Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

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Development and Field Testing of a Suspended Mulberry Branch Harvesting and Stubble Cutting Machine by Tianhong Liu, Yunpeng Yan, Haitao Sui, Fuyang Tian, Yinfa Yan, Bo Zhao, Zhanhua Song

“…In view of the relatively high damage rate of stubble cutting in the mulberry harvesting operations, this paper has conducted in-depth research and designed a mulberry branch harvesting and stubble cutting test machine. The overall structure and key technical parameters of the machine were determined and the design of critical components was theoretically analyzed. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…This study utilized a dataset of 1750 adsorption isotherms comprising adsorption data for 73 organic materials on 50 biochar samples and 30 polymer resins. Machine learning models were developed using eight input parameters, including five Abraham solvation descriptors, total pore volume (Vt), specific surface area (BET), and equilibrium concentration (logCe), with the output parameter being adsorption degree (logKd). …”
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Precision Electrochemical Micro-Machining of Molybdenum in Neutral Salt Solution Based on Electrochemical Analysis by Yuqi Wu, Guoqian Wang, Moucun Yang, Yan Zhang

“…To minimize interference between factors, an orthogonal experiment was used to optimize the parameter combination, determining the optimal machining process parameters. …”
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DATA MINING PENDIDIKAN: PREDIKSI GAYA BELAJAR MAHASISWA TEKNIK MENGGUNAKAN MACHINE LEARNING by Sumarlin Sumarlin, Dewi Anggraini

“…Hasilnya menunjukkan bahwa model yang disarankan mencapai akurasi klasifikasi sebesar 65–78% dengan hanya empat parameter digunakan: nilai akhir, predikat, program studi, dan jenis kelamin.  …”
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BiAF: research on dynamic goat herd detection and tracking based on machine vision by Yun Hou, Mingjuan Han, Wei Fan, Xinyu Jia, Zhuo Gong, Ding Han

“…In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. …”
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Effect of the Cryogenically Treated Copper Nozzle Used in Plasma Arc Machining of S235 Steel by N. Senthilkumar, G. Jayaprakash, N. Nagaprasad, Krishnaraj Ramaswamy

“…In addition, a grey relational analysis is carried out in order to find the best possible machining settings in both conditions. The parameters that were optimized for a nozzle that had been cryogenically treated were 6 bar of gas pressure, 120 amperes of arc current, and 1800 of cutting speed per minute. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. …”
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A comparison of statistical methods for deriving occupancy estimates from machine learning outputs by Lydia K. D. Katsis, Tessa A. Rhinehart, Elizabeth Dorgay, Emma E. Sanchez, Jake L. Snaddon, C. Patrick Doncaster, Justin Kitzes

“…Abstract The combination of autonomous recording units (ARUs) and machine learning enables scalable biodiversity monitoring. …”
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Comparison of Feature Extraction in Support Vector Machine (SVM) Based Sentiment Analysis System by Imam Fahrur Rozi, Irma Maulidia, Mamluatul Hani’ah, Rakhmat Arianto, Dika Rizky Yunianto, Ahmadi Yuli Ananta

“…This study investigates the use of Support Vector Machine (SVM) for sentiment analysis, comparing three feature extraction techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Word2Vec. …”
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Precision-enhanced deburring using machining force feedback control for various casting materials by Ryuji NAKAGAWA, Masakazu FUJIMOTO, Ryosuke TASAKI

“…Deburring experiments were performed to determine appropriate feedback control parameters. The PI control parameters were determined based on an evaluation of the responsiveness and vibration of the machining reaction force, and the target machining reaction force was determined by evaluating the residual height of the workpiece. …”
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Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques by Surafiel Habib Asefa, Yaregal Assabie

“…Additionally, the proposed model significantly reduces the parameter count from 75 million to just 5.4 million, demonstrating substantial computational efficiency while maintaining high accuracy. …”
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Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach by Iancu-Bogdan Teodoru, Zakaria Owusu-Yeboah, Mircea Aniculăesi, Andreea Vasilica Dascălu, Florian Hörtkorn, Alessia Amelio, Irina Lungu

“…This study integrates systematic laboratory testing with advanced machine learning techniques to predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iași, Romania. …”
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Prediction of laser welding qualities of Al alloys using regression and machine learning techniques by Hemant Kumar, Soumyabrata Chakravarty, Nitesh Kuamr, Nikhil Kumar

“…The key weld quality parameters are ultimate load, weld width and penetration depth. …”
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Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques by Syamsul Risal, Fajar Apriyadi, A. Sumardin, Andini Dani Achmad, Annisa Nurul Puteri

“…Previous studies on stroke prediction have been limited by inadequate handling of class imbalance, lack of comprehensive feature selection, and parameter optimization, with accuracy rates usually below 80%. …”
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Method of Generalized Evaluation in the Selecting Factors and Levels of Variation in the Multifactorial Study of Sowing Machines by E. M. Zubrilina, I. A. Markvo, A. S. Zhuravlev, V. I. Novikov, E. V. Neroda

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Best practices for developing microbiome-based disease diagnostic classifiers through machine learning by Peikun Li, Min Li, Wei-Hua Chen

“…The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. …”
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Pengembangan Model Machine Learning untuk Rekomendasi Produk Berdasarkan Analisis Pola Pembelian by Sondang Sibuea, Yohanes Bowo Widodo

“…Penelitian ini bertujuan untuk mengembangkan model machine learning yang mampu merekomendasikan produk secara personalisasi berdasarkan analisis pola pembelian pengguna. …”
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Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel by Fatemeh Mobasheri, Mostafa Khajeh, Mansour Ghaffari-Moghaddam, Jamshid Piri, Mousa Bohlooli

“…In this work, a microwave-assisted extraction improved using machine learning approaches was used to extract tannins and phenolic compounds from pomegranate peel. …”
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Advancing the Engineering Approach to Improving the Quality Cracking Efficiency of Palm Nut Crackiing Machine by Okunola Oluwatobi Israel, Olukunle Olawale John, Adetola Oluwafemi Adeyemi, Akinfiresoye Waleola

“…The best crop and machine parameter for the optimum performance of the Palm Nutcracker are 29.43 mm and 970 rpm, nut size and machine speed.  …”
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