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Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Computers, Materials & Continua 2021年第9期68卷 3079-3100页

作者： Shaheen Khatoon Majed AAlshamari Amna Asif Md Maruf Hasan Sherif Abdou Khaled Mostafa Elsayed Mohsen Rashwan Department of Information System College of Computer Science and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Computers and Artificial Intelligence Cairo UniversityEgypt Faculty of Computing&Information Cairo UniversityEgypt Faculty of Engineering Cairo UniversityEgypt

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made *** response authorities,law enforcement agencies,and the public can use this information to gain situational awareness and improve disaster *** case of emergencies,rapid responses are needed to address victims’requests for *** research community has developed many social media platforms and used them effectively for emergency response and coordination in the ***,most of the present deployments of platforms in crisis management are not automated,and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response *** seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become *** research project aims to develop a generalized Information technology(IT)solution for emergency response and disaster management by integrating social media data as its core *** this paper,we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief *** specifically,we applied state-of-the-art Natural Language Processing(NLP),Machine Learning(ML),and Deep Learning(DL)techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a *** a proof of concept,a case study on the COVID-19 pandemic on the data collected from Twitter is presented,providing evidence that the scientific and operational goals have been achieved.

关键词： Crisis management social media analytics machine learning natural language processing deep learning

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Usability heuristics and qualitative indicators for the usability evaluation of touch screen ventilator systems

Usability heuristics and qualitative indicators for the usab...

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作者： Katre, Dinesh Bhutkar, Ganesh Karmarkar, Shekhar Human-Centered Design and Computing Group C-DAC Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Medical Consultant Pune India

ISBN: (纸本)9783642117619

A ventilator system provides respiratory support to critically ill patients in the Intensive Care Unit. Increasing complexity in the user interface, features and functionalities of ventilator systems can cause medical errors and cost the life of a patient. Therefore, the usability of ventilator systems is most crucial to ensure patient safety. We have evolved a specialized set of heuristics combined with objectively defined usability indicators for the usability evaluation of touch screen based ventilator systems. Our study presents the heuristic evaluation of three touch screen based ventilator systems manufactured by three different companies. The heuristic evaluation has been performed by four different usability evaluators to ensure the reliability of heuristics proposed in this paper. The specialized set of heuristics linked with user interface components and the objectively defined usability indicators are found more reliable in identifying specific usability problems of ventilator systems. © IFIP International Federation for Information Processing 2010.

关键词： Intensive care units

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Providing high-level control and expert assistance in the user interface presentation design 93

Providing high-level control and expert assistance in the us...

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Proceedings of the INTERCHI '93 Conference on Human Factors in computing Systems

作者： Kim, Won Chul Foley, James D. Department of Electrical Engineering and Computer Science The George Washington University Washington DC Graphics Visualization and Usability Center College of Computing Georgia Institute of Technology Atlanta GA

ISBN: (纸本)9051991339

Current user interface builders provide only low-level assistance, because they have knowledge of neither the application, nor the principles by which interface elements are combined effectively. We have developed a framework that unites the knowledge components essential for effective user interface presentation design. The framework consists of an application model (both a data model and a control model), a design process model that supports top-down iterative development, and graphic design knowledge that is used both to place dialog box elements such that their application dependent logical relationships are visually reinforced and to control design symmetry and balance. To demonstrate the framework's viability, we have constructed a tool based on encapsulated design knowledge that establishes high-level style preferences and provides expert assistance for the dialog box presentation design and menu structuring.

关键词： UI design process

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Automated Detection and Classification of Cotton Leaf Diseases: A Computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Ganguly, Swastick Jathin, S. Mohak, M. Sanjay, M. Somashekhar, B.M. Shobharani, N. Amrita School Of Computing Department Of Computer Science Mysuru Campus India Gitam School Of Technology Gitam University Dept Of Computer Science And Engineering Bengaluru Campus India

ISBN: (纸本)9798350371567

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability. © 2024 IEEE.

关键词： Cotton

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Sampled-data models and zero dynamics for nonlinear systems with relative degree two

Sampled-data models and zero dynamics for nonlinear systems ...

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第25届中国控制与决策会议

作者： Cheng Zeng Shan Liang Yingying Su Yangsheng Li College of Automation Chongqing University Department of Mathematics Guiyang university Key Laboratory of Dependable Service Computing in Cyber Physical SocietyMinistry of Education Chongqing University Department of Electric and Electronic Information Engineering Chongqing University of Science and Technology

ISBN: (纸本)9781467355322

This paper derives a more accurate sampled-data model than the Yuz and Goodwin type model for nonlinear systems in the case of the relative degree two, and analyzes the sampling zero dynamics of the sampled-data model to show a condition which assures the stability of the sampling zero dynamics of the proposed model. It is a nature extension of Ishitobi et al.'s result from a single-input single-output (SISO) nonlinear systems to a multi-input multi-output (MIMO) nonlinear systems.

