Prediction Approaches for Smart Cultivation: A Comparative Study by Amitabha Chakrabarty, Nafees Mansoor, Muhammad Irfan Uddin, Mosleh Hmoud Al-adaileh, Nizar Alsharif, Fawaz Waselallah Alsaade

“…To address this issue, the usage of machine learning-based tools has been studied in this paper. …”
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MODELING OF THE PROCESS OF CUTTING MINERAL GROUNDS BY PASSIVE WORKING DRAWNER DURING THE CONSTRUCTION OF CLOSED DRAINAGE by E. Z. Batmanov, T. G. Gasanov, M. R. Guseynov

“…The study of the process of cutting mineral soils with narrow, deep knives on an electronic model makes it possible, at the design stage, to evaluate the effect of changes in various factors and parameters on the operating modes of the drainage machine; if necessary, make changes to the complex of works on the construction of drainage using the trenchless method with the help of the BDM-300 bed-draining machine, as well as to determine the composition and duration of the work operations of the trench-free bed-draining machine.…”
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Research on process optimization and trajectory planning of EA4T axle robot grinding by Feng ZHANG, Zhongli FENG, Feng XU, Deming ZHANG, Xiangrui ZENG, Jianwei MA, Shilei ZHANG

“…The robot machining system program SRC file is generated and subsequently transferred to the robot teach pendant. …”
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Design and Experimental Analysis of an Air-Suction Wheat Precision Hill-Seed Metering Device by Ziheng Fang, Jing Zhang, Jincheng Chen, Feng Pan, Baiwei Wang, Chao Ji

“…Orthogonal experiments were carried out with mould hole diameter, negative pressure size, and seed plate speed as test factors alongside a qualification index, multiple sowing index, and missed sowing index as response indicators—leading to regression equation establishment, which yielded the optimal parameter combination: mould hole diameter at 1.8 mm; gas chamber negative pressure at 3.2 kPa; and a seed plate speed of 74 r·min⁻¹, with the corresponding forwards speed of the machine being 7 km·h⁻¹—resulting in a qualification index of 91.66%, multiple sowing index of 5.98%, and missed sowing index of 2.36%. …”
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An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using attention induced deep convolution neural networks by Louai A. Maghrabi, Alanoud Subahi, Nouf Atiahallah Alghanmi, Turki Althaqafi, Nahla J. Abid, Nasser N. Albogami, Mahmoud Ragab

“…Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. …”
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Responses of surface runoff and soil water-erosion to changes in seasonal land cover and rainfall intensity; the case of Shilansha watershed, Rift Valley Basin of Ethiopia by Assefa Gedle, Tom Rientjes, Alemseged Tamiru Haile, Wolde Mekuria, Paul Hallett, Jo Smith

“…High rainfall intensity had a greater impact when combined with fallow season land cover, while effects were smallest when low rainfall intensity combined with growing season land cover. A calibrated model parameter set for a particular season resulted in deteriorated model performance when applied to other seasons. …”
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Application of X-bar R Control Charts for Process Efficiency Monitoring: A Data-Driven Approach in Quality Management by Aleksy Kwilinski, Maciej Kardas, Nataliia Trushkina

“…Using Minitab Statistical Software, the study analyses the adhesion parameter of Thermoplastic Polyurethane (TPU) film, a material widely used for electronic screen protection. …”
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Research on early warning model of coal spontaneous combustion based on interpretability by Huimin Zhao, Xu Zhou, Jingjing Han, Yixuan Liu, Zhe Liu, Shishuo Wang

“…The grid search algorithm was utilized to optimize the model parameters, ensuring the selection of the most suitable parameter configurations. …”
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Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning by Zhengyi Lu, Hao Liang, Ming Lu, Xiao Wang, Xinqiang Yan, Yuankai Huo

“…Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate B1+ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. …”
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AutoMEX: Streamlining material extrusion with AI agents powered by large language models and knowledge graphs by Haolin Fan, Junlin Huang, Jilong Xu, Yifei Zhou, Jerry Ying Hsi Fuh, Wen Feng Lu, Bingbing Li

“…With minimal human intervention, the framework encompasses a complete workflow, including CAD model generation, printing parameter recommendation, slicing, and machine operation. …”
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Design and Experiment of DEM-Based Layered Cutting–Throwing Perimeter Drainage Ditcher for Rapeseed Fields by Xiaohu Jiang, Zijian Kang, Mingliang Wu, Zhihao Zhao, Zhuo Peng, Yiti Ouyang, Haifeng Luo, Wei Quan

“…To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. …”
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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments by Sarah A. Alzakari, Mohammed Aljebreen, Mashael M. Asiri, Wahida MANSOURI, Sultan Alahmari, Mohammed Alqahtani, Shaymaa Sorour, Wafi Bedewi

“…Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. …”
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Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms by Manal Abdullah Alohali, Sultan Alahmari, Mohammed Aljebreen, Mashael M. Asiri, Achraf Ben Miled, Sami Saad Albouq, Othman Alrusaini, Ali Alqazzaz

“…Researchers have proposed numerous malware detection solutions for the past few years. Machine learning (ML) and deep learning (DL)-based detection models can decrease analysis time and increase malware detection accuracy. …”
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Association of exposure to multiple volatile organic compounds with ultrasound-defined hepatic steatosis and fibrosis in the adult US population: NHANES 2017–2020 by Wentao Shao, Wentao Shao, Pan Gong, Qihan Wang, Fan Ding, Weiyi Shen, Hongchao Zhang, Anhua Huang, Chengyu Liu

“…Vibration Controlled Transient Elastography (VCTE) assessed hepatic steatosis and liver fibrosis via the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), respectively. …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. …”
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Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework by Rui Guo, Xiaodong Huang, Yangjing Xiu, Minglu Che, Jinlong Gao, Shuai Fu, Qisheng Feng, Tiangang Liang

“…In this study, a high-accuracy R/S model was constructed using the AutoGluon framework and traditional machine learning (ML) algorithms with 1,367 R/S samples of grassland in China, integrating climate, soil, terrain and spectral features. …”
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Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

“…In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. …”
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Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study by Jeffrey M Cochran

“…Patch-acquired ACC and ECG data were compared against PSG data to build machine learning classification models to distinguish periods of wake from sleep. …”
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The Impact of Initial Composition on Massive Star Evolution and Nucleosynthesis by Christopher West, Alexander Heger, Benoit Côté, Lev Serxner, Haoxuan Sun

“…We find that initial abundances used in computing stellar structure have a larger impact on the GCE results than the initial abundances used in the large nuclear co-processing network, with the GCH model again being favored when compared to observations. Finally, a machine learning algorithm was used to verify the free parameter values of the GCH model, which were previously found by C. …”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

“…We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. …”
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