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Informatica (Slovenia) 2025年第14期49卷 93-110页

作者： Kaushal, Arush Gupta, Ashok Kumar Sehgal, Vivek Kumar Department of Computer Science and Information Technology Jaypee University of Information Technology Solan171234 India Department of Civil Engineering Jaypee University of Information Technology Solan171234 India

Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dataset. The data, obtained from a laboratory stick-slip friction experiment, simulate real earthquakes through digitized acoustic signals recorded against the time to failure of a granular layer. We introduced a hybrid model combining CatBoost and Support Vector Regression (SVR) to predict the time of the next earthquake, evaluating its performance against individual CatBoost and SVR models. The hybrid model demonstrated superior accuracy with a Mean Absolute Error (MAE) of 0.0825, outperforming the individual models. We implemented feature engineering to optimize the predictive capability of the models. Additionally, we compared our hybrid model's performance with previous studies to validate its efficacy. Our findings underscore the potential of machine learning, particularly hybrid models, in enhancing earthquake prediction accuracy. This study highlights the robustness and effectiveness of the hybrid CatBoost-SVR model, paving the way for advanced AI algorithms in seismology and contributing to improved disaster preparedness and mitigation strategies. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Stick-slip

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Multi-channel mantis search spatial attention convolutional neural network based hyperspectral image change detection

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Multimedia Tools and Applications 2025年 1-22页

作者： Divya, V.M. Department of Computer Science and Engineering East West Institute of Technology Visvesvaraya Technological University Belagavi560018 India

Hyperspectral imaging has transformed remote sensing by offering detailed spectral data for monitoring environmental changes, like urban development and terrestrial cover variations. However, existing change detection techniques often face challenges like underfitting and overfitting. In this research, a novel approach is introduced for hyperspectral image change detection using a Multi-Channel Mantis Search (MCMS). This innovative methodology leverages the power of a Multi-Channel Convolutional Neural Network (MCNN) to process each spectral band independently, facilitating the capture of unique features within each band. The input hyperspectral images are gathered from the Pavia university Dataset and the Indian Pines Dataset. This approach leads to significantly improved feature extraction, enhancing the discrimination of different materials in hyperspectral images. To further enhance the performance of the change detection model, the Mantis Search Algorithm (MSA) is integrated. The MSA is employed to optimize the model's hyperparameters, fine-tuning its architecture and training parameters to achieve superior results. The combination of MCNN and MSA improves hyperspectral image change detection accuracy, achieving 98.21% and 99.31% with the Indian Pines and Pavia university datasets correspondingly. © 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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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Fresco Arbitrary Scale Super Resolution Reconstruction Based on Frequency Domain Transform 9

Fresco Arbitrary Scale Super Resolution Reconstruction Based...

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9th International Symposium on computer and Information Processing technology, ISCIPT 2024

作者： Liu, Jiaqi Cao, Jianfang Taiyuan University of Science and Technology Department of Computer Science and Technology Taiyuan China Xinzhou Normal University Department of Computer Xinzhou China

ISBN: (纸本)9798350388404

In response to the problem that traditional super-resolution models with low reconstruction magnification cannot perfectly reconstruct the details of high-scale murals and cannot present the texture elements of the original painting, the paper proposes a fresco arbitrary scale super-resolution reconstruction based on frequency domain transform. Firsty, the paper fuses the image features of the mural with spatial coordinates. It builds a frequency domain transformation module based on the encoding-decoding architecture to transform the mural image in the frequency domain, learning the frequency, amplitude, and phase characteristics of the mural and enhancing the implicit feature expression of the texture. Secondly, employs a multilayer perceptron as a decoder to learn implicit feature representations of images, thus achieving arbitrary-scale super-resolution. Lastly, constructs a Siamese neural network and uses the trained model as a reconstruction constraint condition for the leading network. Experimental results show that on the mural dataset, compared with popular arbitrary-scale super-resolution reconstruction models, the experimental results show that the PSNR and SSIM of this method are improved by at least 0.09 dB and 0.007 respectively. This method has a better performance in reconstruction quality and image structure. © 2024 IEEE.

关键词： Image enhancement

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A Study of Android Security Vulnerabilities and Their Future Prospects

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 854-869页

作者： Alsumayt, Albandari Elbeh, Heba Elkawkagy, Mohamed Alfawaer, Zeyad Alghamedy, Fatemah H. Alshammari, Majid Aljameel, Sumayh S. Albassam, Sarah Alghareeb, Shahad Alamoudi, Khadijah Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Dammam31441 Saudi Arabia Department of Science Technology and Mathematics College of Art and Sciences Lincoln University Jefferson CityMO United States Department of Information Technology College of Computers and Information Technology Taif University 21944 Taif Saudi Arabia Saudi Aramco Cybersecurity Chair Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam31441 Saudi Arabia

Nowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities,

关键词： Smartphones

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An optimized quad-ensemble short term load forecasting (STLF) method for electricity usage prediction using deep learning

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International Journal of Information technology (Singapore) 2024年第4期16卷 2115-2122页

作者： Pushpa Indora, Sanjeev Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat India

A lot of studies have been conducted for enhancing load accuracy in order to derive decisions about usage of electricity in buildings. In order to accurately predict electricity demand in real time, deep learning models are an apt choice. Therefore, the given paper proposes an ensemble learning method in which the outputs of four deep learning models-feed forward neural network (FFNN), long short-term memory, recurrent neural networks and support vector regression are trained separately and their results are utilized by using secondary FFNN with back-propagating learning to improve the prediction results. The proposed framework considers dataset which consists of electricity consumption values recorded during four different seasons (spring, summer, fall, and winter) in a year. Experiments on real-world dataset shows that the proposed approach is good at handling oscillation features of low-load data by achieving an average mean absolute percentage error (MAPE) of 6.76% which is much better than individual deep learning methods as well recent cited works in the given paper. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Feed forward neural network (FFNN) Long Short-term memory (LSTM) Recurrent neural networks (RNN) Short term load forecasting (STLF) Support vector regression (SVR)

