Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction by Guangli LI, Yiyuan YE, Guangxin XU, Hongbin ZHANG, Guangting WU, Jingqin LYU

“…Second, a novel field-embedded factorization machine (FEFM) is designed to strengthen the interaction intensity between different feature fields by the field pair symmetric matrix. …”
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Low-Damage Grasp Method for Plug Seedlings Based on Machine Vision and Deep Learning by Fengwei Yuan, Gengzhen Ren, Zhang Xiao, Erjie Sun, Guoning Ma, Shuaiyin Chen, Zhenlong Li, Zhenhong Zou, Xiangjiang Wang

“…Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…Despite the increasing interest in Machine Learning (ML) in urology, there have been limited studies on the detection and prediction of varicocelectomy using artificial intelligence. …”
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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine by Hong Yang, Lipeng Gao, Guohui Li

“…Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. …”
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New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines by Daniel Iong, Yang Chen, Gabor Toth, Shasha Zou, Tuija Pulkkinen, Jiaen Ren, Enrico Camporeale, Tamas Gombosi

“…Abstract In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM‐H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM‐H values. …”
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Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique by Gholamali Shafabakhsh, Mostafa Sadeghnejad, Milad Keneshlou

“…Another goal of the paper was to investigate the influence of different parameters, such as temperature (-5, -15, and -25 °C), loading mode (I, II, and I/II), crack geometry (vertical and angular cracks), and nano-modification, on the fracture toughness of HMA by using machine learning technique. …”
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Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach by Daniyal Durmuş Köksal, Yeşim Ahi, Mladen Todorovic

“…This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential of treated wastewater for irrigation. …”
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Theoretical definition of legume grass seeds degree of extraction done by machine for seed extraction by M. V. Simonov, V. J. Mokiev

“…The study of the extraction machine was aimed at drawing analytical formulas which allow mathematically determine the quality indicators of the technological process. …”
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Optimal design of frame structures equipped with viscous dampers using machine learning techniques by Yi Wen, Jianze Wang, Jun Xu, Kaoshan Dai, Yuanfeng Shi, Reza Sharbati

“…To achieve this, the proposed method involves the Machine Learning (ML) techniques of Extreme Gradient Boosting (XGBoost) and a Dynamic Programming (DP) algorithm. …”
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Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study by Klaudia Lenart

“…Unlike machine learning algorithms, the statistical methods performed with similar accuracy even when the change in the marginal distribution parameters’ value was smaller. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. …”
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ASPECTS REGARDING THE TECHNICAL EXPERTISE OF THE ROTATIONAL MECHANISM OF COAL MINING MACHINE - Part I by Marius Liviu CÎRŢÎNĂ, Constanța RĂDULESCU, Alin STĂNCIOIU

“…In this paper we presents technical conditions for the rotational mechanism of the coal mining machine after we made technical expertise. The rehabilitation at the rotational mechanism will be subjected, it will be done by performing the intervention works who will restore both the structural part and the functional part in the normal operating parameters. …”
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ASPECTS REGARDING THE TECHNICAL EXPERTISE OF THE ROTATIONAL MECHANISM OF COAL MINING MACHINE - Part II by Constanța RĂDULESCU, Marius Liviu CÎRŢÎNĂ, Alin STĂNCIOIU

“…In this paper we presents technical conditions for the rotational mechanism of the coal mining machine after we made technical expertise. The rehabilitation at the rotational mechanism will be subjected, it will be done by performing the intervention works who will restore both the structural part and the functional part in the normal operating parameters. …”
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Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms by Aleksander M. Ivanov, Artem V. Gorbarenko, Maria B. Kireeva, Elena S. Povalishnikova

“…The article presents the results of study of the application of machine learning methods to the problem of classification and identification of different river water regimes in a large region – the European territory of Russia. …”
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Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning by Xin Chen, Huanchen Zhao, Beini Wang, Bo Xia

“…Notably, the significance of physical parameters surpassed that of physiological and behavioral factors. …”
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A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method by Sahu Saurabh Kumar, Shekhar Shiena, Khan Akhtar, Soni Dheeraj Lal, Gangwar Prashant Kumar, Gupta Manish

“…In order to achieve this, three specific input parameters were chosen: the spindle speed, feed rate, and depth of cut. …”
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Unveiling degradation patterns in dye-sensitized solar cells: a machine learning perspective by Ahmet Sait Alali, Mahmoud Ashraf, Ahmad Muhammad, Sameh O. Abdellatif

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Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms by Md. Abdullah Al Mamun Hridoy, Chiara Bordin, Andleeb Masood, Khalid Masood

“…The parameters monitored include pH, turbidity, temperature, and dissolved oxygen (DO)—critical indicators of aquatic health and fish productivity. …”
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Predictability of varicocele repair success: preliminary results of a machine learning-based approach by Andrea Crafa, Marco Russo, Rossella Cannarella, Murat Gül, Michele Compagnone, Laura M Mongioì, Vittorio Cannarella, Rosita A Condorelli, Sandro La Vignera, Aldo E Calogero

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A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning by Madina Suleimenova, Kuat Abzaliyev, Madina Mansurova, Symbat Abzaliyeva, Almagul Kurmanova, Guzel Tokhtakulinova, Akbota Bugibayeva, Diana Sundetova, Merei Abdykassymova, Ulzhas Sagalbayeva, Raushan Bitemirova, Zhadyra Yerkin

“…Therefore, in order to detect early aging in the elderly, we have developed a prognostic model based on clinical and immunological markers using machine learning. <b>Methods:</b> This paper analyzes the relationships between immunological markers, clinical parameters, and lifestyle factors in individuals over 60 years of age. …”
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