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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

Procedia Computer Science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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Exploring transposable element-based markers to identify allelic variations underlying agronomic traits in rice

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Plant Communications 2022年第3期3卷 59-71页

作者： Haidong Yan David C.Haak Song Li Linkai Huang Aureliano Bombarely School of Plant and Environmental Sciences Virginia TechBlacksburgVA 24061USA Graduate Program in Genetics Bioinformatics and Computational Biology(GBCB)Virginia TechBlacksburgVA 24061USA Department of Grassland Science Animal Science and Technology CollegeSichuan Agricultural UniversityChengdu 611130China Department of Bioscience Universita degli Studi di Milano(UNIMI)20133 MilanoItaly Instituto de Biologıa Molecular y Celular de Plantas(IBMCP) UPV-CSIC46022 ValenciaSpain

Transposable elements(TEs)are a major force in the production of new alleles during domestication;nevertheless,their use in association studies has been limited because of their *** have developed a TE genotyping pipeline(TEmarker)and applied it to whole-genome genome-wide association study(GWAS)data from 176 Oryza sativa *** accessions to identify genetic elements associated with specific agronomic *** markers recovered a large proportion(69%)of single-nucleotide polymorphism(SNP)-based GWAS peaks,and these TE peaks retained ca.25%of the *** use of TEs in GWASs may reduce false positives associated with linkage disequilibrium(LD)among SNP markers.A genome scan revealed positive selection on TEs associated with agronomic *** found several cases of insertion and deletion variants that potentially resulted from the direct action of TEs,including an allele of LOC_Os11g08410 associated with plant height and panicle length ***,these findings reveal the utility of TE markers for connecting genotype to phenotype and suggest a potential role for TEs in influencing phenotypic variations in rice that impact agronomic traits.

关键词： transposable element marker agronomic trait rice GWAS

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Repurposing transfer learning strategy of computer vision for owl sound classification

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Procedia Computer Science 2023年 216卷 424-430页

作者： Kevin William Gunawan Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes.

关键词： acoustics deep learning bird sound species detection transfer learning

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method 7

Implementation of expert systems in potassium deficiency in ...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Hafizal, Muhammad Titan Putra, Dian Pratama Wirianata, Herry Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta55282 Indonesia Faculty of Forestry STIPER Institute of Agriculture Yogyakarta55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants' condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-To-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants. © 2022 Elsevier B.V.. All rights reserved.

关键词： Expert systems

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Influence of conformational change of chain unit on the intrinsic negative thermal expansion of polymers

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Chinese Chemical Letters 2021年第4期32卷 1515-1518页

作者： Qiangsheng Sun Ke Jin Yuhui Huang Jia Guo Thanyada Rungrotmongkol Phornphimon Maitarad Changchun Wang State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science Fudan UniversityShanghai 200433.China Biocatalyst and Environmental Biotechnology Research Unit Department of BiochemistryFaculty of Science and Program in Bioinformatics and Computational BiologyGraduate SchoolChulalongkom UniversityBangkok 10330Thailand Research Center of Nano Science and Technology Shanghai UniversityShanghai 200444China

Negative thermal expansion(NTE) behavior has roused wide interest for the control of thermomechanical properties of functional *** NTE behaviors have been found in kinds of compounds,it remains challenging for polymers to achieve intrinsic NTE *** this work,we systematically studied the conformational change of dibenzocyclooctadiene(DBCOD) derivatives between chair(C) and twist-boat(TB) forms based on density-functional theo ry(DFT) calculations,and found clear evidence of the relationship between the structure of DBCOD units and the thermal contraction behavior of the related *** order to obtain the polymer with NTE property,two conditions should be met for the thermal contracting DBCOD related units as follows:(i) the TB conformation can turn into C conformation as the temperature increases,and(ii) the volume of C conformation is smaller than that of TB *** rule should offer a guidance to exploration of the new intrinsic NTE polymers in the future.

关键词： Negative thermal expansion Conformational change Polyarylamides Density-functional theory Dibenzocyclooctadiene

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A bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

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Systematic Literature Review On Machine Learning Predictive Models For Indoor Climate In Smart Solar Dryer Dome

Systematic Literature Review On Machine Learning Predictive ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Karli Eka Setiawan Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018–2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models.

关键词： Support vector machines Systematics Recurrent neural networks Power demand Bibliographies Artificial neural networks Predictive models

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A Hydrodynamic Analysis of Water System in Dadahup Swamp Irrigation Area 1

A Hydrodynamic Analysis of Water System in Dadahup Swamp Irr...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Purboseno, Sentot Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Agricultural Engineering Yogyakarta55283 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and a flood embankment structure, which is also connected to three different rivers: Barito River, Mangkatif River, and Kapuas Mumpung River. To quantify flood risk assessments in the area, we perform a hydrodynamic analysis using the unsteady flow method via HEC-RAS software version 4.1.0. We use geometrical profiles from topography measurements and the outer boundary of the Dadahup DIR as input for the hydrologic model. Our results estimate the maximum surface water level from both cross and longitudinal sections in the channels and find that several points near the flood embankment area are prone to flood events. Our modeling approach provides a preliminary assessment for the local government to formulate a flood mitigation plan and policy in the area. © 2021 IEEE.

关键词： Rivers

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Characterization of Antioxidative Flavonoids in Wild and Cultivated Soybean by Ion-Filtering Strategy-Combined Lc-Hrmsn Analysis

SSRN

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SSRN 2024年

作者： No, Kyoung Tai Kim, Kwan Joong Cho, Min Hyung Lee, Sangwon Ha, Jang-Ho Chu, Sung-June Chung, Gyuhwa Kim, Dae-Ok Bioinformatics & Molecular Design Research Center Incheon21983 Korea Republic of Department of Biotechnology Chonnam National University Yeosu59626 Korea Republic of Graduate School of Biotechnology Kyung Hee University Yongin17104 Korea Republic of Interdisciplinary Graduate Program Department of Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Yonsei University Korea Republic of

To profiling flavonoids in wild soybean (WB;Glycine soja Sieb & Zucc.) and cultivated soybean (CB;Glycine max (L.) Merr.), liquid chromatography-high resolution mass spectrometry (LC-HRMS), antioxidant assays, and chemometrics method were used. Total 70 flavonoids were tentatively identified with ion filter strategies containing precursor ion list, mass defect filter, nitrogen rule filter, and diagnostic ion filter from 20,999 features in WB and CB. Semi-quantification result revealed that WB has 1.4-, 93.1-, 6.3-, and 1.9-fold higher contents in isoflavone, flavan, procyanidin, and flavone than that of CB. In antioxidant assays, WB showed 1.89- to 5.83-fold higher antioxidant capacities than CB in four antioxidant capacity assays. In orthogonal partial least squares discriminant analysis, WB and CB were clearly classified, and 31 characteristic flavonoids were identified by variable importance in projection. Furthermore, epicatechin and procyanidins of WB showed significantly positive correlation with antioxidant capacities by Pearson's correlation. © 2024, The Authors. All rights reserved.

关键词： Flavonoids

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia

Time-series Analysis of Correlation between Climatic Paramet...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Joko Pebrianto Trinugroho Digdo Sudigyo Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia.

关键词： Epidemics Temperature distribution Rain Meteorological factors Multimedia systems Time series analysis Weather forecasting

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