Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.
{"title":"Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment","authors":"Kamlesh Lakhwani, Gajanand Sharma, Ramandeep Sandhu, Naresh Kumar Nagwani, Sandeep Bhargava, Varsha Arya, Ammar Almomani","doi":"10.4018/ijcac.324809","DOIUrl":"https://doi.org/10.4018/ijcac.324809","url":null,"abstract":"Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295995","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}
A. G. Jakwa, Abdulsalam Yau Gital, S. Boukari, F. Zambuk
Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.
{"title":"Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing","authors":"A. G. Jakwa, Abdulsalam Yau Gital, S. Boukari, F. Zambuk","doi":"10.4018/ijcac.324758","DOIUrl":"https://doi.org/10.4018/ijcac.324758","url":null,"abstract":"Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49545742","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}
Cloud computing is a new information technology. It is the product of the scientific and technological development of the times and plays an important role in the development of this country. In order to effectively solve the security problem of cloud computing data access, an identity-based privacy protection algorithm for cloud computing is proposed. The user information is stored in the cloud server at the registration stage, and the user identity is verified by signature when the information is obtained. The strong forward secure signature scheme can ensure that the signature is both forward secure and backward secure. At present, most signature schemes based on lattice focus on forward security. Therefore, this article constructs a strong forward secure signature scheme based on lattice and applies this signature scheme to cloud user authentication to ensure security.
{"title":"Privacy Protection of Cloud Computing Based on Strong Forward Security","authors":"Fengyin Li, Junhui Wang, Z. Song","doi":"10.4018/ijcac.323804","DOIUrl":"https://doi.org/10.4018/ijcac.323804","url":null,"abstract":"Cloud computing is a new information technology. It is the product of the scientific and technological development of the times and plays an important role in the development of this country. In order to effectively solve the security problem of cloud computing data access, an identity-based privacy protection algorithm for cloud computing is proposed. The user information is stored in the cloud server at the registration stage, and the user identity is verified by signature when the information is obtained. The strong forward secure signature scheme can ensure that the signature is both forward secure and backward secure. At present, most signature schemes based on lattice focus on forward security. Therefore, this article constructs a strong forward secure signature scheme based on lattice and applies this signature scheme to cloud user authentication to ensure security.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46513965","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}
Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.
{"title":"An Adaptable Approach to Fault Tolerance in Cloud Computing","authors":"Priti Kumari, Parmeet Kaur","doi":"10.4018/ijcac.319032","DOIUrl":"https://doi.org/10.4018/ijcac.319032","url":null,"abstract":"Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135822132","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}
Soumen Swarnakar, Chandan Banerjee, Joydeep Basu, D. Saha
Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.
{"title":"A Multi-Agent-Based VM Migration for Dynamic Load Balancing in Cloud Computing Cloud Environment","authors":"Soumen Swarnakar, Chandan Banerjee, Joydeep Basu, D. Saha","doi":"10.4018/ijcac.320479","DOIUrl":"https://doi.org/10.4018/ijcac.320479","url":null,"abstract":"Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45532432","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}
The presented scheme focuses on active jobs live migration among VMs in 5G cloud framework depending on the software defined networks (SDN) to improve QoS in cloud framework. In this approach, RYU SDN controller is employed, which provides software components that allows software developers to extend network management and control applications for utilizing the features of SDN controller. It currently supports variety of southbound protocols such as OpenFlow, OF-Config, NETCONF, etc., whereas the proposed system uses Mininet prototype network. The destination server selection in the data centre is based on the server distinction based equivalent active weights (SD-EAW) ranking methods. The weight computation necessitate was to recognize non-active and active jobs. A presented SD-EAW scheme utilizes Pareto distribution for the recognition of active and inactive jobs in both continuous and discrete intervals of time. The presented SD-EAW algorithm functions well over all traditional approaches and in turn offers an optimum solution through minimizing the cloud environment's make span.
