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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

Computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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E marketing strategy in health care using IoT and Machine Learning

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Materials Today: Proceedings 2022年 56卷 2087-2091页

作者： Mondal, Tamal Mugaloremutt Jayadeva, Sujay Pani, Rojalin Subramanian, Muthukumar P, Ashokkumar. Sumana, B.K. Department of Botany Hiralal Mazumdar Memorial College for Women Dakshineswar Kolkata700035 India Department of Health System Management Studies JSS Academy of Higher Education & Research Karnataka Mysuru India Odisha Bhubaneswar India Karnataka Tumkuru India Assistant Professor Civil Engineering Sona College of Technology Tamilnadu India PG Department of Business Administration Maharanis Womens Commerce and Management College Karnataka Mysore India

The Internet of Things (IoT) and Machine Learning (ML) have broad applications in a variety of fields, with healthcare system being one of them. The fast growth and enhancement of the web has resulted in the demise of traditional patient care methods and the replacement of them with electronic healthcare systems. The Internet of Things (IoT) technologies provides healthcare professionals and patients with the most up-to-date medical equipment ecosystem possible. Things connected to the Internet of Things (IoT) and machine learning are useful in a variety of categories, ranging from long-distance monitoring of the contemporary environment to mechanical automation. It is impossible to overstate the significance of digital marketing in the delivery of healthcare services. Doctors and medical experts now have the chance to promote their services to people all over the globe via digital marketing, rather than only in their immediate area. Digital marketing enables health professionals to demonstrate their competence to potential patients, to expand their professional area, and to improve the image of the healthcare industry. Furthermore, medical treatment applications have shown a strong desire in Internet of Items (IoT) things since they are less expensive, easier to comprehend, and enhance the personal happiness of patients. In the development of a wearable and implanted healthcare body network connection, a number of difficulties were encountered, which are detailed in this research. It is presented in this paper that an overall view of IoT and Machine Learning focused on healthcare is given in specifics, and then a list of applications that are used in health care that incorporate Machine Learning (ML) and the Internet of Things (IoT) is provided, along with all problems and difficulties that may arise by using this application or handset for healthcare system, as well as their significant application. © 2021

关键词： Internet of things

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Optical Wireless Network Architecture

Optical Wireless Network Architecture

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2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)

作者： Vincent W.S. Chan Joan and Irwin Jacobs Chair Professor Department of Electrical Engineering and Computer Science Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Steve Schwarzman College of Computing AI and Decision System Sector Massachusetts Institute of Technology. President IEEE Communications Society 2020&#x2013 2021.

ISBN: (数字)9784885523366

ISBN: (纸本)9781665486064

Optical wireless network has the potential to serve space-space, space-terrestrial/aircraft, aircraft-aircraft, data centers and metropolitan area networks. This paper addresses architecture features necessary for these networks. Free space optical (FSO) networks have two dimensions that are not encountered in fiber networks and that is: (1) its ability to connect without pre-deployment of infrastructures and (2) the ability to reconfigure its connection topology by beam steering in time scales of mS-S to adapt to traffic loads, switching node states and atmospheric conditions. This paper presents a multi-layer approach to optical wireless networks and how the architecture can be tuned to applications.

关键词： Optical fibers Network topology Metropolitan area networks Wireless networks Optical switches Computer architecture Telecommunication traffic

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Application of deep learning to distinguish multiple deep brain stimulation parameter configurations for the treatment of Parkinson's disease 19

Application of deep learning to distinguish multiple deep br...

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Lemoyne, Robert Mastroianni, Timothy Whiting, Donald Tomycz, Nestor Northern Arizona University Adjunct Professor Department of Biological Sciences FlagstaffAZ United States Founder of Cognition Engineering PittsburghPA United States System Chair Ahn Neuroscience Institute Department of Neurosurgery PittsburghPA United States Neurosurgeon West Penn Allegheny Health System PittsburghPA United States

