Hyperspectral Anomaly Detection by Spatial–Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

“…This algorithm combines spectral extremum detection with information entropy filtering to select the most representative bands by considering multidimensional information. …”
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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Finally, during fracturing design, the optimal productivity prediction model was matched to each interval based on its characteristics to predict post-fracturing productivity. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Signal quality can be improved by traditional noise removal algorithms, but deep learning models outperform non-learning methods. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…Next, Fuzzy Dispersion Entropy (FuzzDE) categorized the components into high-, medium-, and low-complexity subsequences. Then, an Improved Mantis Search Algorithm (IMSA) optimized three distinct models: Bidirectional Long Short-Term Memory (BiLSTM) for high-complexity components, Least Squares Support Vector Regression (LSSVR) for medium-complexity components, and Extreme Learning Machine (ELM) for low-complexity components. …”
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Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks by Nader AL Theeb, Hazem Smadi, Naser Al-qaydeh

“…Furthermore, using BO algorithm for hyperparameters tuning improved the forecasting accuracy. …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…ObjectiveThis study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector machine (SVM) algorithm, offering precise early decision support.MethodsThis study retrospectively included 462 NPC patients, without or with oligometastasis defined by ESTRO/EORTC criteria. …”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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A Classification-Based Blood–Brain Barrier Model: A Comparative Approach by Ralph Saber, Sandy Rihana

“…Feature selection algorithms play a crucial role in identifying the most relevant descriptors, thereby enhancing prediction accuracy. …”
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Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning by Tao Zheng, Hui Xie, Boqiang Liang

“…In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. …”
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Potential of random forest machine learning algorithm for geological mapping using PALSAR and Sentinel-2A remote sensing data: A case study of Tsagaan-uul area, southern Mongolia by Munkhsuren Badrakh, Narantsetseg Tserendash, Erdenejargal Choindonjamts, Gáspár Albert

“…Geological mapping in remote and geologically complex regions can be substantially improved by integrating remote sensing data with machine learning algorithms. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…Conclusion: VMAT-4P is identified as the most effective method for radiotherapy in left-sided breast cancer, providing an excellent balance between optimal target coverage and improved protection of surrounding OAR. …”
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An intelligent SWMM calibration method and identification of urban runoff generation patterns by Zixin Yang, Zixin Yang, Zixin Yang, Jiahong Liu, Jiahong Liu, Jiahong Liu, Youcan Feng, Youcan Feng, Youcan Feng, Jia Wang, Jia Wang, Hao Wang, Hao Wang, Hao Wang, Changhai Li, Changhai Li, Changhai Li

“…This study proposes a universal and effective method to enhance model accuracy by optimizing parameter value ranges through an unsupervised intelligent clustering algorithm. …”
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New QSPR/QSAR Models for Organic and Inorganic Compounds: Similarity and Dissimilarity by Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati

“…<b>Conclusions:</b> A comparison of different methods for the optimization of correlation weights using the Monte Carlo method showed that optimization can be improved using the coefficient of conformism of a correlative prediction (CCCP) or the index of the ideality of correlation (IIC). …”
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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

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A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in M... by Abhijeet Das

“…Our research in Mahanadi River Basin, Odisha, presents an enhanced methodology based on data, specifically designed to be beneficial for Water Quality (WQ) based on Synthetic Pollution Index (SPI) and machine learning models such as Long Short-Term Memory (LSTM) and Sparrow Search Algorithm (SSA), for its analysis and interpretation of extensive, intricate data sets on water quality, as well as the allocation of pollution sources or contributing elements, in order to improve knowledge of the water quality and the planning of monitoring networks for efficient water resource management. …”
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Comparative analysis and selection of the geometry of the microphone array based on MEMS microphones for sound localisation by Andrii Riabko, Tetiana Vakaliuk, Oksana Zaika, Roman Kukharchuk, Yuriy Smorzhevsky

“…The goal is to determine the most effective array architecture and beamforming algorithms to achieve compactness, accuracy, and balanced omnidirectional coverage. …”
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Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states by Chaochen Tang, Xueying Mo, Zhimin Ma, Zhangying Wang

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Predictive Ecological Cooperative Control of Electric Vehicles Platoon on Hilly Roads by Bingbing Li, Weichao Zhuang, Boli Chen, Hao Zhang, Sheng Yu, Jianrun Zhang, Guodong Yin

“…In this paper, a novel strategy based on a decentralized model predictive control (MPC) framework, called predictive ecological cooperative control (PECC), is proposed for vehicle platoon control on hilly roads, aiming to maximize the overall energy efficiency of the platoon. Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories, this strategy employs an approach based on the combination of a long short-term memory network (LSTM) and genetic algorithm (GA) optimization (GA-LSTM) to predict the future speed of the leading vehicle. …”
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