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An extended COPRAS method based on complex q-rung orthopair fuzzy 2-tuple linguistic Maclaurin symmetric mean aggregation operators

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2119-2142页

作者： Naz, Sumera Mehreen, Rida Abbas, Tahir Piñeres-Espitia, Gabriel Butt, Shariq Aziz Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan School of Mathematics Minhaj University Lahore Pakistan Department of Mathematics Abbotabad University Science and Technology Abbottabad Pakistan Universidad de la Costa Barranquilla Colombia School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan

The complex q-rung orthopair fuzzy 2-tuple linguistic set (Cq-ROFTLS), which merges the concepts of complex q-rung orthopair fuzzy sets (Cq-ROFS) and 2-tuple linguistic terms, offers significant advantages in dealing with uncertain and imprecise information during decision-making by effectively representing two-dimensional information within a single set. Notably, the Cq-ROFTLS introduces phase terms that empower experts to express their perspectives flexibly, particularly enhancing its capacity to address periodic elements. To address uncertainty, this approach employs complex values to quantify both membership and non-membership degrees within 2-tuple linguistic environment. Additionally, this research introduces the generalized Maclaurin symmetric mean (MSM) aggregation operator, specifically designed for Cq-ROFTL information. This introduces the Cq-ROFTLMSM and its dual form, the Cq-ROFTL Dual MSM (Cq-ROFTLDMSM), each carrying valuable properties. In cases where the importance of input factors varies, the study proposes the Cq-ROFTL weighted MSM (Cq-ROFTLWMSM) and its dual form, the Cq-ROFTL weighted dual MSM (Cq-ROFTLWDMSM). These operators not only make their debut but also showcase their properties and applications. They flexibly adjust to the significance of inputs, leading to a more refined decision-making process. The methodology extends to address multi-attribute group decision-making (MAGDM) within the Cq-ROFTL framework using the Complex Proportional Assessment (COPRAS) method. The introduction of new aggregation techniques further enhances this approach. A practical illustration involving the selection of the optimal bio-energy production technology (BPT) highlights the real-world effectiveness of the methodology. Through thorough comparisons and a focused exploration of advantages, the study effectively validates the merits of this approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Decision making

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VTLL: Visual-Texture Multi-Modal Fusion for Table Structure Recognition Based on Logic Location Regression

VTLL: Visual-Texture Multi-Modal Fusion for Table Structure ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Fang, Jun Zhang, Chongyang Nanjing University of Science and Technology Nanjing China Nanjing University of Science and Technology Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9781665410205

Tables play a crucial role in both documents and daily life, thus sparking significant interest in the research of automatic table structure recognition(TSR). Recent methods primarily achieve recognition by predicting makeup sequences or adjacency relationships between table cells. However, these methods have some limitations in applications. The former requires additional computation of heuristic rules to recover the structure of the table, which not only increases the computational complexity but also may affect the recognition accuracy. The later relies on huge training data and inefficiency decoders, which not only raises the cost but also limits their application in real-time or large-scale data processing scenarios. At the same time, they often ignore the significance of cell logical location and fail to effectively utilize the rich text information that naturally exists in the table. In this paper, we propose a new framework called VTLL to solve the problem. The method extracts visual and textual features to do adaptive feature fusion, and we also introduce a cascading regressors to predict the fused features multiple times, while combining intra-cell and inter-cell losses. As for the regression header part, we use a mixed context aggregator to understand the inter-cell relationships. We evaluated the proposed methods on several public datasets, experiment results demonstrate that VTLL performs better. © 2024 IEEE.

关键词： Large datasets

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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Hepatitis C Prediction Using Machine Learning and Deep Learning-Based Hybrid Approach with Biomarker and Clinical Data

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Biomedical Materials and Devices 2025年第1期3卷 558-575页

作者： Ripa, Rokiya Uddin, Khandaker Mohammad Mohi Alam, Mir Jafikul Rahman, Md. Mahbubur Department of Computer Science and Engineering Dhaka International University Dhaka 1205 Bangladesh Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka 1216 Bangladesh

Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to liver cirrhosis and eventually liver cancer;therefore, understanding this stage of the virus is essential. Molecular and serological testing approaches are often expensive and difficult to perform for diagnosing HCV infection. Machine learning technology can be effectively employed to identify patterns or associations for diagnosing HCV infection. The study utilized machine learning techniques to develop classification models for hepatitis C illness, aiming to anticipate the virus responsible for the infection. Our research integrates various machine learning algorithms, including Random Forest, Cat Boost, Bagging Classifier, SGD Classifier, Gaussian NB, Bernoulli NB, Multinomial NB, Linear Discriminant Analysis, ANN, and MLP. Prior to training the models, preprocessing methods, including normalization, filtering, and SMOTE, were used to improve the dataset’s attributes. The classification accuracy scores show encouraging results, with ANN scoring 79.63%, MLP scoring 46.29%, Bernoulli NB scoring 79.62%, Cat Boost scoring 98.14%, and Random Forest scoring 99.53%. Among classification methods, random forest demonstrates the highest accuracy in diagnosing HCV infection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification Deep learning Hepatitis C virus Machine learning Predictions

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