Transfer learning models have proven superior to classical machine learning approaches in various text classification tasks, such as sentiment analysis, question answering, news categorization, and natural language inference. Recently, these models have shown exceptional results in natural language understanding (NLU). Advanced attention‐based language models like BERT and XLNet excel at handling complex tasks across diverse contexts. However, they encounter difficulties when applied to specific domains. Platforms like Facebook, characterized by continually evolving casual and sophisticated language, demand meticulous context analysis even from human users. The literature has proposed numerous solutions using statistical and machine learning techniques to predict the sentiment (positive or negative) of online customer reviews, but most of them rely on various business, review, and reviewer features, which leads to generalizability issues. Furthermore, there have been very few studies investigating the effectiveness of state‐of‐the‐art pre‐trained language models for sentiment classification in reviews. Therefore, this study aims to assess the effectiveness of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet in sentiment classification using the Yelp reviews dataset. The models were fine‐tuned, and the results obtained with the same hyperparameters are as follows: 98.30 for RoBERTa, 98.20 for XLNet, 97.40 for BERT, 97.20 for ALBERT, and 96.00 for DistilBERT.
{"title":"Exploring transformer models for sentiment classification: A comparison of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet","authors":"Ali Areshey, Hassan Mathkour","doi":"10.1111/exsy.13701","DOIUrl":"https://doi.org/10.1111/exsy.13701","url":null,"abstract":"Transfer learning models have proven superior to classical machine learning approaches in various text classification tasks, such as sentiment analysis, question answering, news categorization, and natural language inference. Recently, these models have shown exceptional results in natural language understanding (NLU). Advanced attention‐based language models like BERT and XLNet excel at handling complex tasks across diverse contexts. However, they encounter difficulties when applied to specific domains. Platforms like Facebook, characterized by continually evolving casual and sophisticated language, demand meticulous context analysis even from human users. The literature has proposed numerous solutions using statistical and machine learning techniques to predict the sentiment (positive or negative) of online customer reviews, but most of them rely on various business, review, and reviewer features, which leads to generalizability issues. Furthermore, there have been very few studies investigating the effectiveness of state‐of‐the‐art pre‐trained language models for sentiment classification in reviews. Therefore, this study aims to assess the effectiveness of BERT, RoBERTa, ALBERT, DistilBERT, and XLNet in sentiment classification using the Yelp reviews dataset. The models were fine‐tuned, and the results obtained with the same hyperparameters are as follows: 98.30 for RoBERTa, 98.20 for XLNet, 97.40 for BERT, 97.20 for ALBERT, and 96.00 for DistilBERT.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non‐repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose DemocracyGuard platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, DemocracyGuard aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.
{"title":"DemocracyGuard: Blockchain‐based secure voting framework for digital democracy","authors":"Mritunjay Shall Peelam, Gaurav Kumar, Kunjan Shah, Vinay Chamola","doi":"10.1111/exsy.13694","DOIUrl":"https://doi.org/10.1111/exsy.13694","url":null,"abstract":"Online voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non‐repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose <jats:italic>DemocracyGuard</jats:italic> platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, <jats:italic>DemocracyGuard</jats:italic> aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yue Zhao, Farhan Ullah, Chien‐Ming Chen, Mohammed Amoon, Saru Kumari
Identifying malicious intent within a program, also known as malware, is a critical security task. Many detection systems remain ineffective due to the persistent emergence of zero‐day variants, despite the pervasive use of antivirus tools for malware detection. The application of generative AI in the realm of malware visualization, particularly when binaries are depicted as colour visuals, represents a significant advancement over traditional machine‐learning approaches. Generative AI generates various samples, minimizing the need for specialized knowledge and time‐consuming analysis, hence boosting zero‐day attack detection and mitigation. This paper introduces the Deep Convolutional Generative Adversarial Network for Zero‐Shot Learning (DCGAN‐ZSL), leveraging transfer learning and generative adversarial examples for efficient malware classification. First, a normalization method is proposed, resizing malicious images to 128 × 128 or 300 × 300 for standardized input, enhancing feature transformation for improved malware pattern recognition. Second, greyscale representations are converted into colour images to augment feature extraction, providing a richer input for enhanced model performance in malware classification. Third, a novel DCGAN with progressive training improves model stability, mode collapse, and image quality, thus advancing generative model training. We apply the Attention ResNet‐based transfer learning method to extract texture features from generated samples, which increases security evaluation performance. Finally, the ZSL for zero‐day malware presents a novel method for identifying previously unknown threats, indicating a significant advancement in cybersecurity. The proposed approach is evaluated using two standard datasets, namely dumpware and malimg, achieving malware classification accuracies of 96.21% and 98.91%, respectively.
