Pub Date : 2022-12-01DOI: 10.2174/2666255816666221201155914
Xiaoran Chen, Wanbo Yu, Xiang Li
At present, image recognition technology first classifies images and outputs category information through the neural network. Then search. Before retrieval, the feature database needs to be established first, and then one-to-one correspondence. This method is tedious, time-consuming and low accuracy.In the field of computer vision research, researchers have given various image recognition methods to be applied in various fields, and made many research achievements. But at present, the accuracy, stability and time efficiency can't meet the needs of practical work. In terms of UAV image recognition, high accuracy and low consumption are required. Previous methods require huge databases, which increases the consumption of UAVs. Taking aerial transmission line images as the research object, this paper proposes a method of image recognition based on chaotic synchronization. Firstly, the image is used as a function to construct a dynamic system, and the function structure and parameters are adjusted to realize chaos synchronization. In this process, different types of images are identified. At the same time, we research this dynamic system characteristics，and realize the mechanism of image recognition. Compared with other methods, the self-built aerial image data set for bird's nest identification, iron frame identification and insulator identification has the characteristics of high identification rate and less calculation time. It is preliminarily proved that the method of synchronous image recognition is practical, and also worthy of further research, verification and analysis. This article is divided into the following sections:
{"title":"An Image Recognition Method Based On Dynamic System Synchronization","authors":"Xiaoran Chen, Wanbo Yu, Xiang Li","doi":"10.2174/2666255816666221201155914","DOIUrl":"https://doi.org/10.2174/2666255816666221201155914","url":null,"abstract":"\u0000\u0000At present, image recognition technology first classifies images and outputs category information through the neural network. Then search. Before retrieval, the feature database needs to be established first, and then one-to-one correspondence. This method is tedious, time-consuming and low accuracy.In the field of computer vision research, researchers have given various image recognition methods to be applied in various fields, and made many research achievements. But at present, the accuracy, stability and time efficiency can't meet the needs of practical work. In terms of UAV image recognition, high accuracy and low consumption are required. Previous methods require huge databases, which increases the consumption of UAVs. Taking aerial transmission line images as the research object, this paper proposes a method of image recognition based on chaotic synchronization. Firstly, the image is used as a function to construct a dynamic system, and the function structure and parameters are adjusted to realize chaos synchronization. In this process, different types of images are identified. At the same time, we research this dynamic system characteristics，and realize the mechanism of image recognition. Compared with other methods, the self-built aerial image data set for bird's nest identification, iron frame identification and insulator identification has the characteristics of high identification rate and less calculation time. It is preliminarily proved that the method of synchronous image recognition is practical, and also worthy of further research, verification and analysis. This article is divided into the following sections:\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48353330","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 : 2022-11-25DOI: 10.2174/2666255816666221125155715
Roshni singh, Ataussamad
In today’s era, Deep Learning has become the center of recent ascent in the field of artificial intelligence and its models. There are various Artificial Intelligence models that can be viewed as needing more strength for adversely defined information sources. It also leads to a high potential security concern in the adversarial paradigm; the DNN can also misclassify inputs that appear to expect in the result. DNN can solve complex problems accurately. It is empaneled in the vision research area to learn deep neural models for many tasks involving critical security applications. We have also revisited the contributions of computer vision in adversarial attacks on deep learning and discussed its defenses. Many of the authors have given new ideas in this area, which has evolved significantly since witnessing the first-generation methods. For optimal correctness of various research and authenticity, the focus is on peer-reviewed articles issued in the prestigious sources of computer vision and deep learning. Apart from the literature review, this paper defines some standard technical terms for non-experts in the field. This paper represents the review of the adversarial attacks via various methods and techniques along with their defenses within the deep learning area and future scope. Lastly, we bring out the survey to provide a viewpoint of the research in this Computer Vision area.
