Concurrency and Computation-Practice & Experience最新文献

{"title":"Prediction of Network Public Opinion Evolution Trends in Emergent Hot Events","authors":"Xinyan Zhang,&nbsp;Jing Fang","doi":"10.1002/cpe.70125","DOIUrl":"https://doi.org/10.1002/cpe.70125","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, there has been a notable increase in food safety incidents, which has raised considerable public concern. Optimizing food safety supervision and enhancing public trust have become urgent issues to be addressed. This study specifically examines the “tanker mixed with edible oil” incident and employs a variety of methodologies, including text analysis and time series modeling, to conduct a comprehensive analysis of public sentiment, The findings provide a scientific foundation for enhancing regulatory oversight. Relevant data were gathered via Python, public opinion trends were forecast via the ARIMA time series model, and an in-depth analysis of the thematic characteristics associated with each phase of public opinion development was conducted by integrating LDA topic modeling techniques. Meanwhile, this study employs social network analysis to construct an interactive network among users and identify key nodes and pathways involved in the dissemination of public opinion. Through simulation analysis, the following conclusions are drawn: (1) The “tanker mixed with cooking oil” incident exhibited a pronounced trend of negative sentiment that intensified over time. (2) The thematic analysis reveals public concern regarding disarray in food transportation and insufficient regulatory oversight, highlighting a shift in the public's focus. (3) Social network analysis emphasizes the crucial roles played by official media and individual key opinion leaders (KOLs) in shaping public opinion, illustrating how these entities influence the direction of public sentiment through their interactive relationships. Through the empirical analysis of the “tanker mixed with edible oil” incident, this paper verifies the effectiveness of the adopted method, providing an important reference for the risk prevention and control of food safety public opinion and policy-making.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074283","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}

{"title":"Verifiable Cloud-Assisted Multi-Party Private Set Intersection Cardinality","authors":"Gongli Li,&nbsp;Weichen Liu,&nbsp;Lu Li","doi":"10.1002/cpe.70126","DOIUrl":"https://doi.org/10.1002/cpe.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>Private Set Intersection Cardinality (PSI-CA) is a privacy-preserving method designed to compute the size of the intersection between two or more sets without revealing any additional information. In scenarios that require extensive computational resources, outsourcing tasks to cloud servers has emerged as a common solution. Nevertheless, the malicious behavior of cloud servers may lead to incorrect results and pose challenges for clients when verifying correctness. First, for clients with limited computational capabilities, a cloud-assisted multi-party PSI-CA (CMPSI-CA) is introduced, which stores the elements generated by the pseudorandom function generator in a Bloom filter and masks them using the Oblivious Distributed Key PRF (Odk-PRF). Furthermore, to protect against possible malicious behavior of cloud servers, the verifiable cloud-assisted multi-party PSI-CA (VCMPSI-CA) is proposed, leveraging the dual functions with the XOR homomorphic property. When the set size is <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>18</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {2}^{18} $$</annotation>\u0000 </semantics></math> and there are 32 participants, both protocols can be completed in 23.99 and 58.62 s, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074284","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}

