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K-Means Clustering Model to Discriminate Copper-Resistant Bacteria as Bioremediation Agents

Procedia computer science 2021年 179卷 804-812页

作者： Ika Nurlaila Wahyu Irawati Kartika Purwandari Bens Pardamean Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Department of Biology Education Faculty of Education Pelita Harapan University Tangerang Indonesia 15811 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

Copper (Cu) has been excessively used for some valuable commodities and this creates environmental problems. The inorganic element becomes toxic when presents beyond the recommended tolerated concentration. Bacterial-based remediation is seen to be an excellent tool to overcome it as it reduces the copper contamination without yielding any other forms of contamination. There are some pivotal properties in the bacteria render them being considered as bioremediation agents against coppers contamination, namely bioaccumulation and biosorption. In the present study, we question if these bacteria could be clustered into a strong and representative proximity according to their functional properties. Mostly, bacteria are grouped based on their genetic profiles derived from the 16S rRNA sequencing. We propose that our K-Means clustering model can be employed to identify genetically-unlabelled bacteria. But first, a prominent reference should be developed and we are in this phase. We figured out the K-Means clustering model do not pull the same-genus bacteria into the same cluster. Instead, the model gathers into a proximity those isolates with similarity on a functional characteristic termed minimum inhibitory concentration (MIC), regardless their origins and their hierarchy in taxonomy.

关键词： Copper-Resistant Bacteria K-Means Minimum Inhibitory Concentration

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

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The next cryptocurrency price movement prediction application uses patterned datasets

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AIP Conference Proceedings 2024年第1期3116卷

作者： Rizky Parlika Mustafid Basuki Rahmat Diponegoro University Information Systems Doctoral Study Program Graduate School 50241 Jl. Imam Bardjo SH Pleburan Semarang Indonesia University of Pembangunan Nasional “Veteran” Jawa Timur Department of Informaton Technology Faculty of Computer Science 60294 Rungkut Madya Gunung Anyar Surabaya Indonesia

Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin. However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement. This is an exciting reason to conduct further research. A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue. These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application. The experiment’s results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale. It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading. Even though the initial data source comes from Indonesia’s most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally. The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets. At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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Annotated data for semantic role labeling of crisis events in Indonesian Tweets

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Data in Brief 2025年 61卷

作者： Amelia Devi Putri Ariyanto Diana Purwitasari Bilqis Amaliah Chastine Fatichah Muhammad Ghifari Taqiuddin Haikal Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Information Technology and Systems Study Program Widya Husada University Semarang 50146 Indonesia Department of Public Health Faculty of Health Science Universitas Dian Nuswantoro Semarang 50131 Indonesia

Social media platforms like Twitter provide essential real-time information about crisis events. Although the text data generated is rich, its vast volume and unstructured format make manual analysis challenging. information extraction technologies such as Semantic Role Labeling (SRL) are needed to identify a sentence's semantic roles, such as who is the victim, what happened, when and where the event occurred, and what objects are affected in the crisis text to speed up and facilitate the emergency response process. However, the availability of public SRL datasets, especially for Indonesian, still considered a low-resource language, is very limited. We aim to develop an Indonesian-language SRL dataset based on Twitter text focusing on crisis events. This dataset includes entity labels for Named Entity Recognition (NER), another information extraction technique besides SRL. Text data was obtained through a crawling process on Twitter using specific keywords from 2018–2023, then preprocessed to obtain clean and relevant data for crisis events in Indonesia. The cleaned text data was then manually annotated by two experts based on guidelines designed to maintain consistency, resulting in 99,206 tokens labeled with SRL and NER. The high inter-annotator agreement value (Cohen's Kappa >0.90) indicates reliable data quality. This dataset is designed to support the development of automated models for information extraction, such as SRL and NER. The results of this extraction will be used for disaster impact analysis, mapping affected areas, and planning for crisis mitigation. By providing this dataset, the research opens up new opportunities for developing Natural Language Processing (NLP) in Indonesian, especially in crisis event analysis.

