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Yuen, Mantak, Serene Chan, Cheri Chan, Dennis CL Fung, Wai Ming Cheung, Tammy Kwan, and Frederick KS Leung. "Differentiation in key learning areas for gifted students in regular classes." Gifted Education International 34, no. 1 (May 19, 2016): 36–46. http://dx.doi.org/10.1177/0261429416649047.
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Gifted students usually require much less time spent in practising and revising basic skills; instead, they benefit greatly from opportunities to work through the curriculum at a faster pace (acceleration). Teachers currently working with mixed-ability classes do not always find it easy to differentiate their teaching approach in this way, so there is a need to facilitate in-service professional development to provide teachers with practical strategies for implementing effective differentiation for gifted learners. In response, a project for primary school teachers was organized by a university in Hong Kong. The purposes of the project were (a) to enhance the confidence of teachers in planning and delivering differentiated lessons in specific key learning areas (KLAs) with particular reference to gifted students; (b) to empower teachers with knowledge and strategies necessary for designing and implementing a differentiated curriculum in KLA domains and (c) to establish a professional development practice that connects local academics with schools and teachers. The project was implemented by inviting curriculum leaders, panel chairpersons and subject teachers from primary schools to attend a 3-hour lecture and a 6-hour workshop in which differentiation practices were explored. The project was later evaluated based on feedback from participants and university consultants. Overall, the feedback was positive, but suggestions are provided here for enhancing future projects of a similar nature.
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Peres, N. A., S. J. MacKenzie, T. L. Peever, and L. W. Timmer. "Postbloom Fruit Drop of Citrus and Key Lime Anthracnose Are Caused by Distinct Phylogenetic Lineages of Colletotrichum acutatum." Phytopathology® 98, no. 3 (March 2008): 345–52. http://dx.doi.org/10.1094/phyto-98-3-0345.
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Colletotrichum acutatum causes two diseases of citrus, postbloom fruit drop (PFD) and Key lime anthracnose (KLA). PFD is a disease restricted to flowers of sweet orange and most other citrus, and symptoms include petal necrosis, abscission of developing fruit, and the formation of persistent calyces. KLA is a disease of foliage, flowers, and fruits of Key lime only, and symptoms include necrotic lesions on leaves, fruits, twigs, flowers, and blight of entire shoots. The internal transcribed spacers 1 and 2 and the gene encoding the 5.8S ribosomal RNA subunit within the nuclear ribosomal cluster (ITS) and intron 2 of the glyceraldehyde-3-phosphate dehydrogenase gene (G3PD) were sequenced for isolates from PFD-affected sweet orange and KLA-affected Key limes collected in the United States (Florida), Brazil (São Paulo), Mexico, Belize, Costa Rica, and the Dominican Republic to determine if there are consistent genetic differences between PFD and KLA isolates over the geographic area where these diseases occur. Based on the sequence data, isolates clustered into two well-supported clades with little or no sequence variation among isolates within clades. One clade (PFD clade) contained PFD isolates from all countries sampled plus a few isolates from flowers of Key lime in Brazil. The other clade (KLA clade) contained KLA isolates from Key lime foliage from all countries sampled and one isolate from flowers of sweet orange in Mexico. In greenhouse inoculations with PFD and KLA isolates from Florida, isolates from both clades produced PFD symptoms on Orlando tangelo flowers, but KLA-clade isolates produced significantly less severe symptoms. PFD-clade isolates were not pathogenic to Key lime foliage, confirming previous studies. The differentiation of PFD and KLA isolates into two well-supported clades and the pathogenicity data indicate that PFD and KLA are caused by distinct phylogenetic lineages of C. acutatum that are also biologically distinct. PFD is a recently described disease (first reported in 1979) relative to KLA (first reported in 1912) and it had been proposed that strains causing PFD evolved from strains causing KLA eventually losing pathogenicity to Key lime foliage. We reject the hypothesis that PFD strains have diverged from KLA strains recently based on estimated divergence times of haplotypes and it appears that PFD and KLA strains have been dispersed throughout the Americas independently in association with each host.
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Clement, John. "Model based learning as a key research area for science education." International Journal of Science Education 22, no. 9 (September 2000): 1041–53. http://dx.doi.org/10.1080/095006900416901.
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Stein, Sarah J., Campbell J. Mcrobbie, and Ian Ginns. "Recognising Uniqueness in the Technology Key Learning Area: The Search for Meaning." International Journal of Technology and Design Education 10, no. 2 (October 2000): 105–23. http://dx.doi.org/10.1023/a:1008945013123.
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Li, Y., M. Sakamoto, T. Shinohara, and T. Satoh. "AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 117–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-117-2020.
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Abstract. Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately.
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Mudzielwana, Ndileleni Paulinah. "Conceptualisation of Language and Vocabulary Learning Strategies: Key Aspect in Every Curriculum Area." International Journal of Educational Sciences 15, no. 3 (October 2016): 538–46. http://dx.doi.org/10.1080/09751122.2016.11890564.
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Shah, Chandra. "Recurrent Teacher Cost per Student by Key Learning Area: Upper Secondary Schools, Victoria, Australia." Education Economics 6, no. 2 (August 1998): 121–39. http://dx.doi.org/10.1080/09645299800000011.
