scholarly journals Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

2020 ◽  
Vol 8 ◽  
Author(s):  
Zhen Yang ◽  
Zengwu Sun
Keyword(s):  
Teaching And Learning ◽  
Geographic Location ◽  
Prediction Algorithm ◽  
Location Prediction

scholarly journals Hybrid Markov Location Prediction Algorithm Based on Dynamic Social Ties

2015 ◽  
Vol E98.D (8) ◽  
pp. 1456-1464 ◽  
Author(s):  
Wen LI ◽  
Shi-xiong XIA ◽  
Feng LIU ◽  
Lei ZHANG
Keyword(s):  
Social Ties ◽  
Prediction Algorithm ◽  
Location Prediction

Undergraduate perceptions of the knowledge, skills and competencies required of today’s practicing marketer

2017 ◽  
Vol 7 (3) ◽  
pp. 240-260 ◽  
Author(s):  
Stephen Carter ◽  
Amy Chu-May Yeo
Keyword(s):  
Student Perceptions ◽  
Teaching And Learning ◽  
Learning Experience ◽  
Geographic Location ◽  
Education Institution ◽  
Total Sample ◽  
Cross Sectional Study ◽  
Undergraduate Teaching ◽  
Cross Sectional ◽  
Content Type

Purpose The purpose of this paper is to investigate how students in a Malaysian context, as a result of their experience of a Higher Education Institution (HEI) undergraduate teaching and learning experience in the subject of Marketing, perceive the knowledge, skills and competencies required of a practicing marketer and, conversely, what curriculum developers need to do if there is a “shortfall”. Design/methodology/approach Based on a total sample of a UG student population from an Accountancy, Finance and Business Faculty, the primarily descriptive, positivist, cross-sectional study used inferential statistics to measure the relationship between the four components of marketing knowledge, skills and competencies (the marketing mix, performance, social and emotional competencies, and responsible decision making). Findings Quantitative results revealed that all student perceptions of the requirements to be a “fit for purpose” marketer were highly correlated with requirements from the literature, subject benchmarks and practice with few exemptions. Research limitations/implications The findings are based on one institution. Moreover, knowledge, skills and competency requirements by students’ level of study and practitioner experience may vary by type of HEI, organisation and geographic location. Practical implications Recommendations are made for curriculum development to address both employability and career development, particularly in terms of interdisciplinary co-operation and the teaching and learning of concepts. Originality/value Using the student perceptions of the requirements for being a practicing marketer, HEIs can adjust/add to their curriculum by comparing these to documented sources from academia and practice and by making any necessary adjustments by course of study.

scholarly journals Bayesian Learning Neural Network Techniques to Forecast Mobile Phone User Location

2019 ◽  
Vol 8 (9) ◽  
pp. 1233-1237
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Bayesian Learning ◽  
Mobile Network ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Bayesian Neural Network ◽  
Network Location ◽  
Network Methods ◽  
Reality Mining

In the computerized period Location Based Service is a significant pretended in computing frameworks. Aside from the present area, knowing the area of the person's next spot ahead of time that can likewise empower numerous cell phone applications and its overhaul [3].Mobile network location prediction is by and large widely analyzed for use with regards to mobile network location and wireless network communication concerning more effectual mobile network location source administration patterns. Mobile network location extrapolation consents the mobile network and amenities to auxiliary heighten the excellence of provision stages for the mobile phone users. In the present-day a mobile network location prediction algorithm is used feats mobile phone users practises. In this studies the prediction of the location is carried out and the individual’s location are stored and encounters. We introduce an innovative crossbreed Bayesian neural network prototypical for foretelling mobile network locations. We scrutinize diverse analogous execution practises on cell phones of the projected loom and contrast with numerous typical neural network system procedures. In this investigation the outcomes of the projected Bayesian Neural Network through some typical neural network methods in foretelling together subsequent mobile network location and subsequent facility to demand. The Neural Networks of Bayesian learning foresees together mobile Network location and also enhanced provision than typical neural network methods meanwhile this one routines fine originated probability structure to signify vagueness around the associations are erudite. The consequence of training Bayesian learning is a subsequent dissemination through network weights. In this research MCMC method is used to trial N assessments commencing the later weights dissemination [1]. Using reality mining dataset, we exhibit that the proposed methodology can understand the smooth redesign of the expectation execution and perform dynamically [3]. The Simulations algorithms are achieved by means of an Accurate Movement Patterns and confirmation improved forecast accurateness.

scholarly journals A Spatial-Temporal Self-Attention Network (STSAN) for Location Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shuang Wang ◽  
AnLiang Li ◽  
Shuai Xie ◽  
WenZhu Li ◽  
BoWei Wang ◽  
...  
Keyword(s):  
New York ◽  
Geographic Location ◽  
Temporal Information ◽  
Location Prediction ◽  
Trajectory Data ◽  
Attention Network ◽  
Trajectory Representation ◽  
Activity Preference ◽  
Location Based Social Networks ◽  
Significant Attention

With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user’s mobility pattern and predict the users’ next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial-temporal self-attention network), which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users’ dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user’s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.

scholarly journals SANTPEN’s SoTL Journey: Building and Using a SoTL Approach Across Institutions

2016 ◽  
Vol 16 (5) ◽  
pp. 107-122
Author(s):  
Deborah West ◽  
Helen Stephenson
Keyword(s):  
Higher Education ◽  
Teaching And Learning ◽  
Geographic Location ◽  
Learning And Teaching ◽  
Grant Programs ◽  
Sustainable Improvement ◽  
Education Environment ◽  
The Status ◽  
Effective Use ◽  
Teaching In Higher Education