关键词： nonlinear systems, sampled-data models, zero dynamics, relative degree

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Automated Detection of Brain Abnormalities in MRI Images Using Integrated Deep Convolutional Neural Networks With K-Means Algorithm 3

Automated Detection of Brain Abnormalities in MRI Images Usi...

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3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Jaisingh, W. Kavitha, R.K. Saravanan, S. Gour, Radhika School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal466114 India Kumaraguru College of Technology Department of Computer Applications Tamilnadu Coimbatore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Computerization in the medical field has turned out to be a necessity in recent medical development. Radiotherapists and doctors require such computerized systems for the precise identification and treatment of chronic diseases. The field of Machine learning has observed remarkable attention in recent times. Deep neural networks are considered to be one of the popular machine learning models applied in many areas like image analysis, natural language processing, etc. It is also commonly used in both academia and industry. These improvements offer a huge potential in the fields of medical imaging, medical data analysis and diagnostics, and health care. This paper proposes a method for automatically categorizing normal and pathological brain MR images using deep transfer learning. K-Means model along with the Convolutional neural network (CNN) is engaged as a model for deep learning. The proposed method was assessed by focusing on the imaging data offered by the Kaggle dataset. The proficiency of the method was evaluated, and the results were presented. © 2022 IEEE.

关键词： Convolutional neural networks

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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Computer Modeling in engineering & Sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease

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SN Computer Science 2024年第1期5卷 181页

作者： Nramban Kannan, Senthil Kumar Kolla, Bhanu Prakash Sengan, Sudhakar Muthusamy, Rajendiran Manikandan, Raja Patel, Kanubhai K. Dadheech, Pankaj Department of Computer Science and Engineering School of Computing Institute of Science and Technology (Deemed to Be University) Vel Tech Rangarajan Dr. Sagunthala R&D Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Tamil Nadu Trichy 621112 India Charotar University of Science and Technology Gujarat 388421 India Department of Computer Science and Engineering Management and Gramothan (SKIT) Swami Keshvanand Institute of Technology Rajasthan Jaipur 302017 India

Sustainable development is crucial for a prosperous future, but epidemic diseases like Coronavirus Disease 2019 (COVID-19) pose real and complex challenges. The global pandemic, declared by the WHO on March 11, 2020, has led to significant loss of life and challenges in public health, economy, food systems, and social life. This research article aims to develop a Machine Learning (ML) model to predict a patient’s COVID-19 diagnosis, as early diagnosis is fundamental for controlling the spread of COVID-19. An investigation of the literature and a research study is conducted to find suitable mathematical methods and measure how they impact prediction models. This paper analyses COVID-19 pandemic deaths, confirmed cases, and recovered individuals using Time Series Analysis (TSA) to study the disease's impacts and understand the TAS. A forecasting model can predict future COVID cases by analyzing trends in time-series and connecting global changes with government restrictions. Since higher predictive accuracy is the limitation of ensemble learning algorithms, better ML approaches are proposed here. Autocorrelation plots clearly showed the results executed for the considered objectives. The hybrid ARIMA algorithm proposed in this work proved adequate results. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Analysis and visualization Autocorrelation COVID-19 Machine learning Time-series analysis Time-series forecasting

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Optical Satellite Networks

Optical Satellite Networks

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Chan, Vincent W.S. Massachusetts Institute of Technology Research Laboratory of Electronics Steve Schwarzman College of Computing Ai and Decision System Sector Department of Electrical Engineering and Computer Science United States

We will explore the architecture of optical satellite networks at 100G-1Tbps. The challenge is to architect the system and the network protocols with large bandwidth-delay products and the presence of atmospheric turb... 详细信息

ISBN: (纸本)9781957171180

关键词： Atmospheric turbulence

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Evaluation of data delivery on Android application for health screening form

Evaluation of data delivery on Android application for healt...

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International Conference on Cloud computing and Social Networking 2012: "Cloud computing and Social Networking for Smart and Productive Society", ICCCSN 2012

作者： Heo, Seonguk Kang, Kyuchang Bae, Changseok Computer Software and Engineering University of Science and Technology Daejeon Korea Republic of Next-Generation Computing Research Department Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9781467318167

This paper presents an evaluation of data delivery on Android applications implementing a health screening form. Typically, the health screening form provides an integrated view of various data (e.g. lifelog, health log, and medical history) gathered by diverse measuring devices (e.g. hear rate monitor, and inbody). From data delivering perspective, the health screening form also consists of multiple Activity interfaces in a single application or between collaborating applications. Therefore, we need to evaluate efficiency of data delivery among Activity interfaces in a single application or between applications. We use basic interfaces of Android platform such as Intent, Broadcastreceiver, Contentprovider and Socket providing a function of data transfer to evaluate delivery performance. The result of this work can contribute to select efficient data transfer interface while implementing the function of data delivery in Android application © 2012 IEEE.

关键词： Data transfer

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