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Harrison, Richard T., and Claire M. Leitch. "Entrepreneurial Learning: Researching the Interface between Learning and the Entrepreneurial Context." Entrepreneurship Theory and Practice 29, no. 4 (July 2005): 351–71. http://dx.doi.org/10.1111/j.1540-6520.2005.00089.x.
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The context for the research presented in this article arises from increasing interest, by academics and practitioners, in the importance of learning and knowledge in the knowledge–based economy. In particular, we consider the scope for applying concepts of learning within the field of entrepreneurship. While it has gained currency within the field of management, the application of these concepts to entrepreneurship has been limited. In this Introduction to the Special Issue, we review the development of the field of entrepreneurship as a context for the emergence of learning as an area of scholarly attention, summarize a number of key themes emerging from the organizational learning literature, and outline the article selection process and summarize the key elements of each of the included articles. The article concludes with some reflections on future research at the interface between learning and the entrepreneurial context.
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Sari, Rahma Puspita, Kusumastuti Kusumastuti, and Rufia Andisetyana Putri. "Kesesuaian Taman Cerdas sebagai Ruang Publik skala Pelayanan Kelurahan terhadap Konsep Kota Layak Anak (KLA)." Region: Jurnal Pembangunan Wilayah dan Perencanaan Partisipatif 12, no. 1 (November 1, 2017): 71. http://dx.doi.org/10.20961/region.v12i1.12116.
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<pre><em>The Child Friendly City (CFC) is a concept for a city that can guarantee children to get their rights in urban area. The Child Friendly City (CFC) is expected for children to get their rights including </em><em>health, protection, care, education, </em><em>no discrimination, aware of their environment and culture, participate in designing the city, and have the freedom to play. Surakarta is one of the cities in Indonesia which have applied the concept of Child Friendly City (CFC) since 2008. To support implementation of Child Friendly City (CFC) concept, the city must provide a public space for children to be able to transfer their creativity and also have recreational purpose, which is ‘taman cerdas’. The right choice of location, facility, environment, and security need to be considered, because each children go through different phase. The purpose of this study are </em><em>(1) Knowing the suitability of taman cerdas component as the public space of neighborhood service scale to the concept of </em><em>Child Friendly Cities (CFC) </em><em>and (2) Analyze the suitability level of taman cerdas as the public space of neighborhood service scale to the concept of </em><em>Child Friendly Cities (CFC</em><em>). The method used in this research is quantitative research with deductive approach, this research used </em><em>scoring analysis. Survey conducted for this study were primary surveys, consist of observation and interviews, and secondary survey in the form of data. From these results, the advantage of knowing the level of conformity of ‘taman cerdas’ as the public spaces of neighborhood service to the concept of Child Friendly Cities (CFC) has four variables such as strategic location, facilities for playing and learning according to the needs of children, comfortable and healthy environment, as well as comfort and security. Then those variables are used for the assessment tools to eight ‘taman cerdas’ in Surakarta. Based on the results of the study, it can be concluded that there are four ‘taman cerdas’ is match with the variables above, which is ‘taman cerdas’ </em><em>Mojosongo, Joyotakan, Pajang, and Jebres. Meanwhile there are also four </em><em>‘</em><em>taman cerdas</em><em>’</em><em> that not match with the variables of thes research those are </em><em>‘</em><em>taman cerdas</em><em>’</em><em> Sumber, Gandekan, Kadipiro, and Pucangsawit. The conclusion is the city needs to increase the </em><em>the component for taman cerdas as public space service scale of neighborhood for Child Friendly Cities (CFC) at Surakarta.</em></pre>
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Chatti, Mohamed Amine, Anna Lea Dyckhoff, Ulrik Schroeder, and Hendrik Thüs. "Forschungsfeld Learning Analytics." i-com 11, no. 1 (March 1, 2012): 22–25. http://dx.doi.org/10.1524/icom.2012.0007.
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Summary Learning analytics has attracted a great deal of attention in technology enhanced learning (TEL) in recent years as educational institutions and researchers are increasingly seeing the potential that learning analytics has to support the learning process. Learning analytics has been identified as a possible key future trend in learning and teaching (Johnson et al., 2011). Analytics can be a powerful tool to support learning. There are, however, a number of issues that need to be addressed before starting analytics projects. In this paper, we identify various challenges and research opportunities in the emerging area of learning analytics.
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Lynch, Timothy. "Exploring Religious Education and Health and Physical Education Key Learning Area Connections in Primary Schools." International Journal of Humanities Education 10, no. 2 (2013): 11–23. http://dx.doi.org/10.18848/2327-0063/cgp/v10i02/43772.
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Pester, Andreas, and Thomas Klinger. "Distributed Experiments and Distributed Learning." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 06 (May 28, 2020): 19. http://dx.doi.org/10.3991/ijoe.v16i06.13661.
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The paper, an enhanced version of conference key presentation, investigates, how the development of new needs in education and developments in IT triggered technology enhanced learning. Online labs play an important in that area. Federation of labs is the new direction to answer the challenges, which come from the intensive use of online labs in the modern learning environments. VISIR Federation is a concrete attempt to implement such a federation. Distributed labs in a federation need commitments from the participants in organization, sharing lab and learning resources.
