Pub Date : 2021-01-01DOI: 10.4018/IJGHPC.2021070101
V. R. Thakare, K. JohnSingh
The interest in cloud computing and its techniques are gaining exponentially in IT industries because of its cost-effective architecture and services. However, these flexible services of cloud bring many security and privacy challenges due to loss of control over the data. This paper focuses on an analysis of various computational trust models in cloud security environment. The computational trust models that are used to build secure cloud architectures are not available in a blended fashion to overcome security and privacy challenges. The paper aims to contribute to the literature review to assist researchers who are striving to contribute in this area. The main objective of this review is to identify and analyse the recently published research topics related to trust models and trust mechanisms for cloud with regard to research activity and proposed approaches. The future work is to design a trust mechanism for cloud security models to achieve the higher level of security.
{"title":"A Study of Computational Trust Models in Cloud Security","authors":"V. R. Thakare, K. JohnSingh","doi":"10.4018/IJGHPC.2021070101","DOIUrl":"https://doi.org/10.4018/IJGHPC.2021070101","url":null,"abstract":"The interest in cloud computing and its techniques are gaining exponentially in IT industries because of its cost-effective architecture and services. However, these flexible services of cloud bring many security and privacy challenges due to loss of control over the data. This paper focuses on an analysis of various computational trust models in cloud security environment. The computational trust models that are used to build secure cloud architectures are not available in a blended fashion to overcome security and privacy challenges. The paper aims to contribute to the literature review to assist researchers who are striving to contribute in this area. The main objective of this review is to identify and analyse the recently published research topics related to trust models and trust mechanisms for cloud with regard to research activity and proposed approaches. The future work is to design a trust mechanism for cloud security models to achieve the higher level of security.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"220 3 1","pages":"1-11"},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87038593","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}
Pub Date : 2021-01-01DOI: 10.4018/ijghpc.2021010105
V. BalajiPrabhuB., M. Dakshayini
Although Big data analytics, machine learning and cloud technologies have been acknowledged as better enablers in revolutionizing the quality of agricultural systems, in most of the developing nations like India there is no able system to effectively survey the real grocery needs of the society and accordingly educate the farmers to grow and supply the crops. Due to lack of such process, there is no synchronization between demand and supply of food crops, and hence, most of the time farmers suffer with loss and consumers suffer from high varied prices. In order to address this problem, data about the demand, supply, and price variation of various crops of different seasons of the year have been collected and analysed. The analysis results have shown a huge gap between demand and supply of crops. Hence, this work proposes novel machine learning-based data analytics system that forecasts the demand for different food crops and regulates the supply accordingly by assisting the farmers in growing the crops based on the demand. Implementation results have shown 92% reduction in the gap.
{"title":"Machine Learning-Based Decision Support System for Effective Quality Farming","authors":"V. BalajiPrabhuB., M. Dakshayini","doi":"10.4018/ijghpc.2021010105","DOIUrl":"https://doi.org/10.4018/ijghpc.2021010105","url":null,"abstract":"Although Big data analytics, machine learning and cloud technologies have been acknowledged as better enablers in revolutionizing the quality of agricultural systems, in most of the developing nations like India there is no able system to effectively survey the real grocery needs of the society and accordingly educate the farmers to grow and supply the crops. Due to lack of such process, there is no synchronization between demand and supply of food crops, and hence, most of the time farmers suffer with loss and consumers suffer from high varied prices. In order to address this problem, data about the demand, supply, and price variation of various crops of different seasons of the year have been collected and analysed. The analysis results have shown a huge gap between demand and supply of crops. Hence, this work proposes novel machine learning-based data analytics system that forecasts the demand for different food crops and regulates the supply accordingly by assisting the farmers in growing the crops based on the demand. Implementation results have shown 92% reduction in the gap.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"219 1","pages":"82-109"},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76586355","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}
Pub Date : 2020-10-01DOI: 10.4018/ijghpc.2020100103
V. BalajiPrabhuB., M. Dakshayini
Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.
{"title":"Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests","authors":"V. BalajiPrabhuB., M. Dakshayini","doi":"10.4018/ijghpc.2020100103","DOIUrl":"https://doi.org/10.4018/ijghpc.2020100103","url":null,"abstract":"Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"179 1","pages":"35-47"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80061295","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}
Pub Date : 2020-10-01DOI: 10.4018/ijghpc.2020100102
K.Akhil Kumar, Jyoti Thaman
Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.
