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Smart Vision of School Classroom 6th

Smart Vision of School Classroom

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Hantoobi, Sendeyah Wahdan, Ahlam Salloum, Said A. Shaalan, Khaled Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates School of Science Engineering and Environment University of Salford Salford United Kingdom Machine Learning and NLP Research Group Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9783030697167

Modern technology has become an integral part of our daily lives. Every day generates the need for new technologies so that we can coexist and integrate into contemporary life. Virtual reality (VR) is becoming imperative in some life areas, such as medicine, space, and education, for experiments and training. The start of the COVID-19 pandemic revealed the importance of using electronic devices for content delivery. In this paper, the importance of VR in the COVID-19 crises, the current situation of virtual reality, and its applications in the fields of education are discussed. Therefore, state of the art for VR is explained in this study with the new scientific contribution in this field and the new vision of VR in education. Virtual reality has advantages and disadvantages, and both current and future ones are discussed here. Based on our current data, we found that there are tremendous positive developments taking place, but at the same time, there are rules, laws, and societies with certain policies, some of which will promote the environment of virtual reality, and others will oppose it. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Agile Technology and Artificial Intelligent Systems in Business Development

Agile Technology and Artificial Intelligent Systems in Busin...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Melanie Lourens Ramakrishnan Raman P. Vanitha Rajesh Singh Geetha Manoharan Mohit Tiwari Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India M.Kumarasamy college of Engineering Karur Tamil Nadu India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India School of Business SR University Telangana Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Rohtak Road Paschim Vihar Delhi

This article examines how Agile methodology and concepts are used to the delivery of artifical intelligence as well as how Agile has transformed over time. Artificial Intelligence (AI) are wide-ranging set of technologies that can promise various benefits for the company in terms off added business value and customer *** earlier times, organizations/companies are increasingly turning to intelligence technology in order to gain more business value following a deluge of data as well as a strong increase in computational capacity. This is encouraging the incorporation of AI into business operations, but the effects of this adoption need to be investigated more thoroughly. The way that enterprises as well as consumers use information has evolved as a result of the internet’s and smart devices’ exponential growth in data volume. As a result, companies are starting to use AI technologies to embrace agility. Agile technique is the capacity to adapt quickly and effectively to these external situations in order to prosper in an industry that is frequently developing and unpredictable. This research focuses on the entire effects of AI on enterprises, including future changes in business models as well as research, innovation, and market deployment. Additionally, it focuses on several ways to incorporate artificial intelligence methods into scrum *** also provides the information regarding iterative development process can be adapted to A.I evolvment well as the comparison of agile technology and artiofical intelligence in terms of business management development.

关键词： Technological innovation Companies Turning User experience Software Iterative methods Artificial intelligence

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Automatic Multi-disease Diagnosis and Prescription System Using Bayesian Network Approach for Clinical Decision Making

Automatic Multi-disease Diagnosis and Prescription System U...

引用

international conference on Artificial Intelligence and data Engineering, AIDE 2019

作者： Laxmi, P. Gupta, Deepa Radhakrishnan, G. Amudha, J. Sharma, Kshitij Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Paralaxiom Technologies Private Limited Bengaluru India

ISBN: (纸本)9789811535130

A clinical decision support system (CDSS) is used as an aid in decision-making processes of health care providers in their day-to-day activities. This research attempts diagnosis of multiple diseases based on symptoms provided by patients. The work also recommends laboratory tests related to the predicted diseases and medications based on their results. The methodology adopted for implementation of CDSS is the Bayesian network approach. The modeling of the Bayesian network structure was undertaken in consultation with experts from medical domain. Clinical data has been used for estimation of network parameters such as conditional probability tables thereby bringing in machine learning into Bayesian methodology. The model developed is a learning model wherein the system input is saved for future training of the model. The results indicate that Bayesian approach is suitable for implementing a CDSS for multiple disease diagnosis. The proposed work will be useful towards increasing physicians throughput. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Bayesian networks

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Human Trafficking Interdiction Problem: A data Driven Approach to Modeling and Analysis

Human Trafficking Interdiction Problem: A Data Driven Approa...

