Integrated phenotypic and transcriptomic characterization of desmin-related cardiomyopathy in hiPSC-derived cardiomyocytes and machine learning-based classification of disease feat... by Vivien Batoumeni, Yeranuhi Hovhannisyan, Bénédicte Gobert, Keyhan Alvandipour, Jennifer Arthur Ataam, Ombline Conrad, David Hoffmann, Nicolas Wiest-Daesslé, Jochen Dobner, José Américo Nabuco Leva Ferreira Freitas, Hakim Hocini, Zhenlin Li, Laurent Brino, Andrea Rossi, Onnik Agbulut, Peter Sommer, Pierre Joanne, Konstantinos Gkatzis

“…Finaly, we developed a machine learning prediction model to classify cellular phenotypes, which can be used for translational research, including drug candidate screening. …”
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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. …”
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New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs by Patrycja S. Matusik, Zbisław Tabor, Iwona Kucybała, Jarosław D. Jarczewski, Tadeusz J. Popiela

“…Conclusion: The use of AI may optimize the screening process for cardiomegaly on CXRs. Future studies should focus on improving the accuracy of AI algorithms and on assessing the usefulness both of CTR and TCD measurements in screening for cardiomegaly.…”
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The Effect of Extended Smartphone Screen Time on Continuous Partial Attention by M. Fırat

“…Students attributed this finding to hypnotic algorithms, distracting redundancy, marketing and advertising, passive receiver mode, short video flow, and surprising content. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

“…This represents a pioneering application of explainability techniques in the context of cervical cancer screening. Among the classification models explored, including fine-tuned variants of VGGNet and XceptionNet, VGG16-Adapted128 achieved the highest performance, marked by an accuracy of 0.94, precision of 0.94, recall of 0.94, and an F1 score of 0.94. …”
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Big data for imaging assessment in glaucoma by Douglas R. da Costa, Felipe A. Medeiros

“…With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The optimal warping path is weighted by the proportion of gas injection-production to screen pressure monitoring wells. The supervised learning model of formation pressure forecasting is established by three kinds of machine learning algorithms including extreme gradient boosting (XGBoost), support vector regression (SVR), and long short-term memory network (LSTM). …”
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Liquid chromatography-mass spectrometry-based metabolic panels characteristic for patients with prostate cancer and prostate-specific antigen levels of 4–10 ng/mL by Chen Wang, Ting Chen, Teng-Fei Gu, Sheng-Ping Hu, Yong-Tao Pan, Jie Li

“…Based on the identified metabolites, LASSO regression was applied for variable selection, and logistic regression and support vector machine models were developed. Results: The LASSO algorithm’s ability to select variables effectively reduced redundant features and minimized model overfitting. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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Preference-based expensive multi-objective optimization without using an ideal point by Peipei Zhao, Liping Wang, Qicang Qiu

“…The Gaussian process model is built on the objective functions. In the model-based optimization, the projection distance with upper confidence bound (UCB) is developed as the fitness of solutions for each subproblem. …”
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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach by Diego Bravo, Juan Frias, Felipe Vera, Juan Trejos, Carlos Martínez, Martín Gómez, Fabio González, Eduardo Romero

“…The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.…”
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AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening by Rui-Fang Lu, Chao-Yin She, Dan-Ni He, Mei-Qing Cheng, Ying Wang, Hui Huang, Ya-Dan Lin, Jia-Yi Lv, Si Qin, Ze-Zhi Liu, Zhi-Rong Lu, Wei-Ping Ke, Chao-Qun Li, Han Xiao, Zuo-Feng Xu, Guang-Jian Liu, Hong Yang, Jie Ren, Hai-Bo Wang, Ming-De Lu, Qing-Hua Huang, Li-Da Chen, Wei Wang, Ming Kuang

“…Abstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. …”
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Deep Learning-Based Draw-a-Person Intelligence Quotient Screening by Shafaat Hussain, Toqeer Ehsan, Hassan Alhuzali, Ali Al-Laith

“…The primary objective of our research is to streamline the IQ screening process for psychologists by leveraging deep learning algorithms. …”
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Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study by Sunyoung Kim, Jaeyu Park, Yejun Son, Hojae Lee, Selin Woo, Myeongcheol Lee, Hayeon Lee, Hyunji Sang, Dong Keon Yon, Sang Youl Rhee

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Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA by Yang Sen, Wang Hengdi, Cui Yongcun, Li Chang, Tang Yuanchao

“…Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%.…”
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A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD by Jinggeng Gao, Honglei Xu, Yong Yang, Haoming Niu, Jinping Liang, Haiying Dong

“…Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified.…”
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Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition by Xiaomeng LI, Daidou GUO, Xunfang ZHUO, Heng YAO, Chuan QIN

“…Financial security, an important part of national security, is critical for the stable and healthy development of the economy.Digital image watermarking technology plays a crucial role in the field of financial information security, and the anti-screen watermarking algorithm has become a new research focus of digital image watermarking technology.The common way to achieve an invisible watermark in existing watermarking schemes is to modify the carrier image, which is not suitable for all types of images.To solve this problem, an end-to-end robust watermarking scheme based on deep learning was proposed.The algorithm achieved both visual quality and robustness of the watermark image.A random binary string served as the input of the encoder network in the proposed end-to-end network architecture.The encoder can generate the watermark information overlay, which can be attached to any carrier image after training.The ability to resist screen shooting noise was learned by the model through mathematical methods incorporated in the network to simulate the distortion generated during screen shooting.The visual quality of the watermark image was further improved by adding the image JND loss based on just perceptible difference.Moreover, an embedding hyperparameter was introduced in the training phase to balance the visual quality and robustness of the watermarked image adaptively.A watermark model suitable for different scenarios can be obtained by changing the size of the embedding hyperparameter.The visual quality and robustness performance of the proposed scheme and the current state-of-the-art algorithms were evaluated to verify the effectiveness of the proposed scheme.The results show that the watermark image generated by the proposed scheme has better visual quality and can accurately restore the embedded watermark information in robustness experiments under different distances, angles, lighting conditions, display devices, and shooting devices.…”
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Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. by Miriam Leiko Terabe, Miyoko Massago, Pedro Henrique Iora, Thiago Augusto Hernandes Rocha, João Vitor Perez de Souza, Lily Huo, Mamoru Massago, Dalton Makoto Senda, Elisabete Mitiko Kobayashi, João Ricardo Vissoci, Catherine Ann Staton, Luciano de Andrade

“…Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.…”
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