Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"An efficient hybrid approach for forecasting real-time stock market indices","authors":"Riya Kalra ,&nbsp;Tinku Singh ,&nbsp;Suryanshi Mishra ,&nbsp;Satakshi ,&nbsp;Naveen Kumar ,&nbsp;Taehong Kim ,&nbsp;Manish Kumar","doi":"10.1016/j.jksuci.2024.102180","DOIUrl":"10.1016/j.jksuci.2024.102180","url":null,"abstract":"<div><p>The stock market’s volatility, noise, and information overload necessitate efficient prediction methods. Forecasting index prices in this environment is complex due to the non-linear and non-stationary nature of time series data generated from the stock market. Machine learning and deep learning have emerged as powerful tools for identifying financial data patterns and generating predictions based on historical trends. However, updating these models in real-time is crucial for accurate predictions. Deep learning models require extensive computational resources and careful hyperparameter optimization, while incremental learning models struggle to balance stability and adaptability. This paper proposes a novel hybrid bidirectional-LSTM (H.BLSTM) model that combines incremental learning and deep learning techniques for real-time index price prediction, addressing these scalability and memory challenges. The method utilizes both univariate time series derived from historical index prices and multivariate time series incorporating technical indicators. Implementation within a real-time trading system demonstrates the method’s effectiveness in achieving more accurate price forecasts for major stock indices globally through extensive experimentation. The proposed model achieved an average mean absolute percentage error of 0.001 across nine stock indices, significantly outperforming traditional models. It has an average forecasting delay of 2 s, making it suitable for real-time trading applications.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102180"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002696/pdfft?md5=990fa1b67fa197073ed336d80589c08c&pid=1-s2.0-S1319157824002696-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient authentication scheme syncretizing physical unclonable function and revocable biometrics in Industrial Internet of Things","authors":"Xinying Yu ,&nbsp;Kejun Zhang ,&nbsp;Zhufeng Suo ,&nbsp;Jun Wang ,&nbsp;Wenbin Wang ,&nbsp;Bing Zou","doi":"10.1016/j.jksuci.2024.102166","DOIUrl":"10.1016/j.jksuci.2024.102166","url":null,"abstract":"<div><p>Biometric recognition is extensive for user security authentication in the Industrial Internet of Things (IIoT). However, the potential leakage of biometric data has severe repercussions, such as identity theft or tracking. Existing authentication schemes primarily focus on protecting biometric templates but often overlook the “one-authentication multiple-access” mode. As a result, these schemes still confront challenges related to privacy leakage and low efficiency for users who frequently access the server. In this regard, this paper proposes an efficient authentication scheme syncretizing physical unclonable function (PUF) and revocable biometrics in IIoT. Specifically, we design a revocable biometric template generation method syncretizing the user’s biometric data and the device’s PUF to enhance the security and revocability of the dual identity information. Given the generated revocable biometric template and the secret sharing, our scheme implements secure authentication and key negotiation between users and servers. Additionally, we establish an access boundary and an authentication validity period to permit multiple accesses following one authentication, thus significantly decreasing the computational cost of the user-side device. We leverage BAN logic and the ROR model to prove our scheme’s security. Informal security analysis and performance comparison demonstrate that our scheme satisfies more security features with higher authentication efficiency.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102166"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002556/pdfft?md5=bf447ec5a923cea7cdfc3e3a7567340f&pid=1-s2.0-S1319157824002556-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification","authors":"Fan Wang ,&nbsp;Xiaochen Yuan ,&nbsp;Yue Liu ,&nbsp;Chan-Tong Lam","doi":"10.1016/j.jksuci.2024.102200","DOIUrl":"10.1016/j.jksuci.2024.102200","url":null,"abstract":"<div><div>Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102200"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets","authors":"Taner Uçkan","doi":"10.1016/j.jksuci.2024.102162","DOIUrl":"10.1016/j.jksuci.2024.102162","url":null,"abstract":"<div><p>Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102162"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002519/pdfft?md5=58ab4242a07a2504cdd39efa0bbba182&pid=1-s2.0-S1319157824002519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RAPID: Robust multi-pAtch masker using channel-wise Pooled varIance with two-stage patch Detection","authors":"Heemin Kim ,&nbsp;Byeong-Chan Kim ,&nbsp;Sumi Lee ,&nbsp;Minjung Kang ,&nbsp;Hyunjee Nam ,&nbsp;Sunghwan Park ,&nbsp;Il-Youp Kwak ,&nbsp;Jaewoo Lee","doi":"10.1016/j.jksuci.2024.102188","DOIUrl":"10.1016/j.jksuci.2024.102188","url":null,"abstract":"<div><p>Recently, adversarial