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Valiulis, Algirdas Vaclovas, Vytautas Bučinskas, and Eligijus Toločka. "Strengthening Mobility for Better Learning." Solid State Phenomena 220-221 (January 2015): 1014–17. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.1014.
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In a modern environment, during the evolution of international activities, from mobility to international education hubs, universities are searching for new internationalization tools to implement those undertakings more effectively in terms of finance and time. Co-operation with other regions of the world and international openness are the key factors in the development of the European Higher Education Area. To illustrate the situation arising from student mobility of studying technological sciences, the paper analyses information about student mobility in the fields of mechanical engineering and mechatronics for the period 2009–2013.
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Canelas, Dorian A., Jennifer L. Hill, and Andrea Novicki. "Cooperative learning in organic chemistry increases student assessment of learning gains in key transferable skills." Chemistry Education Research and Practice 18, no. 3 (2017): 441–56. http://dx.doi.org/10.1039/c7rp00014f.
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Science and engineering educators and employers agree that students should graduate from college with expertise in their major subject area as well as the skills and competencies necessary for productive participation in diverse work environments. These competencies include problem-solving, communication, leadership, and collaboration, among others. Using a pseudo-experimental design, and employing a variety of data from exam scores, course evaluations, and student assessment of learning gains (SALG) surveys of key competencies, we compared the development of both chemistry content knowledge and transferable or generic skills among students enrolled in two types of large classes: a lecture-based formatversusan interactive, constructive, cooperative learning (flipped classroom) format. Controlling for instructor, as well as laboratory and recitation content, students enrolled in the cooperative learning format reported higher learning gains than the control group in essential transferable skills and competency areas at the end of the term, and more growth in these areas over the course of the term. As a result of their work in the class, the two groups of students reported the most significant differences in their gains in the following areas: “interacting productively to solve problems with a diverse group of classmates,” “behaving as an effective leader,” “behaving as an effective teammate,” and “comfort level working with complex ideas.” Our findings clearly show that cooperative learning course designs allow students to practice and develop the transferable skills valued by employers.
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Lochmiller, Chad R., and Kathleen M. W. Cunningham. "Leading learning in content areas." International Journal of Educational Management 33, no. 6 (September 9, 2019): 1219–34. http://dx.doi.org/10.1108/ijem-03-2018-0094.
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Purpose The purpose of this paper is to report findings from a systematic literature review that explore how recent research on instructional leadership has addressed the role of mathematics and science instruction. Design/methodology/approach Using Hallinger’s (2014) approach to conducting systematic reviews, the review included 109 peer-reviewed articles published since 2008 in leading mathematics and science education journals. An a priori coding scheme based upon key leadership behaviors articulated in Hitt and Tucker’s (2016) unified leadership framework informed the analysis presented. Findings Results indicate that leaders support content area instruction by facilitating high-quality instructional experiences through curricular and assessment leadership. Leadership frequently involves establishing organizational conditions that support teachers’ efforts to improve their own practice instead of direct leadership action on the part of instructional leaders. This support takes different forms and can include distributing leadership to teacher leaders with content area experience as well as using resources strategically to provide professional development or instructional coaching. Originality/value The review strengthens the connections between the instructional leadership, mathematics and science literatures, and identifies some of the leadership practices that these literatures deem important for instructional improvement. The review also reveals the potential for future research exploring the influence of a particular content area on supervisory practice and leadership discourse.
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Annette, Lucy. "A digital learning future." Impact 2021, no. 2 (February 26, 2021): 48–49. http://dx.doi.org/10.21820/23987073.2021.2.48.
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Across the world, digital education is becoming more and more important. It is continuing to grow in popularity among learners and teachers alike and, against the backdrop of COVID-19, has proven vital to enable training and educational development to continue. The European Commission has introduced a new Digital Education Action Plan 2021-2027 that is designed to overcome challenges associated with delivering digital education. It builds on the work of the previous 2018-2020 Action Plan and incorporates learnings from the COVID-19 pandemic. The idea is for world class, inclusive and accessible education to be available digitally to students across Europe. This will involve fostering stronger cooperation between key stakeholders in Member States. Indeed, a strategic dialogue will be launched among EU Member States in order to develop a Council Recommendation on factors that will help facilitate digital education by 2022. A priority area is how digital education can be optimised in education, creating a digital education system that is high-performing and universally useful. A second priority area concerns the development of digital skills and competencies in learners and teachers alike, as well as ensuring online learning resources are relevant, interactive and user-friendly.
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Annette, Lucy. "A digital learning future." Impact 2021, no. 3 (March 29, 2021): 4–5. http://dx.doi.org/10.21820/23987073.2021.3.4.
