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A review of face expression recognition system recommendation for psychiatric disease

AIP Conference Proceedings 2023年第1期2683卷

作者： Ahmad Ilham Suryono Suryono Catur Edi Widodo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University 50241 Semarang-Central Java Indonesia Department of Informatics Faculty of Engineering Universitas Muhammadiyah Semarang 50273 Semarang-Central Java Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University 50275 Semarang – Central Java Indonesia

Research on human mental disorders has started exploring the relationship between human facial expression recognition (FER) and symptoms of mental disorders because cases in FER are similar to symptoms in psychiatric disease. With the tremendous success of machine learning and deep learning, various models and architecture of these techniques are being leveraged to achieve better performance. The purpose of this work is to undertake a review of the most recent work on automatic facial emotion recognition (FER) using deep learning. We emphasize these contributions from the deep learning architecture and facial databases and the researchers’ progress by comparing the recommended technique with the findings produced. The study found that facial expression recognition based on cognitive internet of things (CIoT) in psychiatry disease did not yet exist, so it became a big opportunity to develop this research in the future. It can be concluded that this research can assist scholars by examining previous works and providing insights to improve this field and can be used as recommendations for psychiatric disease.

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Estimating Classification Confidence Using Kernel Densities

arXiv

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arXiv 2022年

作者： Salamon, Peter Salamon, David Cantu, V. Adrian An, Michelle Perry, Tyler Edwards, Robert A. Segall, Anca M. The Department of Mathematics and Statistics The Computational Science Research Center The Viral Information Institute San Diego State University San DiegoCA92182 United States The Department of Mathematics and Statistics San Diego State University San DiegoCA92182 United States The Computational Science Research Center The Department of Biology San Diego State University San DiegoCA92182 United States The Perelman School of Medicine The University of Pennsylvania 425 Johnson Pavilion 3610 Hamilton Walk PhiladelphiaPA19104 United States The Computational Science Research Center San Diego State University San DiegoCA92182 United States Google 1600 Amphitheater Parkway Mountain ViewCA94043 United States The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States Helix 9875 Towne Center Drive San DiegoCA92121 United States The Department of Biology The Computational Science Research Center The Viral Information Institute The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) [1], [2] as well as the classic MNIST benchmark [3]. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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Mineral water production for health therapy based on nanomaterial filters

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AIP Conference Proceedings 2023年第1期2748卷

作者： Mohamad Amin Dewi Nawang Arum Ihya Fakhrurizal Amin Ahya Zhilalikbar Amin Ekowati Retnaningtyas Department of Biology Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No. 5 Malang 65145 Indonesia Poltekkes Kemenkes Malang Malang Indonesia Faculty of Medicine Universitas Indonesia Jakarta Indonesia Study Program of Informatics Engineering Faculty of Computer Sciences Universitas Brawijaya Malang Indonesia

Clean water is one of the most important needs, including for daily drinking, because 70-80% of the human body is water. There are many benefits of consuming adequate drinking water for health. Consuming clean and healthy water is one of the keys to getting a healthy body, because water also plays a role in oxygen transportation throughout the body. To produce clean and healthy water, technological innovations are needed, one of which is to use a nano filter-based device which can reduce solutes, colour, hardness and high metal ions by up to 70%. The purpose of this research is to produce drinking water that is healthy and can be useful in health therapy with nano material-based filters. The resulting water product is drinking water with a pH of 8+. This nano material-based tool has been tested using 3 water samples, namely well water, PDAM water and river water. The results showed that well water with a pH of 8.71, TDS (total dissolved solid) 282 and a test of 4 heavy metals (Pb, Cu, Zn and Cr) were below the threshold and the results after filtering were the most ideal among other water samples and recommended for consumption that can afford for health therapy.