{"title":"Efficient malware detection using hybrid approach of transfer learning and generative adversarial examples with image representation","authors":"Yue Zhao, Farhan Ullah, Chien‐Ming Chen, Mohammed Amoon, Saru Kumari","doi":"10.1111/exsy.13693","DOIUrl":"https://doi.org/10.1111/exsy.13693","url":null,"abstract":"Identifying malicious intent within a program, also known as malware, is a critical security task. Many detection systems remain ineffective due to the persistent emergence of zero‐day variants, despite the pervasive use of antivirus tools for malware detection. The application of generative AI in the realm of malware visualization, particularly when binaries are depicted as colour visuals, represents a significant advancement over traditional machine‐learning approaches. Generative AI generates various samples, minimizing the need for specialized knowledge and time‐consuming analysis, hence boosting zero‐day attack detection and mitigation. This paper introduces the Deep Convolutional Generative Adversarial Network for Zero‐Shot Learning (DCGAN‐ZSL), leveraging transfer learning and generative adversarial examples for efficient malware classification. First, a normalization method is proposed, resizing malicious images to 128 × 128 or 300 × 300 for standardized input, enhancing feature transformation for improved malware pattern recognition. Second, greyscale representations are converted into colour images to augment feature extraction, providing a richer input for enhanced model performance in malware classification. Third, a novel DCGAN with progressive training improves model stability, mode collapse, and image quality, thus advancing generative model training. We apply the Attention ResNet‐based transfer learning method to extract texture features from generated samples, which increases security evaluation performance. Finally, the ZSL for zero‐day malware presents a novel method for identifying previously unknown threats, indicating a significant advancement in cybersecurity. The proposed approach is evaluated using two standard datasets, namely dumpware and malimg, achieving malware classification accuracies of 96.21% and 98.91%, respectively.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats.
{"title":"ResiSC: A system for building resilient smart city communication networks","authors":"Mohammed J. F. Alenazi","doi":"10.1111/exsy.13698","DOIUrl":"https://doi.org/10.1111/exsy.13698","url":null,"abstract":"Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article conducts a systematic mapping and inductive analysis of existing work related to hydrogen energy policy in the Web of Science Core Collection from 1996 to 7 July 2023. First, based on bibliometrics, the research reveals the publication volume trends, influential contributors (countries, authors, organizations and journals). Second, bibliometric and content analysis were applied to vividly demonstrate the evolution of the research topic, discuss leading research topics, and provide valuable insights into issues that deserving appropriate attention in the future. The unequivocal role of policies in supporting the development of hydrogen energy is undeniable. In the future, the focal point of hydrogen energy policies should be concentrated on stimulating the creation of demand for green hydrogen, necessitating the effective evaluation of policy outcomes and ensuring the safety of hydrogen energy. Furthermore, the application scope of hydrogen energy extends beyond the transportation sector, holding potential for expansion into other high carbon‐emitting domains. With the strengthening of international collaboration in hydrogen energy, considerations of energy justice and fairness are poised to become pivotal factors in cooperation, exerting a profound influence on the attainment of long‐term development and environmental sustainability. These critical research directions will shape the future landscape of hydrogen energy policy and serve as an essential resource for policymakers, researchers, and stakeholders in this domain.