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Across the world, digital education is becoming more and more important. It is continuing to grow in popularity among learners and teachers alike and, against the backdrop of COVID-19, has proven vital to enable training and educational development to continue. The European Commission has introduced a new Digital Education Action Plan 2021-2027 that is designed to overcome challenges associated with delivering digital education. It builds on the work of the previous 2018-2020 Action Plan and incorporates learnings from the COVID-19 pandemic. The idea is for world class, inclusive and accessible education to be available digitally to students across Europe. This will involve fostering stronger cooperation between key stakeholders in Member States. Indeed, a strategic dialogue will be launched among EU Member States in order to develop a Council Recommendation on factors that will help facilitate digital education by 2022. A priority area is how digital education can be optimised in education, creating a digital education system that is high-performing and universally useful. A second priority area concerns the development of digital skills and competencies in learners and teachers alike, as well as ensuring online learning resources are relevant, interactive and user-friendly.
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Gong, Faming, Xiaofeng Ji, Wenjuan Gong, Xiangbing Yuan, and Chenyu Gong. "Deep Learning Based Protective Equipment Detection on Offshore Drilling Platform." Symmetry 13, no. 6 (May 27, 2021): 954. http://dx.doi.org/10.3390/sym13060954.
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There is a symmetrical relationship between safety management and production efficiency of an offshore drilling platform. The development of artificial intelligence makes people pay more attention to intelligent security management. It is extremely important to reinforce workplace safety management by monitoring protective equipment wearing using artificial intelligence, such as safety helmets and workwear uniforms. The working environment of the offshore drilling platforms is particularly complex due to small-scale subjects, flexible human postures, oil and gas pipeline occlusions, etc. To automatically monitor and report misconduct that violates safety measures, this paper proposes a personal protective equipment detection method based on deep learning. On the basis of improving YOLOv3, the proposed method detects on-site workers and obtains the bounding box of personnel. The result of candidate detection is used as the input of gesture recognition to detect human body key points. Based on the detected key points, the area of interest (head area and workwear uniform area) is located based on the spatial relations among the human body key points. The safety helmets are recognized using the deep transfer learning based on improved ResNet50, according to the symmetry between the helmets and the workwear uniforms, the same method is used to recognize the workwear uniforms to realize the identification of protective equipment. Experiments show that the proposed method achieves a higher accuracy in the protective equipment detection on offshore drilling platforms compared with other deep learning models. The detection accuracies of the proposed method for helmets and workwear uniforms are 94.8% and 95.4%, respectively.
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Lee, John Chi-kin, Lawrence Ka-ki Ho, Julian Yat-ming Leung, and Cecilia Chuk-ching Lo. "Curriculum Development of the PSHE Key Learning Area in Junior Secondary Schools: Perspectives of Humanities Teachers." Curriculum and Teaching 22, no. 1 (January 1, 2007): 67–86. http://dx.doi.org/10.7459/ct/22.1.05.
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Cooper, Sally-Ann, and Richard A. Collacott. "Depressive episodes in adults with learning disabilities." Irish Journal of Psychological Medicine 13, no. 3 (September 1996): 105–13. http://dx.doi.org/10.1017/s0790966700002664.
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AbstractObjective:In view of the Royal College of Psychiatrists Defeat Depression campaign, to review the existing literature on depressive episodes in people with learning disabilities.Methods:Review of all papers identified to cover this area. Papers were identified by Medline search and also by direct checking of the contents in the preceding five years of eight key learning disabilities journals, the British Journal of Psychiatry, Psychiatric Bulletin and bibliography in Current Opinions in Psychiatry. Papers about children or odd ‘depressive’ symptoms (rather than the syndrome of depression) were excluded.Findings:For over a century it has been recognised that depression can occur in people with learning disabilities. However, the number of studies in this area is very limited. There remain many unanswered questions, such as those of prevalence, clinical presentation and diagnostic criteria, associated social factors, and the evaluation of treatments. Suitable rating scales have yet to be evaluated or devised. The authors propose diagnostic criteria for future use.Conclusions:Research in this important area is very limited. Advances are yet to be made in the clinical assessment and management of the illness.
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Soto Gómez, Encarna, María J. Serván Núñez, and Rosa Caparros-Vida. "Learning to teach with lesson study." International Journal for Lesson and Learning Studies 5, no. 2 (April 11, 2016): 116–29. http://dx.doi.org/10.1108/ijlls-12-2015-0042.
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Purpose – The purpose of this paper is to incorporate Lesson Study (LS) from initial teacher training as a privileged area in which to accompany and tutor future teachers in the complex but necessary scenario of reflective practice in cooperative processes. Design/methodology/approach – This paper presents an innovative educational experience in the university context, incorporating Lesson Study as a strategy for research and improvement of teaching practice, and, in consequence, for students’ learning, in two key subjects in the syllabus: Practicum III and Degree Essay. Findings – A process, positively reviewed by all agents involved, which has shown the need for the reconstruction of the practical knowledge of participating students. Originality/value – This is the first experience in the Spanish university context where LS is introduced as one of the key areas of initial teacher training. The paper presents a work model which may inspire the introduction of Lesson Study as an initial training model during practical training.
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Ali, Mohamed Hassan Mohamed, Said Fathalla, Mohamed Kholief, and Yasser Fouad Hassan. "Learning Non-Taxonomic Relations of Ontologies." International Journal on Semantic Web and Information Systems 17, no. 1 (January 2021): 97–122. http://dx.doi.org/10.4018/ijswis.2021010105.