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IEEE conference on technologies for Homeland Security

作者： A. Sen S. Adeniye K. Basu S. Ravishankar J. Sefair D. Roe-Sepowitz E. Helderop T. Grubesic A. B. Sen School of Computing and Augmented Intelligence Arizona State University School of Social Work Arizona State University School of Information University of Texas Austin Palantir Technologies

ISBN: (纸本)9781665494052

Based on the human trafficking incidence data from the Las Vegas Metropolitan Police Department (LVMPD), we have built a model of movement patterns of traffickers within the contiguous US states. We utilized the model for developing interdiction strategies for the law enforcement authorities, with the goal of maximizing interdiction pay-off within the agency budget, where pay-off is measured in terms of the number of trafficking incidences disrupted. In addition, from the U.S. Interstate Highway Map, we have built a U.S. Interstate Network Graph (USING) to test our interdiction pay-off maximization algorithm. This is a realistic approximation of the U.S. highway system and will be made available to researchers engaged in trafficking interdiction research. Finally, we evaluate our techniques on the data from LVMPD on USING and present the results.

关键词： Road transportation Analytical models Law enforcement US Department of Homeland Security Approximation algorithms data models

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CoLiTec Virtual Observatory Platform for the Cloud computing Analysis of Light Curves for the Variable Stars

CoLiTec Virtual Observatory Platform for the Cloud Computing...

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international Scientific-Practical conference Problems of Infocommunications science and Technology

作者： Sergii Khlamov Vadym Savanevych Olexander Briukhovetskyi Tetiana Trunova Iryna Tabakova Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Systems Engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Western Radio Technical Surveillance Center State Space Agency of Ukraine Mukachevo Ukraine Department of Media Systems and Technologies Kharkiv National University of Radio Electronics Kharkiv Ukraine

A growing of the cloud services and resources capabilities allow moving the desktop software/applications to the servers/clouds/clusters. In this paper we have presented a brief description and several aspects related to the cloud computing, Virtual Observatory (VO) and Software as a Service (SaaS) in astronomy, reviewed them, and provided some major approaches of their realization in scope of the Collection Light Technology (CoLiTec) project as the SaaS called "CoLiTec Virtual Observatory Platform". We have presented a realization of the developed mathematical and computational methods related to the light curve creation of variable stars incapsulated into the CoLiTecVS (CoLiTec Variable Stars) desktop software as a new Software as a Service approach with the graphical user interface (GUI) in the cloud with web access and visualization. All processing stages are performed on the backend and end user has access to the web interface only. The CoLiTec Virtual Observatory Platform was successfully tested on the astronomical data with a lot of investigated variable stars of the different observatories in Ukraine, Slovakia and Thailand using the different types of telescopes and observational conditions.

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Machine learning techniques for short-term forecasting of wind power generation 5th

Machine learning techniques for short-term forecasting of wi...

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5th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2020

作者： Gupta, Yogesh Saraswat, Amit Depatment of Computer Science and Engineering Manipal University Jaipur JaipurRajasthan India Depatment of Electrical Engineering Manipal University Jaipur JaipurRajasthan India

ISBN: (纸本)9789811533822

In recent years, many countries have established their ambitious renewable energy targets to satisfy their future electricity demand with the main aim to foster sustainable and low-emission development. In meeting these targets, the changes to power system planning and operations involve the significant consideration of renewable energy generation through mainly wind energy and solar energy which are more variable and uncertain as compared to the conventional sources (i.e. Thermal and Nuclear Energy). In the present paper, three machine learning methods named as Support Vector Machine, Artificial Neural Network and Multiple Linear Regression are applied to forecast wind power generation on basis of past data of wind direction and wind speed. The impact of input variables such as wind speed and wind direction on wind power generation is investigated and compared. © Springer Nature Singapore Pte Ltd 2021.

关键词： Wind power

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Generalized Net Model Raw Materials Loading and Transportation Processes of Open Construction Sites 15th

Generalized Net Model Raw Materials Loading and Transportati...

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15th National conference of Operational and Systems Research, BOS 2018 and 17th international Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN 2018

作者： Tzanov, Valery Todorova, Lyudmila Zoteva, Dafina Doukovska, Lyubka Scortel LTD 29 Deliiska Vodenitza Street Sofia1582 Bulgaria Department of Bioinformatics and Mathematical Modelling Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Acad. G. Bonchev Street Bl. 105 Sofia1113 Bulgaria Department of Intelligent Systems Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Street Bl. 2 Sofia1113 Bulgaria

ISBN: (纸本)9783030470234

关键词： Materials handling

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Nonlinear Autoregressive Exogenous Neural Network for Earthquake Prediction in Indonesia

Nonlinear Autoregressive Exogenous Neural Network for Earthq...

