Pub Date : 2020-10-01DOI: 10.4018/ijncr.2020100101
Mridu Sahu, T. Jani, Maski Saijahnavi, Amrit Kumar, U. Chaurasiya, Samrudhi Mohdiwale
Rust detection is necessary for proper working and maintenance of machines for security purposes. Images are one of the suggested platforms for rust detection in which rust can be detected even though the human can't reach to the area. However, there are a lack of online databases available that can provide a sizable dataset to identify the most suitable model that can be used further. This paper provides a data augmentation technique by using Perlin noise, and further, the generated images are tested on standard features (i.e., statistical values, entropy, along with SIFT and SURF methods). The two most generalized classifiers, naïve Bayes and support vector machine, are identified and tested to obtain the performance of classification of rusty and non-rusty images. The support vector machine provides better classification accuracy, which also suggests that that the combined features of statistics, SIFT, and SURF are able to differentiate the images. Hence, it can be further used to detect the rust in different parts of machines.
{"title":"Classification of Rusty and Non-Rusty Images: A Machine Learning Approach","authors":"Mridu Sahu, T. Jani, Maski Saijahnavi, Amrit Kumar, U. Chaurasiya, Samrudhi Mohdiwale","doi":"10.4018/ijncr.2020100101","DOIUrl":"https://doi.org/10.4018/ijncr.2020100101","url":null,"abstract":"Rust detection is necessary for proper working and maintenance of machines for security purposes. Images are one of the suggested platforms for rust detection in which rust can be detected even though the human can't reach to the area. However, there are a lack of online databases available that can provide a sizable dataset to identify the most suitable model that can be used further. This paper provides a data augmentation technique by using Perlin noise, and further, the generated images are tested on standard features (i.e., statistical values, entropy, along with SIFT and SURF methods). The two most generalized classifiers, naïve Bayes and support vector machine, are identified and tested to obtain the performance of classification of rusty and non-rusty images. The support vector machine provides better classification accuracy, which also suggests that that the combined features of statistics, SIFT, and SURF are able to differentiate the images. Hence, it can be further used to detect the rust in different parts of machines.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133957176","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/ijncr.2020070105
M. Gooroochurn, D. Kerr, K. Bouazza-Marouf
This paper presents a framework to segment planar or near-planar fluid flow and uses artificial neural networks to characterize fluid flow by determining the rate of flow and source of the fluid, which can be applied in various areas (e.g., characterizing fluid flow in surface irrigation from aerial pictures, in leakage detection, and in surgical robotics for characterizing blood flow over an operative site). For the latter, the outcome enables to assess bleeding severity and find the source of the bleeding. Based on its importance in assessing injuries and from a medical perspective in directing the course of surgery, fluid flow assessment is deemed to be a desirable addition to a surgical robot's capabilities. The results from tests on fluid flows generated from a test rig show that the proposed methods can contribute to an automated characterization of fluid flow, which in the presence of several fluid flow sources can be achieved by tracking the flows, determining the locations of the sources and their relative severities, with execution times suitable for real-time operation.
{"title":"A Machine Learning Approach to Tracking and Characterizing Planar or Near Planar Fluid Flow","authors":"M. Gooroochurn, D. Kerr, K. Bouazza-Marouf","doi":"10.4018/ijncr.2020070105","DOIUrl":"https://doi.org/10.4018/ijncr.2020070105","url":null,"abstract":"This paper presents a framework to segment planar or near-planar fluid flow and uses artificial neural networks to characterize fluid flow by determining the rate of flow and source of the fluid, which can be applied in various areas (e.g., characterizing fluid flow in surface irrigation from aerial pictures, in leakage detection, and in surgical robotics for characterizing blood flow over an operative site). For the latter, the outcome enables to assess bleeding severity and find the source of the bleeding. Based on its importance in assessing injuries and from a medical perspective in directing the course of surgery, fluid flow assessment is deemed to be a desirable addition to a surgical robot's capabilities. The results from tests on fluid flows generated from a test rig show that the proposed methods can contribute to an automated characterization of fluid flow, which in the presence of several fluid flow sources can be achieved by tracking the flows, determining the locations of the sources and their relative severities, with execution times suitable for real-time operation.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115768788","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/ijncr.2020070102
L. Nanni, S. Brahnam, Gianluca Maguolo
Automatic anatomical therapeutic chemical (ATC) classification predicts an unknown compound's therapeutic and chemical characteristics. Predicting the organs/systems an unidentified compound will act on has the potential of expediting drug development and research. That a given compound can affect multiple organs/systems makes automatic ATC classification a complex problem. In this paper, the authors experimentally develop a multi-label ensemble for ATC prediction. The proposed approach extracts a 1D feature vector based on a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds, as defined by the ATC coding system. This 1D vector is reshaped into 2D matrices and fed into seven pre-trained convolutional neural networks (CNN). A bidirectional long short-term memory network (BiLSTM) is trained on the 1D vector. Features extracted from both deep learners are then trained on multi-label classifiers, with results fused. The best system proposed here is shown to outperform other methods reported in the literature.
