Substituted 1,4-naphthoquinones for potential anticancer therapeutics: In vitro cytotoxic effects and QSAR-guided design of new analogs by Veda Prachayasittikul, Prasit Mandi, Ratchanok Pingaew, Supaluk Prachayasittikul, Somsak Ruchirawat, Virapong Prachayasittikul

“…Four QSAR models were successfully constructed using multiple linear regression (MLR) algorithm providing good predictive performance (R: training set = 0.8928–0.9664; testing set = 0.7824–0.9157; RMSE: training set = 0.1755–0.2600; testing set = 0.2726–0.3748). …”
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The quantification of percentage filling of gutta-percha in obturated root canal using image processing and analysis by Pravin R. Lokhande, S. Balaguru

“…Percentage filling of the obturated root canal using X-ray radiography (Dentist's prediction) and proposed algorithm results of the present study were compared. …”
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Error Data Analytics on RSS Range-Based Localization by Shuhui Yang, Zimu Yuan, Wei Li

“…The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.…”
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Machine Learning-Based Cost Estimation Models for Office Buildings by Guolong Chen, Simin Zheng, Xiaorui He, Xian Liang, Xiaohui Liao

“…This paper explores the application of algorithm-optimized back propagation neural networks and support vector machines in predicting the costs of office buildings. …”
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Multi-objective Optimal Allocation of Renewable Energy Distributed Generations and Shunt Capacitors in Radial Distribution System using Corona Virus Herd Optimization by Oluwaseyi Wasiu Adebiyi, Muniru Olajide Okelola, Sunday Adeleke Salimon

“…Furthermore, the highest level of greenhouse gas emission reduction was achieved (says 99.69%) when high priority was placed on the reduction in TGHGe; this shows the significant impact of renewable energy in the distribution system. …”
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Computer Viewing Model for Classification of Erythrocytes Infected with <i>Plasmodium</i> spp. Applied to Malaria Diagnosis Using Optical Microscope by Eduardo Rojas, Irene Cartas-Espinel, Priscila Álvarez, Matías Moris, Manuel Salazar, Rodrigo Boguen, Pablo Letelier, Lucia San Martín, Valeria San Martín, Camilo Morales, Neftalí Guzmán

“…<i>Conclusions:</i> Based on the results, we propose a convolutional neural network model (VGG-19) for malaria diagnosis that can be applied in low-complexity laboratories thanks to its ease of implementation and high predictive performance.…”
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Remote sensing inversion of nitrogen content in silage maize plants based on feature selection by Kejing Cheng, Kejing Cheng, Jixuan Yan, Jixuan Yan, Guang Li, Guang Li, Weiwei Ma, Weiwei Ma, Zichen Guo, Zichen Guo, Wenning Wang, Wenning Wang, Haolin Li, Qihong Da, Qihong Da, Xuchun Li, Xuchun Li, Yadong Yao, Yadong Yao

“…In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…Phytoplankton species biomass data were collected concurrently using standard inverted microscope counts. The SVR algorithm outperformed the other algorithms in predicting phytoplankton abundance and composition in Lake Kinneret, based on the FP measurements. …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…This integration facilitates a more detailed extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. …”
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Personalizing the Marketing with Artificial Intelligence (AI) by Rozhko Viktor I., Pletnova Yelyzaveta S.

“…Another major challenge is the technical limitations of AI. While AI algorithms are capable of analyzing vast amounts of data, they cannot always properly adapt to a changing market environment or predict consumer behavior in non-standard situations. …”
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Use of four next-generation sequencing platforms to determine HIV-1 coreceptor tropism. by John Archer, Jan Weber, Kenneth Henry, Dane Winner, Richard Gibson, Lawrence Lee, Ellen Paxinos, Eric J Arts, David L Robertson, Larry Mimms, Miguel E Quiñones-Mateu

“…In this study, HIV-1 co-receptor usage was predicted for twelve patients scheduled to start a maraviroc-based antiretroviral regimen. …”
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Machine Learning (ML) Based Repair-Count and Periodic Maintenance Policy for Multipurpose CNC Machinery by Alifin Fakhri Ikhwanul, Winarno, Fasa Nadia, Darajatun Rizki Achmad, Kusnadi, Safariyani Eva

“…Several censored data were utilized to predict failure times using machine learning algorithms, such as process temperature, air temperature, rotational speed, and torque. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
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Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production by Tessa Krause, Daniel Nusko, Johannes Rittmann, Luciana Pitta Bauermann, Moritz Kroll, Carlo Holly

“…To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. …”
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Clinical performance validation and four diagnostic strategy assessments of high-sensitivity troponin I assays by Junyi Wu, Yaotong Hua, Yilin Ge, Ke Chen, Siyu Chen, Jiashu Yang, Hui Yuan

“…Diagnostic accuracy was assessed based on sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. …”
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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. by Giacomo Janson, Alessandro Grottesi, Marco Pietrosanto, Gabriele Ausiello, Giulia Guarguaglini, Alessandro Paiardini

“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater by Areej Alhothali, Hifsa Khurshid, Muhammad Raza Ul Mustafa, Kawthar Mostafa Moria, Umer Rashid, Omaimah Omar Bamasag

“…Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. …”
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Identification of a panel of volatile organic compounds in urine for early detection of for bladder cancer by Mai Mao, Yanli Zhang, Haibo Liu, Xiumei Jiang, Yuxiao Zhao, Qi Liu, Zhongfang Niu, Xin Zhang

“…VOCs in urine samples were detected using gas chromatography-ion mobility spectrometry (GC-IMS). Machine learning algorithms were used to establish diagnostic models for predicting BC based on differential expressed VOCs. …”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The research study employs Adaptive Bacterial Foraging (ABF) optimization to refine search parameters, maximizing the predictive accuracy of therapeutic outcomes. The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. …”
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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

“…It's got these brainy models and algorithms that not only soak up information but also play psychic by predicting stuff on brand new data it's never laid eyes on before. …”
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