{"title":"A RYU-SDN Controller-Based VM Migration Scheme Using SD-EAW Ranking Methods for Identifying Active Jobs in the 5G Cloud Framework","authors":"Grace Shalini T., Rathnamala S.","doi":"10.4018/ijcac.319031","DOIUrl":"https://doi.org/10.4018/ijcac.319031","url":null,"abstract":"The presented scheme focuses on active jobs live migration among VMs in 5G cloud framework depending on the software defined networks (SDN) to improve QoS in cloud framework. In this approach, RYU SDN controller is employed, which provides software components that allows software developers to extend network management and control applications for utilizing the features of SDN controller. It currently supports variety of southbound protocols such as OpenFlow, OF-Config, NETCONF, etc., whereas the proposed system uses Mininet prototype network. The destination server selection in the data centre is based on the server distinction based equivalent active weights (SD-EAW) ranking methods. The weight computation necessitate was to recognize non-active and active jobs. A presented SD-EAW scheme utilizes Pareto distribution for the recognition of active and inactive jobs in both continuous and discrete intervals of time. The presented SD-EAW algorithm functions well over all traditional approaches and in turn offers an optimum solution through minimizing the cloud environment's make span.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46712043","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}
The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.
{"title":"A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud","authors":"T. Kassanuk, K. Phasinam","doi":"10.4018/ijcac.318698","DOIUrl":"https://doi.org/10.4018/ijcac.318698","url":null,"abstract":"The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49339136","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}
Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.
{"title":"An Efficient ECK-Secured FCM-Based Firefly Optimization Algorithm for Dynamic Resource Sharing in Multi-Tenant SaaS Service Clouds","authors":"Pallavi G. B.","doi":"10.4018/ijcac.319033","DOIUrl":"https://doi.org/10.4018/ijcac.319033","url":null,"abstract":"Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45376799","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 : 2022-01-01DOI: 10.4018/ijcac.2022010103
V. Barthwal, M. Rauthan, R. Varma
Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be proper management of resources in the datacenter for optimum usage of energy. Virtualization enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, Virtual Machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the Simple Moving Average (SMA) integrated with Linear Regression (LinR), which predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.
{"title":"SMA-LinR: An Energy and SLA-Aware Autonomous Management of Virtual Machines","authors":"V. Barthwal, M. Rauthan, R. Varma","doi":"10.4018/ijcac.2022010103","DOIUrl":"https://doi.org/10.4018/ijcac.2022010103","url":null,"abstract":"Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be proper management of resources in the datacenter for optimum usage of energy. Virtualization enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, Virtual Machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the Simple Moving Average (SMA) integrated with Linear Regression (LinR), which predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70451553","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 : 2022-01-01DOI: 10.4018/ijcac.2022010109
C. AnilB., P. Dayananda, B. Nethravathi, M. Raisinghani
Liver cancer is one the most common forms of cancer. As per statistics in 2018 published by World Health Organization, a quarter of all cancer cases are caused by infections, particularly prevalent in developing countries, including hepatitis B, which is linked to liver cancer. The mortality rate is higher in liver cancer as compared to other types of cancer. Quick and reliable diagnosis tools are of paramount importance for detecting and treating liver cancer in early stage, thus improving the likely course of a medical condition of patient. We have developed a cloud-based solution for liver tumour Segmentation, Classification and Detection in CT images based on GoogleNet architecture of Convolutional Neural Network. Experiment is carried out with training and test sets derived from TCIA repository. The results yield 96.7% accuracy for classification of tumour cells. GoogleNet architecture is used for implementation. The GoogleNet has 70,000 images in diagnosis of malignant tumor in liver cancer, providing a rich database for testing. Our algorithm has been deployed in Azure cloud.
{"title":"Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning","authors":"C. AnilB., P. Dayananda, B. Nethravathi, M. Raisinghani","doi":"10.4018/ijcac.2022010109","DOIUrl":"https://doi.org/10.4018/ijcac.2022010109","url":null,"abstract":"Liver cancer is one the most common forms of cancer. As per statistics in 2018 published by World Health Organization, a quarter of all cancer cases are caused by infections, particularly prevalent in developing countries, including hepatitis B, which is linked to liver cancer. The mortality rate is higher in liver cancer as compared to other types of cancer. Quick and reliable diagnosis tools are of paramount importance for detecting and treating liver cancer in early stage, thus improving the likely course of a medical condition of patient. We have developed a cloud-based solution for liver tumour Segmentation, Classification and Detection in CT images based on GoogleNet architecture of Convolutional Neural Network. Experiment is carried out with training and test sets derived from TCIA repository. The results yield 96.7% accuracy for classification of tumour cells. GoogleNet architecture is used for implementation. The GoogleNet has 70,000 images in diagnosis of malignant tumor in liver cancer, providing a rich database for testing. Our algorithm has been deployed in Azure cloud.","PeriodicalId":51857,"journal":{"name":"International Journal of Cloud Applications and Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70451717","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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