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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Agustini, Eka Puji Gernowo, Rahmad Kurnianingsih Wibowo, Adi Warsito, Budi Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia Faculty of Science and Mathematics Diponegoro Universitty Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350350524

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: The trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights 'Extraction' as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject. © 2024 IEEE.

关键词： Image segmentation

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Eka Puji Agustini Rahmad Gernowo Kurnianingsih Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro Universitty Semarang Indonesia

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: the trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights “Extraction” as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject.

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A Reconfigurable Compute-In-Memory Design with Adaptive Bit-Precision and Power Adjustment

A Reconfigurable Compute-In-Memory Design with Adaptive Bit-...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： I-Chieh Hsu Chun-Fu Chen Dao-Long Huang Yun-Kai Yeh Wei-Chieh Lee Qi-Hua Lin Jinjia Zhou I-Chyn Wey Electrical Engineering Department College of Engineering Chang-Gung University Taiwan School of Electrical Engineering National Taiwan University of Science and Technology Taiwan Master Program in Nano-Electronic Engineering and Design Chang Gung University Taiwan Department of Applied Informatics Faculty of Science and Engineering Hosei University Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

With the successful development of artificial intelligence (AI), Convolutional Neural Networks (CNNs) occupy a large amount of computing time and power consumption in the entire AI computing process. However, in traditional von Neumann architectures, the separation of the computing element and memory leads to the memory-wall problem of memory data transmission bandwidth from memory to processing element when executing multiply-accumulate-based CNN operations. The data transmission time and power consumption all are much higher than the CNN computing part. Therefore, this paper proposes a memory-in-computation design that can flexibly adjust energy usage, achieve high energy efficiency, and support multiple operation frequencies. By controlling the switches of each memory row, unnecessary energy consumption is avoided, leading to a reduction in total power consumption by 15% to 60%. Additionally, by adjusting the pulse width, the charging power consumption of capacitors is reduced by 65% per charge cycle.

关键词： Energy consumption Power demand Capacitors Bandwidth In-memory computing Control systems Energy efficiency Convolutional neural networks Data communication Artificial intelligence

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Low-to-Moderate Arsenic Exposure and Urothelial Tract Cancers with a Long Latent Period of Follow-Up in an Arseniasis Area

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Journal of Epidemiology and Global Health 2023年第4期13卷 807-815页

作者： Liao, Pei-Ju Lee, Chih-Hung Wang, Shu-Li Chiou, Hung-Yi Chen, Chien-Jen Seak, Chen-June Wu, I-Wen Hsu, Kuang-Hung International Program of Health Informatics and Management Chang Gung University Taoyuan Taiwan Master Degree Program in Health and Long-Term Care Industry Chang Gung University Taoyuan Taiwan Division of Nephrology Chang Gung Memorial Hospital Keelung Taiwan Department of Dermatology Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan Institute of Population Health Sciences National Health Research Institutes Zhunan Miaoli County Taiwan Master Program in Applied Epidemiology College of Public Health Taipei Medical University Taipei Taiwan Genomics Research Center Academia Sinica Taipei Taiwan Department of Emergency Medicine New Taipei Municipal Tucheng Hospital New Taipei City Taiwan Department of Emergency Medicine Lin-Kou Medical Center Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Laboratory for Epidemiology Department of Health Care Management and Healthy Aging Research Center Chang Gung University No. 259 Wen-Hwa 1St Road Kwei-Shan Taoyuan 333 Taiwan Research Center for Food and Cosmetic Safety College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan Department of Safety Health and Environmental Engineering Ming Chi University of Technology New Taipei City Taiwan

Background: Arsenic exposure can cause adverse health effects. The effects of long-term low-to-moderate exposure and methylations remain unclear. Objective: This study aims to examine the association between low-to-moderate arsenic exposure and urothelial tract cancers while considering the effects of methylation capacity. Methods: In this study, 5,811 participants were recruited from an arseniasis area in Taiwan for inorganic arsenic metabolite analysis. This follow-up study was conducted between August 1995 and December 2017. We identified 85 urothelial tract cancers in these participants, including 49 bladder and 36 upper urothelial tract cancer cases. A Cox proportional hazards model was employed. Results: The analyses revealed a significant association between concentrations of inorganic arsenic in water > 100 ug/L and bladder cancer occurrence, with a hazard ratio (HR) of 4.88 (95% CI 1.35–17.61). A monotonic trend was observed between concentrations of inorganic arsenic in water (from 0 to > 100 ug/L) and the incidence of urothelial tract cancer, including bladder cancer (p < 0.05) and upper urothelial tract cancers (p < 0.05). Participants with a lower primary methylation index or higher secondary methylation index had a prominent effect. Conclusions: Rigorous regulations and active interventions should be considered for populations with susceptible characteristics. © 2023, The Author(s).

