A deep learning framework for prediction of crop yield in Australia under the impact of climate change by Haydar Demirhan

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Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges by Kossi Dodzi Bissadu, Salleh Sonko, Gahangir Hossain

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Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods by Mohammadhossein Norouzian, Mahan Khakpour, Marko Orosnjak, Atal Anil Kumar, Slawomir Kedziora

“…Therefore, defining an optimal process window is critical to ensuring weld quality. …”
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Predicting the Relative Density of Stainless Steel and Aluminum Alloys Manufactured by L-PBF Using Machine Learning by José Luis Mullo, Iván La Fé-Perdomo, Jorge Ramos-Grez, Ángel F. Moreira Romero, Alejandra Ramírez-Albán, Mélany Yarad-Jácome, Germán Omar Barrionuevo

“…Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). …”
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Unraveling the pivotal role of amylopectin in enhancing hydrothermal stability and filtration control of native corn starch-based drilling fluids for decarbonization by Fuchang You, Lei Pu, Yu Wu, Yancheng Zheng

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Enhanced lightweight infrared object detection algorithm for assistive navigation in visually impaired individuals by Zhimin Bai, Yang Yang, Jian Wang, Zhengyang Li, Jiajun Wang, Chunxin Liu

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Effect of transition metal on the physical and hydrogen storage properties of the dynamically stable novel ARhH3 (A = Mg, Ca, and Sr) hydrides for solid-state hydrogen storage appl... by Md. Rabbi Talukder, Md Rasidul Islam

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Reducing Computational Time in Pixel-Based Path Planning for GMA-DED by Using Multi-Armed Bandit Reinforcement Learning Algorithm by Rafael P. Ferreira, Emil Schubert, Américo Scotti

“…This work presents an artificial intelligence technique to minimise path planning computer processing time for successful GMA-DED 3D printings. …”
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Mentorship Practices and Research Productivity Among Early-Career Educational Psychologists in Universities by Abigail E. Okon, Valentine J. Owan, Mercy V. Owan

Cryo-Rolled AA5052 Alloy: Insights into Mechanical Properties, Formability, and Microstructure by Arun Achuthankutty, Rohith Saravanan, Hariesh Nagarajan, Vidyanand Pasunuri, Nishanth Hari Gopal, Ajith Ramesh, Sumesh Arangot, Dinu Thomas Thekkuden

“…Although several alloys have been reported to undergo solution treatment before cryo-rolling, this study focuses on how post-processing via annealing can lessen the formability constraints usually connected to conventional cryo-rolling. …”
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Reconstruction and enhancement techniques for overcoming occlusion in facial recognition by Filip Pleško, Tomáš Goldmann, Kamil Malinka

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Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. …”
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Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models by Zaher Mundher Yaseen, Farah Loui Alhalimi

“…., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient Boosting (GB), HistGradientBoosting, Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)) were applied in attempt to predict the adsorption efficiency of several heavy metals (i.e., Pb, Cd, Ni, Cu, and Zn) according to different factors including temperature, pH, and biochar characteristics. …”
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Evaluating the efficacy and site-specific performance of machine learning approaches: A comprehensive review of autism detection models by Deblina Mazumder Setu, Tania Islam, Md Maklachur Rahman, Samrat Kumar Dey, Tazizur Rahman

“…Some existing study find out that Gradient Boosting, Extreme Gradient Boosting (XGBoost), DecisionTree (DT), RandomForest (RF), and Light Gradient-Boosting Machine (LGB) demonstrated maximum accuracy scores of 100%, while AdaBoost (AB), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) achieved accuracies of 100%, 100%, 96%, and 96%, respectively. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…Leveraging regression algorithms such as Random Forest, Gradient Boosting, Light Gradient Boosting, Categorical Boosting and Extreme Gradient Boosting, we have modeled the non-linear and temporal dynamics of sensor battery degradation, thereby enabling proactive maintenance strategies, dynamic energy management, and resource allocation. …”
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Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning by Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri

“…The results indicate that light gradient boosting (LGBM) accurately predicts COF of magnesium alloys using the processing procedure, heat treatment, alloy composition, and tribology variables with an R‐squared value of 0.89. …”
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Design and application of human-computer interaction visual communication platform for Guandong culture by integrating RF and light GBM algorithm by Qiao Wu, Ying Jin

“…The platform built reduced the risk of overfitting and improved the generalization ability of the model through the integration of multiple decision trees in random forest. Light gradient boosting machine performed excellently and had high computational efficiency when processing large-scale data through an efficient gradient boosting framework. …”
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Impact of Enzyme–Microbe Combined Fermentation on the Safety and Quality of Soy Paste Fermented with Grass Carp By-Products by Jing Yang, Zihan Li, Xinping Lin, Sufang Zhang, Chaofan Ji

“…Freshwater fish processing produces 30–70% nutrient-rich by-products, often discarded or undervalued. …”
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The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images by Pei Zhang, Qijian Xu, Feng Jiang

“…ObjectiveTo extract and analyze the image features of two-dimensional ultrasound images and elastic images of four thyroid nodules by radiomics, and then further convolution processing to construct a prediction model for thyroid cancer. …”
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Ensemble Machine Learning Model for Classification of Spam Product Reviews by Muhammad Fayaz, Atif Khan, Javid Ur Rahman, Abdullah Alharbi, M. Irfan Uddin, Bader Alouffi

“…Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers (MLP, KNN, and RF) and state-of-the-art boosting approaches like Generalized Boost Regression Model (GBM), Extreme Gradient Boost (XGBoost), and AdaBoost Regression Model in terms of classification accuracy.…”
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