Search results for: online learning management system

Authors: Qiuhu Shao

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Community home-based care service system, which is based on the family pension, supported by community pension and supplied by institutions pension, is an effective pension system to solve the current situation of China's accelerated aging. However, due to the fundamental realities of our country, the government is not able to bear the unilateral supply of the old-age service of the community. Therefore, based on the theory of welfare pluralism, the participation of social organizations in the home-based care service center has become an important part of the diversified supply of the old-age service for the elderly. Meanwhile, the home-based care service industry is still in the early stage, the management is relatively rough, which resulted in a large number of social resources waste. Thus, scientific, objective and long-term implementation is needed for social organizations to participate in home-based care services to guide its performance management. In order to realize the design of the performance management system, the author has done a research work that clarifies the research status of social organization's participation in home-based care service. Relevant theories such as welfare pluralism, community care theory, and performance management theory have been used to demonstrate the feasibility of data envelopment analysis method in social organization performance research. This paper analyzes the characteristics of the operation mode of the home-based care service center, and hackles the national as well as local documents, standards and norms related to the development of the home-based care industry, particularly studies those documents in Nanjing. Based on this, the paper designed a set of performance management PDCA system for home-based care service center in Nanjing and clarified each step of the system in detail. Subsequently, the research methods of performance evaluation and performance management and feedback, which are two core steps of performance management have been compared and screened in order to establish the overall framework of the performance management system of the home-based care service center. Through a large number of research, the paper summarized and analyzed the characteristics of the home-based care service center. Based on the research results, combined with the practice of the industry development in Nanjing, the paper puts forward a targeted performance evaluation index system of home-based care service center in Nanjing. Finally, the paper evaluated and sub-filed the performance of 186 home-based care service centers in Nanjing and then designed the performance optimization direction and performance improvement path based on the results. This study constructs the index system of performance evaluation of home-based care service and makes the index detailed to the implementation level, and constructs the evaluation index system which can be applied directly. Meanwhile, the quantitative evaluation of social organizations participating in the home-based care service changed the subjective impression in the previous practice of evaluation.

Keywords: data envelopment analysis, home-based care, performance management, social organization

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Authors: Vishwas Chakranarayan, Mariyam Al Salman

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Despite the global environmental threats, previous approaches used to overcome these problems have failed to prevent environmental degradation. Scholars believe that entrepreneurs can help conserve habitats, combat climate change, increase freshwater availability, sustain biodiversity, and reduce environmental degradation and deforestation. The pandemic is creating a different ecosystem for fostering the eco-entrepreneurship opportunities. However, attending a course physically is a challenge for many willing learners. Therefore, it is an opportune time to contemplate on developing a social entrepreneurship curriculum which can be offered online.

Keywords: ecopreneurship, environmental problems, environmental degradation, entrepreneurship education

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Authors: Sayed-Mahdi Hashemi-Dehkordi, Ian Baker

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This paper investigates the benefits of implementing distance learning in training courses for the oil and gas industries to reduce lean. Due to the remote locations of many oil and gas operations, scheduling and organizing in-person training classes for employees in these sectors is challenging. Furthermore, considering that employees often work in periodic shifts such as day, night, and resting periods, arranging in-class training courses requires significant time and transportation. To explore the effectiveness of distance learning compared to in-class learning, a set of questionnaires was administered to employees of a far on-shore refinery unit in Iran, where both in-class and distance classes were conducted. The survey results revealed that over 72% of the participants agreed that distance learning saved them a significant amount of time by rating it 4 to 5 points out of 5 on a Likert scale. Additionally, nearly 67% of the participants acknowledged that distance learning considerably reduced transportation requirements, while approximately 64% agreed that it helped in resolving scheduling issues. Introducing and encouraging the use of distance learning in the training environments of oil and gas industries can lead to notable time and transportation savings for employees, ultimately reducing lean in a positive manner.

Keywords: distance learning, in-class learning, lean, oil and gas, scheduling, time, training programs, transportation

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Authors: Selda Uzal Seyfi

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Goat and sheep milk is quite significant in human nutrition. It is considered as more important day by day. This study was carried out in order to determine applied manure management system and their possibilities of improvement in goat and sheep farm in between 2012 and 2013 years. In the study, it was investigated manure management systems of 25 pieces of sheep and goat farms. It was analyzed the manure collecting, storage and treatment features of farms and whether or not they are suitable for animal breeding. As a result of the study, it was determined that the applied manure management systems in the farm were insufficient. Planning the manure management systems in goat and sheep breeding is appropriate technical criteria is useful in respect of the animal welfare, animal health, the health of workers in the barn and environmental pollution.

