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International tourist arrivals modelling and forecasting: A case of Zimbabwe

Sustainable Technology and Entrepreneurship 2023年第1期2卷

作者： Makoni, Tendai Mazuruse, Gideon Nyagadza, Brighton Department of Mathematics & Computer Science School of Natural Sciences Great Zimbabwe University (GZU) Masvingo Zimbabwe Teaching & Learning Institute (TLI) Faculty of Agribusiness & Entrepreneurship Marondera University of Agricultural Sciences & Technology (MUAST) Zimbabwe Department of Marketing (Digital Marketing) Faculty of Agribusiness & Entrepreneurship Marondera University of Agricultural Sciences & Technology (MUAST) Zimbabwe

Zimbabwe is blessed with tourist attractions that draw visitors from all over the world. However, there are no quantitative models available for tourism stakeholders to utilize in decision-making and planning. The country is experiencing foreign currency shortages, which may be alleviated if the tourism industry, which has the power to generate foreign currency, adopted quantitative forecasting techniques that can provide reliable estimates. For planning reasons, resource mobilization, and allocation, accurate tourist projections are critical to the government and other tourism stakeholders. The goal of this research is to model international tourist arrivals in Zimbabwe and develop a quantitative statistical model that can be used to forecast future international tourist visitors. The Zimbabwe National Statistics Agency (ZIMSTAT) provided monthly foreign tourist arrivals data for the period January 2000 to December 2018. After the data revealed non-stationarity and seasonality, a time series technique in the form of the Box-Jenkins approach is applied to the data. The autocorrelation function (ACF), partial autocorrelation function (PACF), and root mean square error (RMSE) revealed that a seasonal autoregressive integrated moving average (SARIMA) model suited well to the data. The model predicted a gradual and seasonal increase in international tourist arrivals. The results of this model could be used by those in charge of tourism marketing to develop effective and efficient marketing strategies so that the country can receive a significant increase in international tourists, which will bring in much-needed foreign currency. It is important for tourism stakeholders and service providers to guarantee the availability of enough transport and accommodation facilities, especially during peak seasons. © 2022 The Author(s)

关键词： ARIMA Forecasting International tourist arrivals SARIMA

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Learning in Audio-visual Context: A Review, Analysis, and New Perspective

arXiv

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arXiv 2022年

作者： Wei, Yake Hu, Di Tian, Yapeng Li, Xuelong The Gaoling School of Artificial Intelligence Beijing Key Laboratory of Big Data Management and Analysis Methods Renmin University of China Beijing100872 China The Department of Computer Science University of Texas at Dallas RichardsonTX75080 United States Northwestern Polytechnical University Xi’an710072 China

Sight and hearing are two senses that play a vital role in human communication and scene understanding. To mimic human perception ability, audio-visual learning, aimed at developing computational approaches to learn from both audio and visual modalities, has been a flourishing field in recent years. A comprehensive survey that can systematically organize and analyze studies of the audio-visual field is expected. Starting from the analysis of audio-visual cognition foundations, we introduce several key findings that have inspired our computational studies. Then, we systematically review the recent audio-visual learning studies and divide them into three categories: audio-visual boosting, cross-modal perception and audio-visual collaboration. Through our analysis, we discover that, the consistency of audio-visual data across semantic, spatial and temporal support the above studies. To revisit the current development of the audio-visual learning field from a more macro view, we further propose a new perspective on audio-visual scene understanding, then discuss and analyze the feasible future direction of the audio-visual learning area. Overall, this survey reviews and outlooks the current audio-visual learning field from different aspects. We hope it can provide researchers with a better understanding of this area. A website including constantly-updated survey is released: https://***/audio-visual-learning/. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Unsupervised graphical user interface learning 28

Unsupervised graphical user interface learning

引用

28th International Workshop on Concurrency, Specification and Programming, CS and P 2019

作者： Czaus, Przemyslaw Department of Mathematical Methods of Computer Science Faculty of Mathematics and Computer Science University of Warmia and Mazury in Olsztyn Sloneczna 54 Olsztyn10-710 Poland

While there are many different papers on automatic testing or generation of interfaces[1, 2, 3, 4], the information concerning similar to human perception user interface learning methods are scarce. Given the recent leaps in Artificial Intelligence (AI) one might find this topic very useful in implementing a solution for communication of AI with applications created for human interaction. User interfaces differ between operating systems, hardware possibilities and implementation. The main objective of the study was the analysis of the applications' Graphic User Interface (GUI) and interaction with the graphic interface elements in general. This method can be used to track the change in application behaviour based on any action from the user, without having knowledge about the underlying object structure. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

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E-Health Telecommunication Systems and Networks 2020年第1期9卷 1-15页

作者： Kanayo Kizito Uka Stanley Ikechukwu Oguoma Chekwube Alphonsus Chukwu Chijioke Izuchukwu Emele Department of Computer Science Imo State University Owerri Nigeria Nnamdi Azikiwe Teaching Hospital Nnewi Nigeria

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

关键词： Artificial Intelligence Expert System Diagnosis Treatment Staphylococcus aureus Infectious Disease

