A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. …”
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A New Incremental Learning Method Based on Rainbow Memory for Fault Diagnosis of AUV by Ying Li, Yuxing Ye, Zhiwei Zhang, Long Wen

“…Third, the RM is combined with the enhanced KAN network, and the stored samples are then combined with new task training data and fed into a multi-branch feature fusion neural network. …”
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Coastal Storm – Surge Combination Risk Identification and Resilience Planning Based on Supply and Demand Assessment of Flood Regulation Ecosystem Services: A Case Study of Fujian D... by Jian TIAN, Tianyu XIU, Suiping ZENG

“…For medium risk–ecological improvement zones (e.g., Jimei District, Xiangcheng District), the focus should be on harmonizing urban – rural ecological patterns through the strategic use of natural topographical features as buffer barriers, adopting mixed land-use approaches to curb urban sprawl, and upgrading eco-oriented infrastructure networks. …”
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Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization by Venkata Nagaraju Thatha, M. Ganesh Karthik, Venu Gopal Gaddam, D. Pramodh Krishna, S. Venkataramana, Kranthi Kumar Lella, Udayaraju Pamula

“…Initially, DenseNet-41 extracts intricate spatial features from histopathological images. These features are then processed by the hybrid AlexNet-GRU model, leveraging AlexNet’s robust feature extraction and GRU’s sequential learning capabilities. …”
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Creating Dynamic Maps of Noise Threat Using PL-Grid Infrastructure by Maciej SZCZODRAK, Józef KOTUS, Bożena KOSTEK, Andrzej CZYŻEWSKI

“…The unique feature of the developed software is a possibility of evaluating auditory effects which are caused by the exposure to excessive noise. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…The model’s performance is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and weighted mean absolute percentage error (WMAPE) and is compared against other models including LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), residual LSTM (ResLSTM), and a variant of GCN-LSTM. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…Hyper-parameter tuning was conducted using Bayesian optimization and cross-validation, while the performance of random forests was evaluated in comparison to gradient boosting, extreme gradient boosting, artificial neural networks, and the generalized linear model.FINDINGS: The random forests models, particularly those utilizing under sampling and cost-sensitive learning, demonstrated superior performance, achieving sensitivity of 0.81, Specificity of 0.85, accuracy of 0.85, and an area under the receiver operating characteristic curve of 0.89 on an independent test set. …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…The model integrates Convolutional Neural Networks (CNNs) to extract spatial features, Long Short-Term Memory (LSTM) networks to model sequential dependencies, Transformer-based attention mechanisms to dynamically weigh environmental factors, and a Multi-Layer Perceptron (MLP) for incorporating vessel-specific and other residual features. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. …”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…This study aims to investigate whether the MSA-ConvLSTM model can enhance the accuracy of predicting regional-scale ETa, considering multiple feature variables. Furthermore, we evaluated different performance indicators, discussed possible reasons for errors in regional ETa prediction, and conducted sensitivity analysis of the model characteristics. …”
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Synthesizing the Academic Administrators’ Competencies: Developing of a Model by Homa Rahmani, Asadollah Khadivi

“…Research shows that competent management can influence the development and evolution of the university and implement the planned changes effectively (Shimoni, 2017). …”
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Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques by Arun Kumar, Aziz Nanthaamornphong

“…The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. …”
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Monitoring Anthropogenically Disturbed Parcels with Soil Erosion Dynamics Change Based on an Improved SegFormer by Zhenqiang Li, Jialin Li, Jie Li, Zhangxuan Li, Kuncheng Jiang, Yuyang Ma, Chuli Hu

“…To address this, we propose a novel ISegFormer model, which integrates the SegFormer network with a pseudo–residual multilayer perceptron (PR–MLP), cross–scale boundary constraint module (CSBC), and multiscale feature fusion module (MSFF). …”
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Community-Based Approaches to Healthy Aging: Lessons from Japanese Blue Zones by Hiroko Oe, Kai Weeks, Sitsada Sartamorn

“…Field observations totaling 40 hours per site allowed direct observation of community activities, environmental features, and social interactions. Document analysis of municipal records, program evaluations, and demographic data provided institutional perspectives on healthy aging initiatives. …”
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Biomedical Data Annotation: An OCT Imaging Case Study by Matthew Anderson, Salman Sadiq, Muzammil Nahaboo Solim, Hannah Barker, David H. Steel, Maged Habib, Boguslaw Obara

“…Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. …”
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Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System by Guowen Dai, Jinjun Tang, Jie Zeng, Yuting Jiang

“…To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). …”
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A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions by Yulong Li, Kai Wang

“…DLERKm utilizes pre-trained language models (ESM-2 and RXNFP), molecular fingerprints, and attention mechanisms to extract enzymatic reaction features for the prediction of Km values. To evaluate the performance of DLERKm, we compared it with a state-of-the-art model (UniKP) on the constructed enzymatic reaction datasets. …”
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Contextual Vulnerability Should Guide Fair Subject Selection in Xenotransplantation Clinical Trials by Gianna Strand

“…Contextual vulnerability is a specific feature of a research environment that increases a subject’s risk of harm. …”
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Machine learning-guided field site selection for river classification by Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

“…Sufficient abundance and variety of field site sampling are crucial for obtaining an accurate reach-scale river classification of a regional stream network in support of scientific research and river management. …”
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User Experience With a Personalized mHealth Service for Physical Activity Promotion in University Students: Mixed Methods Study by Silke Wittmar, Tom Frankenstein, Vincent Timm, Peter Frei, Nicolas Kurpiers, Stefan Wölwer, Axel Georg Meender Schäfer

“…Qualitative findings aligned with the quantitative results, emphasizing students’ appreciation for the personalized, diverse content and social networking features of futur.move. Conclusionsfutur.move demonstrates favorable UX and aligns with student needs, particularly through its personalized content and social features. …”
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