PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans by I-Min Chiu, Teng-Yi Huang, David Ouyang, Wei-Che Lin, Yi-Ju Pan, Chia-Yin Lu, Kuei-Hong Kuo

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Early Detection of Congenital Heart Diseases among Infants Using Artificial Neural Network Algorithm by Lucy Ifeyinwa Ezigbo, Anthony Kwubeghari, Francis Okoye

“…After the data processing stage, Artificial Neural Network (ANN) algorithm is adopted and trained with the data to generate the model for the detection of the CHD. …”
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Machine Learning and Metaheuristic Algorithms for Voice-Based Authentication: A Mobile Banking Case Study by Leili Nosrati, Amir Massoud Bidgoli, Hamid Haj Seyyed Javadi

“…This speech and password matching verification system uses our fuzzy nonlinear support vector machine network classification system, which was trained using the Ali Baba and the forty thieves algorithm. …”
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Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety by Abhishek Kumar Pathak, Ankit Kumar Singh, Pankaj Kumar, Vimal Bhatia, Vimal Bhatia, Vimal Bhatia, Ondrej Krejcar, Ondrej Krejcar

“…The models were trained and evaluated using comprehensive performance metrics, such as accuracy, precision, recall, F1 score, and confusion matrix. …”
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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

“…Technically, we performed a sample denoising process with Tomek links before applying the SMOTE and then followed by sample optimization with an evolutionary algorithm after the SMOTE. A genetic algorithm (GA) as one of the popular evolutionary algorithms is utilized for sample optimization including synthetic samples of SMOTE and original samples from both classes. …”
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Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs by Valeriy G. Narushin, Natalia A. Volkova, Alan Yu. Dzhagaev, Darren K. Griffin, Michael N. Romanov, Natalia A. Zinovieva

“…., image recognition and applications for the detection of egg cracks, egg content and freshness. We comment on how algorithms need to be properly trained and ask what information can be gleaned from egg shape. …”
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Comparison among grazing animal behavior classification algorithms for use with open-source wearable sensors by B.R. dos Reis, S. Sujani, D.R. Fuka, Z.M. Easton, R.R. White

“…Similar accuracies were found when evaluating the RF model on the 3 and 5-second iteration, indicating power saving may be achieved by periodic, rather than continuous sampling. …”
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A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images by Fanxing Meng, Tuo Zhang, Yukun Pan, Xiaojing Kan, Yuwei Xia, Mengyuan Xu, Jin Cai, Fangbin Liu, Yinghui Ge

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A Multihead Attention Deep Learning Algorithm to Detect Amblyopia Using Fixation Eye Movements by Dipak P. Upadhyaya, MS, Gokce Cakir, MD, Stefano Ramat, PhD, Jeffrey Albert, PhD, Aasef Shaikh, MD, PhD, Satya S. Sahoo, PhD, Fatema Ghasia, MD

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Comparative Analysis of A3C and PPO Algorithms in Reinforcement Learning: A Survey on General Environments by Alberto del Rio, David Jimenez, Javier Serrano

“…An evaluation of the environment is needed in terms of algorithm selection, based on specific application needs, balancing between training time and stability. …”
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A Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection by Saeid Sheikhi, Mohammad Taghi Kheirabadi, Amin Bazzazi

“…This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. …”
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Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices by Rica Ramadana, Agus Perdana Windarto, Dedi Suhendro

“…The aim of this study is to evaluate and compare various learning functions within the Backpropagation algorithm to determine the best one for prediction cases. …”
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Fitting Method of Optimal Energy-Running Time Curve Based on Train Operation Data of an Urban Rail Section by Lianbo Deng, Hongda Mei, Wenliang Zhou, Enwei Jing

“…The validation of Guangzhou Metro's actual operation data shows that the energy-running time curve fitted and optimized by our method has lower energy and better continuity and smoothness and could be used for evaluation of train drivers’ performance and energy consumption of train operation diagram.…”
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Enhanced Model for Gestational Diabetes Mellitus Prediction Using a Fusion Technique of Multiple Algorithms with Explainability by Ahmad Hassan, Saima Gulzar Ahmad, Tassawar Iqbal, Ehsan Ullah Munir, Kashif Ayyub, Naeem Ramzan

“…It uses conventional Machine Learning (ML) and advanced Deep Learning (DL) algorithms. Subsequently, it combines the strengths of both ML and DL algorithms using various ensemble techniques. …”
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Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning by Giulia Corniani, Stefano Sapienza, Gloria Vergara-Diaz, Andrea Valerio, Ashkan Vaziri, Paolo Bonato, Peter M. Wayne

“…This approach has the potential to objectively enhance the evaluation of Tai Chi training protocol adherence, learnability, progression in proficiency, and safety in Tai Chi programs, and thus inform training program parameters that are key to achieving optimal clinical outcomes.…”
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Implementation of the Long Short-Term Memory Algorithm in a Chatbot for Dengue Fever Information and Education Services by Heru Budianto, Fahmi Yusuf, Dede Irawan, Muhamad Akhirul Sidik, Aiena Nurhasanah, Sabrina Mauldya, Silmi Nur Afifah

“…The data undergoes a series of preprocessing steps before being used for model development, training, and evaluation. The test results indicate that the developed model achieved an accuracy of 100% during validation with a loss function value of 0.0221. …”
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Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation by Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

“…ResultsA total of 121,498 pulmonary function tests were used in this study, with 85,017 allotted for exploratory data analysis and model development (ie, training dataset) and 36,481 tests reserved for model evaluation (ie, testing dataset). …”
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Determining Atikhisar Reservoir’s Bathymetry from Landsat-5 TM Satellite Images Using the Stumpf Algorithm by Derya Öztürk

“…The study also uses the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to determine the reservoir’s surface area and the Stumpf algorithm to perform the bathymetric mapping. Satellite image-based DBMs were obtained using the linear regression equations created from the blue/green log-ratio values from the Landsat-5 TM satellite image and the values obtained from a 1/5000 scale digital bathymetric map for five different training reference point sets. …”
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The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms by Hongxia Yan, Yixin Tan, Fan Qiao, Zhuotong Zeng, Yaqian Shi, Xueqin Zhang, Lu Li, Ting Zeng, Yi Zhan, Ruixuan You, Xinglan He, Rong Xiao, Xiangning Qiu

“…We developed and validated prediction models with Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms. Model performance was assessed using confusion matrix and evaluation metrics. …”
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Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm by Feng Zhang, Zijie Qiao, Yuxiang Tian, Mingying Wu, Xiayu Xu

“…To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. …”
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