Analysis of Facial Areas to Identify CHD Risks Based on Facial Textures by Budi Sunarko, Agung Adi Firdaus, Yudha Andriano Rismawan, Anan Nugroho

“…This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. …”
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Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning by Hung-Hsien Liu, Chun-Bi Chang, Yi-Sa Chen, Chang-Fu Kuo, Chun-Yu Lin, Cheng-Yu Ma, Li-Jen Wang

“…Background/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. …”
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ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano by Felipe Grijalva, Washington Ramos, Noel Perez, Diego Benitez, Roman Lara, Mario Ruiz

“…Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.…”
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Diagnosis and genetic analysis of polycythemia in children and a novel EPAS1 gene mutationWhat is knownWhat is new? by Hatice Mine Cakmak, Omer Kartal, Ayca Kocaaga, Yasar Bildirici

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Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

“…First, the framework evaluates performance by partitioning data from multiple years into training and validation cohorts. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…The retrained surrogate is further integrated with the iterative ensemble smoother (IES) algorithm for inversion. In the online strategy, the pre‐trained model is adaptively updated and refined with the selected posterior samples in each iteration of IES to continuously adapt to the solution searching path. …”
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Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan

“…We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

“…Furthermore, we have incorporated a tailored Differential Evolution (DE) algorithm designed to navigate the complex hyperparameter space with finesse. …”
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Cross-Language Transfer-Learning Approach via a Pretrained Preact ResNet-18 Architecture for Improving Kanji Recognition Accuracy and Enhancing a Number of Recognizable Kanji by Vasyl Rusyn, Andrii Boichuk, Lesia Mochurad

“…During the implementation of our training algorithms, we trained a model with the CASIA-HWDB dataset with handwritten Chinese characters and used its weights to initialize models that were fine-tuned with a Kuzushiji-Kanji dataset that consists of Japanese handwritten kanji. …”
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Inter-rater reliability of a newly developed gait analysis and motion score by Christina Dürregger, Klemens A Adamer, Michael Pirchl, Michael J Fischer

“…Previous studies indicate that observational-based gait analysis lacks reliability and requires extensive clinical training. Therefore, gait analysis in the clinical practice heavily relies on technical aids. …”
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Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. …”
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Flood resilience assessment of region based on TOPSIS-BOA-RF integrated model by Guofeng Wen, Fayan Ji

“…Finally, based on the obtained weights, learning samples are generated using piecewise linear interpolation and the TOPSIS. Training samples are then input into the Butterfly Optimization Algorithm(BOA) to optimize the key parameters in the Random Forest(RF). …”
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Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network by Deeb Assad Tubail, Mohammed Zourob, Salama Ikki

“…To manage the computational demands of the training phase, we employ a low-complexity algorithm, the Extreme Learning Machine (ELM), which calculates weights and biases through closed-form solution, avoiding complex optimization processes. …”
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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Conclusion We compare the performance of standard Adam, LM, and LM SA on different datasets and computational conditions. Performance was evaluated using Loss function values, predicted Local Distance Difference Test (pLDDT), distance-based Root Mean Square Deviation (dRMSD), and Template Modeling (TM) scores. …”
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Adaptive deep feature representation learning for cross-subject EEG decoding by Shuang Liang, Linzhe Li, Wei Zu, Wei Feng, Wenlong Hang

“…The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects. …”
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Investigation of TsGAN-based multimodal image fusion to augment image pre-processing abilities by Priyanka Bhatambarekar, Gayatri Phade

“…Additionally, “a multiple decision map-based strategy” is introduces for fusion to enhance texture extraction. Empirical evaluations confirm the effectiveness of the proposed approach, highlighting its superiority over existing algorithms in both qualitative and quantitative analysis. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Jingjing Sun, Xin Guan, Xiaojun Sun, Xiaojing Cao, Yepei Tan, Jiarong Liao

“…Using bootstrap sampling, the RFR model generates multiple training subsets from the original data and randomly selects subsets of variables to construct regression trees. …”
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The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature by Zohreh Fazli, Mehran Sadeghi, Mohebat Vali, Parvin Ahmadinejad

“…These include the need for high-quality training data, interpretability of complex AI algorithms, alignment with safety standards, integration with existing systems, and the lack of interdisciplinary expertise. …”
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Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…Approximately 85% of the data was utilised to train the three inputs, while the rest was used to evaluate the predicted model and assess the efficiency using Akaike Information Criterion (AIC) to choose the best fit model. …”
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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

“…Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. …”
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