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Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.
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Gheibi, Omid, Danny Weyns, and Federico Quin. "Applying Machine Learning in Self-adaptive Systems." ACM Transactions on Autonomous and Adaptive Systems 15, no. 3 (September 30, 2020): 1–37. http://dx.doi.org/10.1145/3469440.
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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.
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Pang, Guansong, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. "Deep Learning for Anomaly Detection." ACM Computing Surveys 54, no. 2 (April 2021): 1–38. http://dx.doi.org/10.1145/3439950.
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Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection , has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.
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Mastan Vali, Shaik, and P. Sujatha. "Friendship Identification on Location Based Social Networks Using Ensemble Learning Technique." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 766. http://dx.doi.org/10.14419/ijet.v7i4.36.24239.
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The brisk development of client information and geographic area information in the area built long range interpersonal communication applications, it is logically troublesome for clients to quick and absolutely discover the data they need. With the expedient development and generally abuse of cell phone, area based informal organization (LBSN) has turned out to be one critical stage for some novel applications. The area data will help to find companion relationship, companion suggestion, network identification, and manual for excursion, notice merchandise et cetera. We separated client social relationship, registration separation and registration compose are the three most huge key highlights. After the component extraction, we connected Adaboost troupe classifier with different base classifiers to order. In view of the trial results, Adaboost with Rehashed Incremental Pruning to Deliver Mistake Decrease (RIPPER) gives the best outcome contrasted with other base classifiers.
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Corrigan, Colleen M., Jeff A. Ardron, Mia T. Comeros-Raynal, Erich Hoyt, Giuseppe Notarbartolo Di Sciara, and Kent E. Carpenter. "Developing important marine mammal area criteria: learning from ecologically or biologically significant areas and key biodiversity areas." Aquatic Conservation: Marine and Freshwater Ecosystems 24, S2 (November 2014): 166–83. http://dx.doi.org/10.1002/aqc.2513.
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Raj, Ravi. "Echocardiographic Assessment of Mitral Stenosis: Key Learning Points for Fellows/Residents in Training." Journal of Perioperative Echocardiography 3, no. 1 (2015): 17–24. http://dx.doi.org/10.5005/jp-journals-10034-1030.
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ABSTRACT Mitral stenosis is most commonly due to rheumatic heart disease, even in western countries. Transthoracic echocardiography is the modality of choice for establishing diagnosis, underlying cause, assessment of severity, staging of disease, valve morphology and other associated lesions. Transesophageal echocardiography is recommended in patients considered for percutaneous balloon mitral valvotomy to rule out LA clot, significant MR and to better assess mitral valve morphology. Three-dimensional echocardiography is now increasingly used for estimating mitral valve area and suitability for percutaneous balloon mitral valvotomy. We provide a brief discussion on echocardiographic assessment of mitral stenosis. How to cite this article Raj R. Echocardiographic Assessment of Mitral Stenosis: Key Learning Points for Fellows/Residents in Training. J Perioper Echocardiogr 2015;3(1):17-24.
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Osterman, Karen F. "Communication Skills: A Key to Collaboration and Change." Journal of School Leadership 4, no. 4 (July 1994): 382–98. http://dx.doi.org/10.1177/105268469400400403.
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Communication is frequently identified as an essential aspect of leadership, an argument that is supported strongly by research regarding organizational change and leader effectiveness. This paper reviews that research and argues that this area of study should be formally addressed in preparation programs—for administrators and teachers. The paper also describes an on-going effort to develop communication skills as part of a university-based administrative preparation program that incorporates principles of experiential or problem-based learning and reflective practice.
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Zhang, Fengyi, Xinyuan Cui, Renrong Gong, Chuan Zhang, and Zhigao Liao. "Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations." Journal of Healthcare Engineering 2021 (February 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/6247652.
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This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.
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Tlili, Ahmed, Fathi Essalmi, Mohamed Jemni, Professor Kinshuk, and Nian-Shing Chen. "A Complete Validated Learning Analytics Framework." International Journal of Information and Communication Technology Education 15, no. 3 (July 2019): 42–59. http://dx.doi.org/10.4018/ijicte.2019070104.
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Advances in technology have given the learning analytics (LA) area further potential to enhance the learning process by using methods and techniques that harness educational data. However, the lack of guidelines on what should be taken into considerations during application of LA hinders its full adoption. Therefore, this article investigates the issues that should be considered during the design of LA experience from the data use perspective. The results obtained present a validated LA framework which is composed of eighteen validated key issues that should be considered by various stakeholders in their contexts to enhance designing LA experiences. This framework can also be used by researchers and practitioners to learn more about LA and its designing issues.
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Nonye Sango, Precious, and Rachel Forrester-Jones. "Spirituality and learning disability: a review of UK Government guidance." Tizard Learning Disability Review 19, no. 4 (September 30, 2014): 170–77. http://dx.doi.org/10.1108/tldr-01-2014-0005.