Keywords: goat farm, sheep farm, manure storage, manure management

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Authors: Monette S. De Castro, Veneranda A. Magpantay, Christine B. Adiova, Mark D. Arboleda

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One-hundred-layer chicken farmers were randomly selected and interviewed using structured questionnaires to assess the production system and management practices in layer chicken farms. The respondents belonged to the commercial scale operation. Results showed that the predominant rearing and housing systems were intensive/complete confinement and open-sided, while slatted was the common type of flooring used during the brood-grow period. Dekalb and Lohmann were the common chicken layer strains reared by farmers. The majority of commercial chicken layer farms preferred ready-to-lay (RTL) pullets as their replacement stocks. Selling was the easiest way for farmers to dispose of and utilize poultry manure, while veterinary waste and mortality were disposed of in pits. Biosecurity practices employed by the farmers conformed with the ASEAN Biosecurity Management Manual for Commercial Poultry Farming. Flies and odor were the major problems in most layer farms that are associated with their farm wastes. Therefore, the application of new technologies and husbandry practices through training and actual demonstrations could be implemented to further improve the layer chicken raising in the province.

Keywords: layer chicken farms, marketing, production system, waste management

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Omer Agail

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The purpose of this study was to assess the social skills of students with and without learning disabilities in primary education in Saudi Arabia. A Social Skills Rating Scale for Teachers Form (SSRS-TF) was used to evaluate students' social skills as perceived by teachers. A randomly-selected sample was chosen from students with and without learning disabilities. Descriptive statistics were used to describe the demographic characteristics of participants. Analysis indicated that there were statistically significant differences in SSRS-TF by academic status, i.e. students with learning disabilities exhibit less social skills compared to students without learning disabilities. In addition, analysis indicated that there were no statistically significant differences in SSRS-TF by gender. A conclusion and recommendations are presented.

Keywords: primary education, students with learning disabilities, social skills, social competence

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Authors: K. C. Wong

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This paper demonstrates a number of examples in the hope to shed some light on the possibility of designing future operating systems in a more adaptation-based manner. A modern operating system, we conceive, should possess the capability of 'learning' in such a way that it can dynamically adjust its services and behavior according to the current status of the environment in which it operates. In other words, a modern operating system should play a more proactive role during the session of providing system services to users. As such, a modern operating system is expected to create a computing environment, in which its users are provided with system services more matching their dynamically changing needs. The examples demonstrated in this paper show that user's resource access patterns 'learned' and determined during a session can be utilized to improve system performance and hence to provide users with a better and more effective computing environment. The paper also discusses how to use the frequency, the continuity, and the duration of resource accesses in a session to quantitatively measure and determine user's resource access patterns for the examples shown in the paper.

Keywords: adaptation-based systems, operating systems, resource access patterns, system performance

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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: Alex Elentukh

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The paper is based on data collected from final exams administered during five years of teaching the graduate course in software engineering. The visualization instrument with four distinct personas has been used to improve the effectiveness of each class. The study offers a plethora of clues toward students' behavioral preferences. Diversity among students (professional background, physical proximity) is too significant to assume a single face of a learner. This is particularly true for a body of online graduate students in computer science. Conclusions of the study (each learner is unique, and each class is unique) are extrapolated to demystify the notion of an 'average software engineer.' An immediate direction for an educator is to ensure a course applies to a wide audience of very different individuals. On the other hand, a student should be clear about his/her abilities and preferences - to follow the most effective learning path.