{"title":"Threat of Adversarial Attacks within Deep Learning: Survey","authors":"Roshni singh, Ataussamad","doi":"10.2174/2666255816666221125155715","DOIUrl":"https://doi.org/10.2174/2666255816666221125155715","url":null,"abstract":"\u0000\u0000In today’s era, Deep Learning has become the center of recent ascent in the field of artificial intelligence and its models. There are various Artificial Intelligence models that can be viewed as needing more strength for adversely defined information sources. It also leads to a high potential security concern in the adversarial paradigm; the DNN can also misclassify inputs that appear to expect in the result. DNN can solve complex problems accurately. It is empaneled in the vision research area to learn deep neural models for many tasks involving critical security applications. We have also revisited the contributions of computer vision in adversarial attacks on deep learning and discussed its defenses. Many of the authors have given new ideas in this area, which has evolved significantly since witnessing the first-generation methods. For optimal correctness of various research and authenticity, the focus is on peer-reviewed articles issued in the prestigious sources of computer vision and deep learning. Apart from the literature review, this paper defines some standard technical terms for non-experts in the field. This paper represents the review of the adversarial attacks via various methods and techniques along with their defenses within the deep learning area and future scope. Lastly, we bring out the survey to provide a viewpoint of the research in this Computer Vision area.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45067645","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 : 2022-11-04DOI: 10.2174/2666255816666221104115024
Pundru Chandra Shaker Reddy, Y. Sucharitha
The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.
{"title":"A Design And Challenges In Energy Optimizing\u0000Cr-Wireless Sensor Networks","authors":"Pundru Chandra Shaker Reddy, Y. Sucharitha","doi":"10.2174/2666255816666221104115024","DOIUrl":"https://doi.org/10.2174/2666255816666221104115024","url":null,"abstract":"\u0000\u0000The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict.\u0000\u0000\u0000\u0000Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent.\u0000\u0000\u0000\u0000A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation.\u0000\u0000\u0000\u0000The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes.\u0000\u0000\u0000\u0000Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44973667","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}
Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose Computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. And this can easily interfere with the radiologist's assessment. Convolutional Neural Networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Modified convolutional neural network algorithm to train the denoising model. Make the model to extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.
{"title":"An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network","authors":"Xiaojing Fan, Hanyue Liu, Chunsheng Zhang, Zichao Wang, Qingming Lin, Zhanjiang Lan, Mingyang Jiang, Jie Lian, Xueyan Chen","doi":"10.2174/2666255816666220920150916","DOIUrl":"https://doi.org/10.2174/2666255816666220920150916","url":null,"abstract":"\u0000\u0000Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose Computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. And this can easily interfere with the radiologist's assessment. Convolutional Neural Networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising.\u0000\u0000\u0000\u0000Modified convolutional neural network algorithm to train the denoising model. Make the model to extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising.\u0000\u0000\u0000\u0000We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising.\u0000\u0000\u0000\u0000According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions.\u0000\u0000\u0000\u0000The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42043447","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 : 2022-08-31DOI: 10.2174/2666255816666220831125012
Gnanendra Kotikam, S. Lokesh
Big Data processing is a demanding task, and several big data processing frameworks have emerged during recent decades. The performance of these frameworks greatly dependent on resource management models. YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues.