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Purpose – Despite spirituality being a key aspect of quality of life, it appears to remain a low-priority area for social and health care government policy. The purpose of this paper is to identify and describe what, if at all, UK policy says about spirituality in relation to the care of people with learning disabilities (LD). Design/methodology/approach – A systematic policy review using three government databases: legislation.gov.uk; Department of Health and Directgov (now known as gov.uk) was carried out. Findings – The review identified policy gaps and a general lack of government directives in relation to the spiritual care of people with LD. Whilst research in this area is gathering momentum, practical implementation which makes a real difference to the spiritual experiences of people with LD appears to be sparse. Originality/value – To the authors’ knowledge, this is the first systematic policy review on this subject area, highlighting the need for spirituality to become a more supported aspect of social care within LD services.
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Kim, Minjae, Zhongfeng Zhang, Daejin Kim, and Seungwon Choi. "Deep-Learning-Based Frame Format Detection for IEEE 802.11 Wireless Local Area Networks." Electronics 9, no. 7 (July 18, 2020): 1170. http://dx.doi.org/10.3390/electronics9071170.
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Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, frame format detection is the core precondition. In this study, we present a novel, deep-learning-based frame format detection method for IEEE 802.11 WLANs. Considering that the detection performance of conventional methods is mainly degraded because of poor performance in symbol synchronization and/or channel estimation in environments with a low signal-to-noise ratio, we propose a novel detection method based on a deep learning network to replace conventional detection procedures. The proposed deep-learning network method achieves robust detection directly from the received (Rx) data. Through extensive computer simulations performed in multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), we confirmed that the proposed method exhibits significantly higher frame format detection performance than that of the conventional method.
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Herrera-Ibatá, Diana M. "Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology." Current Topics in Medicinal Chemistry 21, no. 7 (April 26, 2021): 649–60. http://dx.doi.org/10.2174/1568026621666210121153413.
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Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing significant amounts of chemical data obtaining outcomes to find potential drug candidates.
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Eglitis, Daina S., Fran L. Buntman, and Dameon V. Alexander. "Social Issues and Problem-based Learning in Sociology." Teaching Sociology 44, no. 3 (June 22, 2016): 212–20. http://dx.doi.org/10.1177/0092055x16643572.
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This article discusses the use of problem-based learning (PBL) in the undergraduate sociology classroom. PBL shifts students from the role of passive listeners and learners to active knowledge builders and communicators through the use of concise and engaging social problem cases. PBL creates opportunities for building substantive area knowledge, research skills, and problem-solving capacities and fosters student enjoyment. This teaching note describes the key characteristics of PBL, discusses practical approaches to its use in a variety of sociology courses, and offers sample case studies. We evaluate student experiences with PBL and consider its broader applicability.
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Anderson Strachan, Peter. "Managing transformational change: the learning organization and teamworking." Team Performance Management: An International Journal 2, no. 2 (June 1, 1996): 32–40. http://dx.doi.org/10.1108/13527599610114989.
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The area of organizational learning and the learning organization is of burgeoning interest among progressive business organizations, educationalists and consultants concerned with transformational change in turbulent business environments. A key feature of this approach to learning and managing change is a focus on teamworking. Reviews the organizational literature and identifies the role of teamworking in building a learning organization. A challenge facing contemporary business organizations is to redefine and change their organizations in such as way as to be consistent with the learning organization notion, but many difficulties remain to be articulated in the design of such organizations.
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Huang, Zhiqing, Xiang Jia, and Yifan Guo. "State-of-the-Art Model for Music Object Recognition with Deep Learning." Applied Sciences 9, no. 13 (June 29, 2019): 2645. http://dx.doi.org/10.3390/app9132645.
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Optical music recognition (OMR) is an area in music information retrieval. Music object detection is a key part of the OMR pipeline. Notes are used to record pitch and duration and have semantic information. Therefore, note recognition is the core and key aspect of music score recognition. This paper proposes an end-to-end detection model based on a deep convolutional neural network and feature fusion. This model is able to directly process the entire image and then output the symbol categories and the pitch and duration of notes. We show a state-of-the-art recognition model for general music symbols which can get 0.92 duration accurary and 0.96 pitch accuracy .
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Weller, Martin, Jitse Van Ameijde, and Simon Cross. "Learning Design for Student Retention." Journal of Perspectives in Applied Academic Practice 6, no. 2 (September 5, 2018): 41–50. http://dx.doi.org/10.14297/jpaap.v6i2.318.
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Student retention is an issue of increasing interest to higher education institutions, educators and students. Much of the work in this area focuses on identifying and improving interventions that occur during the presentation of a course. This paper suggests that these represent only one set of factors that can influence student withdrawal, and equally important are design based factors that can aid retention throughout the course. The main research question addressed by the paper is what design-related factors impact on student retention. An analysis of student withdrawal at the UK Open University conducted by the researchers produced a synthesis of seven key factors in the design phase that can influence retention. These factors have been given the ICEBERG acronym: Integrated, Collaborative, Engaging, Balanced, Economical, Reflective and Gradual. Examples of how these factors can be implemented are provided, and conclusions focus on how the model has been embedded in the module production process at the Open University.
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Passey, Don, and Joana Zozimo. "Developing mobile learning practices through teacher education." Interactive Technology and Smart Education 13, no. 1 (April 18, 2016): 36–51. http://dx.doi.org/10.1108/itse-01-2016-0002.