{"title":"Exploring Blockchain-Based Patient-Centric Healthcare: A Comprehensive Review","authors":"Deepak Kumar Mishra,&nbsp;Pawan Singh Mehra","doi":"10.1002/cpe.70124","DOIUrl":"https://doi.org/10.1002/cpe.70124","url":null,"abstract":"<div>\u0000 \u0000 <p>Blockchain, an immutable, decentralized, and distributed ledger technology, has the potential to revolutionize the sharing and access of confidential data, particularly in the healthcare sector, where patient-centric systems benefit greatly from its robust security features and ability to ensure data integrity. Recognizing the potential, this paper reviews current research on blockchain-based healthcare architectures that focus on patient-centric solutions, exploring the present state of these architectures and highlighting the growing interest in blockchain applications for enhancing healthcare. The review identifies key trends, challenges, and opportunities within this domain, analyzing the evolution of research over recent years, the types of studies conducted, their geographical distribution, and publication channels. It delves into the prevalent use cases, primary challenges addressed, and contributions of various studies, covering common blockchain platforms, types of blockchains, and the implementation of smart contracts. Guided by four critical questions aimed at improving security and privacy, streamlining patient consent management, fostering interoperability, and ensuring compliance with healthcare data regulations, this research underscores the importance of patient data security through advanced encryption techniques, access controls, and decentralized storage solutions. It also highlights the benefits of patient-controlled data sharing, empowering individuals with greater control over their health information. Despite progress, the review identifies areas requiring further exploration, such as advanced security threats, nuanced and dynamic consent models, comprehensive interoperability solutions, and alignment with evolving healthcare regulations. Addressing these gaps necessitates research into scalable privacy-preserving blockchain architectures that prioritize dynamic consent management and interoperability standards, thus making regulatory compliance possible in an ever-changing healthcare environment.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074286","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}

{"title":"SS-LDP: A Framework for Sparse Streaming Data Collection Based on Local Differential Privacy","authors":"Hongjiao Li,&nbsp;Ming Jin,&nbsp;Jiayi Xu,&nbsp;Zhenya Shi,&nbsp;Anyang Yin","doi":"10.1002/cpe.70119","DOIUrl":"https://doi.org/10.1002/cpe.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>The continuous collection of streaming data in the Internet of Things (IoT) may compromise user privacy, as such data often originates from personal information. Local differential privacy (LDP) is a novel privacy notion that offers a strong privacy guarantee to all users without relying on a trusted data collector. However, existing LDP-based studies mainly focus on static scenarios or perturbation of data points at a single timestamp without sufficiently considering data sparsity, which adds excessive noise and leads to low utility. Therefore, we propose a Framework for Sparse Streaming Data Collection based on Local Differential Privacy (SS-LDP), which aims to provide high utility at each timestamp while satisfying <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>w</mi>\u0000 </mrow>\u0000 <annotation>$$ w $$</annotation>\u0000 </semantics></math>-event LDP. One component is the introduction of an upper-bound optimization mechanism, which reduces the noise scale by combining error minimization with the gradient descent method. Another component of SS-LDP targets the efficient management of privacy resources through two specific strategies. First, significant changes in streaming data are captured by calculating differences between the latest few data points, thereby conserving the privacy budget. Second, an improved sparse privacy budget allocation mechanism quantifies data sparsity at each timestamp using the moving average method, enabling efficient allocation of the privacy budget for each timestamp. SS-LDP is evaluated using two real-world datasets and compared with four baseline methods that satisfy <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>w</mi>\u0000 </mrow>\u0000 <annotation>$$ w $$</annotation>\u0000 </semantics></math>-event privacy. Extensive experiments and theoretical analyses are conducted to demonstrate the superiority of our framework.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944447","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}

{"title":"BRL-Net: A Blockchain-Based Task Offloading Framework Using Smart Contracts for Metaverse","authors":"Priyadarshni Gupta,&nbsp;Praveen Kumar,&nbsp;Shivani Tripathi,&nbsp;Rajiv Misra","doi":"10.1002/cpe.70112","DOIUrl":"https://doi.org/10.1002/cpe.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain-based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q-Networks (DQN) with Bidirectional Long Short-Term Memory (Bi-LSTM), known as BRL-Net (Bi-LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL-Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL-Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944449","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}