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Web Based Emergency Information System for Reporting and Tracking of COVID-19 Infected Persons

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E-Health Telecommunication Systems and Networks 2020年第2期9卷 17-32页

作者： Kanayo Kizito Uka Ikechukwu Stanley Oguoma Chekwube Alphonsus Chukwu Udochukwu Princewill Chuma-Uba Department of Computer Science Imo State University Owerri Nigeria Nnamdi Azikiwe Teaching Hospital Nnewi Nigeria

Quick response to health related issues and reporting has been a huge problem associated with the Nigerian health sector especially when it comes to emergencies. The novel corona virus also known as COVID-19 has put not only the health practitioners into confusion but also to the general public. But in the fight for the solution of this huge pandemic, innovations and technological developments has also assisted in the fight to ensure that this deadly disease is defeated at once. Hence, this paper presents a web based emergency information system for reporting and tracking of COVID-19 infected persons as one of the innovations in fighting this infectious disease. The system is expected to provide a remote access for the general public to report any suspected person with COVID-19, enable questions to be asked, ensure that information on the case of infected and suspected persons with COVID-19 are tracked. This system was achieved following the Agile Model project development life-cycle and the Unified Modeling Language (UML) in respect to that, the front-end design was done with Hypertext Markup Language (HTML5), Cascading Style Sheet (CSS), JavaScript (JS) and Bootstrap Frame work while the back-end development was achieved with Hypertext Preprocessor (PHP5), and My-Structural Query Language (MYSQL). The result after design was a responsive and web based friendly platform for suspected and infected persons of COVID-19 to be reported, tracked and managed remotely.

关键词： Web-Based Infectious Illness Coronavirus Pandemic Information System

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MetAug: Contrastive Learning via Meta Feature Augmentation

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science Engineering The Hong Kong University of Science and Technology Hong Kong

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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Prediction of daily patient visits in respiratory department using deep learning

Prediction of daily patient visits in respiratory department...

引用

International Conference on Computational Intelligence and Security

作者： Xiaobo Song Xinyi Zhang Xiaoli Wang Yuan Li The 20th Research Institute of China Electronics Technology Group Corporation Xi'an Shaanxi China School of Computer Science and Technology Xidian University Xi'an Shaanxi China Basic Course Teaching Department Xi'an Vocational and Technical College Xi'an Shaanxi China

Since climate changes and air pollution have a direct impact on the number of emergency respiratory patients visited, in this paper, we study predicting methods of daily patient visits in respiratory department based on deep learning. We validate our methods via experiments on the actual data of the First Affiliated Hospital of Xi'an Medical University. To improve the accuracy of prediction, we propose four data preprocessing methods, including meteorological lag and time sensitive method, sliding window method, seasonal classification method, as well as correlation analysis method. Experimental results show that the prediction accuracy of the deep learning model is improved from 76.28% to 86.37% after adopting our four preprocessing methods. The maximum error between predicted and actual visits is less than 20.

关键词： Climate change Data processing Patient monitoring Deep learning Medical services Respiratory system Predictive models

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Sequen-C: A multilevel overview of temporal event sequences

arXiv

引用

arXiv 2021年

作者： Castaneda, Jessica Gisela Magallanes Stone, Tony Morris, Paul D. Mason, Suzanne Wood, Steven Villa-Uriol, Maria-Cruz Department of Computer Science Centre for Urgent and Emergency Care Research Department of Infection Immunity and Cardiovascular Disease University of Sheffield United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust

Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric - then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III;which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns. © 2021, CC BY.

关键词： Data visualization

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Interventional Contrastive Learning with Meta Semantic Regularizer

arXiv

引用

arXiv 2022年

作者： Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in this paper, we uncover an ever-overlooked phenomenon: When the CL model is trained with full images, the performance tested in full images is better than that in foreground areas;when the CL model is trained with foreground areas, the performance tested in full images is worse than that in foreground areas. This observation reveals that backgrounds in images may interfere with the model learning semantic information and their influence has not been fully eliminated. To tackle this issue, we build a Structural Causal Model (SCM) to model the background as a confounder. We propose a backdoor adjustment-based regularization method, namely Interventional Contrastive Learning with Meta Semantic Regularizer (ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can be incorporated into any existing CL methods to alleviate background distractions from representation learning. Theoretically, we prove that ICL-MSR achieves a tighter error bound. Empirically, our experiments on multiple benchmark datasets demonstrate that ICL-MSR is able to improve the performances of different state-of-the-art CL methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Big Data Security and Privacy Protection in Colleges and Universities

Big Data Security and Privacy Protection in Colleges and Uni...

引用

International Conference on Intelligent Systems Applications in Multi-modal Information Analytics, 2019

作者： Guo, Jinlan Computer Teaching Department Shanghai University of Political Science and Law Shanghai China

ISBN: (纸本)9783030157388

An increasing number of universities choose to deploy big data and cloud computing to integrate information from various departments, and adopt data mining technology to improve teaching management levels and decision-making abilities. Based on the acquisition, storage, application and publishing of data, this paper proposes the whole technology framework of big data security system from three levels of network, equipment and application, and puts forward to strategies which can protect individual privacy in each link. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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