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Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data

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Geodesy and Geodynamics 2025年第3期16卷 350-368页

作者： Joshi, Anushka Raman, Balasubramanian Mohan, C. Krishna Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Telangana Hyderabad India

The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information. In Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and 1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40, and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks (GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential

关键词： Generative adversarial networks

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Cyber-Resilient IoT-Based Battery Energy Storage Systems in Power Distribution System

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IEEE Transactions on Industry Applications 2025年第3期61卷 4566-4577页

作者： Rafy, Md Fazley Boateng, Ellis Oti Krishnan, Vignesh Venkata Gopala Srivastava, Anurag K. West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26505 United States

Battery Energy Storage Systems (BESS) are critical for addressing the intermittent nature of Distributed Energy Resources (DERs) in power distribution networks. By enabling real-time monitoring and remote control, Internet of Things (IoT)-based BESS controllers are essential to optimize energy storage and ensure efficient management and operation of the power distribution grid. However, the proliferation of Information and Communication Technology (ICT) in IoT-based associated control for BESS introduces cyber vulnerabilities. This paper contributes to a) analyzing the cyber-security challenges and threat landscape faced by IoT-based BESS controllers, b) developing a practical approach to formulate cyber-physical resilience metrics to assess the performance of IoT-based BESS under realistic cyber threat scenarios, c) developing detection and mitigation strategies to improve the cyber-resilience of the IoT based BESS. The proposed cyber-physical resilience metrics and defense mechanisms are validated using a real-time cyber-physical co-simulation testbed, consisting of Typhoon HIL, OpenDSS, and Mininet. The testbed simulates a number of practical-scenarios, and the proposed scheme is effective in quantifying the cyber-physical resilience and mitigating the effects of cyber threats in IoT-based Battery Energy Storage Systems (BESS). © 1972-2012 IEEE.

关键词： Cyber attacks

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 759-772页

作者： Zhong, Xiangnan Ni, Zhen Florida Atlantic University Department of Electrical Engineering and Computer Science Boca RatonFL33431 United States

AI and reinforcement learning (RL) have attracted great attention in the study of multiplayer systems over the past decade. Despite the advances, most of the studies are focused on synchronized decision-making to attain Nash equilibrium, where all the players take actions simultaneously. On the other hand, however, in complex applications, certain players may have an advantage in making sequential decisions and this situation introduces a hierarchical structure and influences how other players respond. The control design for such system is challenging since it relies on solving the coupled Hamilton-Jacobi equation. The situation becomes more difficult when the learning process is exposed to complex uncertainties with unreliable data being exchanged. Therefore, in this article, we develop a new learning-based control approach for a class of nonlinear hierarchical multiplayer systems subject to mismatched uncertainties. Specifically, we first formulate this new problem as a multiplayer Stackelberg-Nash game in conjunction with the hierarchical robust-optimal transformation. Theoretical analysis confirms the equivalence of this transformation and ensures that the designed control policies can achieve stable equilibrium. Then, a two-level neural-RL-based approach is developed to automatically and adaptively learn the solutions. The stability of this online learning process is also provided. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed learning-based robust control design. © 2024 IEEE.

关键词： Reinforcement learning

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3D AIR-UNet: attention–inception–residual-based U-Net for brain tumor segmentation from multimodal MRI

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Neural Computing and Applications 2025年 1-22页

作者： Sharma, Vani Kumar, Mohit Yadav, Arun Kumar Department of Computer Science & Engineering NIT Hamirpur Hamirpur India