{"title":"A Multi-Level Role-Based Provable Data Possession Scheme for Medical Cloud Storage","authors":"Ruizhong Du,&nbsp;Ziyuan Wang,&nbsp;Yuan Wan","doi":"10.1002/cpe.70120","DOIUrl":"https://doi.org/10.1002/cpe.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>Medical institutions are increasingly leveraging cloud servers to store electronic health records (EHRs), highlighting the need for robust data security measures to protect the sensitive personal information they contain. Our study introduces a blockchain-enabled, fine-grained data integrity auditing scheme that not only safeguards the confidentiality and integrity of EHRs within cloud-based healthcare environments but also demonstrates a significant enhancement in data security with our statistical results, reinforcing the trustworthiness of cloud storage for sensitive medical data. The proposed scheme is notable for its support of dynamic user revocation, implementing a multi-tiered role hierarchy that facilitates the efficient access revocation. In this hierarchy, adding new users or updating existing ones involves merely altering the edge labels, thereby obviating the need for a comprehensive recalculation of cryptographic keys. We have developed a smart contract-based access control mechanism to ensure privacy while enabling granular access control. This mechanism leverages password and role-based authentication to empower multi-tiered roles with the ability to perform data integrity audits by their designated permissions. Through security analysis, we have substantiated that our protocol withstands attacks aimed at subversion, counterfeiting, and tag inconsistency. Compared to existing works, our approach uniquely integrates multi-level role hierarchies with blockchain-based dynamic revocation, achieving higher granularity and adaptability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944953","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}

{"title":"Vupsi: Verifiable Unbalanced Private Set Intersection Based on Homomorphic Encryption","authors":"Ruirui Gao,&nbsp;Shuai Shang,&nbsp;Wenqi Zhang,&nbsp;Xiaofen Wang,&nbsp;Ke Huang,&nbsp;Xiong Li","doi":"10.1002/cpe.70122","DOIUrl":"https://doi.org/10.1002/cpe.70122","url":null,"abstract":"<div>\u0000 \u0000 <p>Unbalanced private set intersection (UPSI), a cryptographic technique for securely computing set intersections in asymmetrical setups while preserving privacy, has been extensively studied. However, existing protocols often require clients with small sets to participate in computations, are highly interactive, and lack result verifiability. In this paper, we propose VuPSI, a verifiable unbalanced PSI scheme designed to overcome the limitations of existing protocols. VuPSI offloads the computational burden to the server, reducing client-side processing and simplifying the overall workflow. In addition, VuPSI incorporates an efficient zero-knowledge verification mechanism that allows clients to efficiently verify the correctness of intersection results with minimal computational overhead. This approach significantly improves the reliability of PSI outcomes. Our design implements a low-interaction protocol that ensures scalability and efficiency, especially for large-scale dynamic datasets. Experimental evaluations show that VuPSI is both efficient and practical. Specifically, VuPSI can process 1,024 client-side items and 1,000,000 server-side items within seconds using 32 threads, achieving 40×  the communication efficiency of comparable protocols such as DiPSI. Its lower computational overhead and faster data preprocessing make it well-suited for real-time, dynamic server environments.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944448","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}

{"title":"An Efficient Load Distribution Approach for Optimizing Resources in SDN-Based Edge Computing Environment","authors":"Ajay Nain,&nbsp;Sophiya Sheikh,&nbsp;Mohammad Shahid","doi":"10.1002/cpe.70113","DOIUrl":"https://doi.org/10.1002/cpe.70113","url":null,"abstract":"<div>\u0000 \u0000 <p>In the rapidly evolving networking and communication technology era, the emergence of novel edge computing paradigms helps reduce latency and improve communication efficiency. The advancements of edge computing bring data processing closer to its source, reducing communication distance. Moreover, integrating Software-Defined Networking (SDN) in edge computing enhances network management by decoupling the control plane from the data plane, enabling more flexible and efficient resource allocation in distributed environments. However, scheduling, resource allocation, and load balancing are significant obstacles to enhancing the edge computing resources' performance. Besides, efficient resource allocation and load balancing help to use all resources and optimize the system's performance effectively. To address these issues, this paper proposed an Average-Based Resource Allocation and Load Balancing (ABRL) algorithm for task allocation and load balancing, which aims to minimize the task's completion time and enhance the system's resource utilization. A three-layer SDN-based edge architecture is designed to implement the algorithm that improves the system's performance. The simulation studies have been conducted using the OpenDaylight (ODL) controller and implemented in Python. Experimental results demonstrate that the proposed strategy optimizes makespan, average resource utilization, and level of load balancing under consideration and exhibits better performance than the existing state-of-the-art techniques.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944446","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}

