Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…Among the models, Random Forest emerged as the most effective algorithm, consistently outperforming others on both training and testing datasets. …”
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Equalizer Design: HBOA-DE-trained radial basis function neural networks by Santosh Kumar Das, Satya Ranjan Pattanaik, Pradyumna Kumar Mohapatra, Saroja Kumar Rout, Abdulaziz S. Almazyad, Muhammed Basheer Jasser, Guojiang Xiong, Ali Wagdy Mohamed

“…Additionally, the algorithm has been tested against a situation in burst error and evaluated via bit error probability (BEP) to establish its robustness and performance. …”
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The Problem of Subjectivity in Algorithmic Creativity Organisation by Tetiana Sovhyra

“…It is revealed that algorithmic creativity is actually a kind of mechanised production process, organised in accordance with the features of machine training, and the identified signs of stylistic imitation of traditional practices. …”
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Performance Evaluation of Train Communication Network Based on Cloud Model and Combination Weighting Method by Guoqiang LIU, Deqiang HE, Yanjun CHEN, Xiaoyang YAO

“…Firstly, the performance evaluation system of train communication network and Opnet simulation platform were established, and the comprehensive weight was determined by the combination weighting method; Secondly, the evaluation cloud was generated by the cloud model, and the comprehensive cloud was obtained according to the comprehensive weight and the reverse cloud algorithm; Finally, the similarity between the comprehensive cloud of train communication network performance and the evaluation cloud was measured to realize the evaluation of train communication network performance. …”
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Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review by Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame

“…Early bias mitigation literature has focused on addressing bias in the preparation and development phases of the algorithm life cycle (pre- and in-processing). Post-processing methods, applied at the point of implementation, are less computationally intensive and do not require re-building or training the model, allowing lower-resourced health systems to improve bias in off-the-shelf binary classification models, which are increasingly common within electronic medical records. …”
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Sentinel-2 Masking CNNs Trained on Physics-Supervised Labels by Efrain Padilla-Zepeda, Kevin Alonso, Raquel De Los Reyes, Deni Torres-Roman, Avi Putri Pertiwi, Tobias Storch

“…The model is trained using a large global training dataset from PACO, while a separate validation dataset from the same source is used to monitor performance during learning and prevent overfitting. …”
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Evaluation of College Students’ Emergency Response Capability Based on Questionnaire-TOPSIS Innovative Algorithm by Yanyan Liu, Wei Zhou, Yang Song

“…In this study, the evaluation of college students’ emergency response capability based on the questionnaire-TOPSIS innovative algorithm is conducted. …”
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Design of performance evaluation method for higher education reform based on adaptive fuzzy algorithm by Dakun Yang, Muhammad Sheraz Arshad Malik

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A Talent Cultivation and Performance Evaluation Model Based on a Fuzzy Control Algorithm by Shanshan Jiang

“…Businesses find it difficult to expand when trained personnel are shortages. Evaluating and managing exceptionally gifted persons have become more difficult due to technological developments, which have slowed down technological progress and business expansion. …”
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Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction by Tesfahun Berhane, Tamiru Melese, Abdu Mohammed Seid

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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification by Xiaorou Zheng, Jianxin Jia, Shoubin Dong, Yawei Wang, Runuo Lu, Yuwei Chen, Yueming Wang

“…To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. …”
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Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data by Gabriel Ferreira dos Santos Silva, Roberta Moreira Wichmann, Francisco Costa da Silva Junior, Alexandre Dias Porto Chiavegatto Filho

“…Utilizing data from 2010 to 2016 for training and validation from 2017 to 2019, our analysis included 575,664 pregnancies and assessed five ML algorithms based on key neonatal health indicators recommended by the World Health Organization. …”
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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network by Yiming Chen, Hui Zhang, Chen Zhang, Yi Liu

“…In addition, the particle swarm algorithm is applied in weight clipping to adjust the search step size and direction adaptively. …”
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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…The research employs a systematic approach involving the design, training, and validation of ANN models optimized by these techniques. …”
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Fine-tuning text-to-SQL models with reinforcement-learning training objectives by Xuan-Bang Nguyen, Xuan-Hieu Phan, Massimo Piccardi

“…As reinforcement learning reward, we propose a novel function which better aligns with the text-to-SQL evaluation metrics, applied in conjunction with two strong policy gradient algorithms, REINFORCE and RELAX. …”
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Neural-Driven heuristic for strip packing trained with Black-Box optimization by Mariusz Kaleta, Tomasz Śliwiński

“…In this paper, we propose a neural-driven constructive heuristic that leverages a lightware neural network trained via black-box optimization to dynamically evaluate item placement decisions. …”
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MissPred: A Robust Two-Stage Radar Echo Extrapolation Algorithm for Incomplete Sequences by Ziqi Zhao, Chunxu Duan, Lin Song, Qilin Zhang, Wenda Zhu, Yi Liu

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Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation by Yujiao Li, Yingjie Mu

“…This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. …”
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Evaluating Uncertainty Quantification in Medical Image Segmentation: A Multi-Dataset, Multi-Algorithm Study by Nyaz Jalal, Małgorzata Śliwińska, Wadim Wojciechowski, Iwona Kucybała, Miłosz Rozynek, Kamil Krupa, Patrycja Matusik, Jarosław Jarczewski, Zbisław Tabor

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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

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