Int. J. Model. Simul. Sci. Comput.最新文献_第5页

A deep recursively learning LSTM model to improve cyber security botnet attack intrusion detection 一种深度递归学习LSTM模型，改进网络安全僵尸网络攻击入侵检测

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-24 DOI: 10.1142/s1793962323410180

B. Padmavathi, B. Muthukumar

引用次数: 2

Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network 基于UFS-MSRC算法和LSTM网络的帧间视频伪造检测

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-21 DOI: 10.1142/s1793962323410131

N. Girish, C. Nandini

{"title":"Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network","authors":"N. Girish, C. Nandini","doi":"10.1142/s1793962323410131","DOIUrl":"https://doi.org/10.1142/s1793962323410131","url":null,"abstract":"The forgery involved in region duplication is a common type of video tampering, where the traditional techniques used to detect video tampering are ineffective and inefficient for the forged videos under complex backgrounds. To overcome this issue, a novel video forgery detection model is introduced in this research paper. Initially, the input video sequences are collected from Surrey University Library for Forensic Analysis (SULFA) and Sondos datasets. Further, spatiotemporal averaging method is carried out on the collected video sequences to obtain background information with a pale of moving objects for an effective video forgery detection. Next, feature extraction is performed using the GoogLeNet model for extracting the feature vectors. Then, the Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (UFS-MSRC) approach is used to choose the discriminative feature vectors that superiorly reduce the training time and improve the detection accuracy. Finally, long short-term memory (LSTM) network is applied for forgery detection in the different video sequences. The experimental evaluation illustrated that the UFS-MSRC with LSTM model attained 98.13% and 97.38% of accuracy on SULFA and Sondos datasets, where the obtained results are better when compared to the existing models in video forgery detection.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85001332","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: 1

XDEVS: A hybrid system modeling framework XDEVS:混合系统建模框架

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-18 DOI: 10.1142/s1793962322430012

Kunyu Xie, Lin Zhang, Y. Laili, Xiaohan Wang

引用次数: 1

Reconstruction method of equilibrated flux for a posteriori error estimate of elliptic problems 椭圆型问题后验误差估计的平衡通量重建方法

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-12 DOI: 10.1142/s1793962322500519

F. Han, Yu Liu, Jianguo Wang

引用次数: 0

Impact of variation of size of the initial release mass in the dynamics of landslide generated tsunami 初始释放质量大小变化对滑坡海啸动力学的影响

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-05 DOI: 10.1142/s1793962322500556

J. Kafle, Gr Acharya, Parameshwari Kattel, Puskar R. Pokhrel

{"title":"Impact of variation of size of the initial release mass in the dynamics of landslide generated tsunami","authors":"J. Kafle, Gr Acharya, Parameshwari Kattel, Puskar R. Pokhrel","doi":"10.1142/s1793962322500556","DOIUrl":"https://doi.org/10.1142/s1793962322500556","url":null,"abstract":"When debris flows, landslides, or any gravitational mass flows hit closed or partially open water sources such as seas, oceans, fjords, hydraulic reservoirs, mountain lakes, bays, and landslide dams, it results in tsunami (impulse water waves) by transforming their impact energy to water body, potentially causing damages of infrastructures and human casualties both near field and the distant coastlines. The intensity of hazard depends on the scale, location and process of the landslide, and also on the reservoir volume and topography that surrounds it. Volume or size of the initial release mass that fails and slides along a slope is one of the dominant factors to determine the degree of splash, propagating speed and the amplitudes of the fluid waves, and potential dam breach or water spill over. Here, we numerically integrate the two-phase mass flow model [Pudasaini S. P., J. Geophysi. Res. 117(F03010), 2012] for quasi-three-dimensional, high-resolution simulation results with variation of size of the two-phase initial landslide or debris both longitudinally and laterally. In our numerical experimental results, we observe fundamentally different solid and fluid evolution and wave structures in the reservoir. There are also significant differences in the flow dynamics of solid under water for different volumes of the release mass by extending or contracting the base area along downslope and/or cross-slope directions. The simulation results show that tsunami amplitudes and run out extents are rapidly increased when the volume of the initial release mass in the form of a triangular wedge is increased by increasing the base area through the increment of the length and breadth of the release base. This study can be useful to develop and implement tsunami hazard mitigation strategies to enhance public safety and reduce potential loss due to landslide-generated wave hazards.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76247761","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

