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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Secure E-Voting System for Student Associations in Private University Using Paillier Algorithm

Secure E-Voting System for Student Associations in Private U...

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Student Conference on Research and Development (SCOReD)

作者： Ivan Tenusa Eunike Endariahna Surbakti Fenina Adline Twince Tobing Adhi Kusnadi Informatics Study Program Informatics and Engineering Faculty Universitas Multimedia Nusantara

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The representatives of student associations at Private University are entrusted with the responsibility of empowering, facilitating, and nurturing the growth of the students, lecturers, and alumni within their respective programs. If the elected representatives do not align with the general consensus, problems are bound to arise. Therefore, student participation during the annual voting period is of utmost importance. When voting participation is low, the risk of the majority voice not being heard increases. Data from the General Election Commission in the private university reveals that among the four student associations in the Tech and IT Faculty, three of them have a participation rate lower than 50%.This research aims to develop an E-Voting system for Student Associations within the university, with the intention of replacing the currently utilized Google Form system and enhancing student participation in future voting periods. While constructing an electronic voting system, the security and confidentiality of participants are vital. The Paillier homomorphic encryption algorithm is employed in the system to encrypt and decrypt voting messages sent by voters, ensuring their confidentiality. This algorithm was chosen due to its relative recency, speed, and relevance to this research in comparison to other algorithms. A user acceptance test conducted using the Technology Acceptance Model (TAM) has demonstrated that the application has been successfully developed, with user assessments of 87.50% for perceived ease of use, 72.27% for perceived usefulness, 74.74% for behavioral intention, and 86.46% for actual usage.

关键词： Technology acceptance model Electronic voting systems Internet Cryptography Electronic voting Homomorphic encryption Research and development

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Reducing Dependency on Imported Electricity: Utilizing Renewable Energy and Minimizing Fossil Fuel Power Plant Operations in West Kalimantan

Reducing Dependency on Imported Electricity: Utilizing Renew...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Akbar, Ilham Zakaria, Ariesa Budi Muljadi, Eduard Suwarno Hariyanto, Nanang Corporate University Graduate Program PLN-ITB Pln Auburn United States Auburn University Auburn United States School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376067

This paper discusses West Kalimantan's dependency on imported electricity and fossil fuels and advocates for integrating renewable energy sources, especially solar power. With abundant solar resources and its equatorial location, West Kalimantan presents a significant solar PV generation opportunity. However, its dependence on imported electricity from the ASEAN Grid initiative poses risks to energy security. By promoting solar power as an alternative energy source and reducing reliance on fossil fuels, the paper aims to improve energy independence and sustainability. Integrating solar PV requires assessing frequency stability to ensure system resilience. Moreover, the interconnection between the two grids enhances system resilience and facilitates renewable energy integration. In summary, integrating solar PV power plants into the West Kalimantan system aims to decrease dependence on electricity imports from Sarawak while preserving the system's frequency stability. © 2024 IEEE.

关键词： Frequency stability

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Natural Language Interface to Database (NLIDB) for Interrogative Sentences with Temporal Elements in Indonesian Language

Natural Language Interface to Database (NLIDB) for Interroga...

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2022 International Conference on Data and Software Engineering, ICoDSE 2022

作者： Daniel Thamrin, Hansel Grady Widagdo, Tricya Esterina Undergraduate of Informatics Study Program Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

Natural Language Interface to Database (NLIDB) is a solution that allows users to retrieve data from databases in natural language. Various studies were conducted to translate Indonesian sentences into database queries, but these studies still have weaknesses. In this study, we propose the translation of interrogative sentences in the temporal database accompanied by the addition of several components that can improve the ability of NLIDB. The translation process is carried out by first masking the input sentence and decomposing the sentence into a collection of syntax trees. Then, the existing parsing tree is selected and the best parsing tree will be selected. Then, the process is continued with the analysis of the query components in the interrogative sentences. The results of the analysis will be translated into parts of database query and parts of database query will be rearranged into database queries. This query will be executed and the result can be evaluated by the user and the user can provide feedback according to the system output. The built NLIDB system can translate interrogative sentences for what, who, when, where, how, if, and how many questions with the types of queries that can be handled are current state, time-slice, sequenced, and non-sequenced. © 2022 IEEE.

关键词： Syntactics

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Building Machine Learning Models to Improve Hypertension Diagnosis 15

Building Machine Learning Models to Improve Hypertension Dia...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Haynatzki, Roman Windle, Thomas Windle, John Biomedical Informatics Program University of Nebraska Medical Center 42nd and Emile St OmahaNE68198 United States

Hypertension (HT) is a major risk factor for heart disease, stroke, and kidney disease. A diagnosis of HT is based on two or more blood pressure (BP) readings taken on two or more visits, and such diagnosis may vary depending on the threshold BP values utilized by specific guidelines. Our hypertension identification approach has been recently proposed and validated for HT diagnosis and could be used as ground truth in modeling of latent HT diagnosis. Our approach to modeling of latent HT diagnosis with high precision will leverage analytics to "Big Data", such as electronic health records (EHRs). In this work, we will review the time complexity underlying the classical ML methods XGBoost (XGB) and Artificial Neural Networks (ANN). In particular, we compare the XGB and ANN to leverage their strengths. The performance of all algorithms for diagnosing HT will be characterized using the area under the curve (AUC) approach on a big EHR longitudinal dataset. Predictor variable importance and model interpretability will be assessed using the Shapley values approach. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Internet of Things for Real-Time Multi Room Monitoring System 1

