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Stock price prediction using intelligent models, Ensemble Learning and feature selection 基于智能模型、集成学习和特征选择的股票价格预测
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732101
Mohammad Taghi Faghihi Nezhad, Mahdi Rezaei
{"title":"Stock price prediction using intelligent models, Ensemble Learning and feature selection","authors":"Mohammad Taghi Faghihi Nezhad, Mahdi Rezaei","doi":"10.1109/dchpc55044.2022.9732101","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732101","url":null,"abstract":"The use of artificial intelligence-based models have shown that the stock market is predictable despite its uncertainty and unstable nature. The most important challenge of the proposed models in the stock market is the accuracy of the results and increasing the forecasting efficiency. To overcome this challenge, this paper employs ensemble learning (EL) model using intelligence-based learners and metaheuristic optimization methods to maximize the improvement of forecasting performance. The multiplicity of inputs in the prediction model reduces the speed of execution and increases complexity. The proposed model, with feature selection, increases the accuracy and use as a real-time model. Genetic algorithm (GA) and particle swarm optimization (PSO) technique are used to optimize the aggregation results of the base learners. The evaluation results of stock market dataset show that the proposed model can overcome the market fluctuations and can be used as a reliable model.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82138198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybrid Genetic-Environmental Adaptation Algorithm to Improve Parameters of COCOMO for Software Cost Estimation 改进COCOMO软件成本估算参数的遗传-环境混合自适应算法
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732107
T. Gandomani, Maedeh Dashti, Mina Ziaei Nafchi
{"title":"Hybrid Genetic-Environmental Adaptation Algorithm to Improve Parameters of COCOMO for Software Cost Estimation","authors":"T. Gandomani, Maedeh Dashti, Mina Ziaei Nafchi","doi":"10.1109/dchpc55044.2022.9732107","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732107","url":null,"abstract":"The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8% higher than when these algorithms are separately adopted.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78497338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A collocation method for the numerical solution of a class of linear stochastic integral equations based on Legendre polynomials 基于Legendre多项式的一类线性随机积分方程数值解的配置方法
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732128
A. Yaghoobnia, M. Kazemi
{"title":"A collocation method for the numerical solution of a class of linear stochastic integral equations based on Legendre polynomials","authors":"A. Yaghoobnia, M. Kazemi","doi":"10.1109/dchpc55044.2022.9732128","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732128","url":null,"abstract":"In this paper, a collocation method will introduce. This method is applied to obtain the numerical solution of a class of linear stochastic integral equations. For this purpose, the integrals of these equations are expressed in terms of Legendre polynomials. Then they are applied to the stochastic integral equation and calculate the obtained equations at the node points, where results in a linear system that will solve by conventional methods. Finally, to evaluate the effectiveness of the proposed method, an example is provided, and the numerical results are analyzed.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88357559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An image retrieval approach based on feature extraction and self-supervised learning 一种基于特征提取和自监督学习的图像检索方法
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732156
Maral Kolahkaj
{"title":"An image retrieval approach based on feature extraction and self-supervised learning","authors":"Maral Kolahkaj","doi":"10.1109/dchpc55044.2022.9732156","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732156","url":null,"abstract":"Today, due to the development of technology and the advent of web 2.0 applications, different users prefer to do many of their personal tasks over the Internet. Due to the huge amount of information on the web, retrieving the appropriate information for each user has become a challenging task. Content-based image retrieval is one of the most important research fields in digital image processing domain, which searches the similar images to the target image by extracting visual content from the query image. In this regard, many studies have been conducted to increase the accuracy of image retrieval systems. However, due to the explosive growth of storage resources and the lack of a responsible system for image retrieval, it is still considered as one of the most attractive fields of research. In this paper, a method is proposed that extracts the appropriate features using a hybrid method, and then searches the images that are similar to the target image. In this way, self-supervised learning approach is utilized to provide the most similar images. Experimental results based on the Corel dataset show that the accuracy of the proposed method has increased compared to the other methods.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85807664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Hybrid Collaborative Filtering Technique for Web Service Recommendation using Contextual Attributes of Web Services 基于Web服务上下文属性的Web服务推荐混合协同过滤技术
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732157
Noor Ul Ain, Ali Irfan, N. Iltaf, Mahmood ul Hassan