关键词： Bladder cancer Dose–response relationship Long latency arsenic exposure Methylation capacity Upper urothelial tract cancers

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Trust in government moderates the association between fear of COVID-19 as well as empathic concern and preventive behaviour

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Communications Psychology 2023年第1期1卷 1-16页

作者： Arzu Karakulak Beyza Tepe Radosveta Dimitrova Mohamed Abdelrahman Azzam Amin Plamen Akaliyski Rana Alaseel Batoul Bakkar Wassim Gharz Edine Fatema Mohsen Yousuf Abdulqader Alkamali Danny A. Lizarzaburu Aguinaga Andrii Andres John Jamir Benzon R. Aruta Marios Assiotis Olga Solomontos-Kountouri Hrant Avanesyan Maria Muradyan Norzihan Ayub Maria Bacikova-Sleskova Raushan Baikanova Aidos Bolatov Telman Seisembekov Sunčica Bartoluci Ksenija Bosnar Anna Janovská Darko Katović Franjo Prot David Benitez Rita Rivera Ivanna Bodnar Iuliia Pavlova Judyta Borchet Maria Kazmierczak Aleksandra Lewandowska-Walter Justyna Michalek-Kwiecien Katarzyna Skrzypińska Yunier Broche-Pérez Carmen Buzea Rosalinda Cassibba Pasquale Musso Fabiola Silletti Marta Martín Carbonell Bin-Bin Chen Gordana Ristevska Dimitrovska Dương Công Doanh Alejandra del Carmen Dominguez Espinosa Nelli Ferenczi Regina Fernández-Morales Jorge Gaete Yiqun Gan Suely Giolo Rubia Carla Formighieri Giordani Camila Muhl José Rasia Maria-Therese Friehs Shahar Gindi Biljana Gjoneska Juan Carlos Godoy Lorena Cecilia López Steinmetz Maria del Pilar Grazioso Marinés Mejía Camellia Hancheva Given Hapunda Shogo Hihara Russell Sarwar Kabir Tinka Delakorda Kawashima Kazumi Sugimura Brett Walter Mohd Saiful Husain Md Saiful Islam Sadia Sultana Nino Javakhishvili Ana Makashvili Veljko Jovanović Nor Ba’yah Abdul Kadir Johannes Karl Zhumaly Kauyzbay Richa Khanna Meetu Khosla Martina Klicperová-Baker Iva Poláčková Šolcová Ana Kozina Steven Eric Krauss Rodrigo Landabur Katharina Lefringhausen Yun-Hsia Liang Sadia Malik Denisse Manrique-Millones Stefanos Mastrotheodoros Breeda McGrath Enkeleint A. Mechili Samson Mhizha Diana Miconi Rodrigo Moreta-Herrera Andrej Naterer Arash Nemat Felix Neto Joana Neto Luz Marina Alonso Palacio Hassan Okati-Aliabad Carlos Iván Orellana Ligia María Orellana Sushanta Kumar Mishra Joonha Park Eddy Peralta Petro Petrytsa Saša Pišot Benedicta Prihatin Dwi Riyanti Laura Francisca Sudarnoto Angela Oktavia Suryani Adil Samekin Danielius Serapinas Rasa Pilkaus Istanbul Policy Center Sabanci University Istanbul Turkey Department of Psychology MEF University Istanbul Turkey Department of Psychology Stockholm University Stockholm Sweden Social Psychology Department Doha Institute for Graduate Studies Doha Qatar Mokhtass for Consultations and Research Doha Qatar Department of Sociology and Social Policy Lingnan University Hong Kong SAR China Faculty of Medicine Syrian Private University Damascus Syrian Arab Republic Ipswich Hospital Ipswich UK United Private School Athaiba Oman Universidad César Vallejo Trujillo Peru Department of Physical Education Lviv Polytechnic National University Lviv Ukraine Department of Psychology De La Salle University Manila Philippines Theological School Church of Cyprus Nicosia Cyprus General Psychology Chair Yerevan State University Yerevan Armenia Faculty of Psychology and Education University of Malaysia Sabah Sabah Malaysia Department of Educational Psychology and Health Psychology Pavol Jozef Šafárik University Košice Slovakia Astana Medical University Astana Kazakhstan University of Zagreb Zagreb Croatia Clinical Psychology Department Albizu University Miami FL USA Counseling & Psychological Services Duke University Durham NC USA Lviv State University of Physical Culture Lviv Ukraine Institute of Psychology University of Gdansk Gdansk Poland Polish Society for the Psychology of Religion University of Gdańsk Gdańsk Poland Department of Psychology Universidad Central Marta Abreu de Las Villas Santa Clara Cuba Department of Social Sciences and Communication Transilvania University of Brasov Brasov Romania Department of Education Psychology Communication University of Bari Bari Italy Faculty of Psychology Cooperative University of Colombia Santa Marta Colombia Psychology Department Fudan University Shanghai China Higher Medical School University St. Kliment Ohridski Bitola North Macedonia PHI Psihomedika Bitola North Macedonia National Economics University

With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviours was strongest when trust in the government was weak (both at individual- and country-level). Conversely, the association with empathic prosocial concern was strongest when trust in the government was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how motivations may be shaped by socio-cultural context, and outline how findings may contribute to a better understanding of collective action during global crises.

关键词： Human behaviour Infectious diseases

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Comparison of two pro totype-based multicriteria classification methods

Comparison of two pro totype-based multicriteria classificat...

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2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM 2009

作者： Filho, Amaury T. Brasil Pinheiro, Plácido R. Coelho, André L. V. Costa, Nathanael C. Master of Science Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

ISBN: (纸本)9781424427642

In the present study, we introduce an experimental analysis conducted over two Multicriteria Decision Aid (MCDA) classification methods that have been successfully applied to real world problems. Different from other studies on MCDA classifiers, which put more emphasis on the development of new methods, this work compares the effectiveness of two MCDA nominal classifiers under the same conditions. In such regard, we developed a customized genetic algorithm to calibrate their control parameters automatically under some different sets of reference alternatives, also known as prototypes. Moreover, the experiments we have realized so far, involving different datasets, reveal that there are still some gaps in existing MCDA methods that can lead to an improvement of the methodology for the classification problems. We understand that this sort of empirical assessment is interesting as it reveals how robust/sensitive the MCDA classifiers could be to the choice of cut-off thresholds and prototypes for distinct problems. ©2009 IEEE.

关键词： Classification (of information)

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