ISBN: (纸本)9781728184708

Deep brain stimulation offers the opportunity for patient specific and optimally tuned treatment to ameliorate Parkinson's disease. Imperative for the efficacy of deep brain stimulation is the quantification of therapeutic response. The implementation of inertial sensor systems has considerably augmented the acuity of quantifying the characteristics of the movement disorder through the respective signal data. For example, these inertial sensors constitute effectively wearable and wireless systems that can be mounted about the dorsum of the hand to objectively quantify tremor. Using machine learning algorithms considerable classification accuracy has been achieved. Conformal wearable and wireless inertial sensor systems have been recently developed that can be conveniently mounted about the dorsum of the hand by an adhesive medium with a profile on the order of a bandage. Additionally, these conformal wearable and wireless inertial sensor systems can have their data wirelessly transmitted to a secure Cloud computing environment. This capability offers the opportunity to ascertain the inertial sensor signal for Parkinson's disease tremor response in the context of multiple deep brain stimulation parameter configurations, such as an assortment of amplitude settings. The objective of the research was to quantify and distinguish the response of Parkinson's disease tremor through an assortment of amplitude settings for deep brain stimulation, such as 'Off' setting as a baseline, amplitude set to 1.0 mA, amplitude set to 1.75 mA, amplitude set to 2.5 mA, amplitude set to 3.25 mA, and amplitude set to 4.0 mA, using a deep learning convolutional neural network through TensorFlow with the quantification of tremor by a conformal wearable and wireless inertial sensor system. Considerable classification accuracy was attained to distinguish between these specified deep brain stimulation settings for the amelioration of Parkinson's disease. © 2020 IEEE.

关键词： Adhesives

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Sustainability nexus analytics, informatics, and data (AID): Drought

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Sustainability Nexus Forum 2024年第1期32卷 1-12页

作者： Huning, Laurie S. Bateni, Sayed M. Hayes, Michael Ho, Sarah Quynh-Giang Jayasinghe, Susantha Kumar, Rohini Lima, Carlos Love, Charlotte A. Madani, Kaveh Markonis, Yannis Matin, Mir A. Miao, Chiyuan Motagh, Mahdi Naeger, Aaron de Oliveira, Debora Yumi Read, Laura K. Samaniego, Luis Shokri, Nima Shukla, Shraddhanand Soltanian, Reza Stefanski, Robert Trabelsi, Fatma Tsegai, Daniel Vo, Linh U. C. Wanders, Niko Wens, Marthe Zarei, Azin AghaKouchak, Amir Department of Civil Engineering and Construction Engineering Management California State University Long Beach Long Beach USA Department of Civil and Environmental Engineering University of California Irvine Irvine USA Department of Civil Environmental and Construction Engineering and Water Resources Research Center University of Hawaii Honolulu USA UNESCO-UNISA Africa Chair in Nanoscience and Nanotechnology College of Graduates Studies University of South Africa Pretoria South Africa School of Natural Resources University of Nebraska-Lincoln Lincoln USA Karlsruhe Institute of Technology (KIT) Karlsruher Germany Asian Disaster Preparedness Center (ADPC) Bangkok Thailand Helmholtz Centre for Environmental Research - UFZ Leipzig Germany University of Brasilia Brasília Brazil United Nations University Institute for Water Environment and Health (UNU-INWEH) Richmond Hill Canada United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) Dresden Germany Faculty of Environmental Sciences Czech University of Life Sciences Prague Prague Czech Republic Beijing Normal University Beijing China Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences Potsdam Germany Institute of Photogrammetry and Geoinformation Leibniz University Hannover Hannover Germany NASA Marshall Space Flight Center Huntsville USA Upstream Tech Alameda USA Institute of Environmental Science and Geography University of Potsdam Potsdam Germany Institute of Geo-Hydroinformatics Hamburg University of Technology Hamburg Germany United Nations University Hub on Engineering to Face Climate Change at the Hamburg University of Technology United Nations University Institute for Water Environment and Health (UNU-INWEH) Hamburg Germany Climate Hazards Center University of California Santa Barbara USA Departments of Geosciences and Environmental Engineering University of Cincinnati Cincinnati USA World Meteorological Organization Geneva

