On the machine learning algorithm combined evolutionary optimization to understand different tool designs’ wear mechanisms and other machinability metrics during dry turning of D2... by Muhammad Sana, Muhammad Umar Farooq, Sana Hassan, Anamta Khan

“…Firstly, an economical design of experiment approach is opted to evaluate two inserts with distinct designs with machining parameters such as cutting speed (VCS), feed rate (FR), and depth of cut (DOC). …”
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Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries by Mequannent Sharew Melaku, Lamrot Yohannes, Berhanu Sharew, Mintesnot Hawaz Derseh, Eliyas Addisu Taye

“…The LGBM classifier outperformed among machine learning algorithms and achieved 73% accuracy and an AUC of 0.80. …”
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A Survey of Sampling Methods for Hyperspectral Remote Sensing: Addressing Bias Induced by Random Sampling by Kevin T. Decker, Brett J. Borghetti

“…In this work, we introduce a set of desirable characteristics to evaluate sampling algorithms, with a primary focus on their tendency to induce correlation between training and test data, while also accounting for other relevant factors. …”
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SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING by Abdul Khadeer, Vanaparthi Kiranmai, B. Suvarnamukhi, Ayaz Mohiuddin, Balika Mahesh, P M Suresh, J Arthy, Sasikumar A N, Sudersan Behera, Mohd Ayaz Uddin

“…We were able to see how the results of training the ANN model with different metaheuristics, such as the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), fireworks algorithm (FWA), and chemical reaction optimization (CRO). …”
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Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases by Benjamin Böttcher, Marly van Assen, Roberto Fari, Philipp L. von Knebel Doeberitz, Eun Young Kim, Eugene A. Berkowitz, Felix G. Meinel, Carlo N. De Cecco

“…Abstract Background This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT). …”
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Ways of institutionalizing non-formal education and mechanisms for validating and evaluating learning outcomes obtained outside formal education by Larysa Lukianova, Krzysztof Symela

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Analysis of Traffic Conflicts at Roundabout Entrances and Exits – A Machine Learning Approach for Enhanced Safety by Yuzhou DUAN, Zhipeng LIN, Yulong WANG, Qiaowen BAI

“…The four machine learning algorithms trained a total of 12 models, with RF demonstrating superior training effectiveness. …”
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A Study on Blended Teaching Model Evaluation for English Major Courses in Higher Education: An Uncertainty-Based Approach by Chunmei Xue

“…Traditional trainer-led lectures and coaching sessions are still provided to learners, but they are combined with interactive, self-guided experiences that give employees practical training and allow them to operate as a team. The creation of a decision support system that facilitates the evaluation of blended learning of English courses in higher education for aiding students. …”
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Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation by Samier Uddin Ahammad Shovo, Md. Golam Rabbani Abir, Md. Mohsin Kabir, M. F. Mridha

“…The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…Extensive instrumental parameters, including rheological, tribological, and textural properties of ten different skin creams, were collected, and 22 sensory attribute scores were obtained from trained expert evaluations. Pearson’s correlation analyses and multiple supervised learning algorithms were applied to establish relationships between each sensory attribute and the instrumental parameters, respectively. …”
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PERFORMANCE EVALUATION OF LIGHTWEIGHT OBJECT DETECTION MODELS FOR REAL-TIME PERSONAL PROTECTIVE EQUIPMENT DETECTION IN THE CONSTRUCTION SITES by Herman, Sandy Alferro Dion, Andik Yulianto

“…Custom dataset was used for training the models and then metrics like F1 score, precision, recall mAP50 and mAP50-95 were used to evaluate both models’ performance. …”
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Evaluating rice crop phenology and crop yield in hilly region using satellite imagery and Google Earth Engine by SHWETA POKHARIYAL, N.R. PATEL, AJEET SINGH NAIN, AKARSH S.G., R.S. RANA, R.K. SINGH, RAJEEV RANJAN

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Evaluating the Vulnerability of Hiding Techniques in Cyber-Physical Systems Against Deep Learning-Based Side-Channel Attacks by Seungun Park, Aria Seo, Muyoung Cheong, Hyunsu Kim, JaeCheol Kim, Yunsik Son

“…Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. …”
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Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques by Febriyanti Panjaitan, Win Ce, Hery Oktafiandi, Ghanim Kanugrahan, Yudi Ramdhani, Vito Hafizh Cahaya Putra

“…The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. …”
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Hidden Brain State-Based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-Machine Interfaces by Jieyuan Tan, Xiang Zhang, Shenghui Wu, Zhiwei Song, Yiwen Wang

“…To validate that the extracted internal evaluation could contribute to the decoder training, we compared the decoding performance of decoders trained by different reward models, including manually designed reward, naïve IRL, PCA-IRL, and our proposed HBS-KIRL. …”
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Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

“…ML models were constructed to evaluate the predictive value of maternal parameter changes on preeclampsia combined with FGR. …”
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Screening colorectal cancer associated autoantigens through multi-omics analysis and diagnostic performance evaluation of corresponding autoantibodies by Zan Qiu, Yifan Cheng, Haiyan Liu, Tiandong Li, Yinan Jiang, Yin Lu, Donglin Jiang, Xiaoyue Zhang, Xinwei Wang, Zirui Kang, Lei Peng, Keyan Wang, Liping Dai, Hua Ye, Peng Wang, Jianxiang Shi

“…ELISA results showed that five TAAbs including anti-CKS1B, anti-S100A11, anti-maspin, anti-ANXA3, and anti-eEF2 were potential diagnostic biomarkers during the diagnostic evaluation phase (all P < 0.05). The Random Forest model yielded an AUC of 0.82 (95% CI: 0.78–0.88) on the training set and 0.75 (95% CI: 0.68–0.82) on the test set, demonstrating the robustness of the results. …”
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Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

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Power system corrective control considering topology adjustment: An evolution-enhanced reinforcement learning method by Haoran Zhang, Peidong Xu, Ji Qiao, Yuxin Dai, Yuyang Bai, Tianlu Gao, Fan Yang, Jun Zhang, Jun Hao, Wenzhong Gao

“…The proposed method integrates the double dueling deep Q-network with the evolutionary algorithm, utilizing cumulative rewards to evaluate agents and reduce value estimation errors for corrective actions. …”
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Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. by Maxim Topaz, Maryam Zolnoori, Allison A Norful, Alexis Perrier, Zoran Kostic, Maureen George

“…<h4>Discussion</h4>This was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence.…”
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