Keywords: K.3.2 computer and information science education, learner profiling, adaptive learning, software engineering

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Authors: Amizawati Mohd Amir, Ruhanita Maelah, Zaidi Mohd Noor

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As part of the Malaysia Higher Institutions' Strategic Plan in promoting high-quality research and education, the Ministry of Higher Education has introduced various instrument to assess the universities performance. The aims are that university will produce more commercially-oriented research and continue to contribute in producing professional workforce for domestic and foreign needs. Yet the spirit of the success lies in the commitment of university particularly the academic staff to translate the vision into reality. For that reason, the element of fairness and justice in assessing individual academic staff performance is crucial to promote directly linked between university and individual work goals. Focusing on public research universities (RUs) in Malaysia, this study observes at the issue through the practice of university contemporary performance measurement system. Accordingly management control theory has conceptualized that contemporary performance measurement consisting of three dimension namely strategic, comprehensive and dynamic building upon equity theory, the relationships between contemporary performance measurement system and organizational justice and in turn the effect on academic staff work performance are tested based on online survey data administered on 365 academic staff from public RUs, which were analyzed using statistics analysis SPSS and Equation Structure Modeling. The findings validated the presence of strategic, comprehensive and dynamic in the contemporary performance measurement system. The empirical evidence also indicated that contemporary performance measure and procedural justice are significantly associated with work performance but not for distributive justice. Furthermore, procedural justice does mediate the relationship between contemporary performance measurement and academic staff work performance. Evidently, this study provides evidence on the importance of perceptions of justice towards influencing academic staff work performance. This finding may be a fruitful input in the setting up academic staff performance assessment policy.

Keywords: comprehensive, dynamic, distributive justice, contemporary performance measurement system, strategic, procedure justice, work performance

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Punrapha Praditpong, Surangkana Pipatchokchaiyo

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The objectives of this study were to study the consumer behavior of the Baby boomers, the X & the Y generation towards Online Travel Agents in purchasing tourism-related products and services. The research methodology of this research used the quantitative study and the sample size consisted of 400 questionnaires in five districts of Bangkok. The data was analyzed by frequency, percentage, mean and SD. Moreover, all the hypotheses were tested by One-Way ANOVA and Pearson-Correlation statistics. The research findings were as follows: 1) There were significant effects to the purchasing decision making process towards purchasing tourism related products and services via OTAs; 2) There were different consumer behaviors from the Baby boomers, the X generation and the Y generation towards purchasing tourism related products and services via OTAs, which are explained in detail in finding. The research offers a discussion and presents some recommendations for the OTA websites.

Keywords: consumer behavior, online travel agent, x generations, y generations

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Authors: Liyu Adhi Kasari Sulung

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Poverty has been a major problem in Indonesia. Islamic micro finance (IMF) named Baitul Maal Wat Tamwil (Bmt) plays a prominent role to eradicate this. Indonesia as the biggest muslim country has many successful applied products such as worldwide adopt group-based lending approach, flexible financing for farmers, and gold pawning. The Problems related to these models are operation risk management and internal control system (ICS). A proper ICS will help an organization in preventing the occurrence of bad financing through detecting error and irregularities in its operation. This study aims to seek a proper risk management scheme of credit system in Bmt and internal control system’s rank for every stage. Risk management variables are obtained at the first In-Depth Interview (IDI) and Focus Group Discussion (FGD) with Shariah supervisory boards, boards of directors, and operational managers. Survey was conducted covering nationwide data; West Java, South Sulawesi, and West Nusa Tenggara. Moreover, Content analysis is employed to build the relationship among these variables. Research Findings shows that risk management Characteristics in Indonesia involves ex ante, credit process, and ex post strategies to deal with risk in credit system. Ex-ante control consists of Shariah compliance, survey, group leader reference, and islamic forming orientation. Then, credit process involves saving, collateral, joint liability, loan repayment, and credit installment controlling. Finally, ex-post control includes shariah evaluation, credit evaluation, grace period and low installment provisions. In addition, internal control order sort three stages by its priority; Credit process as first rank, then ex-post control as second, and ex ante control as the last rank.

Keywords: internal control system, islamic micro finance, poverty, risk management

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Gavin J. Baxter, Mark H. Stansfield

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This paper reviews the internal use of blogs and their potential effectiveness as organisational learning tools. Prior to and since the emergence of the concept of ‘Enterprise 2.0’ there still remains a lack of empirical evidence associated with how organisations are applying social media tools and whether they are effective towards supporting organisational learning. Surprisingly, blogs, one of the more traditional social media tools, still remains under-researched in the context of ‘Enterprise 2.0’ and organisational learning. The aim of this paper is to identify the theoretical linkage between blogs and organisational learning in addition to reviewing prior research on organisational blogging with a view towards exploring why this area remains under-researched and identifying what needs to be done to try and move the area forward. Through a review of the literature, one of the principal findings of this paper is that organisational blogs, dependent on their use, do have a mutual compatibility with the interpretivist aspect of organisational learning. This paper further advocates that further empirical work in this subject area is required to substantiate this theoretical assumption.