Drought occurs globally and can have deleterious effects on built and natural systems and societies. With the increasing human footprint on our planet, so has increased the anthropogenic influence on drought and water scarcity, leading to the development of notions of “anthropogenic drought” and “water bankruptcy”. Understanding the human dimension of drought is complex and requires a data-driven nexus approach to better understand the involved processes and address the implications of water deficits around the world. Just as it transcends scales and geographical boundaries, drought is neither restricted to a single hydrologic state in the water cycle nor are its effects confined to one sector. Drought impacts the water, energy, and food sectors, ecosystem services, socioeconomics, public policy, politics, etc. from local to regional and global scales. We argue that drought mitigation strategies and policy developments must be addressed with a multidisciplinary perspective that benefits from a nexus approach rooted in analytics, informatics, and data (AID). The United Nations University (UNU) Sustainability AID Programme employs such an approach to aid the monitoring, forecasting, and projection of drought, both from climatic and anthropogenic perspectives, and its multifaceted impacts across a variety of sectors and spatiotemporal scales. After a broad overview of this UNU Programme’s vision, and to support stakeholders and decision-makers, we present a drought resource database for drought-related information, data, and analysis tools. Our aim is not to compile an exhaustive list of all available data and tools. Instead, we prioritize mature datasets and AID tools while actively highlighting opportunities to develop new data and tools, fostering nexus research.

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COVID-19 test-to-stay program for K-12 schools: Opt-in versus opt-out consent model

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iScience 2024年第1期27卷 108770页

作者： Ivanov, Anton Mukherjee, Ujjal Kumar Bose, Subhonmesh Seshadri, Sridhar Watkins, Ronald England, Albert Charles Suriano, Jacqueline Ahsen, Mehmet Eren Souyris, Sebastian Department of Business Administration University of Illinois at Urbana-Champaign Champaign 61821 IL United States Health Innovation Professor Carle Illinois College of Medicine University of Illinois at Urbana-Champaign United States Electrical and Computer Engineering University of Illinois at Urbana-Champaign Champaign 61821 IL United States Shield Illinois University of Illinois System Champaign 61821 IL United States OSF HealthCare Heart of Mary Medical Center Urbana 61801 IL United States Lally School of Management Rensselaer Polytechnic Institute Troy 12180 NY United States

The Centers for Disease Control and Prevention promoted the Test-to-Stay (TTS) program to facilitate in-person instruction in K-12 schools during COVID-19. This program delineates guidelines for schools to regularly test students and staff to minimize risks of infection transmission. TTS enrollment can be implemented via two different consent models: opt-in, in which students do not test regularly by default, and the opposite, opt-out model. We study the impacts of the two enrollment approaches on testing and positivity rates with data from 259 schools in Illinois. Our results indicate that after controlling for other covariates, schools following the opt-out model are associated with 84% higher testing rate and 30% lower positivity rate. If all schools adopted the opt-out model, 20% of the total lost school days could have been saved. The lower positivity rate among the opt-out group is largely explained by the higher testing rate in these schools, a manifestation of status quo bias. © 2024 The Authors

关键词： Research methodology social sciences Social sciences system management

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Towards sustainable management: Hydrogeochemical evaluation of water supplies across Thailand

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Water research 2025年 284卷 123980页

作者： Jeerapong Laonamsai Supapap Patsinghasanee Kiattipong Kamdee Nantawoot Inseeyong Kwanchai Pakoksung Pavisorn Chuenchum Department of Water Resources Engineering Faculty of Engineering Chulalongkorn University Bangkok 10300 Thailand Center of Excellence in Disaster and Risk Management Information System Chulalongkorn University Bangkok 10300 Thailand. Electronic address: jeerapong.la@chula.ac.th. Department of Water Resources Ministry of Natural Resources and Environment Bangkok 10300 Thailand. Electronic address: supapap.p@dwr.mail.go.th. Thailand Institute of Nuclear Technology (Public Organization) Nakonnayok 26120 Thailand. Electronic address: kiattipong@tint.or.th. State Key Laboratory of Hydroscience & Engineering Tsinghua University Beijing 100084 PR China Department of Hydraulic Engineering Tsinghua University Beijing 100084 PR China. Electronic address: laim20@***. Department of Civil Engineering Faculty of Engineering Burapha University Chonburi 20131 Thailand. Electronic address: kwanchai.pa@buu.ac.th. Center of Excellence in Disaster and Risk Management Information System Chulalongkorn University Bangkok 10300 Thailand UNESCO Chair Programme on Water and Disaster Management and Climate Change Chulalongkorn University Bangkok 10300 Thailand. Electronic address: Pavisorn.C@chula.ac.th.