关键词： Semantic role labeling Named entity recognition Crisis event Twitter data Low-resource languages Indonesian Tweets

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Evaluation of E-government services based on social media using structural equation modeling 4

Evaluation of E-government services based on social media us...

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4th International Conference on information Management and Technology, ICIMTech 2019

作者： Nindito, Hendro Madyatmadja, Evaristus Didik Dian Sano, Albert Verasius Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Study Program Bina Nusantara Institute of Creative Technology Malang65126 Indonesia

ISBN: (纸本)9781728133331

Today many local governments have provided aspiration services or social media based complaints about the community to participate in giving aspirations and report problems that occur around the area. To improve the usefulness of this service, we conduct research with the aim of evaluating and identifying the factors that affect the service's application. The impact of this study is that the government can provide services according to the wishes of the community, because the model found can be a reference for the government to increase public trust and satisfaction for users of government services. The influence on the adoption of Aspiration Services is explained by using perceptions of trust, attitudes, and intentions of use that refer to technology acceptance models (TAM). Data processing and analysis in our research using the SEM-PLS method (Structural Equation Model-Partial Least Square). The results in this research will present that services applying technology that gain a positive effect on the awareness of usability and benefit for service users. © 2019 IEEE.

关键词： Least squares approximations

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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Use of the Web API as a basis for obtaining the latest data on bitcoin prices at 30 exchange places

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IOP Conference Series: Materials science and Engineering 2021年第1期1125卷

作者： Rizky Parlika Arista Pratama Informatics Engineering Study Program Faculty of Computer Science National Development University 'Veteran' East Java Information Systems Study Program Faculty of Computer Science Raya Rungkut Madya Road Gunung Anyar Surabaya

Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.

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Application for Easy Organizing of Event Organizer

Application for Easy Organizing of Event Organizer

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Nizirwan Anwar Wesley Rico Darmawan Gian Wellrich Nathaniel Pranata Sutajaya Muhamad Ali Furqon Harco Leslie Hendric Spits Warnars Faculty of Computer Science Esaunggul University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia Civil Service Police Unit Tangerang Indonesia KelurahanCimone Jaya Kota Tangerang Tangerang Indonesia BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728185194

ISBN: (纸本)9781728185200

These days, Indonesia has entered the era of Industry 4.0 which is marked by the development of all-digital technology and automation. With the increasing rate of technology development that changes people's lives, people are becoming fond of anything fast and instant. With the help of these technological advancements, we create an application that makes it easier for users that are willing to hold events. This application connects 2 entities, which are users and an event organizer. The event organizer can easily find their market and other hands easy as well for the user for finding the market for event organization activities. The business processes in this application were designed with a use case diagram, and the tables in the database were designed with the class diagram and the menu of the application with User Interface (UI). However, this proposed application just only an introduction and needs to be explored for those who are interested to explore entrepreneurship in these areas.

关键词： Organizations User interfaces Mobile applications Reliability Artificial intelligence Informatics Recommender systems

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Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

arXiv

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arXiv 2024年

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Orsmaa, Lotta Kangas, Jari Rasouli, Nastaran Raisamo, Roope Hirvonen, Jussi Mehtonen, Helena Järnstedt, Jorma Mäkitie, Antti Naser, Mohamed Fuller, Clifton Kann, Benjamin Kaski, Kimmo Department of Computer Science Aalto University School of Science Espoo Finland Faculty of Information Technology and Communication Sciences Computing Sciences University of Tampere Tampere Finland Department of Radiology Tampere University Faculty of Medicine and Health Technology Tampere University Hospital Tampere Finland Department of Otorhinolaryngology-Head and Neck Surgery Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki University Hospital Helsinki Finland Department of Radiation Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School BostonMA United States The Alan Turing Institute British Library 96 Euston Rd LondonNW1 2DB United Kingdom

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 ± 0.152 without user interaction and 0.824 ± 0.099 after five interactions, outperforming existing methods in both cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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