{"title":"Trajectory mapping and future charting of hydrogen energy policy: A systematic review","authors":"Shuhan Meng, Xianhua Wu, Hua Li, You Wu","doi":"10.1111/exsy.13696","DOIUrl":"https://doi.org/10.1111/exsy.13696","url":null,"abstract":"This article conducts a systematic mapping and inductive analysis of existing work related to hydrogen energy policy in the Web of Science Core Collection from 1996 to 7 July 2023. First, based on bibliometrics, the research reveals the publication volume trends, influential contributors (countries, authors, organizations and journals). Second, bibliometric and content analysis were applied to vividly demonstrate the evolution of the research topic, discuss leading research topics, and provide valuable insights into issues that deserving appropriate attention in the future. The unequivocal role of policies in supporting the development of hydrogen energy is undeniable. In the future, the focal point of hydrogen energy policies should be concentrated on stimulating the creation of demand for green hydrogen, necessitating the effective evaluation of policy outcomes and ensuring the safety of hydrogen energy. Furthermore, the application scope of hydrogen energy extends beyond the transportation sector, holding potential for expansion into other high carbon‐emitting domains. With the strengthening of international collaboration in hydrogen energy, considerations of energy justice and fairness are poised to become pivotal factors in cooperation, exerting a profound influence on the attainment of long‐term development and environmental sustainability. These critical research directions will shape the future landscape of hydrogen energy policy and serve as an essential resource for policymakers, researchers, and stakeholders in this domain.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fakhri Alam Khan, Abdullah Khan, Muhammad Imran, Awais Ahmad, Gwanggil Jeon
Alzheimer's disease (AD) is a major global health concern that affects millions of people globally. This study investigates the technical challenges in AD analysis and provides a thorough analysis of AD, emphasizing the disease's worldwide effects as well as the predicted increase. It explores the technological difficulties associated with AD analysis, concentrating on the shift in automated clinical diagnosis using MRI data from conventional machine learning to deep learning techniques. This study advances our knowledge of the effects of AD and provides new developments in deep learning for precise diagnosis, providing insightful information for both clinical and future research. The research introduces an innovative deep learning model, leveraging YOLOv5 and variants of YOLOv8, to classify AD images into four (NC, EMCI, LMCI, AD) categories. This study evaluates the performance of YOLOv5 which achieved high accuracy (97%) in multi‐class classification (classes 0 to 3) with precision, recall, and F1‐score reported for each class. YOLOv8 (Small) and YOLOv8 (Medium) models are also assessed for Alzheimer's disease diagnosis, demonstrating accuracy of 97% and 98%, respectively. Precision, recall, and F1‐score metrics provide detailed insights into the models' effectiveness across different classes. Comparative analysis against a transfer learning model reveals YOLOv5, YOLOv8 (Small), and YOLOv8 (Medium) consistently outperforming across six binary classifications related to cognitive impairment. These models show improved sensitivity and accuracy compared to baseline architectures from [32]. In AD/NC classification, YOLOv8 (Medium) achieves 98.43% accuracy and 97.45% sensitivity, for EMCI/LMCI classification, YOLOv8 (Medium) also excels with 92.12% accuracy and 90.12% sensitivity. The results highlight the effectiveness of YOLOv5 and YOLOv8 variants in neuroimaging tasks, showcasing their potential in clinical applications for cognitive impairment classification. The proposed models showcase superior performance, achieving high accuracy, sensitivity, and F1‐scores, surpassing baseline architectures and previous methods. Comparative analyses highlight the robustness and effectiveness of the proposed models in AD classification tasks, providing valuable insights for future research and clinical applications.
{"title":"Advancements in deep learning for Alzheimer's disease diagnosis: A comprehensive exploration and critical analysis of neuroimaging approaches","authors":"Fakhri Alam Khan, Abdullah Khan, Muhammad Imran, Awais Ahmad, Gwanggil Jeon","doi":"10.1111/exsy.13688","DOIUrl":"https://doi.org/10.1111/exsy.13688","url":null,"abstract":"Alzheimer's disease (AD) is a major global health concern that affects millions of people globally. This study investigates the technical challenges in AD analysis and provides a thorough analysis of AD, emphasizing the disease's worldwide effects as well as the predicted increase. It explores the technological difficulties associated with AD analysis, concentrating on the shift in automated clinical diagnosis using MRI data from conventional machine learning to deep learning techniques. This study advances our knowledge of the effects of AD and provides new developments in deep learning for precise diagnosis, providing insightful information for both clinical and future research. The research introduces an innovative deep learning model, leveraging YOLOv5 and variants of YOLOv8, to classify AD images into four (NC, EMCI, LMCI, AD) categories. This study evaluates the performance of YOLOv5 which achieved high accuracy (97%) in multi‐class classification (classes 0 to 3) with precision, recall, and F1‐score reported for each class. YOLOv8 (Small) and YOLOv8 (Medium) models are also assessed for Alzheimer's disease diagnosis, demonstrating accuracy of 97% and 98%, respectively. Precision, recall, and F1‐score metrics provide detailed insights into the models' effectiveness across different classes. Comparative analysis against a transfer learning model reveals YOLOv5, YOLOv8 (Small), and YOLOv8 (Medium) consistently outperforming across six binary classifications related to cognitive impairment. These models show improved sensitivity and accuracy compared to baseline architectures from [32]. In AD/NC classification, YOLOv8 (Medium) achieves 98.43% accuracy and 97.45% sensitivity, for EMCI/LMCI classification, YOLOv8 (Medium) also excels with 92.12% accuracy and 90.12% sensitivity. The results highlight the effectiveness of YOLOv5 and YOLOv8 variants in neuroimaging tasks, showcasing their potential in clinical applications for cognitive impairment classification. The proposed models showcase superior performance, achieving high accuracy, sensitivity, and F1‐scores, surpassing baseline architectures and previous methods. Comparative analyses highlight the robustness and effectiveness of the proposed models in AD classification tasks, providing valuable insights for future research and clinical applications.