Polarimetric Image Discrimination With Depolarization Mueller Matrix by Pengcheng Wang, Qian Chen, Guohua Gu, Weixian Qian, Kan Ren

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Deep Learning Berbasis CNN Untuk Pengenalan Pola Partial Discharge Isolasi Silicone Rubber by Ferlian Seftianto, Sukemi Sukemi, Zainuddin Nawawi

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Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning by Hsiao-Chung Wang, Teng-To Yu, Wen-Fei Peng

“…These defects were inspected using machine vision, confocal microscopy, optical and scanning electron microscopy, acoustic/ultrasonic methods, and inline monitoring and coaxial vision. …”
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Integrating Handcrafted Features with Machine Learning for Hate Speech Detection in Albanian Social Media by Fetahi Endrit, Hamiti Mentor, Susuri Arsim, Zenuni Xhemal, Ajdari Jaumin

“…This suggests that there is no clear pattern for the machine learning models to accurately flag the comments, indicating that Albanian is linguistically challenging to analyze.…”
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Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification by Yiheng Chen, Jiawang Hu, Nanlin Hu, Shikai Wu, Yuan Lu

“…In addition, the sensor array successfully distinguishes 14 odor molecules common in life by pattern recognition algorithms. Eventually, with the assistance of ML, the IISP exhibits 89.2% accuracy in detecting different food odors. …”
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Integrating non-target analysis and machine learning: a framework for contaminant source identification by Peng Liu, Ding Pan, Xin-Yi Jiao, Ji-Ning Liu, Peng-Hui Du, Peng-Cheng Li, Meng-Zhu Xue, Yan-Chao Jin, Cai-Shan Wang, Xue-Rong Wang, Ying-Zhi Ding, Guang-Ning Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xin-Hui Liu, Li-Ping Li

“…Abstract Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. …”
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Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors by Juan Carrillo-Ríos, Ignacio González-Prieto, Ángel González-Prieto, Mario J. Durán, Juan José Aciego

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CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING by Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas

“…The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. …”
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Leader election of dynamic wireless intelligent control machine in sensor network distributed processing by Eko Sediyono, Ferry Wahyu Wibowo, Hindriyanto Dwi Purnomo

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An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine by Xinman Zhang, Qi Xiong, Yixuan Dai, Xuebin Xu, Guokun Song

“…Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. …”
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Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau by Yan Li, Yilong Yu, Shiyuan Ding, Wenjing Dai, Rongguang Shi, Gaoyang Cui, Xiaodong Li

“…This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorization (PMF), Incremental Spatial Autocorrelation, and Bivariate Local Indicators of Spatial Association (BiLISA), to analyze the ecological risk of soil HMs in representative watersheds of the southeastern Tibetan Plateau, focusing on spatial pattern clustering, pollutant source identification, and interaction risk assessment. …”
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Machine learning classification and driver analysis of diel variability in dissolved oxygen in Taihu Lake by Tingting Luo, Yehui Zhang

“…The results underscore the need to account for DO pattern heterogeneity in prediction and management and offer new tools for developing targeted water quality strategies in eutrophic lake systems.…”
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A novel machine learning based approach for iPS progenitor cell identification. by Haishan Zhang, Ximing Shao, Yin Peng, Yanning Teng, Konda Mani Saravanan, Huiling Zhang, Hongchang Li, Yanjie Wei

“…The results also confirm that the morphology and motion pattern of iPS progenitor cells is different from that of normal MEFs, which helps with the machine learning methods for iPS progenitor cell identification.…”
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Machine learning-based detection of medical service anomalies: Kazakhstan’s health insurance data by Maksut Kulzhanov, Alexander Wagner, Abylkair Skakov, Iliyas Mukhamejan, Saya Zhorabek, Ainur B. Qumar

“…These models reliably detected irregularities such as billing duplications, out-of-pattern service provision, and inconsistencies with demographic profiles. …”
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Chemical Space Exploration and Machine Learning-Based Screening of PDE7A Inhibitors by Yuze Li, Zhe Wang, Shengyao Ma, Xiaowen Tang, Hanting Zhang

“…The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest–Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. …”
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Optimizing Reservoir Separability in Liquid State Machines for Spatio-Temporal Classification in Neuromorphic Hardware by Oscar I. Alvarez-Canchila, Andres Espinal, Alberto Patiño-Saucedo, Horacio Rostro-Gonzalez

“…Specifically, our approach addresses two problems: Frequency Recognition (FR) with five classes and Pattern Recognition (PR) with four, eight, and twelve classes. …”
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Mapping China Aquaculture Ponds: Integrating a New Aquaculture Index With Machine Learning by JianChun Chen, Chen Lin, Kun Xue, Ke Song, ZhiGang Cao, RongHua Ma, DanHua Ma, YiJun Tong

“…(b) At the national scale, the spatial distribution of AP shows a pattern of higher density in the north and lower density in the south, with more AP in the east than in the west. …”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Second, a support vector machine (SVM) is used to classify gait patterns between unilateral hip OA patients and HCs. …”
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MODELING AND ANALYSIS OF DYNAMICS IN BEARING SYSTEM OF DRILLING, MILLING AND BORING MACHINE WITH MONO-COLUMN by Yu. V. Vasilevich, S. S. Dovnar, A. S. Truskovsky, I. I. Shumsky

“…FEM-analysis of dynamics in the bearing system of a heavy-duty machine tool has been carried out in the paper. This is a prospective variant for a large-size multi-purpose machine tool with a horizontal sliding ram. …”
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