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RA-ECDM of Silicon Wafers Using Taguchi's Methodology and machine learning algorithms

SILICON 2023年第3期15卷 1511-1526页

作者： Singh, Manpreet Antil, Parvesh Singh, Sarbjit Katal, Nitish Bakshi, Dapinder Kaur Alkesh Punjab State Council Sci & Technol Chandigarh India CCS HAU Coll Agr Engn & Technol Dept Basic Engn Hisar Haryana India Punjab Engn Coll Dept Mech Engn Chandigarh India Indian Inst Informat Technol Sch Elect Una Himachal Prades India

As a prominent machining process, electrochemical discharge machining (ECDM) is used to process materials that are both fragile and difficult to cut. The growing use of this method for the machining of hard to cut materials has paved the way for the discovery of unexploited potential in machining outcomes. In the current study, silicon wafers were machined by rotary assisted electrochemical discharge machining using Taguchi's L-27 orthogonal array, having output process characteristics such as overcut and hole circularity. The optimization through Taguchi's methodology shows that the overcut and circularity of holes improved by 1.40% and 2.44% by using optimal parametric combination as compared to orthogonal array. All output process parameters were again analysed through a machine learning algorithm by determining their root mean squared error (RMSE), R-Squared, mean squared error (MSE), mean absolute error (MAE), prediction speed, and training time. The obtained results show that in regression models of the Gaussian process, overcut and circularity of hole are well predicted by both the exponential and squared exponential regression models. The overcut and circularity of hole models had R-squared values of 0.92 and 0.90, respectively.

关键词： machine learning algorithms RA-ECDM Silicon Wafers Taguchi's Methodology

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Multi-class classification of COVID-19 documents using machine learning algorithms

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2023年第2期60卷 571-591页

作者： Rabby, Gollam Berka, Petr Prague Univ Econom & Business Dept Informat & Knowledge Engn Prague Czech Republic

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classificatio

关键词： Multi-class classification machine learning algorithms Text mining COVID-19

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Rainfall Classification Using machine learning algorithms on Data Mining Platforms

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2025年第2期48卷 109-114页

作者： Turk, Sevtap Istanbul Univ Cerrahpasa Vocat Sch Tech Sci TR-34500 Istanbul Turkiye

Weather conditions directly affect sectors such as agriculture and transport. With climate change, unpredictability is increasing and traditional calculation methods may not be sufficient. In addition to some statistical methods, machine learning algorithms are also used for weather forecasting. This study attempts to classify precipitation using machine learning algorithms on selected meteorological data. The models used are K-nearest neighbors (KNNs), support vector machine (SVM), and multilayer perceptron (MLP). These models were implemented on four different open-source and free data mining platforms. These platforms are Altair AI Studio (formerly Rapidminer), Knime, Orange, and Weka. The dataset includes parameters such as pressure, temperature, humidity, number of rainy days, cloudiness rate, and year and month information. According to the values of these parameters, the data were classified as less rainy, rainy, and very rainy.

关键词： Support vector machines Nearest neighbor methods machine learning algorithms Classification algorithms Rain Data mining Weather forecasting Prediction algorithms Training Humidity Classification machine learning algorithms open-source data mining software weather forecast

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Influence of elevation on plastic-covered greenhouse mapping accuracy using high-resolution satellite imagery and machine learning algorithms

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REMOTE SENSING LETTERS 2023年第4期14卷 413-422页

作者： Veettil, Bijeesh Kozhikkodan Acharki, Siham Van Lang Univ Sci & Technol Adv Inst Lab Ecol & Environm Management Ho Chi Minh City Vietnam Van Lang Univ Fac Appl Technol Sch Technol Ho Chi Minh City Vietnam Abdelmalek Essaadi Univ Fac Sci & Tech Tangier Dept Earth Sci Tetouan Morocco

Proliferation of plastic-covered greenhouse (PCG) farming areas has been unparalleled in recent decades around the globe. Therefore, PCG's proper mapping and continuous monitoring are required to maintain protected farming sustainability and to reduce plastic pollution. Topography is one of the key factors affecting PCG mapping accuracy from satellite imagery. In this research, in order to understand the influence of elevation on PCG mapping accuracy in a Moroccan (with altitude below 200 m) and Vietnamese (an average altitude of 1,500 m) area, five supervised algorithms were applied to high-resolution PlanetScope and RapidEye imagery: maximum likelihood classification (MLC), k-nearest neighbours (k-NN), random forest (RF), support vector machine with linear kernel (SVML) and radial basis kernel (SVMRB). The findings indicated that k-NN (SVMRB) produced the greatest overall accuracy, followed by SVMRB (RF) for Dalat City (Loukkos). The present study also found that RapidEye outperforms PlanetScope in both areas and independently of the machine learning algorithm. Elevation data inclusion significantly improves (1.6% to 6.8%) various satellites' performance, particularly in Dalat City. Thus, this study demonstrated that topography has an impact on PCG mapping, especially in hilly areas.

