PeerJ Computer Science最新文献_第6页

Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm. 基于灰色关联分析和人工蜂群算法优化的支持向量机的短期电力负荷预测。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-21 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1108

Xinfu Pang, Wei Sun, Haibo Li, Yibao Wang, Changfeng Luan

{"title":"Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm.","authors":"Xinfu Pang, Wei Sun, Haibo Li, Yibao Wang, Changfeng Luan","doi":"10.7717/peerj-cs.1108","DOIUrl":"https://doi.org/10.7717/peerj-cs.1108","url":null,"abstract":"Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen similar days in the history, construct a rough set of similar days, perform K-means clustering on the rough sets of similar days, and further construct the set of similar days. The artificial bee colony (ABC) algorithm is then utilized to optimize penalty coefficient and kernel function parameters of the support vector machine (SVM). Finally, the above method is applied on the basis of actual load data in Nanjing for simulation verification, and the results show the effectiveness of the proposed method.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1108"},"PeriodicalIF":3.8,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40646373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Traffic flow simulation of modified cellular automata model based on producer-consumer algorithm. 基于生产者-消费者算法的改进元胞自动机模型交通流仿真。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-20 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1102

Xuefeng Deng, Yi Shao, Jiaxin Song, Hui Wu

{"title":"Traffic flow simulation of modified cellular automata model based on producer-consumer algorithm.","authors":"Xuefeng Deng, Yi Shao, Jiaxin Song, Hui Wu","doi":"10.7717/peerj-cs.1102","DOIUrl":"https://doi.org/10.7717/peerj-cs.1102","url":null,"abstract":"With the rise of new technologies such as the Internet of Vehicles and the Internet of Things, research on the intelligent connected vehicle has become a hot topic in contemporary times. The modeling and simulation of traffic flow are mainly used to analyze the characteristics of traffic flow and study the formation and dissipation mechanism of traffic congestion to better guide the real traffic. Cellular automata are suitable for the simulation of complex giant systems. Because of the randomness and discreteness of vehicle driving, cellular automata are often used to model and analyze traffic flow. This article mainly studies the traffic flow formed by intelligent connected vehicles. Based on the traditional NaSch model, the producer-consumer algorithm is introduced to form a multi-buffer vehicle information access mode, and an improved cellular automata model with random updates is constructed. The simulation results show that the improved cellular automata model improves the traffic congestion significantly compared with the original NaSch model in the intelligent network environment, which is consistent with the actual traffic situation. Therefore, the algorithm proposed in this article can effectively simulate the traffic flow characteristics of intelligent connected vehicles, and provide a theoretical basis for solving traffic problems.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1102"},"PeriodicalIF":3.8,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021). 使用CNN-LSTM深度学习方法分析对全球事件的感知:朝觐1442(2021)的案例。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-20 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1087

Mohd Khaled Shambour

{"title":"Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021).","authors":"Mohd Khaled Shambour","doi":"10.7717/peerj-cs.1087","DOIUrl":"https://doi.org/10.7717/peerj-cs.1087","url":null,"abstract":"Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020-2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1-13, 1442 (July 11-23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people's impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1087"},"PeriodicalIF":3.8,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40558135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

OES-Fed: a federated learning framework in vehicular network based on noise data filtering. 基于噪声数据滤波的车联网联邦学习框架。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-20 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1101

Yuan Lei, Shir Li Wang, Caiyu Su, Theam Foo Ng

{"title":"OES-Fed: a federated learning framework in vehicular network based on noise data filtering.","authors":"Yuan Lei, Shir Li Wang, Caiyu Su, Theam Foo Ng","doi":"10.7717/peerj-cs.1101","DOIUrl":"https://doi.org/10.7717/peerj-cs.1101","url":null,"abstract":"The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1101"},"PeriodicalIF":3.8,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Minimizing features while maintaining performance in data classification problems. 最小化特征，同时保持数据分类问题的性能。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-14 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1081

Surani Matharaarachchi, Mike Domaratzki, Saman Muthukumarana

{"title":"Minimizing features while maintaining performance in data classification problems.","authors":"Surani Matharaarachchi, Mike Domaratzki, Saman Muthukumarana","doi":"10.7717/peerj-cs.1081","DOIUrl":"https://doi.org/10.7717/peerj-cs.1081","url":null,"abstract":"High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible. This article proposes and applies an extended version of such feature selection methods, which selects a smaller feature subset with similar performance to the original subset under a pre-defined threshold. It further validates the suggested extended version of the Principal Component Loading Feature Selection (PCLFS-ext) results by simulating data for several practical scenarios with different numbers of features and different imbalance rates on several classification methods. Our simulated results show that the proposed method outperforms the original PCLFS and existing Recursive Feature Elimination (RFE) by giving reasonable feature reduction on various data sets, which is important in some applications.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1081"},"PeriodicalIF":3.8,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Empirical mode decomposition using deep learning model for financial market forecasting. 利用深度学习的经验模式分解模型进行金融市场预测。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-14 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1076