patches have become frequently used in adversarial attacks in real-world settings, evolving into various shapes and numbers. However, existing defense methods often exhibit limitations in addressing specific attacks, datasets, or conditions. This underscores the demand for versatile and robust defenses capable of operating across diverse scenarios. In this paper, we propose the RAPID (<strong>R</strong>obust multi-p<strong>A</strong>tch masker using channel-wise <strong>P</strong>ooled var<strong>I</strong>ance with two-stage patch <strong>D</strong>etection) framework, a stable solution to restore detection efficacy in the presence of multiple patches. The RAPID framework excels in defending against attacks regardless of patch number or shape, offering a versatile defense adaptable to diverse adversarial scenarios. RAPID employs a two-stage strategy to identify and mask coordinates associated with patch attacks. In the first stage, we propose the ‘channel-wise pooled variance’ to detect candidate patch regions. In the second step, upon detecting these regions, we identify dense areas as patches and mask them accordingly. This framework easily integrates into the preprocessing stage of any object detection model due to its independent structure, requiring no modifications to the model itself. Evaluation indicates that RAPID enhances robustness by up to 60% compared to other defenses. RAPID achieves mAP50 and mAP@50-95 values of 0.696 and 0.479, respectively.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102188"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002775/pdfft?md5=097312e661d7cf2bd4bcbc118fd164bd&pid=1-s2.0-S1319157824002775-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration","authors":"Jiaying Wu ,&nbsp;Lingyun Yuan ,&nbsp;Tianyu Xie ,&nbsp;Hui Dai","doi":"10.1016/j.jksuci.2024.102184","DOIUrl":"10.1016/j.jksuci.2024.102184","url":null,"abstract":"<div><p>Sharding is a critical technology for enhancing blockchain scalability. However, existing sharding blockchain protocols suffer from a high cross-shard ratio, high transaction latency, limited throughput enhancement, and high account migration. To address these problems, this paper proposes a sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration. Firstly, we construct a blockchain transaction account graph network structure to analyze transaction account correlations. Secondly, a modularity-based account transaction reconfiguration algorithm and a detailed account reconfiguration process is designed to minimize cross-shard transactions. Finally, we introduce a transaction processing mechanism for account transaction reconfiguration in parallel with block consensus uploading, which reduces the reconfiguration time overhead and system latency. Experimental results demonstrate substantial performance improvements compared to existing shard protocols: up to a 34.7% reduction in cross-shard transaction ratio, at least an 83.2% decrease in transaction latency, at least a 52.7% increase in throughput and a 7.8% decrease in account migration number. The proposed protocol significantly enhances the overall performance and scalability of blockchain, providing robust support for blockchain applications in various fields such as financial services, supply chain management, and industrial Internet of Things. It also enables better support for high-concurrency scenarios and large-scale network environments.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102184"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002738/pdfft?md5=107fe417689144e59c75fddd0f5b671f&pid=1-s2.0-S1319157824002738-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput systolic array-based accelerator for hybrid transformer-CNN networks","authors":"Qingzeng Song ,&nbsp;Yao Dai ,&nbsp;Hao Lu ,&nbsp;Guanghao Jin","doi":"10.1016/j.jksuci.2024.102194","DOIUrl":"10.1016/j.jksuci.2024.102194","url":null,"abstract":"<div><div>In this era of Transformers enjoying remarkable success, Convolutional Neural Networks (CNNs) remain highly relevant and useful. Indeed, hybrid Transformer-CNN network architectures, which combine the benefits of both approaches, have achieved impressive results. Vision Transformer (ViT) is a significant neural network architecture that features a convolutional layer as its first layer, primarily built on the transformer framework. However, owing to the distinct computation patterns inherent in attention and convolution, existing hardware accelerators for these two models are typically designed separately and lack a unified approach toward accelerating both models efficiently. In this paper, we present a dedicated accelerator on a field-programmable gate array (FPGA) platform. The accelerator, which integrates a configurable three-dimensional systolic array, is specifically designed to accelerate the inferential capabilities of hybrid Transformer-CNN networks. The Convolution and Transformer computations can be mapped to a systolic array by unifying these operations for matrix multiplication. Softmax and LayerNorm which are frequently used in hybrid Transformer-CNN networks were also implemented on FPGA boards. The accelerator achieved high performance with a peak throughput of 722 GOP/s at an average energy efficiency of 53 GOPS/W. Its respective computation latencies were 51.3 ms, 18.1 ms, and 6.8 ms for ViT-Base, ViT-Small, and ViT-Tiny. The accelerator provided a <span><math><mrow><mn>12</mn><mo>×</mo></mrow></math></span> improvement in energy efficiency compared to the CPU, a <span><math><mrow><mn>2</mn><mo>.