关键词： High-resolution imagery topography plastic-covered greenhouse protected horticulture machine learning algorithms

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A review and guide on selecting and optimizing machine learning algorithms for daylight prediction

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BUILDING AND ENVIRONMENT 2023年 244卷

作者： Liu, Qiuping Chen, Yaodong Liu, Yang Lei, Yuanfang Wang, Yibo Hu, Pantin Southwest Jiaotong Univ Sch Architecture Chengdu Peoples R China SWJTU Southwest Jiaotong Univ Chengdu Sichuan Peoples R China

Daylight confers extensive benefits for building occupants and improves energy efficiency;thus, its prediction and performance are significant for design decision-making on building and lighting. However, real-time prediction with high precision is difficult since daylight is spatiotemporal correlated and highly dynamic (e.g., periodic, irregularity). In recent years, numerous studies have been focused on machine learning algorithms due to their high generalization ability, robustness, and accuracy in dealing with complex problems. In contrast, selecting the optimal algorithms for a specific research problem is time-consuming and challenging, especially for new learners. This review attempts to develop a systematic guide for algorithms selection and optimization by: (i) discussing the main principles, model structure, and characteristics of 36 of the most representative algorithms;(ii) reviewing and conducting a whole process statistical analysis on the machine learning-based daylight-prediction studies, while abstracting the statistical issues (regression and classification) and prediction tasks (time series, spatial, and spatiotemporal predictions) of different research topics and purposes;and (iii) integrating the findings, developing the selection and optimization methodologies for algorithms. In conclusion, the order of specific machine learning algorithms is highly recommended for different problem types: (i) for regression, Back Propagation Neural Network (recommendation level = 0.82) > Convolutional Neural Network (0.78) > Kernel Support Vector Regression (0.72);(ii) for classification, Kernel Support Vector machine (0.8) > Radial Basis Function Neural Network (0.56) > Random Forest (0.53);(iii) for time series prediction, Back Propagation Neural Network (0.66) > Random Forest (0.61) > Gated Recurrent Unit Network (0.52) = EXtreme Gradient Boosting (0.52);(iv) for spatial prediction, Back Propagation Neural Network (0.82) > Convolutional Neural Network (0.78

关键词： Daylight prediction machine learning algorithms Time-series prediction Spatial prediction Artificial neural network

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Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with machine learning algorithms

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JOURNAL OF EARTHQUAKE ENGINEERING 2023年第9期27卷 2533-2554页

作者： Coban, Kaan Hakan Sayil, Nilgun Karadeniz Tech Univ Engn Fac Dept Geophys Engn Trabzon Turkey

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M >= 4.0) that occurred in Turkey (1900-2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.

关键词： machine learning algorithms regression trees support vector machines magnitude Regression

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GIS-based landslide susceptibility mapping of the Meghalaya-Shillong Plateau region using machine learning algorithms

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2023年第5期82卷 1-19页

作者： Agrawal, Navdeep Dixit, Jagabandhu Shiv Nadar Univ Disaster Management Lab Delhi NCR Gautam Buddha Nagar 201314 Uttar Pradesh India

Landslides are a common geological hazard causing impairment of public works and loss of lives worldwide and in India, especially in the Himalayan region. The present study aims to map the landslide susceptibility for the Shillong Plateau region of India using different machine learning algorithms, namely artificial neural network (ANN), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) and provides insights into influential factors, with a focus on disaster risk reduction. For this purpose, the geospatial database containing 15 landslide conditioning factors related to regional geo-environmental settings and a landslide inventory with 1330 locations are prepared. The landslide susceptibility maps (LSM) reveal that the south-southeastern portion of Meghalaya, mainly slopes along the southern escarpment, are more susceptible to landslides. The model robustness is demonstrated using the area under the receiver operating characteristic curve (AUC), F1-score, kappa, and other statistical metrics. The XGBoost and RF machine learning models with AUC = 0.971 have shown the best performance, followed by SVM (0.958), KNN (0.951), and ANN (0.945), which is consistent with other applied statistical parameters and higher than the traditional MCDA methods. However, the problem of overestimation is observed in the case of ANN and XGBoost models. The generated LSMs will assist decision-makers and planners in identifying high-risk areas, prioritizing mitigation measures, and guiding regional development.

