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Improved diabetes disease prediction IWFO model using machine learning algorithms

AIP Conference Proceedings 2024年第1期3161卷

作者： R. Gunavathi V. Sivakumar B. Senthil Kumar S. Vijayalakshmi Associate professor School of Science Department of Data Science Christ University Pune India Associate Professor & Program Leader School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Assistant Professor Department of Computer Science Sree Narayana Guru College Coimbatore India

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level.

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Harsawardana Bayu Samodro Bharuno Mahesworo Teddy Suparyanto Don Bosco Surya Atmaja Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans.

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Harsawardana Reza Rahutomo Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Teddy Suparyanto Don Bosco Surya Atmaja Bayu Samoedro Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.

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Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

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Atmospheric and Climate sciences 2022年第1期12卷 54-73页

作者： Asaminew Teshome Jie Zhang Qianrong Ma Stephen E. Zebiak Teferi Demissie Tufa Dinku Asher Siebert Jemal Seid Nachiketa Acharya National Meteorological Agency of Ethiopia Addis Ababa Ethiopia Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/International Joint Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology (NUIST) Nanjing China International Research Institute for Climate and Society (IRI) The Earth Institute Columbia University New York USA NORCE Norwegian Research Center Bjerknes Center for Climate Research Bergen Norway CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS) East Africa Addis Ababa Ethiopia Ethiopian Institute of Agricultural Research Addis Ababa Ethiopia Scuola Superiore Sant’Anna Pisa Italy Center for Earth System Modeling Analysis and Data Department of Meteorology and Atmospheric Science Pennsylvania State University University Park PA USA

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers

关键词： Ethiopia Ensemble June-September Correlation Coefficient Skill

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A search for H i absorption in distant star-forming galaxies with ASKAP-FLASH - I. Selection and analysis of the radio sample

arXiv

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arXiv 2024年

作者： Eden, Sophie L. Sadler, Elaine M. Pimbblet, Kevin A. Mahony, Elizabeth K. Yoon, Hyein E. A. Milne Centre for Astrophysics University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom Sydney Institute for Astronomy School of Physics A28 University of Sydney SydneyNSW2006 Australia ATNF CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Australia Institute for Data Innovation in Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

We present and discuss two catalogues of UV-selected (NUV 2 area of sky covered by the ASKAP FLASH survey and have an impact parameter of less than 20 arcsec to a FLASH radio continuum source. These catalogues are designed to enable a future search for 21 cm H i absorption in and around star-forming galaxies at redshift 0.4 80 percent) star-forming and resemble the overall WiggleZ population. The (g − i) colour evolution with redshift is consistent with a history of active star-formation, but the radio-associated objects are typically redder and have colours similar to high-excitation radio galaxies. The redshift distribution of the two catalogues matches the overall distribution for WiggleZ galaxies, despite their otherwise rare radio properties. These catalogues can be expanded in future as new radio data become available, and a forthcoming paper will present the H i absorption results. © 2024, CC BY.

关键词： Stars

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Rare genetic variation in PTPRB is associated with central serous chorioretinopathy, varicose veins and glaucoma

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Nature Communications 2025年第1期16卷 1-13页

