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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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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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A study on fitness function with parameter-free genetic algorithm for non-destructive estimation of permittivity distribution based on capacitance and electric potential

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IEEJ Transactions on Fundamentals and Materials 2016年第4期136卷 164-173页

作者： Ohchi, Masashi Tomiyama, Kazuyoshi Furukawa, Tatsuya Itoh, Hideaki Fukumoto, Hisao Department of Electrical Electronics and Computer Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Master's Program in Electrical Electronics and Computer Engineering Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga840-8502 Japan

We have carried out experimental and numerical analyses for reconstruction of permittivity distribution using a pair of long distance small size electrodes and additional arc electrodes. A reconstruction method for permittivity distribution that uses a genetic algorithm and numerical analysis (finite element method) based on measured capacitance has been proposed and investigated. However, the conventional method had a problem in accuracy. In this paper, the reconstruction method has been improved by using a Parameter-free Genetic Algorithm(PfGA) based on measured capacitance and electric potential. In addition, we have evaluated the fitness function with PfGA. © 2016 The Institute of Electrical Engineers of Japan.

关键词： Finite element method

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Evaluation of the used of visual electronic banking system application using UTAUT 2 in Bank Central Asia corporation

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IOP Conference Series: Materials science and Engineering 2018年第1期420卷

作者： Ahmad Nurul Fajar Frits Septian Johanes Abba Suganda Girsang Gunawan Wang Information Systems Management Department BINUS Graduate Program – Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The purpose of this study is to determine the factors that encourage the use of visual electronic banking system (VEBS) application in Bank Central Asia Corporation, and provide an idea to increase the use of VEBS. This study using UTAUT 2 model approach. The primary data obtained from interviews, direct observation and questionnaires that distributed and processed using SEM-PLS. The results of this study provide qualitative calculation results of UTAUT 2 variable. The conclusion obtained that interest of the use of application VEBS affected by variables facilitating condition, performance expectancy and behavioral intention. From these conclusions this study provided an idea to the future development of application's features for more effective and efficient use.

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Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method

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Journal of Physics: Conference Series 2020年第1期1471卷

作者： Dedi Dwi Saputra Windu Gata Nia Kusuma Wardhani Ketut Sakho Parthama Hendra Setiawan Sularso Budilaksono Dimas Yogatama Agus Hadiyatna Endah Putri Purnamasari Bryan Pratama Deny Novianti Master of Computer Science-Postgraduate Program Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program University of Mercubuana Jakarta Indonesia Graduate School Master's Degree Computer Science University Paramita Indonesia Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Bani Saleh Jakarta Indonesia Graduate School Master's Degree Computer Science University of Persada Indonesia YAI Jakarta Indonesia Graduate School Master's Degree Computer Science University of Gadjah Mada Jakarta Indonesia

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, 'transform case', 'tokenize', 'token filters by length', 'n-gram', 'stemming' were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

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Hybrid cryptosystem enhanced RSA and RC4 chaotic map: A tutorial

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AIP Conference Proceedings 2024年第1期3222卷

作者： Arief Rahman Hakim Mohammad Andri Budiman Mahyuddin K. M. Nasution Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Since the RSA algorithm is no longer secure and a lot of research has been done to break down RSA security, it is necessary to do research by making a combination of modifications of RSA algorithms, in this case using enhanced RSA using fake modules, with RCCM (RC4 Chaotic Map) algorithms to produce a Hybrid Cryptosystem that is expected to be able to improve the security of secret messages. The combination process is done by encrypting the messages using the RCCM algorithm first, then the messages that have been encrypted back in the encryption using the enhanced RSA algorithm with fake modules. So the security of messages has been improved. As for the results of this study, the key generation process is automatically generated which produces keystream for RCCM and fake public key and fake private key for enhanced RSA. In addition, what distinguishes enhanced RSA from ordinary RSA is the use of four primes p, q, r, and s. as well as the application of chaotic maps to RCCM gives the advantage of the unnecessary use of generating keys manually. With these keys, the process of encryption and decryption can be carried out to generate a secret message. In addition, the program resulting from this research applies the kraitchik factorization algorithm as a medium of attack against the key. The conclusion of this study is that by doing a hybrid cryptosystem enhanced RSA and RCCM can be safer due to the occurrence of double encryption processes. In addition, an enhanced RSA algorithm determines a fake module where the value of X is a prime number so that if attacked using a kraitchik factorization algorithm it is difficult to find a factorization of X values that corresponds to the number of p, q, r, and s.

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Reduction of the dataset of Baitul Mal aid recipients using the principal component analysis (PCA) method to improve C5.0 algorithm classification results

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AIP Conference Proceedings 2024年第1期3222卷

作者： Evi Susanti Sawaluddin Poltak Sihombing Master of Informatics Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and develop. Selection is also needed to prevent aid from being distributed incorrectly. Therefore, the proposed solution is to utilize data mining techniques, especially classification methods (clustering) to identify potential aid recipients who truly deserve to receive aid. This research also uses the Principal Component Analysis (PCA) method to reduce attribute dimensions in the dataset to increase the accuracy of the classification process. By optimizing the classification process through appropriate attribute reduction, this research is expected to make a positive contribution to increasing the efficiency and accuracy of credit risk assessment in the banking sector. The results of this research are: accuracy after the dataset has been reduced by 2 (two) attributes has a higher value when compared to the dataset that has not been reduced, this happens because the attribute reduction process is carried out to reduce attributes that are not or less relevant. So it can be said that the reduction process influences the accuracy of the C5.0 algorithm classification results.

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A Computational Approach for Predicting the Termination of COVID-19

A Computational Approach for Predicting the Termination of C...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Prateek Dutta Abhiroop Sarkar Yash Ambekar Hui Ting Pek F. H. Juwono Gopal Sakarkar Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur M.H India Department of Computer Science G H Raisoni College of Engineering Nagpur M.H India Dept. of Mechatronics and Biomed. Eng Universiti Tunku Abdul Rahman Kajang Selangor Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Johor Malaysia D.Y. Patil Institute of Master of Computer Applications and Management Akurdi Pune M.H India

ISBN: (纸本)9781665486644

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen’s life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field.

关键词： COVID-19 Pandemics Time series analysis Urban areas Sociology Predictive models Prediction algorithms

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