关键词： Landslide susceptibility assessment Landslide risk machine learning algorithms Geospatial analysis Northeast India

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A Smart Gas Sensor Using machine learning algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

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IEEE SENSORS JOURNAL 2023年第11期23卷 11336-11355页

作者： Nasri, Abdelghaffar Boujnah, Aicha Boubaker, Aimen Kalboussi, Adel Icam F-31300 Toulouse France Univ Monastir Dept Phys Fac Sci Monastir 5000 Tunisia

Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Currently, smart, fast, and high sensitivity with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigital electrodes (IDEs), such as electrochemical, resistive, capacitive, and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, and their advantages and limitations are presented. In addition, a comparison between different ML and AI algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart ML algorithms, which provide efficient data processing methods, for the design of smart gas sensors that are highly selective. In addition, the challenges and limitations of ML in gas sensor applications are critically discussed. The study shows the importance of ML with the need for structural optimization to develop and improve smart, sensitive, and selective sensors.

关键词： Sensors Gas detectors machine learning Intelligent sensors Electrodes Sensor phenomena and characterization machine learning algorithms algorithms IDEs structures machine learning (ML) materials selectivity sensitivity smart gas sensor

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Prediction of spring agricultural drought using machine learning algorithms in the southern Songnen Plain, China

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LAND DEGRADATION & DEVELOPMENT 2023年第13期34卷 3836-3849页

作者： Chen, Xiuxue Li, Xiaofeng Jiang, Bo Su, Jiajia Zheng, Xingming Wang, Guangrui Chinese Acad Sci Northeast Inst Geog & Agroecol 4888 Shengbei St Changchun 130102 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun Jingyuetan Remote Sensing Expt Stn Jilin Daan Agroecosyst Natl Observat Res Stn Changchun Jilin Peoples R China Moisture Monitoring Ctr Jilin Prov Changchun Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China

Winter climate conditions have a great effect on spring soil moisture (SM) in cold regions. The frequent occurrence of spring drought events in the Songnen Plain poses a significant threat to food security. In this study, we selected the random forest (RF) algorithm from three currently popular machine learning algorithms to predict the spatial pattern of average SM for the period of April 1-15 and April 16-30 with 1 km resolution in the dry cropland of southern Songnen Plain (SSNP) using 0-10 cm SM data for each April from 81 agro-meteorological stations and winter climate data (e.g., temperature, snowfall), soil properties and topographic relief. Compared to the existing ERA5_Land and SMAP SM products, we obtain higher precision SM data (April 1-15: r = 0.74, RMSE = 0.050 m(3) m(-3);April 16-30: r = 0.73, RMSE = 0.051 m(3) m(-3)). Drought grades were classified based on predicted SM data, and the results indicate agricultural drought was mainly influenced by the change of winter snowfall/snow depth, with western SSNP being more susceptible to drought because of soil properties. Compared with current SM data products, the RF model proposed in this study can implement more accurate prediction of spring soil drought based on winter climate, provide important information for agricultural management departments to prepare for spring cropping and irrigation, and avoid further soil salinization caused by drought.

关键词： cropland machine learning algorithms soil moisture Songnen plain spring agricultural drought

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Evaluation of Tree-Based machine learning algorithms for Network Intrusion Detection in the Internet of Things

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IT PROFESSIONAL 2023年第5期25卷 45-56页

作者： Essa, Mohamed Saied Guirguis, Shawkat Kamal Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Informat Technol Alexandria 21526 Egypt Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Comp Sci & Informat Alexandria 21526 Egypt

The Internet of Things (IoT) is receiving increasing attention from academia and industry. However, improving the security of the IoT environment is critical for fostering trust in it and contributing to its growth in the manufacturing market. This study comparatively analyzes current methods for detecting intruders and malicious activities in IoT networks by introducing tree-based machine learning algorithms. It presents a research gap analysis of the current literature. Furthermore, an empirical evaluation study is presented to explore the potential of tree-based approaches to detect intruders in IoT networks. It compares the performance of bagging and boosting techniques in botnet detection by conducting an extensive experimental benchmarking.

关键词： Industries machine learning algorithms Botnet Network intrusion detection Benchmark testing Boosting Manufacturing

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