Data-driven modeling approaches for pressure drop prediction in a multi-phase flow system by Nezar M. Alyazidi, Aiman F. Bawazir, Ala S. AL-Dogai

“…This study helps the creation of more advanced control algorithms by providing a reliable way to guess when the pressure drops and showing how to choose a model that fits the properties of the multi-phase flow. …”
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Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features by Huanyin Su, Shanglin Mo, Huizi Dai, Jincong Shen

“…In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. …”
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Software and methodological support for additional professional courses: ideas, problems, monitoring by O. D. Prokhorenko, S. V. Morin, O. A. Kozyreva

“…By building prospects for theorizing and implementing ideas for improving the quality of modeling and using software and methodological support for further education courses, we can distinguish in the integrative representation of the direction and prospects of an individual’s achievements three types of solving professional and pedagogical problems theorized in the work (algorithmic, creative-stimulative and innovative-promising).…”
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Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

“…We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. …”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Jun Seo Kim, Jeong Hoon Lee, Yousung Yeon, Doyeon An, Seok Jun Kim, Myung-Giun Noh, Suehyun Lee

“…The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms. Results The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. …”
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Index or illusion: The case of frailty indices in the Health and Retirement Study. by Yi-Sheng Chao, Hsing-Chien Wu, Chao-Jung Wu, Wei-Chih Chen

“…We are developing new algorithms to develop and validate innovative indices.…”
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A New Perspective on Overfeeding in the Intensive Care Unit (ICU): Challenges, Dangers and Prevention Methods by Vlad-Dimitrie Cehan, Alina-Roxana Cehan, Mihai Claudiu Pui, Alexandra Lazar

“…Clinicians must be proficient in interpreting metabolic data, understanding the principles of energy balance, and implementing appropriate feeding algorithms. Interdisciplinary collaboration among critical care teams, including dieticians, physicians, and nurses, is crucial for ensuring consistent and effective nutritional management. …”
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Therapeutic Plasma Exchange in the Management of Postpartum HELLP Syndrome: A Case Report by Parul Jaiswal, Prashant Suryarao, Dipak Kolate

“…Further research is needed to improve diagnostic algorithms and treatment plans due to overlapping clinical presentations.…”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…The advanced optimization algorithms achieve the global minimum of loss functions, which makes the precision and recall metrics superior to the ones for their known counterparts. …”
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Body-part-based individual feral cat identification from camera trap images using deep learning by Rio Rifqi Syah Akbar, Matthew W. Rees, Patricia A. Fleming, Ferdous Sohel

“…This study proposes a body-part-based computer algorithmic approach that uses deep learning for individual identification from photos that can address a common challenge associated with using camera trapping, where often only a partial or obscured view of the objects of interest is presented. …”
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Significance of Chest Computed Tomography Scan Findings at Time of Diagnosis in Patients with COVID-19 Pneumonia by Rajaa Suhail Najim, Ahmed Diaa Abdulwahab, Dina Nasih Tawfeeq

“…Background and objectives: Many published reports demonstrated the cardinal hallmark of COVID-19 infection, such as bilateral ground glass opacities (GGO) with or without consolidation in posterior and peripheral lungs fields. Various CT algorithms including CT scoring system were used to assess quantitively and qualitatively the severity of COVID-19 pneumonia. …”
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Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy by Yongsheng Gao, Guodong Lang, Chenxiao Zhang, Rui Wu, Yanhe Zhu, Yu Zhao, Jie Zhao

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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.…”
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A systematic literature review on integrating AI-powered smart glasses into digital health management for proactive healthcare solutions by Boyuan Wang, Ying Zheng, Xihao Han, Liang Kong, Gexin Xiao, Zunxiong Xiao, Shanji Chen

“…Notably, some cutting-edge smart glasses have adopted advanced artificial intelligence algorithms, particularly large language models (LLMs) with context awareness and human-like interaction capabilities. …”
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Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Com... by L. E. Simms, N. Yu. Ganushkina, M. Van derKamp, M. Balikhin, M. W. Liemohn

“…Abstract We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. …”
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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach by Iven Aabaah, Japheth Kodua Wiredu, Bakaweri Emmanuel Batowise, Nelson Abuba Seidu

“…The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted participant access that hinder the development of robust algorithms. Additional issues include variability in sensor placement, soft tissue artifacts, and low diversity in movement patterns. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). …”
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Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting by Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao

“…However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. …”
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Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model by Juho Han, Sebeen Yoon, Mingyun Kang, Taehoon Kim

“…The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. …”
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