Zebin Jin, Yixiao Jin, Zhiyun Chen

{"title":"Empirical mode decomposition using deep learning model for financial market forecasting.","authors":"Zebin Jin, Yixiao Jin, Zhiyun Chen","doi":"10.7717/peerj-cs.1076","DOIUrl":"https://doi.org/10.7717/peerj-cs.1076","url":null,"abstract":"Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1076"},"PeriodicalIF":3.8,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Evaluation of deepfake detection using YOLO with local binary pattern histogram. 基于局部二值模式直方图的YOLO深度伪造检测评价。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-13 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1086

Štěpán Hubálovský, Pavel Trojovský, Nebojsa Bacanin, Venkatachalam K

{"title":"Evaluation of deepfake detection using YOLO with local binary pattern histogram.","authors":"Štěpán Hubálovský, Pavel Trojovský, Nebojsa Bacanin, Venkatachalam K","doi":"10.7717/peerj-cs.1086","DOIUrl":"https://doi.org/10.7717/peerj-cs.1086","url":null,"abstract":"Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of the video is a critical process due to the negative pattern of the image. In the detection of forged images or videos, various image processing techniques were implemented. Existing methods are ineffective in detecting new threats or false images. This article has proposed You Only Look Once-Local Binary Pattern Histogram (YOLO-LBPH) to detect fake videos. YOLO is used to detect the face in an image or a frame of a video. The spatial features are extracted from the face image using a EfficientNet-B5 method. Spatial feature extractions are fed as input in the Local Binary Pattern Histogram to extract temporal features. The proposed YOLO-LBPH is implemented using the large scale deepfake forensics (DF) dataset known as CelebDF-FaceForensics++(c23), which is a combination of FaceForensics++(c23) and Celeb-DF. As a result, the precision score is 86.88% in the CelebDF-FaceForensics++(c23) dataset, 88.9% in the DFFD dataset, 91.35% in the CASIA-WebFace data. Similarly, the recall is 92.45% in the Celeb-DF-Face Forensics ++(c23) dataset, 93.76% in the DFFD dataset, and 94.35% in the CASIA-Web Face dataset.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1086"},"PeriodicalIF":3.8,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40646374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos. 热监控视频中行人检测的半自动运动约束图割算法。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-12 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1064

Oluwakorede Monica Oluyide, Jules-Raymond Tapamo, Tom Mmbasu Walingo

引用次数: 0

The improved dynamic slicing for spectrum-based fault localization. 基于频谱的改进动态切片故障定位。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-07 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1071

Heling Cao, Fei Wang, Miaolei Deng, Lei Li

{"title":"The improved dynamic slicing for spectrum-based fault localization.","authors":"Heling Cao, Fei Wang, Miaolei Deng, Lei Li","doi":"10.7717/peerj-cs.1071","DOIUrl":"https://doi.org/10.7717/peerj-cs.1071","url":null,"abstract":"Background: Spectrum-based Fault localization have proven to be useful in the process of software testing and debugging. However, how to improve the effectiveness of software fault localization has always been a research hot spot in the field of software engineering. Dynamic slicing can extract program dependencies under certain conditions. Thus, this technology is expected to benefit for locating fault.Methods: We propose an improved dynamic slicing for spectrum-based fault localization under a general framework. We first obtain the dynamic slice of program execution. Secondly, we construct a mixed slice spectrum matrix from the dynamic slice of each test case and the corresponding test results. Finally, we compute the suspiciousness value of each statement in the mixed-slice spectram matrix.Results: To verify the performance of our method, we conduct an empirical study on 15 widely used open-source programs. Experimental results show that our approach achieves significant improvement than the compared techniques.Conclusions: Our approach can reduce approximately 1% to 17.79% of the average cost of code examined significantly.","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1071"},"PeriodicalIF":3.8,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A new imperialist competitive algorithm with spiral rising mechanism for solving path optimization problems. 求解路径优化问题的一种新的螺旋上升机制的帝国主义竞争算法。

IF 3.8

PeerJ Computer Science Pub Date : 2022-09-07 eCollection Date: 2022-01-01 DOI: 10.7717/peerj-cs.1075

Xia Li, Junhan Chen, Lingfang Sun, Jing Li

引用次数: 1