</mo><mn>3</mn><mo>×</mo></mrow></math></span> improvement compared to the GPU, and a <span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> to <span><math><mrow><mn>2</mn><mo>×</mo></mrow></math></span> improvement compared to existing accelerators regarding speed and energy efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102194"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PFEL-Net: A lightweight network to enhance feature for multi-scale pedestrian detection","authors":"Jingwen Tang ,&nbsp;Huicheng Lai ,&nbsp;Guxue Gao ,&nbsp;Tongguan Wang","doi":"10.1016/j.jksuci.2024.102198","DOIUrl":"10.1016/j.jksuci.2024.102198","url":null,"abstract":"<div><div>In the context of intelligent community research, pedestrian detection is an important and challenging object detection task. The diversity in pedestrian target scales and the interference from the surrounding background can result in incorrect and missed detections by the detector, while a large algorithm model can pose challenges for deploying the detector. In response to these issues, this work presents a pedestrian feature enhancement lightweight network (PFEL-Net), which provides the possibility for edge computing and accurate detection of multi-scale pedestrian targets in complex scenes. Firstly, a parallel dilated residual module is designed to expand the receptive field for obtaining richer pedestrian features; then, the selective bidirectional diffusion pyramid network is devised to finely fuse features, and a detail feature layer captures multi-scale information; after that, the lightweight shared detection head is constructed to lightweight the model head; finally, the channel pruning algorithm is employed to further reduce the computational complexity and size of the improved model without compromising accuracy. On the CityPersons dataset, compared to YOLOv8, PFEL-Net increases the <span><math><mrow><mi>m</mi><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span> and <span><math><mrow><mi>m</mi><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn><mo>:</mo><mn>95</mn></mrow></msub></mrow></math></span> by 6.3% and 4.9%, respectively, reduces the number of model parameters by 89% and compresses the model size by 85%, resulting in a mere 0.9 MB. Similarly, excellent performance is achieved on the TinyPerson dataset. The source code is available at <span><span>https://github.com/1tangbao/PFEL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102198"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RSA-RRT: A path planning algorithm based on restricted sampling area","authors":"Lixin Zhang ,&nbsp;Hongtao Yin ,&nbsp;Ang Li ,&nbsp;Longbiao Hu ,&nbsp;Lan Duo","doi":"10.1016/j.jksuci.2024.102152","DOIUrl":"10.1016/j.jksuci.2024.102152","url":null,"abstract":"<div><p>The rapidly-exploring random tree (RRT) algorithm has a wide range of applications in the field of path planning. However, conventional RRT algorithm suffers from low planning efficiency and long path length, making it unable to handle complex environments. In response to the above problems, this paper proposes an improved RRT algorithm based on restricted sampling area (RSA-RRT). Firstly, to address the problem of low efficiency, a restricted sampling area strategy is proposed. By dynamically restricting the sampling area, the number of invalid sampling points is reduced, thus improving planning efficiency. Then, for the path planning problem in narrow areas, a fixed-angle sampling strategy is proposed, which improves the planning efficiency in narrow areas by conducting larger step size sampling with a fixed angle. Finally, a multi-triangle optimization strategy is proposed to address the problem of longer and more tortuous paths. The effectiveness of RSA-RRT algorithm is verified through improved strategy performance verification and ablation experiments. Comparing with other algorithms in different environments, the results show that RSA-RRT algorithm can obtain shorter paths while taking less time, effectively balancing the path quality and planning speed, and it can be applied in complex real-world environments.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102152"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002416/pdfft?md5=f00e2115fc7ea409ec90daa76b1a079f&pid=1-s2.0-S1319157824002416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers","authors":"Mehboob Hussain ,&nbsp;Lian-Fu Wei ,&nbsp;Amir Rehman ,&nbsp;Abid Hussain ,&nbsp;Muqadar Ali ,&nbsp;Muhammad Hafeez Javed","doi":"10.1016/j.jksuci.2024.102170","DOIUrl":"10.1016/j.jksuci.2024.102170","url":null,"abstract":"<div><p>The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102170"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002593/pdfft?md5=8ba14b81a0951bd08637405a78b6250b&pid=1-s2.0-S1319157824002593-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}