DEVELOPMENT AND EVALUATION OF RAISED BED MACHINE TO SUIT FABA BEAN PLANTING by Tarek FOUDA, Abeer ABDELSALAM, Atef SWILAM, Mohamed El DIDAMONY

“…Linear regression analysis were performed to predict the operating parameters for the raised bed machine the machine at different forward speeds during different planting distances.…”
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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Machine learning approaches for forecasting compressive strength of high-strength concrete by Mohammed Shaaban, Mohamed Amin, S. Selim, Islam M. Riad

“…This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. …”
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Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning by Wan-Peng Sun, Ji-Guo Zhang, Yichao Li, Wan-Ting Hou, Fu-Wen Zhang, Jing-Fei Zhang, Xin Zhang

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A machine learning approach to designing and understanding tough, degradable polyamides by Yoshifumi Amamoto, Chie Koganemaru, Ken Kojio, Atsushi Takahara, Sayoko Yamamoto, Kazuki Okazawa, Yuta Tsuji, Toshimitsu Aritake, Kei Terayama

“…In this study, we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 and α-amino acid segments that are mechanically tough and degradable. …”
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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification by Sylvio Barbon, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, Douglas Fernandes Barbin

“…Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. …”
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Analysis of soil suitability for agricultural needs using machine learning methods by Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly, Boyko Andrey, Volneikina Ekaterina

“…This study explores the application of machine learning methods to assess soil suitability for agricultural purposes, focusing on identifying and analysing key factors that influence soil productivity under drought conditions. …”
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Prediction and diagnosis of cardiovascular disease using cloud and machine learning design by K. Babu, A. Gokula Chandar, S. Kannadhasan

“…In order to assess how effectively they work, evaluation parameters are utilised.…”
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Machine learning algorithms to predict the tensile strength of novel composite materials by S. Sathees Kumar, P. Shyamala, Pravat Ranjan Pati

“…A major challenge in their development is the lack of experimental tensile strength data for new formulations. This study develops machine learning (ML) models to predict NFRP tensile strength using publicly available datasets containing parameters such as epoxy group content, density, elastic modulus, curing agent amount, resin consumption, surface density, and matrix–filler ratio. …”
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Design of An Appropriate Sunflower Threshing and Cleaning Machine for Small Scale Farmers. by Kabutetsi Charity

“…The researcher, however, recommended that the fabrication of the machine needs to be done based on the individual part parameters developed and performance testing of the machine to embrace the new technology for effective threshing.…”
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Design of an Appropriate Sunflower Threshing and Cleaning Machine For Small Scale Farmers. by Kabutetsi Charity

“…The researcher, however, recommended that the fabrication of the machine needs to be done based on the individual part parameters developed and performance testing of the machine to embrace the new technology for effective threshing.…”
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Dynamic bandwidth allocation with machine learning in dense WiFi network by Ricardo Alvarado, Bayron Opina, Johan Tellez, Vivian Triana

“…This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. …”
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Design and Experimental Testing of Holly Pruning Machine with Adaptive Adjustment Tool by Xiuhao Yu, Chengming Hou, Liqi Qiu, Xuesong Li, Penghui Yao, Ying Zhao

“…Based on the force analysis and parameters from the experimental results, the machine performed well, with a cutting mechanism motor speed of 20.9 kr/min, a tool tilt angle of 50°, and a walking speed of 0.91 m/s. …”
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Compatibility Model between Encapsulant Compounds and Antioxidants by the Implementation of Machine Learning by Juliana Quintana-Rojas, Rafael Amaya-Gómez, Nicolas Ratkovich

“…A compatibility model between the encapsulant and antioxidant chemicals was built using machine learning (ML) to discover optimal matches without costly and time-consuming trial-and-error experiments. …”
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Development of digital twin of CNC unit based on machine learning methods by Yu. G. Kabaldin, D. A. Shatagin, M. S. Anosov, A. M. Kuzmishina

“…It is shown that the digital twin (electronic passport) of a CNC machine is developed as a cyber-physical system. The work objective is to create neural network models to determine the operation of a CNC machine, its performance and dynamic stability under cutting.Materials and Methods. …”
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RF-Based UAV Detection and Identification Enhanced by Machine Learning Approach by Yash Vasant Ahirrao, Rana Pratap Yadav, Sunil Kumar

“…The system processes the signal data to extract key features and aiding in differentiating emitter types such as Wi-Fi, Bluetooth, or UAV control signals. Machine learning algorithms are trained to make decisions based on these extracted features. …”
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Improved bio-inspired with machine learning computing approach for thyroid prediction by Divya Kesavulu, Kannadasan R

“…The results of the analysis, assessed using parameters such as accuracy, recall, precision, F1-score, and Specificity, clearly show substantial improvements in predictive capability. …”
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Multimodal machine learning for risk-stratified bundled payments in spinal surgery by Kyle A. Mani, Samuel N. Goldman, Thomas Scharfenberger, Vishal Shankar, Manish Bhatta, Rafael De la Garza Ramos, Mitchell S. Fourman, Ananth S. Eleswarapu

“…We develop the first preoperative risk-stratified multimodal machine learning model that integrates structured clinical data and unstructured surgeon notes using natural language processing to predict financial parameters. …”
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Application of Machine Learning for Adaptive Trajectory Control of UAVs Under Uncertainty by Alexander S. Ermilov, Olga A. Saltykova

“…The article explores the potential of applying machine learning (ML) for adaptive trajectory control of unmanned aerial vehicles (UAVs) under uncertainty. …”
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An On-Machine Measuring Apparatus for Dimension and Form Errors of Deep-Hole Parts by Jintao Liang, Xiaotian Song, Kaixin Wang, Xiaolan Han

“…The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. …”
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