Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

“…IntroductionThis paper addresses the growing intersection of machine learning (ML) and psychotherapy by developing a classification model for analyzing topics in therapist remarks. …”
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Rugby Sevens sRPE Workload Imputation Using Objective Models of Measurement by Amarah Epp-Stobbe, Ming-Chang Tsai, Marc Klimstra

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Cortical Adaptation Dynamics in Human-Exoskeleton Interaction Using Multi-Model AMICA by Jasim Naeem, Seongmi Song, Michael Nonte, Courtney A. Haynes, J. Cortney Bradford

“…The human-machine interface is a crucial component of exoskeleton design, and understanding how the human nervous system adapts to and learns to coordinate with wearable robotic systems is essential for optimizing assistive device functionality. …”
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Enhanced Prediction and Evaluation of Hydraulic Concrete Compressive Strength Using Multiple Soft Computing and Metaheuristic Optimization Algorithms by Tianyu Li, Xiamin Hu, Tao Li, Jie Liao, Lidan Mei, Huiwen Tian, Jinlong Gu

“…In the initial stage, several classic machine learning models are selected as base models, and the optimal parameters of these models are obtained using the improved metaheuristic-based gray wolf algorithm. …”
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Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

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Forecasting and decision making of firm’s financial indicators based on the SSA-MLP-BPNN model by Xin Xu

“…By comparing the prediction results of SSA-MLP-BP model with other optimization algorithms, it is found that the SSA optimization algorithm performs superiorly in improving the performance of the MLP-BP model, and it is easier to find the global optimal solution, which improves the prediction accuracy of the model. …”
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Accelerating Fair Federated Learning: Adaptive Federated Adam by Li Ju, Tianru Zhang, Salman Toor, Andreas Hellander

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Attention-Driven AI Model Generalization for Workload Forecasting in the Compute Continuum by Berend J. D. Gort, Godfrey M. Kibalya, Angelos Antonopoulos

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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…These challenges encompass defining a conceptual model outlining cause-and-effect relationships, addressing dissimilarities in data source quantity and information content, grappling with missing or noisy data, fine-tuning model optimization, achieving accurate predictions, and tackling the issue of imbalanced observations across factors. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…These challenges encompass defining a conceptual model outlining cause-and-effect relationships, addressing dissimilarities in data source quantity and information content, grappling with missing or noisy data, fine-tuning model optimization, achieving accurate predictions, and tackling the issue of imbalanced observations across factors. …”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

“…The above five parameters were incorporated into the Ml model. Comprehensive analysis revealed that the Light Gradient Boosting Machine (LightGBM) classification model was the optimal model, with the area under the curve (AUC) of the validation cohort was 0.876, the 95% confidence interval was 0.8139–0.938, the accuracy was 0.843, the sensitivity was 0.805, and the specificity was 0.857, with AUC of validation cohort was 0.876. …”
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Predictive model of malignancy probability in pulmonary nodules based on multicenter data by Yuyan Huang, Yong Chen, Fang He, Li Jiang

“…The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analysis were utilized to identify characteristic predictors. Multiple machine learning classification models were employed for analysis, with the optimal model ultimately selected. …”
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AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm by Xin Shi, Seyed Mohamad Javidan, Yiannis Ampatzidis, Zhao Zhang

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A Multivariate LSTM Model for Short-Term Water Demand Forecasting by Aly K. Salem, Ahmed A. Abokifa

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Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study by Xia Wang, Dan Zhang, Liu Lu, Shujie Meng, Yong Li, Rong Zhang, Jingjie Zhou, Qian Yu, Li Zeng, Jiang Zhao, Yu Zeng, Ru Gao

“…The optimal model was identified through the evaluation of the area under the curve (AUC). …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…This study introduces a novel methodology for predicting cotton yield by integrating machine learning (ML) with mechanistic soil modeling. …”
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A Multilevel and Hierarchical Approach for Multilabel Classification Model in SDGs Research by Berliana Sugiarti Putri, Lya Hulliyyatus Suadaa, Efri Diah Utami

“…Future research can modify the model to utilize a single language input to optimize the term frequency-inverse document frequency (TF-IDF) process, hence, the word meanings from each language are not considered different important words.…”
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