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Carrying Javanese Local Wisdom in Mathematical Model 1

Carrying Javanese Local Wisdom in Mathematical Model

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1st International Conference on Computer, Science, Engineering and Technology, ICComSET 2018

作者： Prabowo, A. Mamat, M. Sukono Ngadiman Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Jenderal Soedirman Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Darul Iman Kuala Terengganu Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran West-Java Indonesia Physical Health and Recreation Education Study Program Faculty of Health Sciences Universitas Jenderal Soedirman Purwokerto Central-Java Indonesia

People of Cikakak Traditional Village, Wangon District, Banyumas Regency, Central-Java Province are known as the followers of Islam Aboge (Alif Rebo Wage). Their religious holidays such as the beginning of fasting month (Ramadan) and Eid al-Fitr are determined based on the rules they have been using so far, namely Month Code or Sandi Bulan and Year Code or Sandi Tahun. This research is conducted using literature study method and field study in the form of interview with respondents. This research aims at producing a mathematical model which matches the function of these two codes. The results obtained are five mathematical equations to determine the names of year, saptawara and pancawara days for the 1st day of each month, and saptawara and pancawara days for other dates. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Functions

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer Science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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Derivative Free Levenberg-Marquardt Method for Solving Fuzzy Nonlinear Equation

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IOP Conference Series: Materials Science and Engineering 2021年第1期1115卷

作者： A U Omesa I M Sulaiman M Mamat M Y Waziri A Shadi M A Zaini Kalfin I Sumiati Department of Mathematics College of Agriculture Zuru Kebbi Nigeria Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Kuala Nerus 22200 Terengganu Malaysia Department of Mathematics Bayero University Kano Nigeria Institute of Engineering Mathematics Universiti Malaysia Perlis Kangar Malaysia Doctor Program of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Master Program in Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

In this paper, we present a derivative-free computational method for solving fuzzy nonlinear equation. Derivative-free technique avoids computing the derivative by generating an estimate to the derivative. This is made possible by inserting the estimate of in Levenberg-Marquardt's method. Numerical experiments are carried out which shows that, the method is efficient.

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Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States

arXiv

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arXiv 2022年

作者： Stolerman, Lucas M. Clemente, Leonardo Poirier, Canelle Parag, Kris V. Majumder, Atreyee Masyn, Serge Resch, Bernd Santillana, Mauricio Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States Department of Mathematics Oklahoma State University StillwaterOK United States NIHR Health Protection Research Unit Behavioural Science and Evaluation University of Bristol Bristol United Kingdom Harvard University T.H. Chan School of Public Health BostonMA United States Department of Geoinformatics - Z-GIS University of Salzburg Salzburg Austria Center for Geographic Analysis Harvard University CambridgeMA United States

The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The design of appropriate and timely mitigation strategies to curb the effects of this and future disease outbreaks requires close monitoring of their spatio-temporal trajectories. We present machine learning methods to anticipate sharp increases in COVID-19 activity in US counties in real-time. Our methods leverage Internet-based digital traces – e.g., disease-related Internet search activity from the general population and clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories from neighboring locations– to monitor potential changes in population-level health trends. Motivated by the need for finer spatial-resolution epidemiological insights to improve local decision-making, we build upon previous retrospective research efforts originally conceived at the state level and in the early months of the pandemic. Our methods –tested in real-time and in an out-of-sample manner on a subset of 97 counties distributed across the US– frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before the onset of local outbreaks (defined as the time when the effective reproduction number Rt becomes larger than 1 consistently). Given the continued emergence of COVID-19 variants of concern –such as the most recent one, Omicron– and the fact that multiple countries have not had full access to vaccines, the framework we present, while conceived for the county-level in the US, could be helpful in countries where similar data sources are available. Copyright © 2022, The Authors. All rights reserved.

关键词： Search engines

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Correlation between digital literacy and environmental literacy of 11th-grade students

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AIP Conference Proceedings 2024年第1期3106卷

作者： Mimien Henie Irawati Al Muhdhar Shinta Amrul Khoirina Muhamad Arjuna Salim Racy Rizky Abdillah Diana Hernawati Diki Muhamad Chaidir Muhammad Iqbal Akbar Lely Mardiyanti Mohammad Qodri Department of Biology Faculty of Mathematics and Sciences Universitas Negeri Malang Malang East Java Indonesia Department of Biologi Education Universitas Siliwangi Tasikmalaya Wesst Java Indonesia Department of Electrical and Informatics Engineering Faculty of Engineering Universitas Negeri Malang Malang East Java Indonesia Study Program of Biological Education Universitas Jambi Jambi Indonesia SMA Negeri 1 Batu Batu East Java Indonesia

