Promoting Digital Health Data Literacy: The Datum Project by Daniel Powell, Laiba Asad, Elissa Zavaglia, Manuela Ferrari

“…The Datum project makes an original contribution to the field of law and ethics in health science research by using novel approaches, such as learning health systems and data banks, to improve equity in health care delivery and by generating multimedia content aimed at encouraging health care users to become better consumers and supporting the collective use of their data. …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. …”
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Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty by Baokun Han, Sixiang Jia, Guifang Liu, Jinrui Wang

“…The process of feature learning is visualized to show the feature extraction process of WGAN-GP. …”
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The Neurorehabilitation Training Toolkit (NTT): A Novel Worldwide Accessible Motor Training Approach for At-Home Rehabilitation after Stroke by Sergi Bermúdez i Badia, M. S. Cameirão

“…The NTT brings together concepts of optimal learning, engagement, and storytelling to deliver a personalized training to its users. …”
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ISLRWR: A network diffusion algorithm for drug-target interactions prediction. by Lu Sun, Zhixiang Yin, Lin Lu

“…Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. …”
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Algorithm and Simulation of Association Rules of Drug Relationship Based on Network Model by Hui Teng, Yukun Ma, Di Teng

“…Network model is suitable for the study of the nonlinear relationship of the pharmaceutical relationship by modeling the data learning. Association rule mining is used to find the potential correlations between the various sets of massive data. …”
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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network by Zhaowen Pei, Liao Chang, Pengfei Xue, Richard J. Harrison

“…Here, we present an easy‐to‐use machine learning framework MagNet for automated morphological recognition of magnetic mineral grains in microscopic images. …”
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An Emerging Theory of School-Based Participatory Science by P. Sean Smith, Christine L. Goforth, Sarah J. Carrier, Meredith L. Hayes, Sarah E. Safley

“…Participatory science conducted in formal K–12 settings has many benefits, including the potential to engage teachers and students authentically in the scientific enterprise and to make learning more meaningful. Despite these benefits and others, school-based participatory science (SBPS) is not widespread. …”
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Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN by Muhammad Asif, Talha Ahmed Khan, Wang-Cheol Song

“…This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. …”
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Driver Distraction Identification with an Ensemble of Convolutional Neural Networks by Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa

“…In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. …”
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A Review of Outcomes and Technique for the Robotic-Assisted Laparoscopic Retroperitoneal Lymph Node Dissection for Testicular Cancer by Zeyad R. Schwen, Mohit Gupta, Phillip M. Pierorazio

“…Encouraged by favorable early oncologic and safety outcomes for treatment of clinical stage (CS) I nonseminomatous germ cell tumor (NSGCT), the R-RPLND affords the same recovery advantages as the laparoscopic retroperitoneal lymph node dissection (L-RPLND) while offering greater dexterity, superior visualization, and a theoretically shorter learning curve for the surgeon. While R-RPLND has a promising future in the management of patients with primary and postchemotherapy NSGCT, larger and more vigorous prospective studies are needed before supplanting the open RPLND as the gold standard approach for primary low-stage NSGCT or becoming an equivalent surgical modality in the postchemotherapy setting.…”
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A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management by Burak Kandemir

“…In this paper, we propose two algorithms, one based on statistical analysis and the other an unsupervised machine learning algorithm using K-means clustering, both of which differ-entiate seasonal and non-seasonal products where an item’s variation is evaluated with respect to seasonality of the product group it belongs to. …”
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Lightweight attention-based SAR ship detector by YU Nanjing, FENG Daquan, ZHU Ying, ZHANG Hengjia, LU Ping

“…Synthetic aperture radar (SAR) remote sensing images have been widely applied in military reconnaissance and traffic supervision, owing to their all-weather and all-day abilities. With excellent learning performance, convolutional neural networks are employed in the SAR ship detection algorithms. …”
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The Relationship Between Mathematical Literacy and Early Childhood's Habits of Mind by Efrida Mandasari Dalimunthe, Raisatunnisa Raisatunnisa, Agung Kaisar Siregar

“…So it can be concluded that developing habits of mind through well-planned learning can strengthen early childhood mathematical literacy. …”
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Models for information propagation on graphs by Oliver R. A. Dunbar, Charles M. Elliott, Lisa Maria Kreusser

“…Finally, we apply the front propagation models on graphs to semi-supervised learning via label propagation and information propagation on trust networks.…”
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Using Story Frame Approach to Increase Students’ Reading Comprehension at Senior High School by Putri Nurlia, Sri Ramadhona, Sinta Dwi Devita

“…This study found that students reading post-test score in experimental class using story frame strategy had a higher score of 76,67 higher than the student control class and a score post-test of 67,62, based on the formula, the difference in control class learning without Story Frame strategy. In addition, the Story Frame technique has made their reading comprehension more important. …”
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A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete by Zhangabay Nurlan

“…Results demonstrate powerful potential in the learning section as well as approximation in the testing phase. …”
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Potential Source Density Function: A New Tool for Identifying Air Pollution Sources by In Sun Kim, Yong Pyo Kim, Daehyun Wee

“…Abstract Potential source density function (PSDF) is developed to identify, that is, locate and quantify, source areas of ambient trace species based on Gaussian process regression (GPR), a machine-learning technique. The PSDF model requires backward trajectories and sampling data at a receptor site in the calculation as in the conventional model to locate source areas of ambient trace species, such as the potential source contribution function (PSCF). …”
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Preparing Advanced Clinicians and Practitioners: A Model for Mentorship in Occupational Therapy Practice by Sarah A. Schoen, Bryan M. Gee, Mim Ochsenbein

“…Positive outcomes were associated with this one mentorship program, suggesting a need for more in-person, structured mentored learning experiences. Mentorship is recommended as a method to address the growing need within the profession to support career development, build knowledge, skill and attitudes, and aspirations/commitment as well as enhance professionalism/professional development.…”
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The Greenhouse Effect: Using Student-Generated Agricultural Data to Warm Up Students for Data-Based Decision Making by Shonda Kuiper, Abhishek Chakraborty, Tyler George, Lisa W. Kay, Lawrence M. Lesser, Ginger Rowell, Dennis Pearl, Scott Crawford, Anna Olsen

“…This allowed us to evaluate the differences in student learning and attitudes when students have a stronger connection with the data through an interactive—and hopefully fun—data-collection process than they do when they are given a worksheet with a pre-collected dataset. …”
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