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Triboinformatic Approaches for Composite Coatings on Titanium Alloys 1

11th International Conference on Industrial Tribology, IndiaTrib-2022

作者： Naveen Kumar, K. Barman, Utpal Masset, Patrick J. Rao, T.V.V.L.N. Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai600036 India Faculty of Computer Technology Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India Lukasiewicz Research Network Krakow Institute of Technology ul. Zakopiańska 73 Kraków30-418 Poland Faculty of Engineering Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India

ISBN: (数字)9789819992645

ISBN: (纸本)9789819992638

Ti-6Al-4 V is an (α + β) titanium alloy that has been most widely used in automotive, aerospace, and biomedical applications due to the extensive material properties of high strength, toughness, high strength-to-weight ratio, and biocompatibility. Machine learning (ML) algorithms of data-driven methods provide a better understanding of the correlation between material properties and tribological properties. Correlations of tribological test variables (sliding speed, sliding distance, and normal load) with the tribological properties (coefficient of friction and wear rate) were studied using machine learning algorithms. A total of 41 data points based on the Ti-6Al-4 V alloy coating were divided into training and testing sets in the ratio of 80:20. The ML-based algorithms, which include Decision Tree (DT) and Random Forest (RF) algorithms, have been studied to predict the wear rates and coefficient of friction. The evaluation metrics like MAE, MSE, and RMSE are used to find the best suitable algorithm for the predictions. Using the data analysis, the coefficient of friction and wear rates have been satisfactorily predicted from the considered data sets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tribology

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ZnO Nanorods Coated Glass Substrate Based on Side Coupling Technique for Lactose Sensing Application

ZnO Nanorods Coated Glass Substrate Based on Side Coupling T...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Marlina Jahava Mohd Hafiz Jali Md. Ashadi Md Johari Mohd Adili Norashikin Haziezol Helmi Mohd Yusof Aminah Ahmad Faculty of Electrical Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Information Technology and Computer Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia

A study has been conducted to investigate the sensing performance of zinc oxide (ZnO) nanorods coated glass substrate using the side coupling approach for lactose sensing application. The concentration of lactose solution was varied from 0% to 100% in order to examine the performance of the proposed sensor, which is influenced by the scattering and absorption of light. Coated glass substrates with optical side coupling could solve the lower output coupling voltage existed in the recent approach. Experimental results show that the proposed approach improved by a factor of 2.5 as compared to the uncoated glass substrate when exposed to variation concentration of lactose solution. The average sensitivity of the sensor was observed to be 0.0201 V/%Concentration throughout the tested %Concentration levels. The utilization of affordable and uncomplicated sensors allows for the precise identification of alterations in the refractive index solution, hence presenting potential applications in the domains of environmental and optical sensing.

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Development of Mobile Application Monitoring for Thai Food and Beverage Based on Portable Near-Infrared Spectroscopy technology

Development of Mobile Application Monitoring for Thai Food a...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Natthapon Pannurat Montri Phothisonothai Thanat Sooknuan Saran Kampeephat Wichupong Wiboonjaroen Department of Computer Engineering Faculty of Engineering at Sriracha Kasetsart University Sriracha Chonburi Thailand Faculty of Engineering and Technology Rajamangala University of Technology Isan Nakornrachasima Thailand

The paper aims to propose “Portable NIRS technology for Thai Food Industry” with applications using non-destructive based technology called Near-Infrared Spectroscopy (NIRS) for determining whether the sampled food is expired or not. This proposed NIRS technology will be applicable especially for the agricultural and food industries. The basic process of the proposed system can be divided into the following two parts, i.e., 1) a sensor device that detects samples and 2) a mobile application developed for connecting to the NIRS device. The binary k-nearest neighbors algorithm (k-NN) is proposed to classify the five common Thai local samples, i.e., soy milk (SM), fresh milk (FM), salad dressing (SD), corn milk (CM), and banana in coconut milk (BM). The output results are classified into two classes, i.e., Unexpired (Un) and expired (Ex), based on the wavelength measuring from the proposed NIRS system.

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A Hybrid Three-Port Topology for Urban Charging Stations

A Hybrid Three-Port Topology for Urban Charging Stations

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Mohammadreza Khodaparast Klidbari Naser Souri Zahra Sadat Habibolahi Hamid Montazeri Hedeshi Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Bradley Department of Electrical and Computer Engineering Virginia Tech United States Department of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96% peak efficiency.