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Purpose This paper explores the context, development and outcomes of a cross-European in-service training programme, developing mobile learning practices in school classrooms – MLEARN – in The Netherlands, England, Greece and Italy. This paper focuses on: contextual backgrounds; a training needs analysis; and initial, mid-term and final outcomes from teachers and learners. Design/methodology/approach Evidence was gathered about: contextual backgrounds through literature reviews and key informant responses; a training needs analysis through online survey responses from teachers and trainers; and outcomes of uses from five surveys, largely completed online by teachers and learners, after some 2, 3, 5, 9 and 13 months of use. Findings Findings from survey periods were compared to identify shifts and gaps. The training programme led to successful outcomes; there was rapid uptake and use of the mobile devices, as well as shifts in emphasis across the period of the pilot, dependent on contextual factors. But long-term integration requires focus on specific learning activities, as well as on integration beyond a one-year period. Social Implications Uses of mobile devices are increasing, across countries and age ranges. How devices can be used to support learning and teaching in contemporary contexts is not temporally independent. Country curricula, legislation, training programme access, and teacher and learner awareness and perceptions, all influence practice. Originality/value Teachers have limited access to training programmes in this field. This study investigated a key pilot in this under-developed research area. Key factors need to be considered when programmes are developed and run.
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Jiang, Hangjin, and Xiaodan Fan. "The Two-Step Clustering Approach for Metastable States Learning." International Journal of Molecular Sciences 22, no. 12 (June 19, 2021): 6576. http://dx.doi.org/10.3390/ijms22126576.
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Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein–protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.
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Pham, D. T., and A. A. Afify. "Machine-learning techniques and their applications in manufacturing." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 219, no. 5 (May 1, 2005): 395–412. http://dx.doi.org/10.1243/095440505x32274.
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Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.
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Pérez-Sianes, Javier, Horacio Pérez-Sánchez, and Fernando Díaz. "Virtual Screening Meets Deep Learning." Current Computer-Aided Drug Design 15, no. 1 (December 14, 2018): 6–28. http://dx.doi.org/10.2174/1573409914666181018141602.
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Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
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Alamia, Andrea, Oleg Solopchuk, Alessandro D'Ausilio, Violette Van Bever, Luciano Fadiga, Etienne Olivier, and Alexandre Zénon. "Disruption of Broca's Area Alters Higher-order Chunking Processing during Perceptual Sequence Learning." Journal of Cognitive Neuroscience 28, no. 3 (March 2016): 402–17. http://dx.doi.org/10.1162/jocn_a_00911.
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Because Broca's area is known to be involved in many cognitive functions, including language, music, and action processing, several attempts have been made to propose a unifying theory of its role that emphasizes a possible contribution to syntactic processing. Recently, we have postulated that Broca's area might be involved in higher-order chunk processing during implicit learning of a motor sequence. Chunking is an information-processing mechanism that consists of grouping consecutive items in a sequence and is likely to be involved in all of the aforementioned cognitive processes. Demonstrating a contribution of Broca's area to chunking during the learning of a nonmotor sequence that does not involve language could shed new light on its function. To address this issue, we used offline MRI-guided TMS in healthy volunteers to disrupt the activity of either the posterior part of Broca's area (left Brodmann's area [BA] 44) or a control site just before participants learned a perceptual sequence structured in distinct hierarchical levels. We found that disruption of the left BA 44 increased the processing time of stimuli representing the boundaries of higher-order chunks and modified the chunking strategy. The current results highlight the possible role of the left BA 44 in building up effector-independent representations of higher-order events in structured sequences. This might clarify the contribution of Broca's area in processing hierarchical structures, a key mechanism in many cognitive functions, such as language and composite actions.
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Breslin, Dermot. "Entrepreneurial learning; intuiting, scanning, internalizing and routinizing." Learning Organization 26, no. 6 (September 9, 2019): 604–16. http://dx.doi.org/10.1108/tlo-04-2018-0054.
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Purpose Despite an increasing number of publications focusing on the phenomenon of entrepreneurial learning, it is still unclear how this learning process differs from wider organizational learning. This paper aims to address this gap by highlighting four key processual dimensions unique to entrepreneurial learning: intuiting, scanning, internalizing and routinizing. Design/methodology/approach Drawing on various conceptual and empirical papers published in this area over the past 20 years, common threads in the literature are identified, which point towards these four key dimensions of entrepreneurial learning. Findings It is thus argued that the ability of the entrepreneurial team to learn form and adapt to changes in the external market involves all four dimensions of intuiting, scanning, internalizing and routinizing. Intuiting involves drawing on prior knowledge to create new opportunity sets, and skills. These ideas and skills are then tested in the market, through scanning and market research. Internalizing allows the entrepreneurial team to question taken for granted assumptions, as existing ways of working and views of the world are continually adapted. Finally, routinization is the process whereby the entrepreneurial team accumulates a situated knowledge of the changing world around them, and in the process, frees up valuable cognitive resources, needed in the continual process of intuiting, scanning and internalizing. Originality/value It is argued that the adaptability of entrepreneurial ventures hinges on all four processual dimensions.