The quality of Thailand's surface water supply is under significant threat from both natural hydrogeochemical processes and rising anthropogenic activities. In this study, 270 water samples collected nationwide in 2022 were comprehensively analyzed using advanced hydrogeochemical characterization, multivariate statistical techniques, and standardized water quality indices (WQIs). The results reveal distinct spatial variations across regions. The Northern and Eastern regions are dominated by calcium-bicarbonate water types, with 65 % of samples showing evidence of extensive mineral dissolution from rock weathering, while the Southern region exhibits low mineralization, with 80 % of samples having Total Dissolved Solids (TDS) below 100 mg/L due to high precipitation. In contrast, the Central and Northeastern regions are characterized by sodium-chloride water types; here, 70 % of samples exceeded TDS levels of 1000 mg/L, reflecting the combined effects of evaporative concentration and agricultural runoff. Notably, critical health-related findings include nitrate concentrations that surpassed the World Health Organization (WHO) threshold of 50 mg/L in 40 % of samples from the Central and Northeastern regions, and arsenic levels that exceeded the WHO limit of 10 µg/L in 30 % of samples. Additionally, microbial analyses indicated that 25 % of samples had Escherichia coli and Total Coliform Bacteria count above safe levels, emphasizing significant fecal contamination risks. Cluster analysis further delineated distinct contamination sources and water types, emphasizing the need for region-specific interventions such as enhanced sanitation, optimized nutrient management, and rigorous monitoring programs. These findings provide a crucial baseline for future water quality monitoring and serve as a strategic framework for developing targeted water management policies aimed at protecting public health and promoting sustainable water use in Thailand and similar tropical regions.

关键词： Anthropogenic activities Hydrogeochemistry Surface water Thailand Water management Water quality

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FORWARD

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Seminars in vascular surgery 2023年第3期36卷 393页

作者： Sharon C Kiang Chief Division of Vascular Surgery Chair Institutional Review Board (IRB) PI: Center for Artificial Intelligence and Vascular Engineering (CAIVE) VA LOMA LINDA HEALTHCARE SYSTEM Vice-Chair for Research Associate Professor Department of Surgery LOMA LINDA UNIVERSITY| School of Medicine 11175 Campus Street Suite 21123 Loma Linda California 92350 (909) 558-8648 . fax (909) 558-034. Electronic address: skiang@llu.edu.

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Accelerating progress on sustainable development goals: Assessing secondary school students' knowledge of climate change actions

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IOP Conference Series: Earth and Environmental Science 2021年第1期665卷

作者： PF Tunji-Olayeni F Adegboye A Oluwatobi G Adeyemi O Olagunju A Okoro ES Osabuohien Regional Centre of Expertise (RCE Ogun) Building Technology Department Banking and Finance Department Estate Management Department Civil Engineering Department Centre for Entrepreneurial Development Studies (CEDS) Professor of Economics & Chair Centre for Economic Policy & Development Research (CEPDeR) Covenant University Ota Nigeria Architecture Department

The youth plays significant role in realising the sustainable development goals (SDGs). What young people know about the SDGs and how to engage with the SDGs can contribute significantly to the realisation of the SDGs within the shortest possible time. Specifically, climate change is central to the SDGs because of its substantial nexus with economic, social and environmental outcomes for all regions of the world. Thus, this study investigates climate change awareness among secondary school students to guide climate change mitigation strategies and practices among young people for the attainment of the SDGs. The study adopts a qualitative research approach using content analysis of the essays on climate change written and presented by students of different secondary schools in Ota, Nigeria. The analysis reveals, among other things, that the students are knowledgeable about climate change and that their schools are the most prevalent source of climate change knowledge. The most-reported climate change effect from the essays include: rise in global temperatures, melting of ice, flooding, rise in sea level, drought, extinction of terrestrial and marine life and health challenges. Common mitigation strategies suggested by the students entail awareness, tree planting, use of low carbon vehicles, use of energy-saving bulbs, reduction of carbon emissions, and recycling of waste. With adequate climate change knowledge and mitigation strategies, many young people can contribute and engage practically with the climate change discourse, thereby improving the climate change statistics in their regions and accelerating progress on the SDGs.

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