Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach by Asraar Anjum, Abdul Aabid, Meftah Hrairi

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Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network by Miao Zhang, Yao Zhang, Qian Cen, Shixun Wu

“…Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. …”
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A Generalized Zero-Shot Deep Learning Classifier for Emotion Recognition Using Facial Expression Images by Vishal Singh Bhati, Namita Tiwari, Meenu Chawla

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Machine learning prediction of combat basic training injury from 3D body shape images. by Steven Morse, Kevin Talty, Patrick Kuiper, Michael Scioletti, Steven B Heymsfield, Richard L Atkinson, Diana M Thomas

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NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning by Jianliang Wang, Chen Chen, Jiacheng Wang, Zhaosheng Yao, Ying Wang, Yuanyuan Zhao, Yi Sun, Fei Wu, Dongwei Han, Guanshuo Yang, Xinyu Liu, Chengming Sun, Tao Liu

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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology by Isaac Y. Tian, Jason Liu, Michael C. Wong, Nisa N. Kelly, Yong E. Liu, Andrea K. Garber, Steven B. Heymsfield, Brian Curless, John A. Shepherd

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Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture by Nezha Kharraz, István Szabó

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Predicting ash content and water content in coal using full infrared spectra and machine learning models by Suprapto Suprapto, Antin Wahyuningtyas, Kartika Anoraga Madurani, Yatim Lailun Ni'mah

“…The aim of this study was to predict ash and water contents in coal samples using machine learning regression techniques, specifically LassoCV, RidgeCV, ElasticNetCV and LassoLarsCV. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

“…Abstract Machine learning offers great promise for fast and accurate binding affinity predictions. …”
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Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning by Jian Wang, Bo Zhang, Dong Yin, Jinxin Ouyang

“…To identify and locate faults of small-current grounded distribution networks under high-impedance fault with weak characteristics, a fault comprehensive identification method for distribution networks based on voltage-ampere curves and deep ensemble learning is proposed. First, the correlations of the voltage-ampere curves with the fault causes, fault types, and fault distances are analyzed to illustrate the feasibility of using three-phase and zero-sequence voltage-ampere curves as input features. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. …”
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Vocabulary learning in EFL context: do primary school English Subject textbooks provide structured support? by John Misana Biseko

“…However, a comprehensive analysis revealed critical shortcomings that undermine incidental vocabulary learning—the necessary complement to intentional approaches. …”
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FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications by Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru

Subjects: “…Broad learning…”
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Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms by R. Kabilan, V. Chandran, J. Yogapriya, Alagar Karthick, Priyesh P. Gandhi, V. Mohanavel, Robbi Rahim, S. Manoharan

“…This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. …”
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Creativity in numbers: Uncovering students' potential through diagnostic learning evaluation at SMK Pasundan 4 Bandung by Ainil Kaafil Hasanah, Lulu Banusalam, Muhammad Rizqi Febrian

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Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence by Ali Hammoud, Alaa Iskandar, Béla Kovács

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Detection and Segmentation of Glioma Tumors Using an Improved Visual Geometry Group (IVGG) Deep Learning Structure by Parameswari Alagarsamy, Vinoth Kumar Kalimuthu, Bhavani Sridharan

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Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection by Sunil Kaushik, Akashdeep Bhardwaj, Ahmad Almogren, Salil bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen, Habib Hamam

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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

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