Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos, Elpiniki Papageorgiou

“…The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges’ assessments) were comparable at 79% and 80.17%, respectively. …”
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Models, systems, networks in economics, engineering, nature and society by V.V. Khryashchev, A.L. Priorov, N.V. Kotov, K.I. Malygin

“…A study was conducted to evaluate the efficiency of using a number of classical and neural network image segmentation algorithms. …”

Shape Penalized Decision Forests for Imbalanced Data Classification by Rahul Goswami, Aindrila Garai, Payel Sadhukhan, Palash Ghosh, Tanujit Chakraborty

“…The proposed approach enhances predictive performance and generalization by leveraging ensemble learning strategies such as bagging and adaptive boosting. We evaluate the method on twenty benchmark tabular imbalanced datasets, spanning diverse sample sizes and imbalance ratios, and demonstrate its superiority over several state-of-the-art data-level and algorithmic-level methods. …”
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Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review by Hein Minn Tun, Hanif Abdul Rahman, Lin Naing, Owais Ahmed Malik

“…Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers’ trust in AI-CDSS tools were transparency, usability, and clinical reliability. …”
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The potential for productive use of the digital service LearningApps in teaching foreign languages by Natalia A. Katalkina, Nadezhda V. Bogdanova

“…Interactive game simulators serve to develop algorithmic thinking of students in foreign language lessons. …”
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A deep multiple self-supervised clustering model based on autoencoder networks by Ling Zhu, Zijin Liu, Guangyu Liu

“…The proposed model effectively integrates the advantages of autoencoder and fuzzy C-Means clustering, performing multi-level clustering evaluations throughout multiple iterations of the autoencoder network training process. …”
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Improved Convolutional Neural Networks for Course Teaching Quality Assessment by Yun Liu

“…Experimental results show that SLRCN algorithm has the best performance in training set and test set. …”
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Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers by Evan Scope Crafts, Umberto Villa

“…In this setting, approximate ground-truth posterior samples can be obtained, enabling principled evaluation of the performance of posterior sampling algorithms. …”
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Heterogeneity-Aware Personalized Federated Neural Architecture Search by An Yang, Ying Liu

“…Furthermore, we develop a model-heterogeneous FL algorithm called heteroFedAvg to facilitate collaborative model training for the discovered personalized models. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…Image features are extracted using the pre-trained convolutional neural network model Inception ResNetV2 and image data is clustered by K-means clustering algorithm. …”
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Skincare Recommendation System Based on Facial Skin Type with Real-Time Weather Integration by Gabrielle Sheila Sylvagno, Theresia Herlina Rochadiani

“…The dataset exhibits diversity in skin types, allowing for a more valid evaluation of the CNN model. The training and testing process involved splitting the data into training and testing sets, with augmentation applied to the training data to enhance the feature diversity across classes. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…Furthermore, a fault diagnosis method based on CBAM-MobileNetV2 is proposed to achieve fault diagnosis of mine hoist main bearings under small sample data. At the algorithmic level, the Wasserstein distance metric is introduced to solve the problem of vanishing training gradients in generative adversarial networks. …”
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P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…The system was additionally evaluated in a test set of images (N=180; positive for cirrhosis=64) obtained through Butterfly POCUS. …”
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Enhancing Rare Class Performance in HOI Detection with Re-Splitting and a Fair Test Dataset by Gyubin Park, Afaque Manzoor Soomro

“…To overcome this problem, a Re-Splitting algorithm has been developed. This algorithm implements DreamSim-based clustering and performs k-means-based partitioning to restructure the train–test splits. …”
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AI-powered literature search: some observations and concerns by Farooq Azam Rathore, Fareeha Farooq

“…A lack of understanding of the mechanisms and algorithms can negatively influence their ability to critically evaluate the relevance and reliability of the AI-generated outputs. …”
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Noninvasive prediction of meningioma brain invasion via multiparametric MRI⁃based brain⁃tumor interface radiomics by CHENG Xing, WANG Zhi⁃chao, LI Hua⁃ning, WANG Xie⁃feng, YOU Yong⁃ping

“…Through five⁃fold cross⁃validation in the training set and evaluation in the testing set, comparative analysis of the predictive performance of 18 model⁃thickness combinations (6 ML algorithms × 3 BTI thicknesses) showed that the XGBoost model constructed with a 1.00 cm BTI thickness demonstrated exceptional performance. …”
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Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs by Michal Maciusowicz, Grzegorz Psuj

“…Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). …”
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Short-Term Energy Consumption Prediction in Korean Residential Buildings Using Optimized Multi-Layer Perceptron by Fazli Wahid, Do Hyeun Kim

“…Two main training algorithms namely scaled conjugate gradient, and Levenberg-Marquardt back propagation algorithms were used for training. …”
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Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis. by Leonardo V Castorina, Filippo Grazioli, Pierre Machart, Anja Mösch, Federico Errica

“…The DS algorithm controls the distance between training and testing peptides based on both sequence and structure, allowing for a more nuanced evaluation of model performance. …”
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Semantic Segmentation-Driven Knowledge Distillation-Based Infrared Visible Image Fusion Framework by Xingshuo Wang

“…However, many existing fusion algorithms overly emphasize visual quality and traditional statistical evaluation metrics while neglecting the requirements of real-world applications, especially in high-level vision tasks. …”
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