Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review by Yuling Guo, Ghulam Qanmber, Zhao Liu, Han Yang, Jun Li, Zhikun Yang, Zhongxian Li, Linlin Yang, Zuoren Yang

“…These include the need for more accurate models and advanced algorithms to predict hormone-receptor interactions and metabolic pathways. …”
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Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas by Ao-Yu Li, Jie Bu, Hui-Ni Xiao, Zi-Yue Zhao, Jia-Lin Zhang, Bin Yu, Hui Li, Jin-Ping Li, Tao Xiao

“…We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). …”
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A mineral-strength conversion model based on LIBS technology and rapid batch testing and application of uniaxial compressive strength by Qinghe ZHANG, Xiaorui WANG, Chuanbing WANG, Weiguo LI, Fengqiang GONG, Xinsheng ZHANG, Jingguo LI

“…The mass fraction of mineral components is analyzed via the support vector regression (SVR) algorithm. In the end, a mineral-strength conversion model is established to calculate the UCS from the predicted values of mineral component concentrations, and its rationality and scientific validity are validated by the standard mechanical tests. …”
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Performance of Noise Map Service Working in Cloud Computing Environment by Karolina MARCINIUK, Maciej SZCZODRAK, Bożena KOSTEK

“…In the paper, a noise map service designated for the user interested in environmental noise is presented. Noise prediction algorithm and source model, developed for creating acoustic maps, are working in the cloud computing environment. …”
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Transient Attracting Profiles in the Great Pacific Garbage Patch by L. Kunz, A. Griesel, C. Eden, R. Duran, R. Duran, B. Sainte-Rose

“…<p>A major challenge for cleanup operations in the Great Pacific Garbage Patch is the daily prediction of plastic concentrations that allows identifying hotspots of marine debris. …”
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Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets by Marzuraikah Mohd Stofa, Siti Raihanah Abdani, Asraf Mohamed Moubark, Muhammad Ammirrul Atiqi Mohd Zainuri, Ahmad Asrul Ibrahim, Nor Azwan Mohamed Kamari, Mohd Asyraf Zulkifley

“…Monitoring of sea ice concentration through remote sensing technology is critical in predicting sea ice-related hazards, facilitating safe ship navigation and enabling polar environmental research. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

“…ObjectiveIn this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models. …”
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Exploring the Potential Regulatory Mechanisms of Mitophagy in Ischemic Cardiomyopathy by Li Z, Kong J, Xi S, Jin Z, Yang F, Zhu Z, Liu L

“…Finally, 45 drugs targeting the biomarkers were predicted, such as Salmeterol, Histamine, Rotavirus vaccine, etc. …”
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Research on the spatial layout of the elderly care industry based on explainable machine learning: A case study of Hangzhou(基于可解释性机器学习的养老产业空间布局研究)... by 曾笑奇(ZENG Xiaoqi), 赵秋皓(ZHAO Qiuhao), 冯友建(FENG Youjian)

“…However, existing research has not sufficiently explored the factors influencing the spatial layout of elderly care facilities or how to optimally predict these layouts. This study uses Hangzhou's urban area as a case study to fill this gap. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). …”
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Personalized Federated Learning for Heterogeneous Residential Load Forecasting by Xiaodong Qu, Chengcheng Guan, Gang Xie, Zhiyi Tian, Keshav Sood, Chaoli Sun, Lei Cui

“…Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. …”
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Design of nonlinear rubber vibration isolators with low resonant frequency and high load bearing capacity by Xiaoqi Zhang, Xin Li, Fei Du, Fucai Hu, Li Wang, Yongsheng Yu

“…The rubber isolator static and dynamic behavior prediction models suitable for engineering applications are first established, where Yeoh model is adopted for rubber strong nonlinear feature prediction. …”
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Evaluasi Kinerja MLLIB APACHE SPARK pada Klasifikasi Berita Palsu dalam Bahasa Indonesia by Antonius Angga Kurniawan, Metty Mustikasari

“…The result of confusion matrix was also good, because the number of correct predictions was far more than the number of incorrect ones. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
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An integrated approach of feature selection and machine learning for early detection of breast cancer by Jing Zhu, Zhenhang Zhao, Bangzheng Yin, Canpeng Wu, Chan Yin, Rong Chen, Youde Ding

“…With the increasing application of machine learning technology in the medical field, algorithm-based diagnostic tools provide new possibilities for early prediction of breast cancer. …”
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Traffic signal active control method for short-distance intersections. by Yulin Tian, Shuqing Liu, Lu Wei, Zhen Li, Shaohu Tang, Yuchen Zhang, Tao Zhu

“…Aiming at the existing problems about the overflow prevention goal and the overall traffic efficiency guarantee being difficult to optimize at the same time in the signal control process of short-distance intersections scenario, this paper proposes a traffic signal active control method based on key state prediction. In order to construct the key state evolution trend of short-distance intersection scenarios, this paper proposes the concept of overflow index for short-distance road sections and designs the prediction method of overflow index. …”
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Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR by Shengbiao Wu, Huaning Li, Xianpeng Chen

“…First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression algorithm is used for parametric modeling to solve the problems of low accuracy and poor prediction performance; third, the support vector of the parameter model is optimized by the differential evolution whale algorithm (DWA) to improve the convergence and generalization ability of the model in actual tuning. …”
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Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China by Yunjin WANG, Fujian ZHOU, Hang SU, Leyi ZHENG, Minghui LI, Fuwei YU, Yuan LI, Tianbo LIANG

“…A separated fracturing effect prediction model is proposed, with the fracturing learning curve decomposed into two parts: (1) overall trend, which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance; and (2) local fluctuation, which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight. …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…Importantly, our model demonstrates a high capacity for predicting clinically relevant PHLF. The clinicians' prediction assisted by our model was substantially improved over the clinician-only predictions (AUC = 0.778 vs. 0.637, P = 0.009). …”
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Efficient search, mapping, and optimization of multi‐protein genetic systems in diverse bacteria by Iman Farasat, Manish Kushwaha, Jason Collens, Michael Easterbrook, Matthew Guido, Howard M Salis

“…We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram‐positive and gram‐negative bacterial hosts. …”
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