{"title":"Anatomical Therapeutic Chemical Classification (ATC) With Multi-Label Learners and Deep Features","authors":"L. Nanni, S. Brahnam, Gianluca Maguolo","doi":"10.4018/ijncr.2020070102","DOIUrl":"https://doi.org/10.4018/ijncr.2020070102","url":null,"abstract":"Automatic anatomical therapeutic chemical (ATC) classification predicts an unknown compound's therapeutic and chemical characteristics. Predicting the organs/systems an unidentified compound will act on has the potential of expediting drug development and research. That a given compound can affect multiple organs/systems makes automatic ATC classification a complex problem. In this paper, the authors experimentally develop a multi-label ensemble for ATC prediction. The proposed approach extracts a 1D feature vector based on a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds, as defined by the ATC coding system. This 1D vector is reshaped into 2D matrices and fed into seven pre-trained convolutional neural networks (CNN). A bidirectional long short-term memory network (BiLSTM) is trained on the 1D vector. Features extracted from both deep learners are then trained on multi-label classifiers, with results fused. The best system proposed here is shown to outperform other methods reported in the literature.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"53 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935798","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/ijncr.2020070103
Anusha Vangala, Sachi Pandey, P. Parwekar, Ikechi Augustine Ukaegbu
A wireless sensor network consists of a number of sensors laid out in a field with mobile sinks dynamically aggregating data from the nodes. Sensitive applications such as military environment require the sink to identify if a sensor that it visits is legitimate, and in turn, the sensor has to ensure that the sink is authenticated to access its sensitive data. For the system to intelligently learn the credentials of non-malicious sink and non-malicious sensors based on the dynamically observed data, four approaches using access control lists, authenticator tokens, message digests, and elliptic curve variant of RSA algorithm are proposed along with the formal logic for correctness. The experimented data is analysed using false acceptance rate, false rejection rate, precision, and curve analysis parameters. The approaches are further compared based on the attacks they are vulnerable to and execution time, ultimately concluding that exchange of message digests and elliptic curve RSA algorithm are more widely applicable.
{"title":"Intelligent Authentication Model in a Hierarchical Wireless Sensor Network With Multiple Sinks","authors":"Anusha Vangala, Sachi Pandey, P. Parwekar, Ikechi Augustine Ukaegbu","doi":"10.4018/ijncr.2020070103","DOIUrl":"https://doi.org/10.4018/ijncr.2020070103","url":null,"abstract":"A wireless sensor network consists of a number of sensors laid out in a field with mobile sinks dynamically aggregating data from the nodes. Sensitive applications such as military environment require the sink to identify if a sensor that it visits is legitimate, and in turn, the sensor has to ensure that the sink is authenticated to access its sensitive data. For the system to intelligently learn the credentials of non-malicious sink and non-malicious sensors based on the dynamically observed data, four approaches using access control lists, authenticator tokens, message digests, and elliptic curve variant of RSA algorithm are proposed along with the formal logic for correctness. The experimented data is analysed using false acceptance rate, false rejection rate, precision, and curve analysis parameters. The approaches are further compared based on the attacks they are vulnerable to and execution time, ultimately concluding that exchange of message digests and elliptic curve RSA algorithm are more widely applicable.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963737","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}
Visual tracking requirement is increasing day by day due to the availability of high-performance digital cameras at low prices. Visual tracking becomes a complex problem when cameras suffer with unwanted and unintentional motion, resulting in motion-blurred unstabilized video. The problem in hand becomes more challenging when the target of interest is to be detected automatically in this unstabilized video. This paper presents a comprehensive single intelligent solution for these problems. The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video keeping intentional motion only using speeded-up robust features (SURF) technique. Motion smear due to unstabilization is also removed, providing sharp stabilized video output with video quality enhancement of up to 20dB. Gabor filter is used innovatively for auto-detection of target of interest in each stabilized frame. Then the target is tracked using SURF method.