The dependence modeling of the caterpillar eggs yield of the laboratory population of Lymantria dispar on the duration of diapause 实验种群虫卵产量与滞育时间的关系模型不一致

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-04-05 DOI: 10.1142/s1793962322500404

A. Sedelnikov

引用次数: 0

PFCMVO: Political fractional competitive multi-verse optimization enabled deep neuro fuzzy network for student performance estimation in spark environment PFCMVO:基于政治分数竞争多元优化的深度神经模糊网络在火花环境下的学生成绩评估

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-28 DOI: 10.1142/s1793962322500507

A. Baruah, S. Baruah

{"title":"PFCMVO: Political fractional competitive multi-verse optimization enabled deep neuro fuzzy network for student performance estimation in spark environment","authors":"A. Baruah, S. Baruah","doi":"10.1142/s1793962322500507","DOIUrl":"https://doi.org/10.1142/s1793962322500507","url":null,"abstract":"Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, [Formula: see text]-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80772049","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: 1

Network security analysis for cloud computing environment 云计算环境下的网络安全分析

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-28 DOI: 10.1142/s1793962322500544

Linjiang Xie, Feilu Hang, W. Guo, Zhenhong Zhang, Hanruo Li

引用次数: 2

Online shopping behavior analysis for smart business using big data analytics and blockchain security 使用大数据分析和区块链安全的智能商业在线购物行为分析

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-28 DOI: 10.1142/s1793962322500532

Weiwei Wei, C. Sivaparthipan, P. Kumar

{"title":"Online shopping behavior analysis for smart business using big data analytics and blockchain security","authors":"Weiwei Wei, C. Sivaparthipan, P. Kumar","doi":"10.1142/s1793962322500532","DOIUrl":"https://doi.org/10.1142/s1793962322500532","url":null,"abstract":"Technological advancement in a modern environment is approaching universality. The realm and uses of information technology (IT) and Blockchain Security have been extensively broadened. Any business wishing to boost its market prospects would certainly keep records of the perspectives and behaviors of its consumers. The firms employ advanced technological concepts, skills, and methods to comprehend their applicants. Additional information and facts are analyzed to improve judgment. Business analytics and Blockchain Security professionals have a positive opinion. Online shopping behavior analysis (OSBA) is proposed in this research. It addresses a smooth transition from a prediction method to a gradual information strategy that learns the clients’ needs and achieves their electronic trading revenues. Any commercial enterprise must have limitless entry to information. That contains population revenues, industrial patterns, competition and customer information, productivity measurements, computations, and much more. Corporate information has a significant role in this undertaking. Experimental information is collected periodically to evaluate the evidence and provide fresh discoveries and operations that provide fresh perspectives. The major consideration principal element assessment approach is utilized with big data analytics, Blockchain Security, and fuzzy interference system to assess the essential purchasing variables for customers. It achieves an accuracy of 89% and an [Formula: see text] score of 87%. Models like support vector machine, convolutional neural network, deep neural network, random forest, fuzzy logic, and decision tree (DT) are compared with the OSBA model’s simulation results (DT). Fuzzy interference, big data, and Blockchain Security analytics improve the OSBA model’s performance.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76480459","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

Acceptance sampling to aid in the verification of computational simulations 辅助验证计算模拟的验收抽样

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-28 DOI: 10.1142/s1793962322500441

Andrew J. Collins, Erika F. Frydenlund, Christopher J. Lynch, R. M. Robinson

引用次数: 0