Brain tumors are ranked highly among the leading causes of cancer-related fatalities. Precise segmentation and quantitative assessment of brain tumors are crucial for effective diagnosis and treatment planning. However, manual segmentation is often laborious, challenging, and prone to errors, necessitating the creation of a fully automated brain tumor segmentation approach. This article introduces "3D AIR-UNet," an end-to-end architecture aiming to automate the segmentation of brain tumors from MRI data. The presented model employs an encoder–decoder architecture, with carefully constructed inception–residual units replacing the usual convolution layers used in UNet. The inception–residual block combines the advantages of inception modules and residual connections to provide a powerful feature extraction mechanism. It captures extensive multi-scale information by combining different filter sizes. This block’s design is effective at handling complex 3D data patterns, making it a vital component of sophisticated neural network architecture. Moreover, an attention mechanism further boosts the capability of the model to differentiate between tumor and non-tumor regions, leading to improved localization and contextual understanding. Additionally, skip connections are employed between the encoder and decoder at each level to speed up the training process. The proposed 3D AIR-UNet architecture demonstrated encouraging outcomes, attaining dice scores of 0.9218 for the whole tumor, 0.9019 for the tumor core, and 0.8788 for the enhancing tumor when evaluated on the BraTS 2020 dataset. Comparative analysis with contemporary methods suggests that 3D AIR-UNet notably enhances the segmentation accuracy of brain tumor subregions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Decoding

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Improving fish image detection speed with hybrid VGG16 and darknet

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Multimedia Tools and Applications 2025年第12期84卷 10551-10566页

作者： Sirigineedi, Manikanta Jagan Mohan, R.N.V. Sahu, Bandita Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering SRKR Engineering College Andhra Pradesh Bhimavaram534201 India

Fish classification and object detection are crucial tasks in the fishery industry. The use of computer vision and deep learning techniques can help automate these tasks and improve the efficiency of the fishery industry. In this paper, we propose a hybrid model of VGG16 and Darknet for fish classification and object detection. The VGG16 model is a popular deep learning model for image classification, and the Darknet model is a state-of-the-art object detection model. The hybrid model combines the strengths of both models to achieve better performance in fish classification and object detection. The proposed model is trained on a dataset of fish images, which is collected from various sources. The dataset includes images of different fish species, sizes, and orientations. The model is trained to classify fish species and detect fish objects in the images. The results of the experiments show that the proposed model outperforms the individual VGG16 and Darknet models in terms of accuracy and speed. The model achieves the training time for 500 sessions with all 951 images was only 1 h 12 min, which is very fast. Additionally, the size of the model weight file is 13 MB, which is relatively small. This suggests that the model is lightweight and efficient, which is desirable in many applications. In conclusion, the proposed hybrid model of VGG16 and Darknet is an effective solution for fish classification and object detection. The model can be applied in real-world fishery scenarios to improve the efficiency and accuracy of fishery tasks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fish

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COVID-19 emergency decision-making using q-rung linear diophantine fuzzy set,differential evolutionary and evidential reasoning techniques

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Applied Mathematics(A Journal of Chinese Universities) 2025年第1期40卷 182-206页

作者： G Punnam Chander Sujit Das Department of Computer science and Engineering National Institute of TechnologyWarangal 506004India

In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.

关键词： COVID-19 q-rung linear diophantine fuzzy set differential evolutionary evidential reasoning decision-making

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Geometric Interpretation of Sensitivity to Structured Uncertainties in Spintronic Networks

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IEEE Control Systems Letters 2025年 9卷 192-197页

作者： O'Neil, S.P. Jonckheere, E.A. Schirmer, S. United States Military Academy Department of Electrical Engineering and Computer Science NY United States University of Southern California Dept of Electrical and Computer Engineering CA United States Swansea University Faculty of Science and Engineering Physics United Kingdom

We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence of the differential sensitivity on the fidelity (error), and we further demonstrate that this dependence does not require a trade-off between the fidelity and sensitivity. We prove that for closed systems, ideal performance in the sense of perfect state transfer is both necessary and sufficient for optimal robustness in terms of vanishing sensitivity. We demonstrate the utility of this geometric interpretation of the sensitivity by applying the model to explain the sensitivity versus fidelity error data in two examples. © 2017 IEEE.

关键词： Sensitivity analysis

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