{"title":"A Hybrid Collaborative Filtering Technique for Web Service Recommendation using Contextual Attributes of Web Services","authors":"Noor Ul Ain, Ali Irfan, N. Iltaf, Mahmood ul Hassan","doi":"10.1109/dchpc55044.2022.9732157","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732157","url":null,"abstract":"Quality of Service (QoS) Aware recommender system considers the quality of service to recommend personalized web services to the user. Quality of Service parameters also includes response time and throughput that a user receives when invoking a web service. There exist numerous collaborative filtering techniques that tend to predict Quality of Service value; however, existing techniques only use the client-side information of QoS and neglect the service's contextual attributes. This paper proposes a new Web Service Recommendation System that will consider the contextual attributes of Web services. The proposed method collects the contextual properties from WSDL files to cluster Web services based on their attribute similarities. Thus, more accurate neighbour selection takes place and prediction value is determined using QoS record; in addition to this, a user-influenced prediction value is also determined. To map both, service, and user influence on QoS prediction, a hybrid memory-based CF model is developed. The effectiveness and reliability of the proposed system is verified by the results of experiments.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77958544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detecting IoT Attacks using Multi-Layer Data Through Machine Learning 通过机器学习使用多层数据检测物联网攻击
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732117
Hina Alam, Muhammad Shaharyar Yaqub, Ibrahim Nadir
{"title":"Detecting IoT Attacks using Multi-Layer Data Through Machine Learning","authors":"Hina Alam, Muhammad Shaharyar Yaqub, Ibrahim Nadir","doi":"10.1109/dchpc55044.2022.9732117","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732117","url":null,"abstract":"Internet of Things (IoT) devices is being used in countless network applications. However, due to their insecure nature, the wide adoption of these devices has also increased the possibility of cyber-attacks. There is a need for a robust security mechanism to detect and safeguard against numerous threats. Machine Learning (ML) techniques have been used to detect attacks on different networking layers but training only the network, transport, or link-layer data has proven to be inadequate. Thus, opening paths for attackers to take control and penetrate the networks. Leveraging from this inadequacy, we have employed Machine Learning technology to detect attacks on IoT devices using the application, transport, and network layer data. In particular, we have focused on feature extraction of Application layer data to identify nefariousness in packets. Furthermore, for packet classification, we are also extracting features from the network layer and transport layer. Our simulation results have promised accuracy of 88% and 92% using different ML algorithms. We have also identified possible future work that can be used to validate the solution.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91101493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Mining Frequent Spatial Patterns in Image Databases with 17D-SPA Representation 基于17D-SPA表示的图像数据库频繁空间模式挖掘
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732084
K. Borna, Parsa Mohammadrezaei
{"title":"Mining Frequent Spatial Patterns in Image Databases with 17D-SPA Representation","authors":"K. Borna, Parsa Mohammadrezaei","doi":"10.1109/dchpc55044.2022.9732084","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732084","url":null,"abstract":"In this paper, we propose a new algorithm for mining frequent patterns in image databases. Our method mines patterns with more accuracy than a previously known algorithm and can be beneficial, where small differences in object locations can change the pattern. In this regard, we generate larger patterns from smaller ones and check whether the support for each candidate is less than a user-specified amount.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86678815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimized Power Consumption Formula for Designing IoT-Based Systems 基于物联网系统设计的优化功耗公式
高性能计算技术 Pub Date : 2022-03-02 DOI: 10.1109/dchpc55044.2022.9732149
Abolfazl Rajaiyan, Somayeh Sobati-Moghadam
{"title":"Optimized Power Consumption Formula for Designing IoT-Based Systems","authors":"Abolfazl Rajaiyan, Somayeh Sobati-Moghadam","doi":"10.1109/dchpc55044.2022.9732149","DOIUrl":"https://doi.org/10.1109/dchpc55044.2022.9732149","url":null,"abstract":"Smart homes are currently being empowered by Internet of Things (IoT) innovations and applications. IoT-based systems are designed to achieve low power consumption and optimal performance. In many situations, IoT devices are powered by batteries, and power efficiency is the most crucial need for them to operate reliably for long periods of time. Low power consumption and increasing system lifetime are two important problems of IoT. This paper describes a formula for designing IoT systems depending on the available power of the resource. This paper presents an effective technique to reduce power consumption. The proposed method is based on the available power in the resources of an IoT system. First, the power consumption is calculated to meet the system's requirements, and then the system is developed appropriately. Furthermore, by constructing a smart home system, the quantity of power storage in a specific situation is investigated, and a formula for designing IoT systems based on the available power has been presented.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89435887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Deep Learning in Healthcare 医疗保健领域的深度学习
高性能计算技术 Pub Date : 2021-01-28 DOI: 10.1007/978-3-030-60265-9_8
L. Priya, A. Sathya, S. ThangaRevathi
{"title":"Deep Learning in Healthcare","authors":"L. Priya, A. Sathya, S. ThangaRevathi","doi":"10.1007/978-3-030-60265-9_8","DOIUrl":"https://doi.org/10.1007/978-3-030-60265-9_8","url":null,"abstract":"","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80166207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 34
Towards Computation Offloading in Edge Computing: A Survey 边缘计算中的计算卸载:综述
高性能计算技术 Pub Date : 2019-01-01 DOI: 10.1007/978-981-32-9987-0_1
Xiaolan Cheng, Xin Zhou, Congfeng Jiang, Jian Wan
{"title":"Towards Computation Offloading in Edge Computing: A Survey","authors":"Xiaolan Cheng, Xin Zhou, Congfeng Jiang, Jian Wan","doi":"10.1007/978-981-32-9987-0_1","DOIUrl":"https://doi.org/10.1007/978-981-32-9987-0_1","url":null,"abstract":"","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77367837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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