Intelligent Assessment Systems in Medical Education: A Systematic Review by MAJID ALIZADEH, MARYAM JAFAR SAMERI

“…Introduction: Using Intelligent Assessment Systems (IAS) in medical education has garnered attention as an advanced tool for enhancing educational processes and student evaluation. These systems leverage advanced algorithms to rapidly assess the students’ performance and learning progress. …”
Get full text

Artificial Intelligence in Education: Analysis and Assessment of Legal Knowledge Using AI Tools by Anvar J. Khurramov, Mavluda A. Axmedshaeva, Fiyuza A. Mukhitdinova, Gulnora A. Xudayberdiyeva, Shahnoza S. Almosova, Makhmud M. Makhamatov, Sherzod R. Khayitov

“…Recommendations include clear institutional guidelines, targeted teacher training, and the adoption of AI systems that align with legal norms and moral standards in education. …”
Get full text

Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study by Alyce S Adams, Catherine Lee, Gabriel Escobar, Elizabeth A Bayliss, Brian Callaghan, Michael Horberg, Julie A Schmittdiel, Connie Trinacty, Lisa K Gilliam, Eileen Kim, Nima S Hejazi, Lin Ma, Romain Neugebauer

“…We randomly split the data into training (38,569/48209; 80%) and testing (9,640/48209; 20%) datasets. …”
Get full text

A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

“…The decision basis of the optimal model was further analyzed using Shapley Additive Explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME).ResultsIn total, 12 characteristic variables were identified using the Boruta algorithm. Among the five machine learning models, random forest model: AUC (training set: 0.840, test set: 0.779), accuracy (training set: 0.781, test set: 0.742); SVM: AUC (training set: 0.809, test set: 0.768), accuracy (training set: 0.751, test set: 0.728); XGBoost: AUC (training set: 0.734, test set: 0.748), accuracy (training set: 0.668, test set: 0.722); logistic regression: AUC (training set: 0.672, test set: 0.691), accuracy (training set: 0.651, test set: 0.656); lasso-stacking model: AUC (training set: 0.877, test set: 0.791), accuracy (training set: 0.796, test set: 0.762). …”
Get full text

Representation of few-group homogenized cross section by multi-variate polynomial regression by Nguyen Dinh Quoc Dang, Masiello Emiliano

“…Furthermore, its evaluation algorithm can be adapted for GPU utilization, similar to polynomial interpolation with the Newton method [5].…”
Get full text

Gated recurrent deep learning approaches to revolutionizing English language learning for personalized instruction and effective instruction by Bo Sun

“…The training and evaluation architecture and dataset are detailed with an emphasis on optimization techniques. …”
Get full text

AzSLD: Azerbaijani sign language dataset for fingerspelling, word, and sentence translation with baseline softwareZenodo by Nigar Alishzade, Jamaladdin Hasanov

“…This approach enables robust training and evaluation of gesture recognition algorithms. …”
Get full text

Advanced Financial Fraud Malware Detection Method in the Android Environment by Jaeho Shin, Daehyun Kim, Kyungho Lee

“…Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. …”
Get full text

Defect detection in textiles using back propagation neural classifier by Subrata Das, Amitabh Wahi, Suresh Jayaram

“…After successful training of the neural network on train dataset, the performance of the trained neural network was evaluated on the test dataset. …”
Get full text

Topical Issues of Drug Safety, Possibilities of Improving of Pharmacovigilance by M. V. Zhuravleva, B. K. Romanov, G. I. Gorodetskaya, O. V. Muslimova, V. S. Krysanova, E. Yu. Demchenkova

“…Presents methods to identify causal relationships between adverse reaction and drug, evaluation of its degree of validity (questionnaires, algorithms, and scale), as already recommended by the WHO, and the new modifi ed versions. …”
Get full text

A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network by Yulianto Triwahyuadi POLLY, Adriana FANGGIDAE, Juan Rizky Mannuel LEDOH, Clarissa Elfira AMOS PAH, Bertha S. DJAHI, Kisan Emiliano Rape TUPEN

“…The evaluation was based on three main aspects: accuracy, F1 score, and training time. …”
Get full text

The Application of Artificial Intelligence in Medical Diagnostics: Implications for Sports Medicine by Michał Bolek, Dominika Musialska, Aleksandra Kędzia, Bartosz Jagieła, Monika Fidyk, Magda Minkiewicz, Maciej Dyda

“…These AI-driven tools can predict injury risks by evaluating training loads, biomechanics, and real-time physiological signals. …”
Get full text

Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández, Francisco Manuel Arrabal-Campos

“…Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.7</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>), while TRAIn achieves the highest fidelity (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.5</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>) at a modest computational cost. …”
Get full text

METHODS OF MATHEMATICAL STATISTICS AS A MEANS OF PROFESSIONAL COMPETENCE FORMATION OF STUDENTS-ECOLOGISTS by S. I. Toropova

“…The main methodological and organizational problems in the training of environmental students in statistical data analysis are revealed: orientation of the existing training programs and methods of professional education towards the “average” student; insufficient quantity of professionoriented study guides; large volume of statistical information, necessary for studying, and limitation of temporary opportunities for its understanding and evaluation; a lack of future ecologists’ experience on application of mathematical statistics toolkit; etc. …”
Get full text

Incentivization and Aggregation Schemes for Federated Learning Applications by Vikas Hassija, Vaibhav Chawla, Vinay Chamola, Biplab Sikdar

“…The aggregation technique uses Polyak-averaging to aggregate weights of local models, with the weightage assigned to each local model being proportional to its accuracy on the test dataset. Performance evaluation shows that the federated learning model formed using our aggregation approach achieves the performance level of machine learning as we perform more iterations and performs slightly better than the model formed using the FedAvg algorithm. …”
Get full text

Multiple Channel Integration Quality Assessment Method Using NARX by Xiaolei Wang, Yingzhao He

“…Then, this study uses the dataset preprocessed with the GRA algorithm for training the NARX model. Then, this study uses the trained model to evaluate the quality of multiple channel integration comprehensively. …”
Get full text

A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (<i>u</i>, <i>v</i> + rh) model outperforms the MR-ConvLSTM (<i>u</i>, <i>v</i> + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation.…”
Get full text

Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Mette Sagbakken, Ahmed Ghannam, Fuad H. Abuadas, Joel Somerville, Joel Somerville, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair

“…Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT), and the risk of bias evaluated through ROBINS-I.ResultsThis review encompassed 25 studies, from which six overarching themes emerged.Education and trainingAI-powered simulations and content-creation platforms enriched nursing curricula by presenting realistic clinical scenarios, which consistently yielded deeper student engagement, enhanced case-management performance, and higher satisfaction scores. …”
Get full text

Refining CT image analysis: Exploring adaptive fusion in U-nets for enhanced brain tissue segmentation. by Bang-Chuan Chen, Chung-Yi Shen, Jyh-Wen Chai, Ren-Hung Hwang, Wei-Chuan Chiang, Chi-Hsiang Chou, Wei-Min Liu

“…<h4>Conclusion</h4>Our adaptive fusion strategy significantly diminishes FAs and enhances the training efficacy of DL-based ILS algorithms, surpassing individual U-Net models. …”
Get full text

DQN-Based Shaped Reward Function Mold for UAV Emergency Communication by Chenhao Ye, Wei Zhu, Shiluo Guo, Jinyin Bai

“…In this paper, we establish a meticulously crafted virtual simulation environment and leverage advanced deep reinforcement learning algorithms to train UAVs agents. Notwithstanding, the development of reinforcement learning algorithms is beset with challenges such as sparse rewards and protracted training durations. …”
Get full text