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

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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Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： F E Gunawan A S Budiman B Pardamean E Djuana S Romeli N Hananda C Harito D P B Aji D N N Putri Stevanus Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT. Impack Pratama Industri Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia. Mechanical Engineering Department. Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia

Solar dryer is typically used for agricultural purposes in Indonesia. There are many economically important crops requiring storage or drying under particular environmental conditions such as temperature and humidity. High temperatures inside solar dryer prevents the growth of microorganism, and quickly reduce moisture content from the substance. A hybrid solar dryer is generally considered to provide the most optimum solution, however solar panels may be expensive and they still only provide heat or energy in the daytime. Hence, we propose here a new kind of hybrid solar dryer for 24/7 optimum conditions for crops - enabled by recent advances in energy technologies as well as Industry 4.0. This study aims to create an efficient, affordable and a self-sufficient intelligent energy system that will be applied to agriculture for storage or drying purposes by measuring the energy needs for the optimal drying system. Therefore, it is crucial to estimate and assess the critical energy needs for such new systems in order to optimize and design such smart solar dryer (SSD) system especially for Indonesia's agricultural needs. We use design experience of our industry partner (PT Impack Pratama Industri, Indonesia) who has been working extensively on such solar dryer dome (SDD) based on polycarbonate material (only solar irradiation, no other technologies) and theoretical framework based on first principles in thermodynamics to estimate and assess critical energy needs for such dome with all the smart technologies. The calculation was performed based on Mollier diagram and the result still a rough estimation of energy required.

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

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Computational intelligence and neuroscience 2023年第1期2023卷 4776770页

作者： Sujatha Krishnamoorthy Yaxi Zhang Seifedine Kadry Muhammad Attique Khan Majed Alhaisoni Nasser Mustafa Weifeng Yu Abdullah Alqahtani Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Wenzhou Zhejiang Province China. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Wenzhou Zhejiang Province China. Department of Neurology Wenzhou Central Hospital Medical Group Wenzhou 325000 China. Department of Applied Data Science Noroff University College Kristiansand Norway. Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 UAE. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Computer Science HITEC University Taxila Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia. Wenzhou-Kean University School of Science and Technology Wenzhou Zhejiang Province China. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia.

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences

arXiv

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arXiv 2025年

作者： Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernández-Díaz, Raúl Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C.E. Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece 1Department of Biomedical Sciences University of Padova Padova35121 Italy Department of Biomedical Sciences University of Padova Via Ugo Bassi 58/B Padova35131 Italy Athena Research & Innovation Center Aigialias & Chalepa Marousi15125 Greece VIB Data Core VIB.AI Center for AI & Computational Biology Technologiepark-Zwijnaarde 75 Ghent9052 Belgium ELIXIR Europe Hub EMBL-EBI South Building Wellcome Genome HinxtonCB10 1SD United Kingdom Plaça Eusebi Güell 1-3 Barcelona08034 Spain Department of Pharmacy and Biotechnology University of Bologna Bologna40126 Italy Computational Genomics Platform IRCCS University Hospital of Bologna Bologna40138 Italy ZB MED Information Centre for Life Sciences Gleueler Straße 60 Cologne50931 Germany SequenceAnalysis.co.uk United Kingdom University of East Anglia Research Park NorwichNR4 7TJ United Kingdom Department of Computational Biology University of Lausanne Genopode 2024.3 Lausanne1015 Switzerland Swiss Institute of Bioinformatics Amphipôle Quartier UNIL-Sorge Lausanne1015 Switzerland Institute for Fundamental Biomedical Science Biomedical Sciences Research Center Alexander Fleming Vari Greece Department of Informatics & Telematics School of Digital Technology Harokopio University Athens Greece School of Medicine University College Dublin Belfield Dublin 4 Ireland Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin 4 Ireland IBM Research Dublin DublinD15 HN66 Ireland Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki57001 Greece Department of Biology National & Kapodistrian University of Athens Athens15772 Greece Department of Computer Science University of Manchester Oxford Road ManchesterM19 3PL United Kingdom Luxembourg National Data Service 6 avenue des Hauts-Fourneaux Esch-sur-AlzetteL-4362 Luxembourg Insti

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI *** Codes 92 © 2025, CC BY-NC-ND.