In the current higher education environment, providing high quality teaching and learning experiences to students has moved beyond desirable to essential. Quality improvement takes many forms, but one core aspect to ensure sustainable improvement is the development of a culture of scholarship of teaching and learning (SoTL). Developing such an institutional culture is surprisingly challenging yet essential to improving the status of teaching in higher education (HE), being successful in teaching and learning awards and grants, and, improving the student experience. The Australian Government’s Promoting Excellence Network initiative funds networks to foster collaboration between HE institutions to improve outcomes in national learning and teaching award and grant programs. Supported by this funding, the South Australian / Northern Territory Promoting Excellence Network (SANTPEN), a grouping of six institutions, formed. Bringing together a diverse network of institutions, similar only by virtue of geographic location is challenging. This paper describes the first three years of SANTPEN’s journey from the context of our own development with the concept of SoTL and how we applied this to build a culture of SoTL in and between our institutions. It also demonstrates how a modest budget can be put to effective use to benefit those immediately involved, institutional objectives and the aims of the national funding body. We provide evidence of this effectiveness and conclude with our collective aspirations for the future of SANTPEN and other likeminded and funded networks.

Tracking Algorirthm Based on Location Prediction for Wireless Sensor Network

2012 ◽  
Vol 562-564 ◽  
pp. 1856-1860 ◽  
Author(s):  
Tao Liang ◽  
Jian Bing Guo ◽  
Hai Yan Ren
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Target Tracking ◽  
Target Position ◽  
Wireless Sensor ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Recovery Mechanism ◽  
Position Prediction ◽  
Tracking Technology

In many wireless sensor networks tracking technologies,location-based pediction target tracking technology have a natural advantage of reducing the energy consumption of nodes in tracking the process. However, the existing location-based prediction target tracking algorithm is not perfect.So Location-Based Pediction Target Tracking Algorirthm (LBP) was proposed. There are three parts in LBP,target position prediction algorithm based self-simlar,target tracking based on dynamic wakeup cluster(DWC),and three levels recovery mechanism.

scholarly journals A Novel Location Prediction Algorithm of Mobile Users For Cellular Networks

2015 ◽  
pp. 58
Author(s):  
Giang Minh Duc ◽  
Le Manh ◽  
Do Hong Tuan
Keyword(s):  
Execution Time ◽  
Mobile Network ◽  
Mobile User ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
Second Phase ◽  
Location Prediction ◽  
Mobility Patterns ◽  
Two Phase ◽  
Computing Systems

Predicting the location of a mobile user is one of  the  important  issues  in  mobile  computing  systems. Applications of the location prediction include adjusting the bandwidth of the mobile network, the location based services  (LSB),  smart  handover,  etc.  However,  the applications  require  the  execution  time  of  the  User Mobility  Patterns  Mining  (UMPMining)  algorithm  be instantaneous.  In  this  paper,  we  propose  a  new algorithm named Find_UMP for mining next location of a  mobile  user.  Our  algorithm  includes  two  phase  as follows.  In  the  first  phase  (Find_UMP_1),  we  make  to reduce the complexity of the UMPMining algorithm. In the second phase (Find_UMP_2), we perform to reduce the  number  of  transactions  of  the  paths  database. Results  of  our  experiments  show  that  our  proposed algorithm  outperforms  the  UMPMining  algorithm  in terms of the execution time.

scholarly journals Internet como plataforma para la enseñanza y aprendizaje musical / Internet as a Platform for Teaching and Learning Music

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Jose Palazon Herrera
Keyword(s):  
Teaching And Learning ◽  
Geographic Location ◽  
Music Technology ◽  
Music Schools ◽  
Internet Connection ◽  
Instrumental Study ◽  
Online Music ◽  
Instrumental Practice ◽  
Teaching Learning

ABSTRACTCurrently, one of the least favored by the advent of online music technology areas has been the instrumental practice. Until a few years ago, playing an instrument has been synonymous with having a teacher next to a student in any school, conservatory and even in the classrooms of music schools, colleges, etc., which was vetoed instrumental study all one that could not have that presentiality teacher. In recent years have appeared online initiatives of various nature and origin, both institutional and individual, starting to encourage anyone to study instrument regardless of their geographic location, interests, or other circumstances, having only a good internet connection. Virtual conservatories, video platforms like YouTube or similar video podcasts, and more, have revolutionized the current outlook, favoring the teaching-learning instrumental in a natural way.RESUMENEn la actualidad, uno de los ámbitos musicales menos favorecidos por la irrupción de las tecnologías online ha sido el de la práctica instrumental. Hasta hace muy pocos años, tocar un instrumento ha sido sinónimo de tener un profesor al lado del alumno en cualquier academia, conservatorio e incluso en las aulas de música de colegios, institutos, etc., con lo que quedaba vetado el estudio instrumental a todo aquél que no podía contar con esa presencialidad del profesor. En estos últimos años han ido apareciendo iniciativas online de diversa naturaleza y procedencia, tanto a nivel institucional como particular, que empiezan a favorecer que cualquier persona pueda estudiar instrumento con independencia de su ubicación geográfica, intereses particulares, u otras circunstancias, disponiendo únicamente de una buena conexión a Internet. Conservatorios virtuales, videoconferencias, plataformas como YouTube o similares o los videopodcasts, entre otras opciones, han revolucionado el panorama actual, favoreciendo los procesos de enseñanza-aprendizaje instrumental de una manera natural. Contacto principal: [email protected]

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