Keywords: Enterprise 2.0, blogs, organisational learning, social media tools

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Authors: Lusia Ndahafa Nghitotelwa

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India is a home to many languages, cultures, traditions, castes and religions. This diversity, when observed in education, appears to be challenging and difficult to manage with respect to including everyone in the educational system. But in order to achieve this, attempts to understand the complexity of the issue and find some solutions for including everyone in education has been made in India since independence, regardless of the students’ background. Despite that, the challenge is still topical. Plenty of students are left out of the system due to the lack of awareness and appropriate management of these diversities. Therefore, the present paper makes an attempt to study the awareness and management of diversity in Indian schools. Existing studies on diversity in Indian schools, along with how measures and which measures have been taken to accommodate and retain everyone in school, have been looked at, and a thorough critical analysis of findings has been narrated. It was found that a lot of efforts have been conjugated to include and educate children of all castes, religions, and linguistic backgrounds. Furthermore, the awareness of inclusive education among teachers and society members is moderate, but teachers lack the necessary skills and knowledge on how to deal with students with special educational needs in regular classes. Also, the management is aware of inclusive education, but the management does not include teachers in decision-making. Moreover, it was found that the poor management of inclusion services and retention of special needs students in Indian schools results in their poor effective integration into the workforce. Finally, the management was found to have stringent admission criteria, which has the effect of hindering some students from entering the educational system. Based on the results of the study, it is clear that the implementation of inclusive education is still a challenge in India. However, there are promising results in tackling the issue. All children should be given an opportunity to learn together with other children in order to broaden their interest and challenge their potential.

Keywords: awareness, management, inclusive education, students

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Authors: Beom-Ryeol Choi, Won-Poong Lee, Jin-Young Choi, Young-Hak Shin, Dong-Jun Won

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A Microgrid (MG) has a major role in power system. There are numerous benefits, such as ability to reduce environmental impact and enhance the reliability of a power system. Hence, Multi-MG (MMG) consisted of multiple MGs is being studied intensively. This paper proposes the optimal power exchange of MMG with hierarchical coordination. The whole system architecture consists of two layers: 1) upper layer including MG of MG Center (MoMC) which is in charge of the overall management and coordination and 2) lower layer comprised of several Microgrid-Energy Management Systems (MG-EMSs) which make a decision for own schedule. In order to accomplish the optimal power exchange, the proposed coordination algorithm is applied to MMG system. The objective of this process is to achieve optimal operation for improving economics under the grid-connected operation. The simulation results show how the output of each MG can be changed through coordination algorithm.

Keywords: microgrids, multi-microgrids, power exchange, hierarchical coordination

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Authors: Nirmani Wijenayake, Louise Lutze-Mann, Lucy Jo, John Wilson, Vivian Yeung, Dean Lovett, Kim Snepvangers

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Sessional academic staff at universities have the most influence and impact on student learning, engagement, and experience as they have the most direct contact with undergraduate students. A blended technology-enhanced program was created for the development and support of sessional staff to ensure adequate training is provided to deliver quality educational outcomes for the students. This program combines innovative mixed media educational modules, a peer-driven support forum, and face-to-face workshops to provide a comprehensive training and support package for staff. Additionally, the program encourages the development of learning communities and peer mentoring among the sessional staff to enhance their support system. In 2018, the program was piloted on 100 sessional staff in the School of Biotechnology and Biomolecular Sciences to evaluate the effectiveness of this model. As part of the program, rotoscope animations were developed to showcase ‘typical’ interactions between staff and students. These were designed around communication, confidence building, consistency in grading, feedback, diversity awareness, and mental health and wellbeing. When surveyed, 86% of sessional staff found these animations to be helpful in their teaching. An online platform (Moodle) was set up to disseminate educational resources and teaching tips, to host a discussion forum for peer-to-peer communication and to increase critical thinking and problem-solving skills through scenario-based lessons. The learning analytics from these lessons were essential in identifying difficulties faced by sessional staff to further develop supporting workshops to improve outcomes related to teaching. The face-to-face professional development workshops were run by expert guest speakers on topics such as cultural diversity, stress and anxiety, LGBTIQ and student engagement. All the attendees of the workshops found them to be useful and 88% said they felt these workshops increase interaction with their peers and built a sense of community. The final component of the program was to use an adaptive e-learning platform to gather feedback from the students on sessional staff teaching twice during the semester. The initial feedback provides sessional staff with enough time to reflect on their teaching and adjust their performance if necessary, to improve the student experience. The feedback from students and the sessional staff on this model has been extremely positive. The training equips the sessional staff with knowledge and insights which can provide students with an exceptional learning environment. This program is designed in a flexible and scalable manner so that other faculties or institutions could adapt components for their own training. It is anticipated that the training and support would help to build the next generation of educators who will directly impact the educational experience of students.

