Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction by Santosh Adhikari, Larry Leigh, Dinithi Siriwardana Pathiranage

“…In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). …”
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Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy by Qincheng Qiao, Juan Cao, Wen Xue, Jin Qian, Chuan Wang, Qi Pan, Bin Lu, Qian Xiong, Li Chen, Xinguo Hou

“…Background Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early identification is crucial for improving patient outcomes. Corneal confocal microscopy (CCM) can non-invasively detect changes in corneal nerve fibers (CNFs), making it a potential tool for the early diagnosis of DPN. …”
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Spectral analysis of gas dynamic processes in the inlet system of a piston engine with turbocharger by L. V. Plotnikov, Y. M. Brodov, B. P. Zhilkin, D. S. Shestakov, L. E. Osipov

“…The experiments were carried out on a single-cylinder piston engine model, which could be equipped with a turbocharger. …”
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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. …”
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Fire Hawk Optimizer Adjusted Tri-Stage (1+PI)-PI-TID Cascade Controller for Automatic Generation Control of PSI by Moses Awal, Michael Robson Atim, Jimmy Nabende Wanzala, Johnes Obungoloch, Mohamed Barakat

“…The results demonstrate significant improvements in the performance of ITAE by 84.3%, 10.7%, 42.1%, and 16.7% for scenario 1 across all four tested models. …”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. …”
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…Subsequently, both classical and quantum-inspired models are trained and optimized. The classical models utilized Genetic Algorithms (CGA) and Particle Swarm Optimization (CPSO) for hyperparameter tuning, while the quantum-inspired models employed Quantum Genetic Algorithms (QGAs) and Quantum Particle Swarm Optimization (QPSO). …”
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Fish Detection Using Deep Learning by Suxia Cui, Yu Zhou, Yonghui Wang, Lujun Zhai

“…Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. …”
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A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications by Qingmiao Zhang, Yuhao Zhao, Hanzhi Dong, Junhui Zhao

“…Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.…”
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Measurement of Intratumor Heterogeneity and Its Changing Pattern to Predict Response and Recurrence Risk After Neoadjuvant Chemotherapy in Breast Cancer by Mingxi Zhu, Qiong Wu, Xiaochuan Geng, Huaying Xie, Yan Wang, Ziping Wu, Yanping Lin, Liheng Zhou, Shuguang Xu, Yumei Ye, Wenjin Yin, Jia Hua, Jingsong Lu, Yaohui Wang

“…Lower heterogeneity was independently associated with pCR (OR, 0.401 [95%CI: 0.21, 0.76]; <i>p</i> = 0.007). The model combining heterogeneity and clinical characteristics demonstrated improved specificity (True Negative Rate 0.857 vs. 0.698) and accuracy (Accuracy 0.828 vs. 0.753) compared to the clinical model. …”
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Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer by Taisheng Zeng, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang, Mengde Ling

“…Method: The proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. …”
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CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…Afterwards, a deep CNN network and a residuals network are taken to establish the IQA models. Meanwhile, the transfer learning strategy is introduced here to accelerate the training and improve the robustness of the model considering the fact that the ultrasound image samples are not abundant. …”
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Advances in the application of machine learning technology in the field of environmental health by ZHENG Yu, LI Cheng, HU Guiping, JIA Guang

“…As the data sharing and availability in environmental and health research continue to improve, the number of large datasets for environmental and human health has increased dramatically. …”
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The potential role of next-generation sequencing in identifying MET amplification and disclosing resistance mechanisms in NSCLC patients with osimertinib resistance by Xiao Xiao, Xiao Xiao, Ren Xu, Ren Xu, Jun Lu, Beibei Xin, Chenyang Wang, Kexin Zhu, Hao Zhang, Xinyu Chen

“…With FISH results as gold standard, enumeration algorithm was applied to establish the optimal model for identifying MET amplification using gene copy number (GCN) data.ResultsThe optimal model for identifying MET amplification was constructed based on the GCN of MET, BRAF, CDK6 and CYP3A4, which achieved a 74.0% overall agreement with FISH and performed well in identifying MET amplification except polysomy with a sensitivity of 85.7% and a specificity of 93.9%. …”
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Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning by Reagan Lewis, Teun Kostermans, Jan Wilhelm Brovold, Talha Laique, Marko Ocepek

“…The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. …”
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Charting the path forward: CT image quality assessment - an in-depth review by Siyi Xun, Qiaoyu Li, Xiaohong Liu, Pu Huang, Guangtao Zhai, Yue Sun, Peter H. N. de With, Mingxiang Wu, Tao Tan

“…At present, Artificial intelligence (AI)-based CT-IQA is becoming a notable trend. It helps improve the accuracy of CT scanning equipment and the performance of CT system reconstruction algorithms, and provides a new idea for CT image processing algorithms. …”
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Digital Twin for a Frequency Mixer Used as a Phase Sensor by Carlos Pires, Manuel Abreu, Isabel Godinho, Rui Agostinho, João A. Sousa

“…The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. …”
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Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture by Yassine Bouslihim, Abdelkrim Bouasria, Ahmed Jelloul, Lotfi Khiari, Sara Dahhani, Rachid Mrabet, Rachid Moussadek

“…Model validation using independent test datasets demonstrated strong performance, with R2 values of 0.78 for P and 0.80 for K. …”
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Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease by Ons Masmoudi, Afef Abdelkrim, Emna Harigua-Souiai

“…Abstract The success of AI-based pipelines in Drug Discovery (DD) heavily depends on three components: the dataset (size, content, imbalance ratio), the encoding system and the predictive model. This study focuses on optimizing these elements to develop robust predictive models for the biological activity of chemical molecules. …”
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth by Rui Zhou, Ziqian Liu, Tongtong Wu, Xianwei Pan, Tongtong Li, Kaiting Miao, Yuru Li, Xiaohui Hu, Haigang Wu, Andrew M. Hemmings, Beier Jiang, Zhenzhen Zhang, Ning Liu

“…With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency. …”
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