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generative artificial intelligence (AI) has developed as an effective tool for time series predicting, revolutionizing the typical methods of prediction. Different classical approaches that depend on existing approaches and assumptions, generative AI controls advanced deep learning (DL) approaches like generative adversarial networks (GANs) and recurrent neural networks (RNNs), to identify designs and connections in time series data. DL has accomplished major success in optimizing performances connected with AI. In the financial area, it can be extremely utilized for the stock market predictive, trade implementation approaches, and set of optimizers. Stock market predictive is the most important use case in this field. GANs with advanced AI approaches have become more significant in recent times. However, it can be utilized in image‐image‐translation and other computer vision (CV) conditions. GANs could not utilized greatly for stock market prediction because of their effort to establish the proper set of hyperparameters. This study develops an integrated spotted hyena optimization algorithm with generative artificial intelligence for time series forecasting (SHOAGAI‐TSF) technique. The purpose of the SHOAGAI‐TSF technique is to accomplish a forecasting process for the utilization of stock price prediction. The SHOAGAI‐TSF technique uses probabilistic forecasting with a conditional GAN (CGAN) approach for the prediction of stock prices. The CGAN model learns the data generation distribution and determines the probabilistic prediction from it. To boost the prediction results of the CGAN approach, the hyperparameter tuning can be performed by the use of the SHOA. The simulation result analysis of the SHOAGAI‐TSF technique takes place on the stock market dataset. The experimental outcomes determine the significant solution of the SHOAGAI‐TSF algorithm with other compared methods in terms of distinct metrics.
{"title":"Integrating spotted hyena optimization technique with generative artificial intelligence for time series forecasting","authors":"Reda Salama","doi":"10.1111/exsy.13681","DOIUrl":"https://doi.org/10.1111/exsy.13681","url":null,"abstract":"Generative artificial intelligence (AI) has developed as an effective tool for time series predicting, revolutionizing the typical methods of prediction. Different classical approaches that depend on existing approaches and assumptions, generative AI controls advanced deep learning (DL) approaches like generative adversarial networks (GANs) and recurrent neural networks (RNNs), to identify designs and connections in time series data. DL has accomplished major success in optimizing performances connected with AI. In the financial area, it can be extremely utilized for the stock market predictive, trade implementation approaches, and set of optimizers. Stock market predictive is the most important use case in this field. GANs with advanced AI approaches have become more significant in recent times. However, it can be utilized in image‐image‐translation and other computer vision (CV) conditions. GANs could not utilized greatly for stock market prediction because of their effort to establish the proper set of hyperparameters. This study develops an integrated spotted hyena optimization algorithm with generative artificial intelligence for time series forecasting (SHOAGAI‐TSF) technique. The purpose of the SHOAGAI‐TSF technique is to accomplish a forecasting process for the utilization of stock price prediction. The SHOAGAI‐TSF technique uses probabilistic forecasting with a conditional GAN (CGAN) approach for the prediction of stock prices. The CGAN model learns the data generation distribution and determines the probabilistic prediction from it. To boost the prediction results of the CGAN approach, the hyperparameter tuning can be performed by the use of the SHOA. The simulation result analysis of the SHOAGAI‐TSF technique takes place on the stock market dataset. The experimental outcomes determine the significant solution of the SHOAGAI‐TSF algorithm with other compared methods in terms of distinct metrics.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.
{"title":"Health indicator construction based on normal states through FFT‐graph embedding","authors":"GwanPil Kim, Jason J. Jung, David Camacho","doi":"10.1111/exsy.13689","DOIUrl":"https://doi.org/10.1111/exsy.13689","url":null,"abstract":"Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement.
{"title":"GRDATFusion: A gradient residual dense and attention transformer infrared and visible image fusion network for smart city security systems in cloud and fog computing","authors":"Jian Zheng, Seunggil Jeon, Xiaomin Yang","doi":"10.1111/exsy.13685","DOIUrl":"https://doi.org/10.1111/exsy.13685","url":null,"abstract":"The infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.
{"title":"Entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced dataset","authors":"Anil Kumar, Dinesh Singh, Rama Shankar Yadav","doi":"10.1111/exsy.13679","DOIUrl":"https://doi.org/10.1111/exsy.13679","url":null,"abstract":"Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：