software program of English language on the achievement of undergraduate level students studying in tribal area like Sarguja Division, Chhattisgarh, India. To testify the effect of an innovative teaching in the graduate classroom in tribal area 50 students were randomly selected and separated into two groups. The first group of 25 students were taught English grammar i.e., passive voice/narration, through traditional method using chalk and blackboard asking some formal questions. The second group, the experimental one, was taught English grammar i.e., passive voice/narration, using computer, projector with power point presentation of grammatical items. The statistical analysis was done on the students’ learning capacities and achievement. The result was extremely mesmerizing not only for the teacher but for taught also. The process of the recapitulation demonstrated that the students of experimental group responded the answers of the questions enthusiastically with innovative sense of learning. In light of the findings of the study, it was recommended that teachers and professors of English ought to use self-made instructional program in their teaching process particularly in tribal areas.

Keywords: achievement computer assisted language learning, use of instructional program

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Authors: Xiao Wen Xu

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There exists a challenge of accommodating/integrating people at risk and those from various backgrounds in urban areas. The success of interdependence as a tool for survival largely rests on the mutually beneficial relationships amongst individuals within a given society. One approach to meeting this challenge has been written by Ivan Illich in his book, Tools for Conviviality, where he defines 'conviviality' as interactions that help individuals. With the goal of helping the community and applying conviviality as a principle to actors in both natural and social realms of Moss Park in Toronto, the proposal involves redesigning the park and buildings as a series of different health care, extended learning, employment support, armoury, and recreation facilities that integrate the exterior landscape as treatment, teaching, military, and recreation areas; in other words, the proposal links services with access to park space. While buildings are traditionally known to physically provide shelter, parks embody shelter and act as service, as people often find comfort and relief from being in nature, and Moss Park, in particular, is home to many people at risk. This landscape is not only an important space for the homeless community but also the rest of the neighborhood. The thesis proposes that the federal government rebuilds the current armoury, as it is an obsolete building while acknowledging the extensive future developments proposed by developers and its impact on public space. The neighbourhood is an underserved area, and the new design develops not just a new armoury, but also a complex of interrelated services, which are completely integrated into the park. The armoury is redesigned as an integral component of the community that not only serves as training facilities for reservists but also serves as an emergency shelter in sub-zero temperatures for the homeless community. This paper proposes a new design for Moss Park through examining how 'park buildings', interconnected buildings and parks, can foster empowering relationships that create a supportive public realm.

Keywords: conviviality, natural, social, Ivan Illich

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Authors: Naira Poghosyan

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In knowledge society the content of the studies, the methods used and the requirements for an educator’s professionalism regularly undergo certain changes. It follows that in knowledge society the aim of education is not only to educate professionals for a certain field but also to help students to be aware of cultural values, form human mutual relationship, collaborate, be open, adapt to the new situation, creatively express their ideas, accept responsibility and challenge. In this viewpoint, the development of communicative language competence requires a through coordinated approach to ensure proper comprehension and memorization of subject-specific words starting from high school level. On the other hand, ESP (English for Specific Purposes) teachers and practitioners are increasingly faced with the task of developing and exploiting new ways of assessing their learners’ literacy while learning and teaching ESP. The presentation will highlight the latest achievements in this field. The author will present some practical methodological issues and principles associated with learning, teaching and assessing ESP skills of the learners, using the two software programs of EXE 2.0 and Hot Potatoes 6. On the one hand the author will display the advantages of the two programs as self-learning and self-assessment interactive tools in the course of academic study and professional development of the CLIL learners, on the other hand, she will comprehensively shed light upon some methodological aspects of working out appropriate ways of selection, introduction, consolidation of subject specific materials via EXE 2.0 and Hot Potatoes 6. Then the author will go further to distinguish ESP courses by the general nature of the learners’ specialty identifying three large categories of EST (English for Science and Technology), EBE (English for Business and Economics) and ESS (English for the Social Sciences). The cornerstone of the presentation will be the introduction of the subject titled “The methodology of teaching ESP in non-linguistic institutions”, where a unique case of teaching ESP on Architecture and Construction via EXE 2.0 and Hot Potatoes 6 will be introduced, exemplifying how the introduction, consolidation and assessment can be used as a basis for feedback to the ESP learners in a particular professional field.

