The development of bionic trends in agricultural mechanics by L. F. Babitskiy, V. Y. Moskalevich, I. V. Sobolevsky

“…For bionic modeling of the working bodies of tillage machines the closest biological prototypes limbs are burrowing animals and insect burrowing animals and aquatic organisms. …”
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Newly synthesized 1-butyl-3-methylimidazolium p-toluenesulfonate ionic liquid for acid corrosion of API 5L X70 steel: Experimental, DFT/MD-simulation, statistical and machine learn... by Daniel Iheanacho Udunwa, Okechukwu Dominic Onukwuli, Simeon Chukwudozie Nwanonenyi, Callistus Nonso Ude, Innocent Okechi Arukalam, Remy Uche

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Predicting Gender from Twitter Text Messages Using Methods Based on Artificial Intelligence and Text Analysis by Md. Nyem Hasan Bhuiyan, Anisur Rahman

“…Also, in order to increase the learning rate, the RMSprop optimizer was used in these 2 models. ML-based methods include five different models: LR, XGBoost, Support Vector Classification (SVC), RF, and AdaBoost, all of which were converted from text to numerical vectors using a one-hot encoding method. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Unlike traditional methods where phenotypic changes are learnt or detected by machine learning models, the use of mRNA specific two-dimensional images can yield superior results due to increased spatial informativeness. …”
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Prediction of permissioned blockchain performance for resource scaling configurations by Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang, Chuck Yoo

“…To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. …”
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The uncertainty inherent to DEM simulations of interlocking particles by Lukas Maier, Michael Mitterlindner, Hadie Benabchiasli, Gregor Fasching, Stefan Radl

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Impact of Gait Parameters and Their Variability on Fall Risk Assessment Accuracy Using Wearable Sensor by Jinghao Cai, Zeyang Guan, Jiachen Wang, Ziyun Ding, Yibin Li, Rui Song, Huanghe Zhang

“…However, the impact of these gait parameters and their variability on the overall accuracy of fall risk prediction models remains an open question. This study introduced three fundamental machine learning models—logistic regression, support vector machines (SVM), and an artificial neural network—to predict fall risk among 163 frail older adults. …”
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Contrastive learning and mixture of experts enables precise vector embeddings in biological databases by Logan Hallee, Rohan Kapur, Arjun Patel, Jason P. Gleghorn, Bohdan B. Khomtchouk

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Field-scale evaluation of low-elevation and mobile drip irrigation systems by Masoumeh Hashemi, Matt Yost, Jonathan Holt

“…Finally, relative yield changes were simulated using the most accurate machine learning models for each irrigation technology. …”
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Critical statistical assessment of data in metal additive manufacturing by Raymond Wong, Anh Tran, Bogdan Dovgyy, Claudia Santos Maldonado, Minh-Son Pham

“…Firstly, majority of studies report only high quality builds, these imbalances in reporting result in weak correlation between process parameters, properties and consolidation, limiting the ability of machine learning models to generalize beyond optimized conditions. …”
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A new approach to atom-to-atom mapping using the naive Bayesian classifier by A.I. Khayrullina, T.I. Madzhidov, R.I. Nugmanov, V.A. Afonina, I.I. Baskin, A.A. Varnek

“…The presence of AAM is a key factor for establishing the mechanism and type of reaction, searching for similarities and substructures, modeling, checking the quality of data. A new approach has been proposed to the search for optimal atomic-atom mapping in chemical reactions based on the use of machine learning methods. …”
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Performance of Sentiment Classification on Tweets of Clothing Brands by Muhammad Shafiq Jalani, Hu Ng, Timothy Tzen Vun Yap, Vik Tor Goh

“…The word embeddings are fed into classification models namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic Regression (LR) and Multilayer Perceptron (MLP) by comparing their accuracy performances.  …”
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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…To address the above problems, we proposed a typing evaluation method for KASP primers by integrating deep learning and traditional machine learning algorithms, called TAL-SRX. First, three algorithms are used to optimize the performance of each model in the Stacking framework respectively, and five-fold cross-validation is used to enhance stability. …”
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Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation by Tamer Addissouky, Majeed M. A. Ali, Ibrahim El Tantawy El Sayed, Mahmood Hasen Shuhata Alubiady

“…This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…Future advancements include refining diagnostics through machine learning and natural language processing and integrating collaborative AI models for holistic, personalized care. …”
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RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification by Giacomo Chiarot, Sebastiano Vascon, Claudio Silvestri, Idoia Ochoa

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Exploring the Impact of Artificial Intelligence on Pain and Anxiety in Surgical Patients: A Systematic Review by Etienne El-Helou

“…Results showed that AI interventions in pain management include machine learning models that predict recovery outcomes, personalize pain management, and optimize opioid dosages. …”
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Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis by Haihua Ying, Andri Pranolo, Zalik Nuryana, Andini Isti Syafitri

“…Neuropsychological evaluations are valuable in neurosurgery because they comprehensively evaluate cognitive, affective, and behavioral functioning to optimize patient outcomes. Incorporating artificial intelligence (AI) into neuropsychology offers optimistic advances, with machine learning models assisting in classifying behavioral, cognitive, and functional impairments while minimizing the number of tests. …”
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MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices by Peng Zhang, Qinghua Ma, Yi Li, Min Cui

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Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review by Mirjam Bonanno, Mirjam Bonanno, Davide Cardile, Davide Cardile, Piergiuseppe Liuzzi, Piergiuseppe Liuzzi, Antonio Celesti, Giuseppe Micali, Francesco Corallo, Angelo Quartarone, Angelo Quartarone, Francesco Tomaiuolo, Rocco Salvatore Calabrò

“…BackgroundArtificial intelligence (AI), in the form of machine learning (ML) or deep learning (DL) models, can aid clinicians in the diagnostic process and/or in the prognosis of critically medical conditions, as for patients with a disorder of consciousness (DoC), in which both aspects are particularly challenging. …”
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