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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mohammad Prasanto Bimantio Dian Pratama Putra Teddy Suparyanto Amallia Ferhat Nanda Satya Nugraha Alam Ahmad Hidayat Bens Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia Department of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.

关键词： Decision making Education Government Transforms Data collection Agriculture Recording

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

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AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Continuous Spatiotemporal Transformers

arXiv

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arXiv 2023年

作者： de Oliveira Fonseca, Antonio Henrique Zappala, Emanuele Caro, Josue Ortega van Dijk, David Interdepartmental Neuroscience Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Neuroscience Yale University New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Yale University New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems is that they are fundamentally discrete time and space models and thus have no guarantees regarding continuous sampling. To address this challenge, we present the Continuous Spatiotemporal Transformer (CST), a new transformer architecture that is designed for modeling of continuous systems. This new framework guarantees a continuous and smooth output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other spatiotemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems, including learning brain dynamics from calcium imaging data. © 2023, CC BY-NC-SA.

关键词： Sobolev spaces

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Scalable privacy-preserving cancer type prediction with homomorphic encryption

arXiv

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arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

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An efficient linear mixed model framework for meta-analytic Association studies across multiple contexts 21

An efficient linear mixed model framework for meta-analytic ...

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21st International Workshop on Algorithms in bioinformatics, WABI 2021

作者： Jew, Brandon Li, Jiajin Sankararaman, Sriram Sul, Jae Hoon Bioinformatics Interdepartmental Program University of California Los AngelesCA United States Department of Human Genetics University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States Department of Computational Medicine University of California Los AngelesCA United States Department of Psychiatry and Biobehavioral Sciences University of California Los AngelesCA United States

ISBN: (纸本)9783959772006

Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale. © Brandon Jew, Jiajin Li, Sriram Sankararaman, and Jae Hoon Sul;licensed under Creative Commons License CC-BY 4.0 21st International Workshop on Algorithms in bioinformatics (WABI 2021).

关键词： bioinformatics

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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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Meteorological parameters for PV installations in Indonesia

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Many countries are still dependent on fossil fuels as their primary energy supply. However, there is a growing concern about the sustainability and the environmental issue of fossil fuels. Therefore, it is important to utilize renewable resources as the energy supply, such as solar energy. As a country with a tropical climate, Indonesia has a great potential to use solar energy, due to the abundance of sunlight exposure. The present study aims to analyze several meteorological parameters for the installation of photovoltaic (PV) technology in Jakarta, Indonesia. The meteorological data provided by the Ministry of Environment and Forestry, Indonesia from 2008-2021 were used at three different locations in Jakarta. Data visualizations were used to show the difference between each variable in different locations. In addition, the statistical difference between the variables in the three locations was also examined using the Mann-Whitney U test. The test shows that all parameters are evenly distributed in the three locations except for a moderate difference in air temperature in the two locations. Furthermore, the monthly analysis of the parameters also indicates that the dry season, especially from August to September, would be the optimal time to utilize PV technology. Hence, this study would be useful to give preliminary insight regarding a site-specified assessment of PV plants in Jakarta based on local meteorological data.

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