Explainable AI for Spectral Analysis of Electromagnetic Fields by Dimitris Kalatzis, Agapi Ploussi, Ellas Spyratou, Theodor Panagiotakopoulos, Efstathios P. Efstathopoulos, Yiannis Kiouvrekis

“…A comparative evaluation of six machine learning algorithms was conducted: XGBoost, LightGBM, Random Forests, k-Nearest Neighbors, Neural Networks and Decision Trees to assess prediction performance across each frequency band. …”
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Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

“…Second, each window generates an output probability, which quantifies prediction confidence based on the true class probability (TCP). …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. …”
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Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring by Xiaokai Chen, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, Kang Yu

“…Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.…”
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Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications by Sita Rani, Raman Kumar, B. S. Panda, Rajender Kumar, Nafaa Farhan Muften, Mayada Ahmed Abass, Jasmina Lozanović

“…Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. …”
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Masked Feature Modeling for Generative Self-Supervised Representation Learning of High-Resolution Remote Sensing Images by Shiyan Pang, Hanchun Hu, Zhiqi Zuo, Jia Chen, Xiangyun Hu

“…This methodology has several advantages: 1) The hybrid CNN + Transformer architecture not only retains the advantages of the local feature representation of the CNN architecture but also has the full-text information modeling capabilities of the Transformer architecture; 2) the feature extraction network outputs multiscale features, and it is easier to add upsampling and a skip connection to improve the accuracy of the downstream dense prediction task; and 3) the pretrained MFM can be applied to various downstream tasks through fine-tuning with limited samples. …”
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State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach by Sesidhar Dvsr, Chandrashekhar Badachi, Chandrashekar Nagawaram, Panduranga Chary Kondoju, C. Dhanamjayulu, Innocent Kamwa

“…This paper mainly focusses on the relationship between dataset characteristics and data stationarity, exploring battery behaviour prediction and related dataset comprehension techniques. …”
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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…Data analysis and parameter prediction were conducted using a variety of machine learning and deep learning models including Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), among which the LSTM model demonstrated superior performance, particularly at multiple critical time points. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell.…”
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MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. Meanwhile, a tiny prediction head is incorporated to make the model focus on the low-level features, thus improving its ability to detect small objects. …”
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Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking by Yao Yang, Xu Sun, Bin Liu, Yunshu Zhang, Tong Xie, Junchen Li, Jifeng Liu, Qingkai Zhang

“…Through machine learning algorithms, the diagnostic model was established. Further elucidating the mechanisms and regulatory network of the model gene in UC were GSVA, immunological correlation analysis, transcription factor prediction, immunofluorescence, and single-cell analysis. …”
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OMAL: A Multi-Label Active Learning Approach from Data Streams by Qiao Fang, Chen Xiang, Jicong Duan, Benallal Soufiyan, Changbin Shao, Xibei Yang, Sen Xu, Hualong Yu

“…To solve these two issues, we propose a novel online multi-label active learning (OMAL) algorithm that considers simultaneously adopting uncertainty (using the average entropy of prediction probabilities) and diversity (using the average cosine distance between feature vectors) as an active query strategy. …”
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easyspec: An Open-source Python Package for Long-slit Spectroscopy by Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido

“…This package is built upon the well-established long-slit spectroscopy routines of the Image Reduction and Analysis Facility (IRAF), integrating modern coding techniques and advanced fitting algorithms based on Markov chain Monte Carlo simulations. …”
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Target determination for transcranial magnetic stimulation in patients with a pharmacotherapy-resistant depressive episode based on the individual parameters of resting-state funct... by A. G. Poydasheva, D. O. Sinitsyn, I. S. Bakulin, N. A. Suponeva, N. V. Maslennikov, E. E. Tsukarzi, S. N. Mosolov, M. A. Piradov

“…The investigation can be considered to be pilot in searching for algorithms to enhance the efficiency of rTMS DLPFC in pharmacotherapy-resistant depression using the algorithm for personification of target selection. …”
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O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments by V. C. Bharathi, S. Syed Abuthahir, Monelli Ayyavaraiah, G. Arunkumar, Usama Abdurrahman, Sardar Asad Ali Biabani

“…Simulation results show that O2O-PLB outperforms traditional resource allocation methods in resource usage, response times, and latency reduction. Based on the experimental results, the O2O-PLB algorithm significantly outperforms the benchmark algorithms across essential performance metrics at varying task loads. …”
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Accurate GRACE terrestrial water storage estimations via a new fusion method by Min Dai, Hao Zhou, Wenjing Ma, Shuyun Zheng, Mingyang Xia, Yaozong Li, Zhicai Luo

“…A 62.8% reduction in average uncertainty and a 1.54-fold improvement in the SNR are similarly achieved globally. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Downscaling Satellite Night-Time Light Imagery While Addressing the Blooming Effect by Nikolaos Tziokas, Ce Zhang, Alexandros Tziokas, Qunming Wang, Peter M. Atkinson

“…We compared several image fusion algorithms for downscaling while reducing the blooming effect. …”
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