The rapid development of technology, information, and communication requires students to master digital literacy. In addition, the rapid development of technology also affects environmental conditions, so it is necessary to master environmental literacy to understand and interpret environmental conditions, as well as build students’ awareness of environmental issues. This study aims to determine the correlation between digital literacy and student environmental literacy. The population in this research includes 11th-grade students of SMAN 1 Batu. The research sample amounted to 102 students, and the sampling technique used was purposive random sampling. Data were collected through digital literacy questionnaires, environmental literacy questionnaires, and environmental literacy tests. The data were tested for normality and homogeneity prerequisites and then analyzed using the Pearson Correlation statistical test. The results obtained show that the average digital literacy score of students is 105.1, including the high category, and the average student environmental literacy score is 279.7, included in the medium category. The high level of digital literacy will make students more critical in accessing technology and digital media. Digital literacy includes the process of finding, using multiple sources, evaluating, and using information to produce original products, with the maximum application of digital literacy, which is expected to support students’ environmental literacy in providing knowledge, understanding, and building an attitude of caring for the environment. The data obtained shows that there is a positive relationship between digital literacy and environmental literacy.

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Coconut waste to green nanomaterial: Large scale synthesis of N-doped graphene nano sheets

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Nano-Structures and Nano-Objects 2023年 36卷

作者： Siburian, Rikson Tang, Liang Wei Alias, Yatimah Tok, Alfred Iing Yoong Goei, Ronn Simanjuntak, Crystina Tarigan, Kerista Paiman, Suriati Goh, Boon Tong Anshori, Isa Kurniawan, Cepi Postgraduate Program Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Carbon and Frankincense Research Center Universitas Sumatera Utara Medan 20155 Indonesia Department of Chemistry Faculty of Science Universiti Malaya Kuala Lumpur 50603 Malaysia University Malaya Centre for Ionic Liquids (UMCiL) Universiti Malaya Kuala Lumpur 50603 Malaysia School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore Department of Physics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan 20155 Indonesia Physics Department Faculty of Science Universiti Putra Malaysia Selangor (Serdang) Seri Kembangan 43400 Malaysia Low Dimensional Materials Research Centre Department of Physics Faculty of Science University of Malaya Kuala Lumpur 50603 Malaysia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung 40132 Indonesia Research Center of Nanoscience and Nanotechnology Bandung Institute of Technology Bandung 40132 Indonesia Chemistry Department Faculty of Mathematics and Natural Sciences Universitas Negeri Semarang Semarang 50229 Indonesia Functional Material Research Center Universitas Negeri Semarang Semarang 50229 Indonesia

In this paper, a breakthrough was achieved in large-scale production of N-doped Graphene Nano Sheets (N-GNS) using coconut fruits. The main objectives were to produce N-GNS on a large scale and assess its electrical conductivity. The process involved two key steps: first, producing GNS from coconut fruits through pyrolysis, and then generating N-GNS by doping with nitrogen using ammonia solution at room temperature. Various analytical techniques were used to characterize the produced N-GNS, including X-ray Diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS), Transmission Electron Microscopy (TEM), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, and electrochemical cyclic voltammetry and electrochemical impedance spectroscopy. The XRD data indicated that the C (002) peak of N-GNS shifted to a higher 2θ value (2θ = 24.72°) compared to GNS (2θ = 23.86°), suggesting an incorporation of nitrogen into carbon structure in N-GNS. This was further supported by XPS data, which identified N-pyridine (BE = 402.0 eV) and C-N (BE = 286.8 eV) in N-GNS. TEM images showed that N-GNS had a flat surface, and the distance between graphene layers slightly expanded (0.36 nm) compared to graphene layers (0.34 nm). SEM images and EDX data validated the morphology, resembling honeycomb lattices, with a significant content of N atoms. Raman data successfully identified the D-band and G-band in N-GNS, further validating its production. Most importantly, N-GNS exhibited electrical conductivity, making it a promising candidate for conductive and supporting materials in various applications. The significance of this research extends beyond N-GNS production, opening up new avenues for the sustainable synthesis of valuable nanomaterials from coconut fruit waste, contributing to both graphene technology and environmental conservation. © 2023 Elsevier B.V.

关键词： C-sp2 Electrical conductivity Graphene nano sheets N-graphene nano sheets N-pyridine XPS

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