Keywords: ESP competences, ESP skill assessment/ self-assessment tool, eXe 2.0 / HotPotatoes software program, ESP teaching strategies and techniques

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Authors: Elizabeth D. Ramos

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Peace education supports a holistic appreciation for the value of life and the interdependence of all living systems. Peace education aims to build a culture of peace. One way of building a culture of peace is through environmental stewardship. This study sought to find out the environmental stewardship practices in selected Higher Education Institutions (HEIs) in the Philippines and how these environmental stewardship practices lead to building a culture of peace. The findings revealed that there is still room for improvement in implementing environmental stewardship in schools through academic service learning. In addition, the following manifestations are implemented very satisfactorily in schools: 1) waste reduction, reuse, and recycling, 2) community service, 3) clean and green surroundings. Administrators of schools in the study lead their staff and students in implementing environmental stewardship. It could be concluded that those involved in environmental stewardship display an acceptable culture of peace, particularly, solidarity, respect for persons, and inner peace.

Keywords: academic service learning, environmental stewardship, leadership support, peace, solidarity

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Skyler Kim

Abstract:

An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Christian Luarca

Abstract:

The study aims to define the efficiency of e-Trainings by the use of cloud platform as part of the onboarding process for IT support engineers. Traditional lecture based trainings involves human resource to guide and assist new hires as part of onboarding which takes time and effort. The use of electronic medium as a platform for training provides a two-way basic communication that can be done in a repetitive manner. The study focuses on determining the most efficient manner of learning the basic knowledge on IT support in the shortest time possible. This was determined by conducting the same set of knowledge transfer categories in two different approaches, one being the e-Training and the other using the traditional method. Performance assessment will be done by the use of Service Tracker Assessment (STA) Tool and Service Manager. Data gathered from this ongoing study will promote the utilization of e-Trainings in the IT onboarding process.

Keywords: cloud platform, e-Training, efficiency, onboarding

Procedia PDF Downloads 139

Authors: Khader Musbah Titi

Abstract:

In this era, traditional education systems do not meet the new challenges created by emerging technologies. On the other hand, eLearning offers all the necessary tools to meet these challenges. Using the Internet has brought numerous benefits to most educational institutions; it has also stretched traditional problems of plagiarism, cheating, stealing, vandalism, and spying into the cyberspace. This research discusses these issues in an eLearning environment. It attempts to provide suggestions and possible solutions to some of these issues. The main aim of this research is to conduct a survey at Irbid National University (INU), one of the oldest and biggest universities in Jordan, to study information related to moral and ethical issues in e-learning environment that affect the construction of the students’ characters in the future. The study will focus on student’s behavior and actions through the Internet using Learning Management System (LMS). Another aim of this research is to analyze the opinions of the instructors and last year students at INU about ethical behavior and interaction through LMS. The results show that educational institutes that use LMS should focus on student character development along with field knowledge. According to disadvantages, the results of the study showed that most of students behave unethically in their online activities (cheating, plagiarism, copy/paste etc.) while studying online courses through LMS. The result showed that instructors play a major role in the character development of students. The result also showed that academic institute must have variant mechanisms and strict policy in LMS to control unethical actions of students.

Keywords: LMS, cyber ethics, e-learning, IT ethics, students’ behaviors

Procedia PDF Downloads 229

Authors: Napasri Suwanajote

Abstract:

The purposes of this research were to analyse and evaluate successful factors in OTOP production process for the developing of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, marketing management

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

Abstract:

Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Lei Zhu, Nan Li

Abstract:

Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

Procedia PDF Downloads 131