Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…However, limitations persist, such as the dependency on large, high-quality annotated datasets for training DL models, potential challenges in model generalizability across diverse clinical settings, and the “black-box” nature of some complex algorithms, alongside crucial needs for rigorous clinical validation, data standardization, and ethical management of sensitive information. …”
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Decentralized Coordination of Temperature Control in Multiarea Premises by Maria Yukhymchuk, Volodymyr Dubovoi, Viacheslav Kovtun

“…The criteria for control quality are defined and evaluated. The proposed coordination algorithms make it possible to optimize the operating modes of the system automatically when its structure and/or settings are changed. …”
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Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment by Aravam Babu, A. Bagubali

“…FL enables decentralized training on IoT devices, reducing the risk of data breaches. …”
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Novel Method of Immunoepitope Recognition, Long-Term Immunity Markers, Immunosuppressive Domens and Vaccines against COVID-19 by E. P. Kharchenko

“…To develop a new immunoinformation method for recognizing immunoepitopes, to identify in the viral proteins possible potential markers to induce long-term immunity and to evaluate by them the vaccines against Covid-19. Materials and methods. …”
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Sleep Staging Using Compressed Vision Transformer With Novel Two-Step Attention Weighted Sum by Hyounggyu Kim, Moogyeong Kim, Wonzoo Chung

“…Numerical experiments show notable performance enhancement of the proposed scheme in comparison with the state-of-the-art algorithms, particularly for small training datasets, which validates the resilience of the proposed method against overfitting. …”
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Determination of the Effect of Some Properties on Egg Yield with Regression Analysis Met-hod Bagging Mars and R Application by Demet Canga, Mustafa Boğa

“…Earth (enhanced adaptive regression through hinges) and caret (classification and regression training), mda (Mixture Discriminant Analysis) packages were used in R STUDIO program to provide a stronger solution of regression problems in the created MARS and Bagging MARS algorithm. …”
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Multi-Skilled Project Scheduling for High-End Equipment Development Considering Newcomer Cultivation and Duration Uncertainty by Yaohui Liu, Ronggui Ding, Shanshan Liu, Lei Wang

“…Therefore, we put forward an adaptive simulation–optimization approach featuring two-fold: a simulation module capable of dynamically adjusting sample sizes based on convergence feedback and evaluating solutions with improved efficiency and stable accuracy; a tailored non-dominated sorting genetic algorithm II (NSGA-II) with adaptive evolutionary operators that enhance search effectiveness and ensure the identification of a well-distributed Pareto front. …”
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Detecting Keratoconus in Adolescents with Anterior Segment Optical Coherence Tomography by Burcu Yücekul, Anika Förster, H. Burkhard Dick, Suphi Taneri

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Machine vision-based automatic fruit quality detection and grading by Amna, Muhammad Waqar AKRAM, Guiqiang LI, Muhammad Zuhaib AKRAM, Muhammad FAHEEM, Muhammad Mubashar OMAR, Muhammad Ghulman HASSAN

“…Different image processing algorithms including pre-processing, thresholding, morphological and bitwise operations combined with a deep leaning algorithm, i.e., convolutional neural network (CNN), were applied to fruit images for the detection of defective fruit. …”
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Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors by Akira Minamisawa, Shogo Okada, Ken Inoue, Mami Noguchi

“…We then developed a feature extraction method related to daily activity patterns based on a clustering algorithm and analyzed its effectiveness. In the experimental evaluation, we trained binary classification models to classify dementia scale scores based on the Mini-Mental State Examination (MMSE) from these datasets. …”
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Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study by Emrah Aydın, Taha Eren Sarnıç, İnan Utku Türkmen, Narmina Khanmammadova, Ufuk Ateş, Mustafa Onur Öztan, Tamer Sekmenli, Necip Fazıl Aras, Tülin Öztaş, Ali Yalçınkaya, Murat Özbek, Deniz Gökçe, Hatice Sonay Yalçın Cömert, Osman Uzunlu, Aliye Kandırıcı, Nazile Ertürk, Alev Süzen, Fatih Akova, Mehmet Paşaoğlu, Egemen Eroğlu, Gülnur Göllü Bahadır, Ahmet Murat Çakmak, Salim Bilici, Ramazan Karabulut, Mustafa İmamoğlu, Haluk Sarıhan, Süleyman Cüneyt Karakuş

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Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel by Kefan Yang, Youmin Wang, Guoqing Wang

“…The BP neural network model related to the molding process parameters, volume shrinkage, and warpage deformation was built, and the trained network model was optimized with the ant colony algorithm. …”
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Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study by Yafang Zhang, Shilin Lu, Chuan Peng, Shichong Zhou, Irene Campo, Michele Bertolotto, Qian Li, Zhiyuan Wang, Dong Xu, Yun Wang, Jinshun Xu, Qinfu Wu, Xiaoying Hu, Wei Zheng, Jianhua Zhou

“…Materials and methods This international multicenter retrospective study recruited patients with confirmed TGCT between 2015 and 2023. A pre-trained SR reconstruction algorithm was applied to enhance native resolution (NR) images. …”
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Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data by Munib Mesinovic, Peter Watkinson, Tingting Zhu

“…We demonstrate that XMI-ICU maintains reliable predictive performance across different prediction horizons (6, 12, 18, and 24 hours) during ICU stay while also achieving successful external validation in a separate patient cohort from MIMIC-IV without any previous training on that dataset. We also evaluated the framework for clinical risk analysis by comparing it to the standard APACHE IV system in active use. …”
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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. …”
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Assessment of flight fatigue using heart rate variability and machine learning approaches by Dalong Guo, Cong Wang, Yufei Qin, Lamei Shang, Aijing Gao, Baosen Tan, Yubin Zhou, Guangyun Wang

“…The accurate identification of flight fatigue is crucial for managing pilot training intensity and preventing aviation accidents. …”
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Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano by H. M. Mohan, S. Anitha, Rifai Chai, Sai Ho Ling

“…The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. …”
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Predicting Freeze-Thaw States in Alaska Permafrost Landscapes using climate Reanalysis and Machine Learning by A. Ahajjam, M. Soaper, R. Chance, J. Chandler, T. Pasch

“…Furthermore, the study evaluates the impact of four training approaches (location-specific, cross-location, location-agnostic, and depth-agnostic) on model performance, addressing the challenge of using prediction methods in real-world scenarios.…”
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Robust screening of atrial fibrillation with distribution classification by Pierre-François Massiani, Lukas Haverbeck, Claas Thesing, Friedrich Solowjow, Marlo Verket, Matthias Daniel Zink, Katharina Schütt, Dirk Müller-Wieland, Nikolaus Marx, Sebastian Trimpe

“…It achieves state-of-the-art performance and unprecedented robustness on the screening problem while only leveraging one interpretable feature and little training data. We illustrate these advantages by evaluating on other data sources (cross-data-set) and through sensitivity studies. …”
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Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…The developed model’s results were evaluated utilizing a number of statistical metrics to identify the most reliable algorithm for wear and COF prediction. …”
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