An overview of artificial intelligence based automated diagnosis in paediatric dentistry by Suba B. Rajinikanth, Densingh Samuel Raj Rajkumar, Akshay Rajinikanth, Ponsekar Abraham Anandhapandian, Bhuvaneswarri J.

“…The field of AI, deep machine learning and CNN's is an upcoming and newer area, with new developments this will open up areas for more sophisticated algorithms in multiple layers to predict accurately, when compared to experienced Paediatric dentists.…”
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Enhanced Performance of New Scaling-Free CORDIC for Memory-Based Fast Fourier Transform Architecture by C. Paramasivam, Sandeep Singh Chauhan, Veerpratap Meena, A. Sreejagathi, B. A. V. N. Hasini, K. L. K. Kishore, T. V. N. G. Vamsikrishna, M. Durga Ananta Sai, Abdessamad Didi

“…The angle of convergence (AOC) of the algorithm is 57.1°, and it is extended to 180° using the pre-rotation operation and optimized shift value prediction technique. …”
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AI-driven healthcare: Fairness in AI healthcare: A survey. by Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang

“…We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. …”
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The Effect of Derived Features on Art Genre Classification with Machine Learning by Didem Abidin

“…Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. …”
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Non-Gaussian Process Dynamical Models by Yaman Kindap, Simon Godsill

“…Probabilistic dynamical models used in applications in tracking and prediction are typically assumed to be Gaussian noise driven motions since well-known inference algorithms can be applied to these models. …”
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SURGNET: An Integrated Surgical Data Transmission System for Telesurgery by Sriram Natarajan, Aura Ganz

“…In this paper we propose SURGNET, a telesurgery system for which we developed the architecture, algorithms and implemented it on a testbed. The algorithms include adaptive packet prediction and buffer time adjustment techniques which reduce the negative effects caused by the lossy and time varying networks. …”
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Establishment and validation of post-PCI nomogram in elderly patients with acute coronary syndromes by Xing-Yu Zhu, Zhi-Meng Jiang, Xiao Li, Fei-Fei Su, Jian-Wei Tian

“…When comparing our clinical prediction model to the widely used GRACE scoring system, the results showed that our model had slightly better predictive efficacy for the dataset involved in this study. …”
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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. Feature selection was conducted using gradient-boosted recursive feature elimination. …”
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Sensor Image, Anomaly Detection Method for Hydroelectric Dam Structure Using Sensors Measurements and Deep Learning by Van-Phuong Ha, Dinh-Van Nguyen, Trong-Chuong Trinh, Duc-Cuong Quach, Van HuyBui

“…To better prevent future disasters, machine-learning algorithms have been employed. Often, these algorithms are trained on historical sensor data to predict future events. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“…The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. …”
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Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia by Fangmin Zhong, Fangyi Yao, Zihao Wang, Jing Liu, Bo Huang, Xiaozhong Wang

“…Additionally, patients with a high risk score were also predicted to exhibit a favorable response to anti-PD-1 therapy, suggesting that these individuals may derive greater benefits from immunotherapy.ConclusionThe NET-related signature, derived from a combination of diverse machine learning algorithms, has promising potential as a valuable tool for prognostic prediction, preventive measures, and personalized medicine in patients with AML.…”
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NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE by Anna Bakurova, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, Elina Tereschenko

“…The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. …”
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A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health by Bassel Hammoud, Aline Semaan, Lenka Benova, Imad H. Elhajj

“…Its effectiveness is tested, using 8 ML algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, XGBoost, CatBoost, and Artificial Neural Network) to predict the feeling of protection among healthcare workers during the COVID-19 pandemic, based on a global online survey, then validated on two other outputs. …”
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Advancing lakes algal chlorophyll estimation in the contiguous USA: A comparative study of machine learning models and satellite data by Md Mamun, Xiao Yang

“…We assess the performance of four ML algorithms (random forest, extra tree regressor, bagging regressor, and xgboost model), discern the most influential spectral bands and indices, and compare these methods to established remote sensing techniques for CHL-a prediction. …”
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Method of terminal control in ascent segment of unmanned aerial vehicle with ballistic phase by N. Y. Polovinchuk, S. V. Ivanov, M. Y. Zhukova, D. G. Belonozhko

“…Such algorithms have good convergence and injection accuracy due to the prediction of parameters during the flight at a shorter time interval.Discussion and Conclusions. …”
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Single target tracking in high-resolution satellite videos: a comprehensive review by Xin Huang, Ding Wang, Qiqi Zhu, Ying Zheng, Qingfeng Guan

“…To identify emerging research trends and opportunities, we classify established and emerging algorithms into three main categories and evaluate their performance on typical satellite videos. …”
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Using Explainable AI to Measure Feature Contribution to Uncertainty by Katherine Elizabeth Brown, Douglas A. Talbert

“…\textit{Uncertainty} measures the algorithm’s lack of trust in its predictions, and this information is important for practitioners using machine learning-based decision support. …”
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Simulation of Minefield Installation in a Video Game Engine by Maksym Maksymov, Oleksii Neizhpapa, Oleksandr Toshev, Maksym Kiriakidi

“…The first objective of this research is to improve damage prediction algorithms, enabling the simulation to more accurately estimate the consequences of ships passing through a minefield. …”
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A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints by Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang

“…By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. …”
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Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home program by Maria Glòria Bonet-Papell, Maria Glòria Bonet-Papell, Georgina Company-Se, María Delgado-Capel, Beatriz Díez-Sánchez, Lourdes Mateu-Pruñosa, Roger Paredes-Deirós, Jordi Ara del Rey, Lexa Nescolarde

“…Various classification algorithms (bagged trees, KNN, LDA, logistic regression, Naïve Bayes, and the support vector machine [SVM]) were implemented to predict readmission, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and the Matthews Correlation Coefficient (MCC).ResultsSignificant differences were observed in IL-6, Hs-TnT, CRP (p < 0.001), and ferritin (p < 0.01) between the first day of conventional hospitalization and the first day of HaH for patients who were not readmitted. …”
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