{"title":"Intelligent Visual Tracking in Unstabilized Videos","authors":"Kamlesh Verma, D. Ghosh, Harsh Saxena, Himanshu Singh, Rajeev Marathe, Avnish Kumar","doi":"10.4018/ijncr.2020070104","DOIUrl":"https://doi.org/10.4018/ijncr.2020070104","url":null,"abstract":"Visual tracking requirement is increasing day by day due to the availability of high-performance digital cameras at low prices. Visual tracking becomes a complex problem when cameras suffer with unwanted and unintentional motion, resulting in motion-blurred unstabilized video. The problem in hand becomes more challenging when the target of interest is to be detected automatically in this unstabilized video. This paper presents a comprehensive single intelligent solution for these problems. The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video keeping intentional motion only using speeded-up robust features (SURF) technique. Motion smear due to unstabilization is also removed, providing sharp stabilized video output with video quality enhancement of up to 20dB. Gabor filter is used innovatively for auto-detection of target of interest in each stabilized frame. Then the target is tracked using SURF method.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133031864","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/ijncr.2020070101
G. Thippeswamy, H. Chandrakala
Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.
{"title":"Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features","authors":"G. Thippeswamy, H. Chandrakala","doi":"10.4018/ijncr.2020070101","DOIUrl":"https://doi.org/10.4018/ijncr.2020070101","url":null,"abstract":"Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128472068","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 : 2020-04-01DOI: 10.4018/ijncr.2020040105
Paulraj Joseph, J. Norman
Cybercrimes catastrophically caused great financial loss in the year 2018 as powerful obfuscated malware known as ransomware continued to be a continual threat to governments and organizations. Advanced malwares capable of system encryption with sophisticated obscure keys left organizations paying the ransom that hackers demand. Since every individual is vulnerable to this assault, cyber forensics play a vital role either in educating society or combating the attacks. As cyber forensics is classified into many subdomains, memory forensics is the domain that leads in curbing these types of attacks. This article gives insight on importance of memory forensics and provides widespread analysis on working of ransomware, recognizes the workflow, provides the ways to overcome this attack. Furthermore, this article implements user defined rules by integrating into powerful search tools known as YARA to detect and prevent the ransomware attacks.
{"title":"Systematic Memory Forensic Analysis of Ransomware using Digital Forensic Tools","authors":"Paulraj Joseph, J. Norman","doi":"10.4018/ijncr.2020040105","DOIUrl":"https://doi.org/10.4018/ijncr.2020040105","url":null,"abstract":"Cybercrimes catastrophically caused great financial loss in the year 2018 as powerful obfuscated malware known as ransomware continued to be a continual threat to governments and organizations. Advanced malwares capable of system encryption with sophisticated obscure keys left organizations paying the ransom that hackers demand. Since every individual is vulnerable to this assault, cyber forensics play a vital role either in educating society or combating the attacks. As cyber forensics is classified into many subdomains, memory forensics is the domain that leads in curbing these types of attacks. This article gives insight on importance of memory forensics and provides widespread analysis on working of ransomware, recognizes the workflow, provides the ways to overcome this attack. Furthermore, this article implements user defined rules by integrating into powerful search tools known as YARA to detect and prevent the ransomware attacks.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565670","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 : 2020-04-01DOI: 10.4018/ijncr.2020040104
P. Parwekar, A. Bakambekova, Talgat Bizhigit, Yeldar Toleubay
Different access network types are characterized by a variety of attributes which include link bandwidths, physical media, capacity, and reliability. Therefore, the question of accurately identifying whether the sender uses a wired ethernet connection or a wireless LAN connection comes into place. This article aims to analyse, simulate, validate, and improve the existing classification scheme which is based on measuring entropy of packet pair inter-arrival times and median. A riverbed modeller (former OPNET) is used for simulating the different scenarios. Small-scale experiment conducted on campus at the Nazarbayev University (NU) validates the insignificance of the packet probe size chosen for the classification.