Internet of Things for Real-Time Multi Room Monitoring Syste...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Tae, Kim Sung Suhartomo, Antonius Vincent President University Study Program of Electrical Engineering Bekasi Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350306156

Electronic devices have become an integral component in our daily activities. In fact, the consumption of electrical energy in households reached 49% in 2018, and is expected to increase to 58% in 2050. Concurrently, the LPG is the second highest consumed energy in the household and is expected to increase to 37% in 2050. However, the devices utilizing these energies could lead to a danger such as housefire if not operated and treated carefully. An Internet of Things (IoT)-based smart home monitoring system would solve this problem, especially by effectively monitoring several parameters in the house simultaneously. In this study, a system is built to monitor and control three rooms in the house independently and simultaneously. The sensors and/or actuators in all rooms are controlled by NodeMCU ESP8266s. All microcontrollers are linked via the Internet to a local server, where all data is analyzed, saved, and transmitted to the user using the MQTT protocol. The monitoring is done by the user via MQTT dashboard in the smartphone. Following testing, the three microcontrollers are fully capable of exchanging data with the user's smartphone. The humidity changed by 1%, while the temperature changed by 0.1 °C. Perfect data interchange is possible between the relays, flame, gas, and movement activity sensor. © 2023 IEEE.

关键词： Informatization

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ECO-LÓGICA: A prototype for carbon dioxide pollution level prediction

ECO-LÓGICA: A prototype for carbon dioxide pollution level ...

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2024 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2024

作者： Boneth, Guillermo Augusto Duran Costaguta, Rosanna Rico-Bautista, Dewar University Francisco de Paula Santander Systems Engineering Program Ocaña Colombia National University of Santiago Del Estero Institute for Research in Informatics and Information Systems Santiago del Estero Argentina University Francisco de Paula Santander Department of Systems and Informatics Ocaña Colombia

ISBN: (纸本)9798331516901

This paper describes the ECO-LÓGICA prototype, a useful tool for the decision-making process related to air pollution. The ECO-LÓGICA system can generate a wide range of reports, including those based on real data collected through the monitoring of carbon dioxide emissions. Additionally, it employs a specialized neural network to predict future air pollution values resulting from these emissions. The performance of the prototype was validated using data supplied by three Colombian environmental monitoring stations. The applied metrics reach values close to the maximums, demonstrating adequate performance. © 2024 IEEE.

关键词： Environmental monitoring

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Optimizing Advantage Actor-Critic with Policy Gradient and Deep Q-learning to Maximize Profit in Forex Trading Prediction 1

Optimizing Advantage Actor-Critic with Policy Gradient and D...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Baradja, Abdillah Gernowo, Rahmat Wibowo, Adi Universitas Diponegoro Doktoral Program of Information System Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350306156

Financial markets, such as forex markets, are inherently complex, with high volatility, noise, trends, and market shocks. Therefore, designing effective trading strategies is crucial for maximizing investment returns. The implementation of deep reinforcement learning (DRL) in financial trading has become an attractive research topic in recent years. This study proposes Advantage Actor-Critic-based algorithms (A2C) algorithms to learn forex trading strategies using deep reinforcement schemes. The ensemble trading strategy was obtained by combining the best features of the two algorithms' Policy Gradient as Actor and Q-learning as Critic, resulting in a robust strategy that adapts to different market situations. Our experiment on the EURUSD currency on the 4 hour candlestick timeframe using five years of data history was divided into three parts for training, validating, and testing. The A2C agent learns to buy, sell, and hold a trade with the goals of maximizing profits and reducing losses. Our proposed method provides an effective and robust solution to various financial trading problems using deep reinforcement learning. Our proposed methods provide effective and robust solutions to various financial trading problems using deep reinforcement learning. The evaluation results improved from each training, where the Cumulative Return evaluation increased from 29.65 to 112.74, the Maximum Drawdown reduced from 36.18% to 4.48%, the Sharp Ratio increased from 0.94 to 2.47%, and the AHPR increased from 0.19% to 3.56%. This indicates that longer training data result in higher cumulative rewards and an increase in total profit. © 2023 IEEE.

关键词： Reinforcement learning

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Automatic quantitative and morphometric analysis of muscle fibers 30

Automatic quantitative and morphometric analysis of muscle f...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Da Silva, Sergio A. Santos, Eduardo O. Carvalho, Mateus F. T. Flores, Franklin C. Almeida, Fernanda L. A. Costa, Cecilia E. M. Costa, Yandre M. G. State University of Maringá Department of Informatics Maringá Brazil State Univerity of Maringá Graduate Program in Physiological Sciences Maringá Brazil State University of Maringá Graduate Program in Biological Sciences Maringá Brazil

ISBN: (纸本)9798350337297

In this work, we propose an automated solution to support researchers of histological sciences labs on the analysis of muscle fibers microscopic images. For this purpose, we developed a dataset carefully curated composed of semi-seriated muscle samples. The manual annotation of these images is laborious and tiring, and the results may become compromised as the human gets tired. Thus, we propose a framework to address this problem which starts with a preprocessing step, aiming to mitigate discrepancies regarding the color variation frequently found in this type of image, followed by the use of Mask R-CNN, to automatically perform muscle fibers segmentation and also to extract the smallest diameter of these fibers, which is an important measure for further analysis. Experiments showed that there is no statistically significant difference between the results obtained manually and those obtained using our proposal. The dataset used in the experiments is also a contribution of this work. © 2023 IEEE.

关键词： Muscle

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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