作者： Rämö, Joel T. Gorman, Bryan R. Weng, Lu-Chen Jurgens, Sean J. Singhanetr, Panisa Tieger, Marisa G. van Dijk, Elon HC Halladay, Christopher W. Wang, Xin Hauser, Blake M. Kim, Soo Hyun Brinks, Joost Choi, Seung Hoan Luo, Yuyang Pyarajan, Saiju Nealon, Cari L. Gorin, Michael B. Wu, Wen-Chih Anthony, Scott A. Roncone, David P. Sobrin, Lucia Kaarniranta, Kai Yzer, Suzanne Palotie, Aarno Peachey, Neal S. Turunen, Joni A. Boon, Camiel JF Ellinor, Patrick T. Iyengar, Sudha K. Daly, Mark J. Rossin, Elizabeth J. Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Massachusetts Eye and Ear Boston MA United States Cardiovascular Disease Initiative Broad Institute of MIT and Harvard Cambridge MA United States Cardiovascular Research Center Massachusetts General Hospital Boston MA United States Center for Data and Computational Sciences (C-DACS) VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Booz Allen Hamilton McLean VA United States Department of Experimental Cardiology Amsterdam Cardiovascular Sciences Heart Failure & Arrhythmias Amsterdam UMC University of Amsterdam Amsterdam Netherlands Mettapracharak Eye Institute Mettapracharak (Wat Rai Khing) Hospital Nakhon Pathom Thailand New England Eye Center Tufts Medical Center Boston MA United States Department of Ophthalmology Leiden University Medical Center Leiden Netherlands Center of Innovation in Long Term Services and Supports Providence VA Medical Center Providence RI United States Harvard Medical School Boston MA United States Department of Biostatistics Boston University Boston MA United States VA Cooperative Studies Program VA Boston Healthcare System Boston MA United States Department of Medicine Brigham and Women’s Hospital and Harvard School of Medicine Boston MA United States Eye Clinic VA Northeast Ohio Healthcare System Cleveland OH United States Department of Ophthalmology David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Department of Human Genetics David Geffen School of Medicine Stein Eye Institute University of California Los Angeles Los Angeles CA United States Section of Cardiology Medical Service VA Providence Healthcare System Providence RI United States Harvard Medical School Department of Ophthalmology Massachusetts Eye and Ear Boston MA United States Department of Ophthalmology Unive

Central serous chorioretinopathy is an eye disease characterized by fluid buildup under the central retina whose etiology is not well understood. Abnormal choroidal veins in central serous chorioretinopathy patients have been shown to have similarities with varicose veins. To identify potential mechanisms, we analyzed genotype data from 1,477 patients and 455,449 controls in FinnGen. We identified an association for a low-frequency (allele frequency = 0.5%) missense variant (rs113791087) in PTPRB, the gene encoding vascular endothelial protein tyrosine phosphatase (odds ratio=2.85, P = 4.5 × 10^-9). This was confirmed in a meta-analysis of 2,452 patients and 865,767 controls from 4 studies (odds ratio=3.06, P = 7.4 × 10^-15). Rs113791087 was associated with a 56% higher prevalence of retinal abnormalities (35.3% vs 22.6%, P = 8.0 × 10^-4) in 708 UK Biobank participants and, surprisingly, with increased risk of varicose veins (odds ratio=1.31, P = 2.3 × 10^-11) and reduced risk of glaucoma (odds ratio=0.82, P = 6.9 × 10^-9). Predicted loss-of-function variants in PTPRB, though rare in number, were associated with central serous chorioretinopathy in All of Us (odds ratio=17.09, P = 0.018). These findings highlight the significance of vascular endothelial protein tyrosine phosphatase in diverse ocular and systemic veno-vascular diseases. © The Author(s) 2025.

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An artificial intelligence method for vessel detection in images of cardiac catheterization

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Informatics in Medicine Unlocked 2023年 39卷

作者： Chien, Ting-Ying Ting, Hsien-Wei Li, Hao-Wei Chang, Hsiao-Huang Department of Computer Science and Engineering Yuan Ze University 320 Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University 320 Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University 320 Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare 242 New Taipei City Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Veterans General Hospital Taipei 11217 Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan

Coronary artery disease (CAD) is one of the top ten leading causes of death in countries worldwide. There are several methods used by physicians to diagnose CAD. One common method is the use of X-ray angiogram (XRA) images. When employing a computer-aided automatic analysis method to locate blood vessels in XRA images, problems such as complex background noise, low contrast, and difficulty in separating blood vessels from the background are encountered, especially for blood vessels with a low contrast to the background noise. In this study, we used various image preprocessing methods to highlight blood vessels from complex backgrounds and different types of blood vessel, followed by the employment of four superpixel segmentation methods to partition an image into multiple segments, and finally the utilization of Weighted Voting to determine the best superpixel algorithm. A genetic algorithm was used to identify the best weights by which to determine the locations of blood vessels in images of cardiac catheterization. Our proposed method achieved an average F1-score of 73.4% in the analysis of five patients, which was better than the other superpixel algorithms tested. The results of this research will be helpful for future SYNTAX score analysis. © 2023

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