{"title":"Software-Based Validation of the Differentiation Scheme for Ethernet and Wireless LAN Access Network Types Using an End-to-End Approach","authors":"P. Parwekar, A. Bakambekova, Talgat Bizhigit, Yeldar Toleubay","doi":"10.4018/ijncr.2020040104","DOIUrl":"https://doi.org/10.4018/ijncr.2020040104","url":null,"abstract":"Different access network types are characterized by a variety of attributes which include link bandwidths, physical media, capacity, and reliability. Therefore, the question of accurately identifying whether the sender uses a wired ethernet connection or a wireless LAN connection comes into place. This article aims to analyse, simulate, validate, and improve the existing classification scheme which is based on measuring entropy of packet pair inter-arrival times and median. A riverbed modeller (former OPNET) is used for simulating the different scenarios. Small-scale experiment conducted on campus at the Nazarbayev University (NU) validates the insignificance of the packet probe size chosen for the classification.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134286051","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 : 2020-04-01DOI: 10.4018/ijncr.2020040101
K. Vani, K. P. Kumar, Geethika Kodali, Naveen Pothineni, S. Aravapalli
This article presents criminal bioinformatics approach which turned out to be fast, exact, and definitive in the evaluation and the investigation of crude DNA profiling information. The most problematic scenario for mixture interpretation, however, is when the amount of DNA is limited for one or more of the sources in a mixture. The present study has examined the utility of legal bioinformatics application to Short Tandem Repeats (STR) information. The DNA profiling information is overseen and investigated on the grounds of the different loci display and changeability in various people. The authors have consolidated a similar general idea Inconstancy in STR areas can be utilized to recognize one DNA profile from another.
{"title":"An Exploration of Mixed DNA Samples by Forensic Biological Data","authors":"K. Vani, K. P. Kumar, Geethika Kodali, Naveen Pothineni, S. Aravapalli","doi":"10.4018/ijncr.2020040101","DOIUrl":"https://doi.org/10.4018/ijncr.2020040101","url":null,"abstract":"This article presents criminal bioinformatics approach which turned out to be fast, exact, and definitive in the evaluation and the investigation of crude DNA profiling information. The most problematic scenario for mixture interpretation, however, is when the amount of DNA is limited for one or more of the sources in a mixture. The present study has examined the utility of legal bioinformatics application to Short Tandem Repeats (STR) information. The DNA profiling information is overseen and investigated on the grounds of the different loci display and changeability in various people. The authors have consolidated a similar general idea Inconstancy in STR areas can be utilized to recognize one DNA profile from another.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122074881","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 : 2020-04-01DOI: 10.4018/ijncr.2020040103
G. Rani, J. Bansal
Desktop grids make use of unused resources of personal computers provided by volunteers to work as a huge processor and make them available to users that need them. The rate of heterogeneity, volatility, and unreliability is higher in case of a desktop grid in comparison to conventional systems. Therefore, the application of fault tolerance strategies becomes an inevitable requirement. In this article, a hybrid fault tolerance strategy is proposed which works in three phases. First, two phases deal with the task and resource scheduling in which appropriate scheduling decisions are taken in order to select the most suitable resource for a task. Even if any failure occurs, it is then recovered in the third phase by using rescheduling and checkpointing. The proposed strategy is compared against existing hybrid fault tolerance scheduling strategies and ensures a 100% success rate and processor utilization and outperforms by a factor of 3.5%, 0.4%, and 0.1% when turnaround time, throughput, and makespan, respectively, are taken into account
{"title":"A Hybrid Batch Mode Fault Tolerance Strategy in Desktop Grids","authors":"G. Rani, J. Bansal","doi":"10.4018/ijncr.2020040103","DOIUrl":"https://doi.org/10.4018/ijncr.2020040103","url":null,"abstract":"Desktop grids make use of unused resources of personal computers provided by volunteers to work as a huge processor and make them available to users that need them. The rate of heterogeneity, volatility, and unreliability is higher in case of a desktop grid in comparison to conventional systems. Therefore, the application of fault tolerance strategies becomes an inevitable requirement. In this article, a hybrid fault tolerance strategy is proposed which works in three phases. First, two phases deal with the task and resource scheduling in which appropriate scheduling decisions are taken in order to select the most suitable resource for a task. Even if any failure occurs, it is then recovered in the third phase by using rescheduling and checkpointing. The proposed strategy is compared against existing hybrid fault tolerance scheduling strategies and ensures a 100% success rate and processor utilization and outperforms by a factor of 3.5%, 0.4%, and 0.1% when turnaround time, throughput, and makespan, respectively, are taken into account","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125023335","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}
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