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international conference on Advanced Mechatronic Systems (ICAMechS)

作者： Nur Hasanah Zhihong Man Zhenwei Cao Jinchuan Zheng School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9781665463812

In this work, a nonlinear autoregressive exogenous neural network (NARXNN) is combined with the extended Kalman filter (EKF) algorithm for predicting the earthquake magnitudes in Indonesia. It is seen that the proposed neural model has a feedback loop from the output to the input layer, and such a recurrent model structure is selected based on the dynamic characteristics of the real earthquake magnitude data of Indonesia. The trained neural model with the real earthquake data shows a superior capability of producing smaller Mean Squared Error (MSE) and high accurate prediction error compared to a regular NARXNN and Feed-Forward Neural Network (FFNN) models. The experiments with the earthquake dataset of the last few years from the Meteorology, Climatology, and Geophysics Agency of Indonesia, are carried out for training the proposed recurrent neural model and then predict the next 15 earthquake magnitudes in Indonesia. The experiment results demonstrate that the proposed neural model has an excellent performance and will play an important role on earthquake magnitude prediction in Indonesia in the near future.

关键词： Training Mechatronics Heuristic algorithms Earthquakes Machine learning Predictive models Prediction algorithms

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Analysis of Vegetation Indices in the Cotton Crop in South Asia region using UAV Imagery

Analysis of Vegetation Indices in the Cotton Crop in South A...

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international conference on Emerging technologies, ICET

作者： Noor us Sabah Muhammad Usama Zuhair Zafar Muhammad Shahzad Muhammad Moazam Fraz Karsten Berns School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Aerospace and Geodesy Technical University of Munich (TUM) Munich Germany Department of Computer Science Robotics Research Lab Technische Universität Kaiserslautern (TUK) Kaiserslautern Germany

ISBN: (纸本)9781665459938

Cotton is one of the most economically significant crops in the South Asia region. Following the decline in cotton production in recent years, it is necessary to study and analyse cotton growth patterns by using modern precision agriculture techniques to enhance crop yield and quality. Multispectral imagery plays a vital role in estimating crop quantification by computing vegetation indices at different phenological stages of the crop. It facilitates in estimating plant health and impact on yield before the visible symptoms appear. In this study, four different vegetation indices maps are computed using two-band multispectral imagery acquired through Unmanned Aerial Vehicle (UAV). The UAV multispectral data is collected from two fields with different sowing dates using Sentera NDVI single sensor. The variation in the vegetation indices map generated is analysed by vegetation index thresholding and k-means clustering. By using Sentinel-2A imagery, an average value of each vegetation index for every phenological stage is validated. The correlation coefficient of each vegetation index conveys a positive progression trend and validates the accuracy of UAV data computed results. In conclusion, vegetation indices computed using multispectral UAV imagery conveys vital information regarding the progression of crop phenology and growth pattern.

关键词： Image resolution Asia Vegetation mapping Crops Autonomous aerial vehicles Market research Cotton

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Stock Price Prediction System with Improved LSTM

Stock Price Prediction System with Improved LSTM

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North Karnataka Subsection Flagship international conference (NKCon), IEEE

作者： Preethi R Singh Manohar N Rampilla Mahesh Computer Science Department Amrita School of Computing Mysuru Campus Amrita Vishwa Vidyapeetham India Management and Commerce Department Amrita School of Arts Humanities and Commerce Mysuru Campus Amrita Vishwa Vidyapeetham India

High Volatility in the Stock Market makes forecasting the stock price challenging globally. With the introduction of state-of-the-art technologies like Artificial Intelligence and Machine Learning, programmed prediction methods have proven to be more significant in forecasting the prices of stocks. In this work, the Recurrent Neural Network-based model, LSTM, is utilized to forecast the day's high and low costs of the three tyre companies: MRF Limited, CEAT Limited, and JK Tyre Industries and Limited, based on the company's historical data. The LSTM model uses technical values like RSI, SMA, etc., for training to make better predictions with higher accuracy. The system uses standard indicators: MAPE and MSE, Precision-Recall, etc., to evaluate the model's performance.

关键词： Training Industries Recurrent neural networks Machine learning Predictive models Tires data models

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