关键词： Reusability

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Disease spectrum analysis of hospitalized children in China: A study of 18 tertiary children's hospitals

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Pediatric Investigation 2019年第3期3卷 159-164页

作者： Guoshuang Feng Yueping Zeng Jian Tian Xinyu Wang Jun Tai Fei Song Xin Zhang Xin Xu Jun Chen Tieliu Shi Xin Ni Big Data and Engineering Research Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijingChina Medical Record Management Office Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Hospital Administration Office Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Department of Otolaryngology Head and Surgery Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Department of Scientific Research Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Information Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina The Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEastChina Normal UniversityShanghaiChina

Importance: Morbidity and mortality of children are important indicators of the performance of the public health system in any country. In China, the children's disease spectrum has gradually changed in recent years. However, the gender- and age-specific disease spectrum for hospitalized children under 15 years old is still unclear. Objective: To explore the gender- and age-based distribution of diseases in hospitalized children under 15 years in China. Methods: Medical records home page data for 2016 to 2018 were collected from 18 tertiary children's hospitals in China. The gender- and age-specific disease spectrum was analyzed, using the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Results: The most common diseases were those of the respiratory system (25.7% of all 2232142 hospitalized children). The top three diseases for boys were diseases of the respiratory system (25.6%), diseases of the digestive system (11.4%) and certain conditions originating in the perinatal period (8.6%). The top three diseases for girls were diseases of the respiratory system (25.9%), certain conditions originating in the perinatal period (10.1%), and factors influencing health status and contact with health services (9.4%).The most common diseases for children under 1 year old were certain conditions originating in the perinatal period (38.1%). For all other age groups, the most common conditions were respiratory diseases (33.8% for those aged 1–3 years, 25.2% for those aged 4–6 years, and 12.2% for those aged 7–14 years). Interpretation: This study analyzed the medical records home pages of 18 children's hospitals to provide the first overview of the disease spectrum and its gender- and age-specific distribution among children in China.

关键词： Disease spectrum Hospitalized children China

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Blood Transfusion and Risk of Venous Thromboembolism: A Population-Based Cohort Study

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Thrombosis and Haemostasis 2019年第1期120卷 156-167页

作者： Shih-Yi Lin Yun-Lung Chang Hung-Chieh Yeh Cheng-Li Lin Chia-Hung Kao Graduate Institute of Biomedical Sciences and School of Medicine College of Medicine China Medical University Taichung Taiwan Division of Nephrology and Kidney Institute China Medical University Hospital Taichung Taiwan Management Office for Health Data China Medical University Hospital Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Nuclear Medicine and PET Center China Medical University Hospital Taichung Taiwan Center of Augmented Intelligence in Healthcare China Medical University Hospital Taichung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

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Crowdsourcing annotation system of object counting dataset for deep learning algorithm 2

Crowdsourcing annotation system of object counting dataset f...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Cenggoro, Tjeng Wawan Tanzil, Fidelson Aslamiah, Ayu Hidayah Karuppiah, Ettikan Kandasamy Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crowdsourcing

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram data 3

Transfer Learning from Chest X-Ray Pre-trained Convolutional...

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3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Rahutomo, Reza Budiarto, Arif Karuppiah, Ettikan Kandasamy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation United States

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Deep learning

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FRI0585 HIGH-THROUGHPUT METHODOLOGY FOR EMR-BASED IDENTIFICATION OF CLINICAL SUB-PHENOTYPES IN COMPLEX PATIENT POPULATIONS

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Annals of the Rheumatic Diseases 2020年 79卷 897-897页

作者： M. Maurits T. Huizinga M. Reinders S. Raychaudhuri E. Karlson E. Van den Akker R. Knevel Leiden University Medical Center (LUMC) Rheumatology Leiden Netherlands Delft University of Technology (TU Delft) Bioinformatics Delft Netherlands Leiden University Medical Center (LUMC) Molecular Epidemiology Leiden Netherlands Brigham and Women's Hospital Rheumatology Boston United States of America Brigham and Women's Hospital Data Sciences Boston United States of America