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Morgan, Felicia N., and Deborah Brown McCabe. "Learning Why We Buy." Journal of Marketing Education 34, no. 2 (July 2, 2012): 140–55. http://dx.doi.org/10.1177/0273475312450387.
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Marketing educators have long recognized the value of engendering students’ deep learning of course content via experiential pedagogies. In this article, the authors describe a semester-long, team-based retail audit project that is structured to elicit active student engagement with consumer behavior course material via concrete, hands-on, real-world experience. For the project, students form teams to organize and conduct an observational audit of a live retail setting. In the process of completing the project, students engage with course content on their own, with their team members, and importantly, within a focal store environment, thus experiencing for themselves the effects of that content on their own shopping behavior, as well as that of others. Compelled by the project’s active pedagogy to engage in discovery, students learn not only the “what” and “why” of marketing concepts, strategies, and techniques but also “how to” implement them. Anchored in conceptual perspectives relevant to the project, the article explains the components and structure of the project and explicates its key benefits with an emphasis on the students’ perspectives. The article includes results of qualitative and quantitative analyses that support the effectiveness of the project and suggests future directions for extending pedagogical research in this area.
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Lee, Thomas, Susan Mckeever, and Jane Courtney. "Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy." Drones 5, no. 2 (June 17, 2021): 52. http://dx.doi.org/10.3390/drones5020052.
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With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future.
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Norman, Heidi. "Australian Indigenous Tertiary Studies: A Discussion with Professor David Boud on Experience-Based Learning and the Transformation of University Courses." Australian Journal of Indigenous Education 41, no. 1 (August 2012): 34–39. http://dx.doi.org/10.1017/jie.2012.6.
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This article critically examines the possibility of using Problem-Based Learning as an approach to teaching and learning and curriculum design in Indigenous studies. This approach emphasises the potential for Experience-Based Learning or Problem-Based Learning as a model that frames the curriculum and pedagogical activities to encourage student engagement with key issues in ways they find meaningful. It takes the form of an interview with Professor David Boud, a well-known scholar in this area.
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Indrawati, Mega, Cahyo Prihadi, and Ayu Siantoro. "The Covid-19 Pandemic Impact on Children’s Education in Disadvantaged and Rural Area Across Indonesia." International Journal of Education (IJE) 8, no. 4 (December 30, 2020): 19–33. http://dx.doi.org/10.5121/ije.2020.8403.
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This study used a mixed methods approach to capture key findings on COVID-19 impacts on education and challenges that hinder continued education particularly in disadvantaged and rural areas. The respondents were 900 parents, 943 children, 15 teachers and education officials in 594 villages in 9 provinces of Indonesia. The results showed that 1 out of 3 children stopped learning and children learned less. Children had limited access to widely supported online learning. While the offline method was more applicable, still it had some challenges. Children’s mental health was affected and they experienced excessive emotions of fear and anxiety. Many parents were not ready to support children in learning school subjects while doing livelihood activities at the same time. Some children experienced domestic violence. Parents did not have a spare budget to provide distance learning facilities and had to sacrifice education over basic needs. The risk of drop out of school was heightened.
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Lorente, Maria Paz Sesmero, Elena Magán Lopez, Laura Alvarez Florez, Agapito Ledezma Espino, José Antonio Iglesias Martínez, and Araceli Sanchis de Miguel. "Explaining Deep Learning-Based Driver Models." Applied Sciences 11, no. 8 (April 7, 2021): 3321. http://dx.doi.org/10.3390/app11083321.
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Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with “black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.
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Shannon, Benjamin J., Sanjeev Neil Vaishnavi, Andrei G. Vlassenko, Joshua S. Shimony, Jerrel Rutlin, and Marcus E. Raichle. "Brain aerobic glycolysis and motor adaptation learning." Proceedings of the National Academy of Sciences 113, no. 26 (May 23, 2016): E3782—E3791. http://dx.doi.org/10.1073/pnas.1604977113.
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Ten percent to 15% of glucose used by the brain is metabolized nonoxidatively despite adequate tissue oxygenation, a process termed aerobic glycolysis (AG). Because of the known role of glycolysis in biosynthesis, we tested whether learning-induced synaptic plasticity would lead to regionally appropriate, learning-dependent changes in AG. Functional MRI (fMRI) before, during, and after performance of a visual–motor adaptation task demonstrated that left Brodmann area 44 (BA44) played a key role in adaptation, with learning-related changes to activity during the task and altered resting-state, functional connectivity after the task. PET scans before and after task performance indicated a sustained increase in AG in left BA 44 accompanied by decreased oxygen consumption. Intersubject variability in behavioral adaptation rate correlated strongly with changes in AG in this region, as well as functional connectivity, which is consistent with a role for AG in synaptic plasticity.
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Cheng, Shicheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You, and Xueqing Tian. "GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures." Applied Sciences 11, no. 7 (April 4, 2021): 3239. http://dx.doi.org/10.3390/app11073239.
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The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end–to–end auto–encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state–of–the–art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph–level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node–level and graph–level representations contributes most in a relatively dense network.