Keywords: designing effective instruction, enhancing student learning, implementing effective strategies, professional development

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Authors: Skyler Kim

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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: Helene Thieblemont, Fariborz Haghighat

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Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control.

Keywords: anticipatory control, model predictive control, solar radiation forecast, thermal storage

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Authors: Kuo-Kai Lin, Po-Lun Chang

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Research Background and Purpose: Following the development of the Internet and multimedia, the Internet and information technology have become crucial avenues of modern communication and knowledge acquisition. The advantages of using mobile devices for learning include making learning borderless and accessible. Mobile learning has become a trend in disease management and health promotion in recent years. End-stage renal disease (ESRD) is an irreversible chronic disease, and patients who do not receive kidney transplants can only rely on hemodialysis or peritoneal dialysis to survive. Due to the complexities in caregiving for patients with ESRD that stem from their advanced age and other comorbidities, the patients’ incapacity of self-care leads to an increase in the need to rely on their families or primary caregivers, although whether the primary caregivers adequately understand and implement patient care is a topic of concern. Therefore, this study explored whether primary caregivers’ health care provisions can be improved through the intervention of an automatic speech intelligent system, thereby improving the objective health outcomes of patients undergoing long-term dialysis. Method: This study developed an automatic speech intelligent system with healthcare functions such as health information voice prompt, two-way feedback, real-time push notification, and health information delivery. Convenience sampling was adopted to recruit eligible patients from a hemodialysis center at a regional teaching hospital as research participants. A one-group pretest-posttest design was adopted. Descriptive and inferential statistics were calculated from the demographic information collected from questionnaires answered by patients and primary caregivers, and from a medical record review, a health care scale (recorded six months before and after the implementation of intervention measures), a subjective health assessment, and a report of objective physiological indicators. The changes in health care behaviors, subjective health status, and physiological indicators before and after the intervention of the proposed automatic speech intelligent system were then compared. Conclusion and Discussion: The preliminary automatic speech intelligent system developed in this study was tested with 20 pretest patients at the recruitment location, and their health care capacity scores improved from 59.1 to 72.8; comparisons through a nonparametric test indicated a significant difference (p < .01). The average score for their subjective health assessment rose from 2.8 to 3.3. A survey of their objective physiological indicators discovered that the compliance rate for the blood potassium level was the most significant indicator; its average compliance rate increased from 81% to 94%. The results demonstrated that this automatic speech intelligent system yielded a higher efficacy for chronic disease care than did conventional health education delivered by nurses. Therefore, future efforts will continue to increase the number of recruited patients and to refine the intelligent system. Future improvements to the intelligent system can be expected to enhance its effectiveness even further.

Keywords: automatic speech intelligent system for health care, primary caregiver, long-term hemodialysis, health care capabilities, health outcomes

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Authors: Swee Kun Yap, Sachin Jangam, Suraj Vasudevan