{"title":"YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy","authors":"Gnanendra Kotikam, S. Lokesh","doi":"10.2174/2666255816666220831125012","DOIUrl":"https://doi.org/10.2174/2666255816666220831125012","url":null,"abstract":"\u0000\u0000Big Data processing is a demanding task, and several big data processing frameworks have emerged during recent decades. The performance of these frameworks greatly dependent on resource management models.\u0000\u0000\u0000\u0000YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling.\u0000\u0000\u0000\u0000In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster.\u0000\u0000\u0000\u0000Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44349255","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 : 2022-08-29DOI: 10.2174/2666255816666220829103359
Shuliang Huan
On the retrieval of spatiotemporal information of chorography (STIC), one of the most important topics is how to quickly pinpoint the desired STIC text out of the massive chorography databases. Domestically, there are not diverse means to retrieve the spatiotemporal information from chorography database. Emerging techniques like data mining, artificial intelligence (AI), and natural language processing (NLP) should be introduced into the informatization of chorography. This study intends to devise an information retrieval method for STIC based on deep learning, and fully demonstrates its feasibility. Firstly, the authors explained the flow for retrieving and analyzing the data features of STIC texts, and established a deep hash model for STIC texts. Next, the data matching flow was defined for STIC texts, the learned hash code was adopted as the memory address of STIC texts, and the hash Hamming distance of the text information was computed through linear search, thereby completing the task of STIC retrieval. Our STIC text feature extraction model learned better STIC text features than the contrastive method. It learned many hash features, and differentiated between different information well, when there were many hash bits. In addition, our hash algorithm achieved the best retrieval accuracy among various methods. Finally, the hash features acquired by our algorithm can accelerate the retrieval speed of STIC texts. These experimental results demonstrate the effectiveness of the proposed model and algorithm.
{"title":"A Retrieval Method for Spatiotemporal Information of Chorography Based on Deep Learning","authors":"Shuliang Huan","doi":"10.2174/2666255816666220829103359","DOIUrl":"https://doi.org/10.2174/2666255816666220829103359","url":null,"abstract":"\u0000\u0000On the retrieval of spatiotemporal information of chorography (STIC), one of the most important topics is how to quickly pinpoint the desired STIC text out of the massive chorography databases. Domestically, there are not diverse means to retrieve the spatiotemporal information from chorography database. Emerging techniques like data mining, artificial intelligence (AI), and natural language processing (NLP) should be introduced into the informatization of chorography.\u0000\u0000\u0000\u0000This study intends to devise an information retrieval method for STIC based on deep learning, and fully demonstrates its feasibility.\u0000\u0000\u0000\u0000Firstly, the authors explained the flow for retrieving and analyzing the data features of STIC texts, and established a deep hash model for STIC texts. Next, the data matching flow was defined for STIC texts, the learned hash code was adopted as the memory address of STIC texts, and the hash Hamming distance of the text information was computed through linear search, thereby completing the task of STIC retrieval.\u0000\u0000\u0000\u0000Our STIC text feature extraction model learned better STIC text features than the contrastive method. It learned many hash features, and differentiated between different information well, when there were many hash bits.\u0000\u0000\u0000\u0000In addition, our hash algorithm achieved the best retrieval accuracy among various methods. Finally, the hash features acquired by our algorithm can accelerate the retrieval speed of STIC texts. These experimental results demonstrate the effectiveness of the proposed model and algorithm.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47014983","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 : 2022-08-23DOI: 10.2174/2666255816666220823163913
Ankur Kumar, Avinash Kaur
This research is being conducted to study fuzzy system with improved rule base. Rule base is an important part of any fuzzy inference system designed. Rules of a fuzzy system depend on the number of features selected. Selecting an optimized number of features is called feature selection. All features (parameters) play an important role in the input to the system, but they have a different impact on the system performance. Some features do not even have a positive impact of classifier on multiple classes. Reduced features, depending on the objective to be achieved require fewer training rules, Thereby, improving the accuracy of the system. Learning is an important mechanism to automate fuzzy systems. The overall purpose of the research is to design a general fuzzy expert system with improvements in the relationship between interpretability and accuracy by improving the feature selection and learning mechanism processes through nature-inspired techniques or innovating new methodologies for the same.