{"title":"Cuckoo Search Algorithm With Ensemble Strategy for Continuous Optimization Problems","authors":"Jiatang Cheng,&nbsp;Kaike Tu,&nbsp;Yan Xiong","doi":"10.1002/cpe.70116","DOIUrl":"https://doi.org/10.1002/cpe.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>Cuckoo search (CS) algorithm is a simple and effective optimization technique. However, CS algorithm may encounter the issue of premature convergence as the complexity of the problem increases. To address this challenge, a cuckoo search algorithm with ensemble strategy, called CSES, is presented in this paper. Specifically, three new search strategies with diverse properties are designed to boost the competitiveness. After that, according to the idea of selective ensemble, a priority roulette method is employed to select the appropriate search strategy at distinct phases of the evolution process, so as to produce more promising results. Furthermore, the effectiveness evaluation of CSES algorithm is carried out on 58 benchmark functions from CEC 2013 and CEC 2017 test suites and several real-world problems including chaotic time series prediction and transformer fault classification. Simulation outcomes illustrate that the introduced CSES is superior to five recently developed CS variants in terms of search accuracy and robustness, for example it provides 10 and 12 better performance improvements on <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>30</mn>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <annotation>$$ 30D $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>50</mn>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <annotation>$$ 50D $$</annotation>\u0000 </semantics></math> optimization of the CEC 2013 benchmarks, and produces 19 and 11 better performance improvements on <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>30</mn>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <annotation>$$ 30D $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>50</mn>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <annotation>$$ 50D $$</annotation>\u0000 </semantics></math> optimization of the CEC 2017 benchmarks, respectively. Moreover, CSES also exhibits more superiority compared to several other advanced evolutionary methods, including butterfly optimization algorithm (BOA), dung beetle optimizer (DBO), electric eel foraging optimization (EEFO), jellyfish search (JS) and wild horse optimizer (WHO), and yields 25 better performance improvements on <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>30</mn>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <annotation>$$ 30D $$</annotation>\u0000 </semantics></math> optimization of the CEC 2013 benchmarks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914087","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}

{"title":"Prediction of Carbon Emission Rights Trading Prices Based on the CNN–LSTM Model in the Context of Carbon Peak: Taking Guangdong Province as an Example","authors":"Tinggui Chen,&nbsp;Jiawen Ye,&nbsp;Yanping Zhou,&nbsp;Qing Yu,&nbsp;Shanshan Wang,&nbsp;Gongfa Li","doi":"10.1002/cpe.70121","DOIUrl":"https://doi.org/10.1002/cpe.70121","url":null,"abstract":"<div>\u0000 \u0000 <p>Carbon emissions are a significant contributor to global warming. As one of the largest carbon emitters in the world, China is committed to establishing a carbon emission trading market to address the challenges posed by climate change. The carbon price is a fundamental component of the carbon financial market. Accurately predicting it can improve environmental quality, reduce energy demand, and promote economic growth. This study uses price data from the Guangdong carbon market as a case study and employs a hybrid model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for carbon price forecasting. The findings indicate that: (1) the CNN–LSTM model exhibits optimal predictive performance when the sliding window is set to a size of 5 on the basis of previous carbon price data. (2) By incorporating significant indicator features from the Guangdong pilot carbon price dataset while maintaining a sliding window size of 5, the model achieves superior predictive accuracy, as evidenced by a Goodness of Fit (<i>R</i>²) of 0.8622 and a mean absolute error (MAE) of 0.0228, resulting in the most favorable comprehensive evaluation index. (3) The integration of one-dimensional convolutional layers with LSTM layers in the CNN–LSTM model effectively leverages the strengths of CNNs for local feature extraction and the capabilities of LSTMs for modeling time series data. This approach leads to a substantial improvement in predictive performance compared with alternative models such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), and LSTM.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 12-14","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914088","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}