关键词： Switching frequency Soft switching Prototypes Switches DC-DC power converters Zero voltage switching Charging stations Electric vehicle charging Topology Complexity theory

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Brain Tumor Segmentation from 3D MRI Scans Using U-Net

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SN computer Science 2023年第4期4卷 386页

作者： Montaha, Sidratul Azam, Sami Rakibul Haque Rafid, A.K.M. Hasan, Md. Zahid Karim, Asif Department of Computer Science and Engineering Daffodil International University 102/1 Sukrabad Mirpur Rd Dhaka 1207 Bangladesh Faculty of Science and Technology Charles Darwin University Ellengowan Drive Casuarina 0909 NT Australia

A fully automated system based on three-dimensional (3D) magnetic resonance imaging (MRI) scans for brain tumor segmentation could be a diagnostic aid to clinical specialists, as manual segmentation is challenging, arduous, tedious and error prone. Employing 3D convolutions requires large computational cost and memory capacity. This study proposes a fully automated approach using 2D U-net architecture on BraTS2020 dataset to extract tumor regions from healthy tissue. All the MRI sequences are experimented with the model to determine for which sequence optimal performance is achieved. After normalization and rescaling, using optimizer Adam with learning rate 0.001 on T1 MRI sequence, we get an accuracy of 99.41% and dice similarity coefficient (DSC) of 93%, demonstrating the effectiveness of our approach. The model is further trained with different hyper-parameters to assess the robustness and performance consistency. © 2023, The Author(s).

关键词： 3D MRI Brain tumor segmentation BraTS dataset U-Net

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Sentiment Analysis of Hashtag Activism on Social Media Twitter

Sentiment Analysis of Hashtag Activism on Social Media Twitt...

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2023 International Conference on computer Science, Information technology and engineering, ICCoSITE 2023

作者： Sidharta, Sidharta Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University BINUS Graduated Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter's API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research. © 2023 IEEE.

关键词： Sentiment analysis

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Machine Learning for Asthma Prediction: Analyzing Health Data for Early Detection

Machine Learning for Asthma Prediction: Analyzing Health Dat...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： TS. Sundresan Perumal Banu Priya.S Kumutha Duraisamy Faculty of Science and Technology Universiti Sains Islam Malaysia Malaysia Department of Computer Science and Engineering Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Asthma is a persistent respiratory condition that significantly affects people worldwide, making the prediction of asthma flare-ups crucial for enhancing patient care and optimizing healthcare resources. Despite progress in the field of machine learning, current predictive models often face challenges due to their dependence on historical clinical data alone. This paper evaluates existing asthma prediction models and introduces a new approach utilizing Bernoulli distribution and logistic regression to enhance prediction accuracy. Current models fre- quently depend on clinical variables such as coexisting conditions, past exacerbations, and healthcare engagement. Widely used, methodologies include decision trees, random forests, and logistic regression; however, these models generally perform poorly when evaluated on varied real-world datasets. A notable drawback is their inability to factor in environmental influences, such as air quality, weather patterns, and pollen counts, all of which play a role in asthma exacerbations. In future research, we will further investigate and refine techniques to better integrate a variety of data sources, aiming to make asthma prediction models more robust, dependable, and suitable for diverse populations.

关键词： Logistic regression Adaptation models Accuracy Atmospheric modeling Machine learning Medical services Predictive models Real-time systems Random forests Asthma

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Lunar Crater Detection based YoloV5 using CCD Data

Lunar Crater Detection based YoloV5 using CCD Data

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2022 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Tang, Kexin Liang, Jiarui Yan, Pengcheng Tian, Xiaolin Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering China Macau University of Science and Technology Lunar and Planetary Science Laboratory Space Science Institute China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

This paper proofs that one-stage object detection neural network of YoloV5 also performs well on lunar crater detection. The experiment is based on CCD data provided by LROC camera carried by NASA's Lunar Reconnaissance Orbiter (LRO), and gets a detection result of nearly 75% on test set without any preprocessing operation on the original image. © 2022 IEEE.

关键词： NASA

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A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification

arXiv

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arXiv 2025年

作者： Farshchiha, Shayli Asoudeh, Salman Kuhshuri, Maryam Shavali Eisaei, Mehrshad Azadi, Mohamadreza Hesaraki, Saba Isfahan University of Technology Department of Electrical and Computer Engineering Faculty of Engineering Isfahan Iran Faculty of Computer College of Technical and Engineering University of Velayat Iran University of Isfahan Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan Iran University of Mazandaran Mazandaran Iran Department of Computer Science Shamsipour technical college Tehran Iran Islamic Azad University Science and Research Branch Faculty of Mechanics Electrical Power and Computer Iran

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patterns is established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score. © 2025, CC BY-SA.

关键词： Lung cancer

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