Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. …”
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Outpatient diagnosis of endogenous intoxication in surgery by A. A. Solomakha, A. P. Vlasov, V. I. Gorbachenko

“…The problems of diagnosis, treatment, prevention and prediction of purulent diseases in surgery can be solved thanks to advanced digital technologies.Aim of the study. …”
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Molecular insights into ulcerative colitis and orbital inflammation by Kang Tan, Pei Liu, Zixuan Wu, Xi Long, Yunfeng Yu, Pengfei Jiang, Qinghua Peng

“…GO enrichment, PPI networks, and transcription factor prediction were performed using Cytoscape plugins (cytoHubba and iRegulon). …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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The unwell patient with advanced chronic liver disease: when to use each score? by Oliver Moore, Wai-See Ma, Scott Read, Jacob George, Golo Ahlenstiel

“…Incorporating artificial intelligence to personalise predictive algorithms may provide the most effective prognostication for all clinical phenotypes. …”
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Point-of-care prognostication in moderate Covid-19: Analytical validation and prognostic accuracy of a soluble urokinase plasminogen activator receptor (suPAR) rapid test. by Arjun Chandna, Raman Mahajan, Priyanka Gautam, Lazaro Mwandigha, Sabine Dittrich, Vikash Kumar, Jennifer Osborn, Pragya Kumar, Constantinos Koshiaris, George M Varghese, Yoel Lubell, Sakib Burza

“…AUC of ELISA = 0.70 [95% CI = 0.63 to 0.76]; p = 0.12) or as part of a published multivariable prediction model (AUC of RDT-based model = 0.74 [95% CI = 0.66 to 0.83] vs. …”
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Computational Intelligence-Based Structural Health Monitoring of Corroded and Eccentrically Loaded Reinforced Concrete Columns by Somain Sharma, Harish Chandra Arora, Aman Kumar, Denise-Penelope N. Kontoni, Nishant Raj Kapoor, Krishna Kumar, Arshdeep Singh

“…Machine learning (ML)-based prediction models are beneficial in dealing with such complex databases. …”
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Development of a 101.6K liquid‐phased probe for GWAS and genomic selection in pine wilt disease‐resistance breeding in Masson pine by Jingyi Zhu, Qinghua Liu, Shu Diao, Zhichun Zhou, Yangdong Wang, Xianyin Ding, Mingyue Cao, Dinghui Luo

“…The DNNGP (deep neural network‐based method for genomic prediction) model demonstrated superior performance in GS, achieving a maximum predictive accuracy of 0.71. …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Despite its practical importance, the direct impact of parallel shaft misalignment on bearing fault prediction remains unaddressed, mainly in the existing literature. …”
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The impact of a coach-guided personalized depression risk communication program on the risk of major depressive episode: study protocol for a randomized controlled trial by JianLi Wang, Cindy Feng, Mohammad Hajizadeh, Alain Lesage

“…Individuals are eligible, if they: (1) are 18 years or older, (2) have not had a depressive episode in the past two months, (3) are at high risk of MDE based on the sex-specific risk predictive algorithms for MDE (predicted risk of 6.5% + for men and of 11.2% + for women), (4) can communicate in either English or French, and (5) agree to be contacted for follow-up interviews. …”
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Flood-tech frontiers: smart but just? A systematic review of AI-driven urban flood adaptation and  associated governance challenges by Johannes Bhanye

“…Key limitations include the poor transferability of AI models across geographies, a lack of participatory design, and risks of algorithmic exclusion in already marginalized urban areas. …”
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Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…This study demonstrates how combining optical and microwave remote sensing data, the GEE platform, transfer learning, and cotton cropland mapping algorithms can enhance insights into precision agricultural systems.…”
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Accurate and affordable multi-cancer early detection and localization via plasma cfDNA multi-omic profiling by Pin Cui, Weihuang He, Mingji Feng, Hanming Lai

“…By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites, based on which we built in silico classifiers for prediction of each of the four groups previously mentioned, achieving considerable performance of AUC ranging from 0.8896 to 0.959. …”
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Genomic profiling, implications for genotype-based treatment of 131 patients with phenylketonuria and characterization of novel p.Pro416Leu PAH variant by K. Klaassen, B. Kecman, S. Stankovic, J. Komazec, S. Pavlovic, Maja Stojiljkovic, M. Djordjevic

“…We detected one novel variant, p.Pro416Leu, which was classified as pathogenic, based on computational algorithms prediction, with destabilization as the mechanism of the effect upon PAH protein. …”
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Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques by J. Priskilla Angel Rani, C. Yesubai Rubavathi

“…This study uses AquaMeld and Multi-Layer Perceptron with Recurrent Neural Network (MLP-RNN) algorithms to create a complete recommendation system. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. …”
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Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence by Kristóf Ágrez, Pál Vakli, Béla Weiss, Zoltán Vidnyánszky, Nóra Bunford

“…Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. …”
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Investigating the impact of fatty acid profiles on biodiesel lubricity using artificial intelligence techniques by Atthaphon Maneedaeng, Attasit Wiangkham, Atthaphon Ariyarit, Anupap Pumpuang, Ekarong Sukjit

“…To further analyze the impact of fatty acid composition on lubricity, an artificial intelligence (AI)-based approach using the Adaptive Boosting (AdaBoost) algorithm was implemented. The AI model effectively predicts wear scar diameter, friction coefficient, and film formation, providing insights into the complex interactions between fatty acid profiles and tribological performance. …”
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A multimodal approach for ADHD with coexisting ASD detection for children by Jungpil Shin, Sota Konnai, Md. Maniruzzaman, Yoichi Tomioka, Yong Seok Hwang, Akiko Megumi, Akira Yasumura

“…Each task had two conditions: trace and predict. Various statistical features were derived from pen tablet and fNIRs data for each task. …”
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Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer. by Skye Hung-Chun Cheng, Tzu-Ting Huang, Yu-Hao Cheng, Tee Benita Kiat Tan, Chen-Fang Horng, Yong Alison Wang, Nicholas Shannon Brian, Li-Sun Shih, Ben-Long Yu

“…The 18-gene scoring algorithm defines scores as: <21, low risk; ≥21, high risk. …”
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