International Journal of Advanced Computer Science and Applications最新文献_第9页

Deep Learning-based Sentence Embeddings using BERT for Textual Entailment 基于BERT的深度学习句子嵌入的文本蕴涵

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.01408108

M. Alsuhaibani

引用次数: 1

Research on Recommendation Model of College English MOOC based on Hybrid Recommendation Algorithm 基于混合推荐算法的大学英语MOOC推荐模型研究

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140464

Yifang Ding, J. Hao

{"title":"Research on Recommendation Model of College English MOOC based on Hybrid Recommendation Algorithm","authors":"Yifang Ding, J. Hao","doi":"10.14569/ijacsa.2023.0140464","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140464","url":null,"abstract":"Establishing a reasonable and efficient compulsory education balance index system is very important to boost the all-around of compulsory education development, and then realize the course recommendation for students with different attributes. Based on this, the research aimed at the problems in college English education and evaluation, aimed to establish a college English MOOC education and evaluation system based on the improved neural network recommendation algorithm. The research first constructed the college English MOOC education and evaluation data elements, and then established a genetic algorithm improved neural network algorithm (BP Neural Network Optimization Algorithm Based on Genetic Algorithm, GA-BP), and finally analyzed the effect of the assembled model. These results show that the fitness of the GA-BP model reaches the set expectation when the evolutionary algebra reaches 10 times, and its fitness is 0.6. The corresponding threshold and weight are obtained, and the threshold and weight are substituted into the model. After repeated iterative training, the model finally reached an error of 10-3 when it was trained 12 times, and the expected accuracy was achieved. The R value of each set hovered around 0.97, and the fitting degree was high, which showed that the GA-BP model proposed in the study had a better fitting degree. The difference between the expected value and the output value is mainly distributed in the [-0.08083, 0.06481] interval. To sum up, the GA-BP model proposed in the study has an excellent effect on college English education and evaluation. This evaluation model has a faster learning rate and a higher prediction accuracy and more stable performance. Keywords—Genetic algorithm; education quality assessment; BP neural network; college English MOOC","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86519764","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

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices 基于Sentinel-2影像和光谱指数的干旱区土地覆盖分类特征优选

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140312

Mohammed Saeed, Asmala Ahmad, O. Mohd

引用次数: 0

A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level 一种优化决策者满意度的单值五边形中性几何规划方法

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140439

Satyabrata Nath, P. Das, P. Debnath

{"title":"A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level","authors":"Satyabrata Nath, P. Das, P. Debnath","doi":"10.14569/ijacsa.2023.0140439","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140439","url":null,"abstract":"Achieving the desired level of satisfaction for a decision-maker in any decision-making scenario is considered a challenging endeavor because minor modifications in the process might lead to incorrect findings and inaccurate decisions. In order to maximize the decision-maker’s satisfaction, this paper proposes a Single-valued Neutrosophic Geometric Programming model based on pentagonal fuzzy numbers. The decision-maker is typically assumed to be certain of the parameters, but in reality, this is not the case, hence the parameters are presented as neutrosophic fuzzy values. The decision-maker, with this strategy, is able to achieve varying levels of satisfaction and dissatisfaction for each constraint and even complete satisfaction for certain constraints. Here the decision maker aims to achieve the maximum level of satisfaction while maintaining the level of hesitation and minimizing dissatisfaction in order to retain an optimum solution. Furthermore, transforming the objective function into a constraint adds one more layer to the Ndimensional multi-parametrizes and . The advantages of this multi-parametrized proposed method over the existing ones are proven using numerical examples. Keywords—Decision making; pentagonal neutrosophic numbers; single-valued neutrosophic geometric programming; multi-parametric programming","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"75 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80664918","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

Improved 3D Rotation-based Geometric Data Perturbation Based on Medical Data Preservation in Big Data 基于大数据医疗数据保存的改进三维旋转几何数据摄动

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140592

Jayanti Dansana, M. R. Kabat, P. Pattnaik

{"title":"Improved 3D Rotation-based Geometric Data Perturbation Based on Medical Data Preservation in Big Data","authors":"Jayanti Dansana, M. R. Kabat, P. Pattnaik","doi":"10.14569/ijacsa.2023.0140592","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140592","url":null,"abstract":"— With the rise in technology, a huge volume of data is being processed using data mining, especially in the healthcare sector. Usually, medical data consist of a lot of personal data, and third parties utilize it for the data mining process. Perturbation in health care data highly aids in preventing intruders from utilizing the patient’s privacy. One of the challenges in data perturbation is managing data utility and privacy protection. Medical data mining has certain special properties compared with other data mining fields. Hence, in this work, the machine learning (ML) based perturbation approach is introduced to provide more privacy to healthcare data. Here, clustering and IGDP-3DR processes are applied to improve healthcare privacy preservation. Initially, the dataset is pre-processed using data normalization. Then, the dimensionality is reduced by SVD with PCA (singular value decomposition with Principal component analysis). Then, the clustering process is performed by IFCM (Improved Fuzzy C means). The high-dimensional data are divided into several segments by IFCM, and every partition is set as a cluster. Then, improved Geometric Data Perturbation (IGDP) is used to perturb the clustered data. IGDP is a combination of GDP with 3D rotation (3DR). Finally, the perturbed data are classified using a machine learning (ML) classifier - kernel Support Vector Machine- Horse Herd Optimization (KSVM-HHO) to classify the perturbed data and ensure better accuracy. The overall evaluation of the proposed KSVM-HHO is carried out in the Python platform. The performance of the IGDP-KSVM-HHO is compared over the two benchmark datasets, Wisconsin prognostic breast cancer (WBC) and Pima Indians Diabetes (PID) dataset. For the WBC dataset, the proposed method obtains an overall accuracy of 98.08% perturbed data, and for the PID dataset, the proposed method obtains an overall accuracy of 98.04%.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"70 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80672950","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 Adaptive Testcase Recommendation System to Engage Students in Learning: A Practice Study in Fundamental Programming Courses 应用自适应测试案例推荐系统让学生参与学习:程式设计基础课程的实践研究