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Experiential learning is dubbed as a highly effective way to enhance learning. Virtual reality (VR) is thus a helpful tool in providing a safe, memorable, and interactive learning environment. A class of 49 fluid mechanics students participated in starting up a pump, one of the most used equipment in the chemical industry, in VR. They experience the process in VR to familiarize themselves with the safety training and the standard operating procedure (SOP) in guided mode. Students subsequently observe their peers (in groups of 4 to 5) complete the same training. The training first brings each user through the personal protection equipment (PPE) selection, before guiding the user through a series of steps for pump startup. One of the most common feedback given by industries include the weakness of our graduates in pump design and operation. Traditional fluid mechanics is a highly theoretical module loaded with engineering equations, providing limited opportunity for visualization and operation. With VR pump, students can now learn to startup, shutdown, troubleshoot and observe the intricacies of a centrifugal pump in a safe and controlled environment, thereby bridging the gap between theory and practical application. Following the completion of the guided mode operation, students then individually complete the VR assessment for pump startup on the same day, which requires students to complete the same series of steps, without any cues given in VR to test their recollection rate. While most students miss out a few minor steps such as the checking of lubrication oil and the closing of minor drain valves before pump priming, all the students scored full marks in the PPE selection, and over 80% of the students were able to complete all the critical steps that are required to startup a pump safely. The students were subsequently tested for their recollection rate by means of an online quiz 3 weeks later, and it is again found that over 80% of the students were able to complete the critical steps in the correct order. In the survey conducted, students reported that the VR experience has been enjoyable and enriching, and 79.5% of the students voted to include VR as a positive supplementary exercise in addition to traditional teaching methods. One of the more notable feedback is the higher ease of noticing and learning from mistakes as an observer rather than as a VR participant. Thus, the cycling between being a VR participant and an observer has helped tremendously in their knowledge retention. This reinforces the positive impact VR has on learning.

Keywords: experiential learning, learning by doing, pump, unit operations, virtual reality

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Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 126

Authors: Gerges Samaan Henin Abdalla

Abstract:

Ultra-secured methods of banking services have been introduced to the customer, such as online banking. Banks have begun to consider electronic banking (e-banking) as a way to replace some traditional branch functions by using the Internet as a distribution channel. Some consumers have at least one account at multiple banks and access these accounts through online banking. To check their current net worth, clients need to log into each of their accounts, get detailed information, and work toward consolidation. Not only is it time consuming, but it is also a repeatable activity with a certain frequency. To solve this problem, the concept of account aggregation was added as a solution. Account consolidation in e-banking as a form of electronic banking appears to build a stronger relationship with customers. An account linking service is generally referred to as a service that allows customers to manage their bank accounts held at different institutions via a common online banking platform that places a high priority on security and data protection. Consumers have at least one account at multiple banks and access these accounts through online banking. To check their current net worth, clients need to log into each of their accounts, get detailed information, and work toward consolidation. The article provides an overview of the account aggregation approach in e-banking as a new service in the area of e-banking.

Keywords: compatibility, complexity, mobile banking, observation, risk banking technology, Internet banks, modernization of banks, banks, account aggregation, security, enterprise development

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Authors: M. Ndaw, G. Mendy, S. Ouya

Abstract:

Information system risk management helps to reduce or eliminate risk by implementing appropriate controls. In this paper, we propose a quantification model of controls impact on information system risks by automatizing the residual criticality estimation step of FMECA which is based on a inductive reasoning. For this, we defined three equations based on type and maturity of controls. For testing, the values obtained with the model were compared to estimated values given by interlocutors during different working sessions and the result is satisfactory. This model allows an optimal assessment of controls maturity and facilitates risk analysis of information system.

Keywords: information system, risk, control, FMECA method

Procedia PDF Downloads 355

Authors: Naoki Okamura

Abstract:

From a perspective of moral education, this study has examined the experiences of a group of college students who volunteered in disaster areas after the magnitude 9.0 Earthquake, which struck the Northeastern region of Japan in March, 2011. The research, utilizing the method of grounded theory, has uncovered that most of the students have gone through positive changes in their development of moral and social characters, such as attaining deeper sense of empathy and caring personalities. The study expresses, in identifying the nature of those transformations, that the importance of volunteer work should strongly be recognized by the colleges and universities in Japan, in fulfilling their public responsibility of creating and building learning communities that are responsible and caring.

Keywords: moral development, moral education, service learning, volunteer learning

Procedia PDF Downloads 320

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

Abstract:

Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 155

Authors: Asma Ameur, Dhafer Malouche

Abstract:

Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

Procedia PDF Downloads 136

Authors: Yu-Tzu Chen, Luh-Maan Chang

Abstract:

Construction is an inherently risky industry. The projects have been dominated by reactive actions owing to non-routine in nature. The on-site activities are especially crucial for successful project control. In order to alter actions from reactive to proactive, this paper presents an on-site data collection system utilizing advanced technology RFID and GPS in assisting on-site management with near real time progress monitoring.

Keywords: On-Site management, progress monitoring, RFID, GPS

Procedia PDF Downloads 568