Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions by Maria Sigopi, Cletah Shoko, Timothy Dube

“…Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.…”
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Clays Are Not Created Equal: How Clay Mineral Type Affects Soil Parameterization by P. Lehmann, B. Leshchinsky, S. Gupta, B. B. Mirus, S. Bickel, N. Lu, D. Or

“…Clay mineral‐informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.…”
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Efficient neural network training method for unsteady flow field prediction based on data pool by Qiuchi Min, Tianyu Li, Guanxiong Li, Shuyuan Liu, Laiping Zhang, Xiaogang Deng

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Predicting Survival Rates in Brain Metastases Patients from Non‐Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity by Fuxing Deng, Gang Xiao, Guilong Tanzhu, Xianjing Chu, Jiaoyang Ning, Ruoyu Lu, Liu Chen, Zijian Zhang, Rongrong Zhou

“…Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. …”
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Impact of Subjective and Objective Green Space Characteristics on Mental Health Benefits: An Explainable Machine Learning Approach by Ke LI, Yipei MAO, Yongjun LI

“…Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. …”
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A new machine learning method for rainfall classification: temporal random tree by Kokten Ulas Birant, Bita Ghasemkhani, Özlem Varlıklar, Derya Birant

“…Traditional classification algorithms usually assume that all samples in a dataset contribute equally to the training of a machine learning model, which is not always the case. …”
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The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems by András Adolf, Csaba Márton Köllőd, Gergely Márton, Ward Fadel, István Ulbert

“…Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. …”
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synax: A Differentiable and GPU-accelerated Synchrotron Simulation Package by Kangning Diao, Zack Li, Richard D. P. Grumitt, Yi Mao

“…We further demonstrate the potential of AD in enabling full posterior inference using gradient-based inference algorithms. Using synax with HMC to perform inference over a four-parameter test model, we attain a twofold improvement compared to standard random walk Metropolis–Hastings (RWMH). …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…What sets this study apart from previous ones is its novel demonstration of how these techniques significantly reduce training time and model complexity while maintaining or even improving performance, confirming the effectiveness of strategic feature utilization in strengthening Industrial IoT security by balancing accuracy, speed, and model size.…”
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A reproducible approach for the use of aptamer libraries for the identification of Aptamarkers for brain amyloid deposition based on plasma analysis. by Cathal Meehan, Soizic Lecocq, Gregory Penner

“…Results were analysed using multiple machine learning algorithms from the Scikit-learn package along with clinical variables including cognitive status, age and sex to create predictive models. …”
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Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network. by Zhengqiang Xiong, Manhui Lin, Zhen Lin, Tao Sun, Guangyi Yang, Zhengxing Wang

“…Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.…”
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Lightweight image super-resolution network based on muti-domain information enhancement by KOU Qiqi, LIU Gui, JIANG He, CHEN Liangliang, CHENG Deqiang

“…By processing information across different feature domains, both global and local low-frequency and high-frequency features were optimized, significantly improving the model’s performance in detail recovery and image reconstruction. …”
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Innovative novel regularized memory graph attention capsule network for financial fraud detection. by Xiangting Shi, Xiaochen Wang, Yakang Zhang, Xiaoyi Zhang, Manning Yu, Lihao Zhang

“…This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. …”
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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

“…Even though most of these methods target individual event types, their reported performance results leave significant room for improvement. The research presented here adopts a novel approach to visually inspect the waveform, similar to how specialists work, to develop a single model that can detect and determine the location of both sleep spindles and K-complexes. …”
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Gesture Recognition Achieved by Utilizing LoRa Signals and Deep Learning by Peihao Zhang, Baofeng Zhao

“…This study proposes a novel gesture recognition system based on LoRa technology, integrating advanced signal preprocessing, adaptive segmentation algorithms, and an improved SS-ResNet50 deep learning model. …”
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Training a Minesweeper Agent Using a Convolutional Neural Network by Wenbo Wang, Chengyou Lei

“…Although there is room for improvement in sample efficiency and training stability in the DQN model, its greater generalization ability makes it highly promising for application in more complex decision-making tasks.…”
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Impact of fine-tuning parameters of convolutional neural network for skin cancer detection by Zaib Unnisa, Asadullah Tariq, Nadeem Sarwar, Irfanud Din, Mohamed Adel Serhani, Zouheir Trabelsi

“…The literature has employed numerous Machine Learning (ML) and Deep Learning (DL) algorithms to detect skin cancer. ML algorithms perform well for small datasets but cannot comprehend larger ones. …”
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Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches by M. Sadeghi malekabadi, A.R. Davari

“…This paper introduces an innovative approach to address this issue, leveraging a combination of neural network-based reduced order modeling and a multi-objective genetic algorithm. This methodology aims to optimize the aerodynamic and aero-acoustic characteristics of an S8xx-series airfoil, including the trailing edge serration geometry. …”
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A comparison study and multi-objective optimization of a DICI engine fueled with petroleum diesel and Fischer-Tropsch diesel by Panpan Cai, Zheng Jing, Chunhua Zhang, Bing Han, Gang Li, Wenlong Song

“…These models are fully verified for reproducibility and generalization ability. …”
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Surrounding Object Material Detection and Identification Method for Robots Based on Ultrasonic Echo Signals by Bo Zhu, Tao Geng, Gedong Jiang, Zheng Guan, Yang Li, Xialun Yun

“…This method primarily adopts the 16-dimensional feature vector extracted from intrinsic mode functions that we gain from empirical mode decomposition as inputs of machine learning algorithms to recognize different materials, and we use K-nearest neighbor, decision tree, and support vector machine algorithms on the feature vector set to decide the best classifier, and its acoustic theoretical model is established additionally. …”
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