Background: Heterogeneity in disease populations complicates discovery of risk factors. To identify risk factors for subpopulations of diseases, we need analytical methods that can deal with unidentified disease subgroups. Objectives: Inspired by successful approaches from the Big data field, we developed a high-throughput approach to identify subpopulations within patients with heterogeneous, complex diseases using the wealth of information available in Electronic Medical Records (EMRs). Methods: We extracted longitudinal healthcare-interaction records coded by 1,853 PheCodes[1] of the 64,819 patients from the Boston's Partners-Biobank. Through dimensionality reduction using t-SNE[2] we created a 2D embedding of 32,424 of these patients (set A). We then identified distinct clusters post-t-SNE using DBscan[3] and visualized the relative importance of individual PheCodes within them using specialized spectrographs. We replicated this procedure in the remaining 32,395 records (set B). Results: Summary statistics of both sets were comparable ( Table 1 ). We found 284 clusters in set A and 295 in set B, of which 63.4% from set A could be mapped to a cluster in set B with a median (range) correlation of 0.24 (0.03 – 0.58). Clusters represented similar yet distinct clinical phenotypes; e.g. patients diagnosed with “other headache syndrome” were separated into four distinct clusters characterized by migraines, neurofibromatosis, epilepsy or brain cancer, all resulting in patients presenting with headaches ( Fig. 1 & 2 ). Though EMR databases tend to be noisy, our method was also able to differentiate misclassification from true cases; SLE patients with RA codes clustered separately from true RA cases. Conclusion: We have shown that EMR data can be used to identify and visualize latent structure in patient categorizations, using an approach based on dimension reduction and clustering machine learning techniques. Our method can identify misclassified patients as well as sepa

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A deep learning-based method for automatic segmentation of proximal femur from quantitative computed tomography images

arXiv

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arXiv 2020年

作者： Zhao, Chen Keyak, Joyce H. Tang, Jinshan Kaneko, Tadashi S. Khosla, Sundeep Amin, Shreyasee Atkinson, Elizabeth J. Zhao, Lan-Juan Serou, Michael J. Zhang, Chaoyang Shen, Hui Deng, Hong-Wen Zhou, Weihua College of Computing Michigan Technological University HoughtonMI49931 United States Department of Radiological Sciences Department of Mechanical and Aerospace Engineering Department of Biomedical Engineering Chao Family Comprehensive Cancer Center University of California Irvine IrvineCA92868 United States Department of Radiological Sciences University of California Irvine IrvineCA92868 United States Division of Endocrinology Department of Medicine Mayo Clinic RochesterMN United States Division of Epidemiology Department of Health Sciences Research Division of Rheumatology Department of Medicine Mayo Clinic RochesterMN United States Division of Biomedical Statistics and Informatics Department of Health Sciences Research Mayo Clinic RochesterMN United States Tulane Center of Bioinformatics and Genomics Tulane University School of Public Health and Tropical Medicine New OrleansLA70112 United States Department of Radiology Tulane University School of Medicine New OrleansLA70112 United States School of Computing Sciences and Computer Engineering University of Southern Mississippi HattiesburgMS39406 United States College of Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Tulane Center for Bioinformatics and Genomics Department of Biostatistics and Data Science Tulane School of Public Health and Tropical Medicine 1440 Canal Street Suite 1610 New OrleansLA70112 United States

Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experiments to evaluate the effectiveness of the proposed segmentation method. In the experiments, a QCT dataset which included 397 QCT subjects was used. For the QCT image of each subject, the ground truth for the proximal femur was delineated by a well-trained scientist. During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models;the rest of the subjects were used to evaluate the performance of models. Results: Visual comparison demonstrated high agreement between the model prediction and ground truth contours of the proximal femur portion of the QCT images. In the entire cohort, the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and a specificity of 0.9992. In addition, an R2 score of 0.9956 (p Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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