{"title":"Literature review on devlopment of feature selection and learning mechanism for fuzzy rule based system","authors":"Ankur Kumar, Avinash Kaur","doi":"10.2174/2666255816666220823163913","DOIUrl":"https://doi.org/10.2174/2666255816666220823163913","url":null,"abstract":"\u0000\u0000This research is being conducted to study fuzzy system with improved rule base. Rule base is an important part of any fuzzy inference system designed. Rules of a fuzzy system depend on the number of features selected. Selecting an optimized number of features is called feature selection. All features (parameters) play an important role in the input to the system, but they have a different impact on the system performance. Some features do not even have a positive impact of classifier on multiple classes. Reduced features, depending on the objective to be achieved require fewer training rules, Thereby, improving the accuracy of the system. Learning is an important mechanism to automate fuzzy systems. The overall purpose of the research is to design a general fuzzy expert system with improvements in the relationship between interpretability and accuracy by improving the feature selection and learning mechanism processes through nature-inspired techniques or innovating new methodologies for the same.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41506531","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 : 2022-08-19DOI: 10.2174/2666255816666220819124133
Jyotsna, P. Nand
It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, and minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration. IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices. This research focuses on analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms. The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper. The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researches to develop and choose algorithms close to their case studies.
{"title":"Assessment of various scheduling and load balancing algorithms in integrated cloud-fog environment","authors":"Jyotsna, P. Nand","doi":"10.2174/2666255816666220819124133","DOIUrl":"https://doi.org/10.2174/2666255816666220819124133","url":null,"abstract":"\u0000\u0000It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, and minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration.\u0000\u0000\u0000\u0000IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices.\u0000\u0000\u0000\u0000This research focuses on analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms.\u0000\u0000\u0000\u0000The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper.\u0000\u0000\u0000\u0000The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researches to develop and choose algorithms close to their case studies.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45366445","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 : 2022-08-15DOI: 10.2174/2666255816666220815145203
R. Saravanaguru, Gokul Geetha Narayanan
Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is expected to be with the aim of having migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and identified research gaps to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.
{"title":"Secure Virtual Machine Live Migration using Advanced Metric Encryption","authors":"R. Saravanaguru, Gokul Geetha Narayanan","doi":"10.2174/2666255816666220815145203","DOIUrl":"https://doi.org/10.2174/2666255816666220815145203","url":null,"abstract":"\u0000\u0000Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is expected to be with the aim of having migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and identified research gaps to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42574143","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 : 2022-08-04DOI: 10.2174/2666255816666220804161549
P. Mathur, Pooja, K. Veer
Electrocardiogram (ECG) is a visual representation of the heartbeat that can be used to detect cardiac problems. It helps in detection of normal or abnormal state of heart diseases. So, it’s difficult to detect the cardio logical status by naked eyes. So, features extraction from ECG signal is crucial to recognise heart disorders. After selecting significant features, classification can be done by machine learning (ML), and deep learning (DL). Most of the methods utilised to classify the electrocardiogram are based on 1-D electrocardiogram data. These methods focus on extracting the attributes wavelength and time of each waveform as an input but these algorithms behave different during selecting classification technique. Various ECG construal algorithms based on signal processing approaches have been planned in recent years. Few studies shows how optimisation techniques are helpful for feature selection and classification with ML and DL. This works compares the studies based on ML and DL. It also depicts how optimisation methods increases the accuracy, sensitivity and specificity of data.
{"title":"Comparison Of Soft Computing And Optimization Techniques In Classification Of Ecg Signal","authors":"P. Mathur, Pooja, K. Veer","doi":"10.2174/2666255816666220804161549","DOIUrl":"https://doi.org/10.2174/2666255816666220804161549","url":null,"abstract":"\u0000\u0000Electrocardiogram (ECG) is a visual representation of the heartbeat that can be used to detect cardiac problems. It helps in detection of normal or abnormal state of heart diseases. So, it’s difficult to detect the cardio logical status by naked eyes. So, features extraction from ECG signal is crucial to recognise heart disorders. After selecting significant features, classification can be done by machine learning (ML), and deep learning (DL). Most of the methods utilised to classify the electrocardiogram are based on 1-D electrocardiogram data. These methods focus on extracting the attributes wavelength and time of each waveform as an input but these algorithms behave different during selecting classification technique. Various ECG construal algorithms based on signal processing approaches have been planned in recent years. Few studies shows how optimisation techniques are helpful for feature selection and classification with ML and DL. This works compares the studies based on ML and DL. It also depicts how optimisation methods increases the accuracy, sensitivity and specificity of data.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45875112","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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