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.01406118

Tien Vu-Van, Huy Tran, Thanh-Van Le, Hoang-Anh Pham, Nguyen Huynh-Tuong

引用次数: 0

A Novel Framework for Detecting Network Intrusions Based on Machine Learning Methods 基于机器学习方法的网络入侵检测新框架

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140755

B. Omarov, Nazgul Abdinurova, Zhamshidbek Abdulkhamidov

{"title":"A Novel Framework for Detecting Network Intrusions Based on Machine Learning Methods","authors":"B. Omarov, Nazgul Abdinurova, Zhamshidbek Abdulkhamidov","doi":"10.14569/ijacsa.2023.0140755","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140755","url":null,"abstract":"—In the rapidly evolving landscape of cyber threats, the efficacy of traditional rule-based network intrusion detection systems has become increasingly questionable. This paper introduces a novel framework for identifying network intrusions, leveraging the power of advanced machine learning techniques. The proposed methodology steps away from the rigidity of conventional systems, bringing a flexible, adaptive, and intuitive approach to the forefront of network security. This study employs a diverse blend of machine learning models including but not limited to, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. This research explores an innovative feature extraction and selection technique that enables the model to focus on high-priority potential threats, minimizing noise and improving detection accuracy. The framework's performance has been rigorously evaluated through a series of experiments on benchmark datasets. The results consistently surpass traditional methods, demonstrating a remarkable increase in detection rates and a significant reduction in false positives. Further, the machine learning-based model demonstrated its ability to adapt to new threat landscapes, indicating its suitability in real-world scenarios. By marrying the agility of machine learning with the concreteness of network intrusion detection, this research opens up new avenues for dynamic and resilient cybersecurity. The framework offers an innovative solution that can identify, learn, and adapt to evolving network intrusions, shaping the future of cyber defense strategies.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"4 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84894582","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

Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification 基于边缘检测的虹膜识别方法在飞行模拟器用户识别中的应用

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140425

Sundas Khan, Samra Urooj Khan, Onyeka J. Nwobodo, K. Cyran

{"title":"Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification","authors":"Sundas Khan, Samra Urooj Khan, Onyeka J. Nwobodo, K. Cyran","doi":"10.14569/ijacsa.2023.0140425","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140425","url":null,"abstract":"— To meet the increasing security requirement of authorized users of flight simulators, personal identification is becoming more and more important. Iris recognition stands out as one of the most accurate biometric methods in use today. Iris recognition is done through different edge detection methods. Therefore, it is important to have an understanding of the different edge detection methods that are in use these days. Specifically, the biomedical research shows that irises are as different as fingerprints or the other patterns of the recognition. Furthermore, because the iris is a visible organism, its exterior look can be examined remotely using a machine vision system. The main part of this paper delves into concerns concerning the selection of the best results giving method of the recognition. In this paper, three edge detection methods, namely Canny, Sobel and Prewitt, are applied to the image of eye (iris) and their comparative analysis is discussed. These methods are applied using the Software MATLAB. The datasets used for this purpose are CASIA and MMU. The results indicate that the performance of Canny edge detection method is best as compared to Sobel and Prewitt. Image quality is a key requirement in image-based object recognition. This paper provides the quality evaluation of the images using different metrics like PSNR, SNR, MSE and SSIM. However, SSIM is considered best image quality metric as compared to PSNR, SNR and MSE.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"54 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83613365","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

Proactive Acquisition using Bot on Discord 在Discord上使用Bot进行主动获取

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.0140533

N. Cahyani, D. Pratama, N. H. A. Rahman

引用次数: 0

Optimal Scheduling using Advanced Cat Swarm Optimization Algorithm to Improve Performance in Fog Computing 利用先进的Cat群算法提高雾计算性能的最优调度

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International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI: 10.14569/ijacsa.2023.01407114

Xiaoyan Huo, Xue-ming Wang

{"title":"Optimal Scheduling using Advanced Cat Swarm Optimization Algorithm to Improve Performance in Fog Computing","authors":"Xiaoyan Huo, Xue-ming Wang","doi":"10.14569/ijacsa.2023.01407114","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01407114","url":null,"abstract":"—Fog computing can be considered a decentralized computing approach that essentially extends the capabilities offered by cloud computing to the periphery of the network. In addition, due to its proximity to the user, fog computing proves to be highly efficient in minimizing the volume of data that needs to be transmitted, reducing overall network traffic, and shortening the distance that data must travel. But this technology, like other new technologies, has challenges, and scheduling and optimal allocation of resources is one of the most important of these challenges. Accordingly, this research aims to propose an optimal solution for efficient scheduling within the fog computing environment through the application of the advanced cat swarm optimization algorithm. In this solution, the two main behaviors of cats are implemented in the form of seek and tracking states. Accordingly, processing nodes are periodically examined and categorized based on the number of available resources; servers with highly available resources are prioritized in the scheduling process for efficient scheduling. Subsequently, the congested servers, which may be experiencing various issues, are migrated to alternative servers with ample resources using the virtual machine live migration technique. Ultimately, the effectiveness of the proposed solution is assessed using the iFogSim simulator, demonstrating notable reductions in execution time and energy consumption. So, the proposed solution has led to a 20% reduction in execution time while also improving energy efficiency by more than 15% on average. This optimization represents a trade-off between improving performance and reducing resource consumption.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85316991","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