{"title":"Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment","authors":"K.Akhil Kumar, Jyoti Thaman","doi":"10.4018/ijghpc.2020100102","DOIUrl":"https://doi.org/10.4018/ijghpc.2020100102","url":null,"abstract":"Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"49 1","pages":"13-34"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90648123","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}
Pub Date : 2020-10-01DOI: 10.4018/ijghpc.2020100106
Wei Zhang, Yanli Du, Qinghua Bai
In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot SLAM algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter SLAM algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the SLAM positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot SLAM information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot SLAM is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.
{"title":"An Optimized Coverage Robot SLAM Algorithm Based on Improved Particle Filter for WSN Nodes","authors":"Wei Zhang, Yanli Du, Qinghua Bai","doi":"10.4018/ijghpc.2020100106","DOIUrl":"https://doi.org/10.4018/ijghpc.2020100106","url":null,"abstract":"In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot SLAM algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter SLAM algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the SLAM positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot SLAM information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot SLAM is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"41 1","pages":"76-88"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79895644","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}
Pub Date : 2020-10-01DOI: 10.4018/ijghpc.2020100104
Anamala Balaji Manju, Sumathy Subramanian
With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.
{"title":"Fog-Assisted Privacy Preservation Scheme for Location-Based Services Based on Trust Relationship","authors":"Anamala Balaji Manju, Sumathy Subramanian","doi":"10.4018/ijghpc.2020100104","DOIUrl":"https://doi.org/10.4018/ijghpc.2020100104","url":null,"abstract":"With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"34 1","pages":"48-62"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73603611","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}
Pub Date : 2020-10-01DOI: 10.4018/ijghpc.2020100101
Ming Chen, Jinghua Yan, Tieliang Gao, Huan Ma, Li Duan, Qiguang Tang
Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.
{"title":"An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication","authors":"Ming Chen, Jinghua Yan, Tieliang Gao, Huan Ma, Li Duan, Qiguang Tang","doi":"10.4018/ijghpc.2020100101","DOIUrl":"https://doi.org/10.4018/ijghpc.2020100101","url":null,"abstract":"Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"9 1","pages":"1-12"},"PeriodicalIF":1.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79596413","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}
Pub Date : 2020-07-01DOI: 10.4018/ijghpc.2020070101
Nancy Victor, Daphne Lopez
The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.
{"title":"sl-LSTM: A Bi-Directional LSTM With Stochastic Gradient Descent Optimization for Sequence Labeling Tasks in Big Data","authors":"Nancy Victor, Daphne Lopez","doi":"10.4018/ijghpc.2020070101","DOIUrl":"https://doi.org/10.4018/ijghpc.2020070101","url":null,"abstract":"The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"19 1","pages":"1-16"},"PeriodicalIF":1.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89414111","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}
Pub Date : 2020-07-01DOI: 10.4018/ijghpc.2020070103
Sukhnandan Kaur Johal, R. Mohana
Various natural language processing tasks are carried out to feed into computerized decision support systems. Among these, sentiment analysis is gaining more attention. The majority of sentiment analysis relies on the social media content. This web content is highly un-normalized in nature. This hinders the performance of decision support system. To enhance the performance, it is required to process data efficiently. This article proposes a novel method of normalization of web data during the pre-processing phase. It is aimed to get better results for different natural language processing tasks. This research applies this technique on data for sentiment analysis. Performance of different learning models is analysed using precision, recall, f-measure, fallout for normalize and un-normalize sentiment analysis. Results shows after normalization, some documents shift their polarity i.e. negative to positive. Experimental results show normalized data processing outperforms un-normalized data processing with better accuracy.
{"title":"Effectiveness of Normalization Over Processing of Textual Data Using Hybrid Approach Sentiment Analysis","authors":"Sukhnandan Kaur Johal, R. Mohana","doi":"10.4018/ijghpc.2020070103","DOIUrl":"https://doi.org/10.4018/ijghpc.2020070103","url":null,"abstract":"Various natural language processing tasks are carried out to feed into computerized decision support systems. Among these, sentiment analysis is gaining more attention. The majority of sentiment analysis relies on the social media content. This web content is highly un-normalized in nature. This hinders the performance of decision support system. To enhance the performance, it is required to process data efficiently. This article proposes a novel method of normalization of web data during the pre-processing phase. It is aimed to get better results for different natural language processing tasks. This research applies this technique on data for sentiment analysis. Performance of different learning models is analysed using precision, recall, f-measure, fallout for normalize and un-normalize sentiment analysis. Results shows after normalization, some documents shift their polarity i.e. negative to positive. Experimental results show normalized data processing outperforms un-normalized data processing with better accuracy.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"24 1","pages":"43-56"},"PeriodicalIF":1.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78734387","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：