Pub Date : 2017-12-01DOI: 10.1109/PDCAT.2017.00028
N. Lynn, Zin Mar Kyu
Melanoma, one type of skin cancer is considered o the most dangerous form of skin cancer occurred in humans. However it is curable if the person detects early. To minimize the diagnostic error caused by the complexity of visual interpretation and subjectivity, it is important to develop a technology for computerized image analysis. This paper presents a methodological approach for the classification of pigmented skin lesions in dermoscopic images. Firstly, the image of the skin to remove unwanted hair and noise, and then the segmentation process is performed to extract the affected area. For detecting the melanoma skin cancer, the meanshift algorithm that segments the lesion from the entire image is used in this study. Feature extraction is then performed by underlying ABCD dermatology rules. After extracting the features from the lesion, feature selection algorithm has been used to get optimized features in order to feed for classification stage. Those selected optimized features are classified using kNN, decision tree and SVM classifiers. The performance of the system was tested and compare those accuracies and get promising results.
{"title":"Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images","authors":"N. Lynn, Zin Mar Kyu","doi":"10.1109/PDCAT.2017.00028","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00028","url":null,"abstract":"Melanoma, one type of skin cancer is considered o the most dangerous form of skin cancer occurred in humans. However it is curable if the person detects early. To minimize the diagnostic error caused by the complexity of visual interpretation and subjectivity, it is important to develop a technology for computerized image analysis. This paper presents a methodological approach for the classification of pigmented skin lesions in dermoscopic images. Firstly, the image of the skin to remove unwanted hair and noise, and then the segmentation process is performed to extract the affected area. For detecting the melanoma skin cancer, the meanshift algorithm that segments the lesion from the entire image is used in this study. Feature extraction is then performed by underlying ABCD dermatology rules. After extracting the features from the lesion, feature selection algorithm has been used to get optimized features in order to feed for classification stage. Those selected optimized features are classified using kNN, decision tree and SVM classifiers. The performance of the system was tested and compare those accuracies and get promising results.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128916831","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00035
Fu-Shiung Hsieh
Reconfigurable Manufacturing Systems (RMS) is a paradigm to flexibly deal with frequent changing demand and technologies. With the advancement of technology and more and more sensors and machines are connected, the world quickly enter the era of Internet of Things (IoT), which provides infrastructure for RMS. However existing studies lack a formalism that provides a framework for the development of RMS, from modeling, design to implementation. In particular, an important issue is design of dynamic process planner for RMS. This paper focuses on the development of a dynamic process planning method for the development of RMS. Modeling and managing RMS in manufacturing sector are challenging issues due to the complex workflows in the system. Recent progress in artificial intelligence and bio-inspired optimization technology provides a solid background to develop a framework to provide dynamic process planning for RMS in IoT-enabled manufacturing environment. In this paper, we propose a process planning method based on multi-agent systems (MAS) using Petri Nets to specify the workflows and capabilities of resources in the system and develop a solution algorithm based on a meta-heuristic method to solve the process planning problem based on discrete Particle swarm optimization (DPSO) approach The proposed method is illustrated by a several examples.
{"title":"A Meta-Heuristic Approach for Dynamic Process Planning in Reconfigurable Manufacturing Systems","authors":"Fu-Shiung Hsieh","doi":"10.1109/PDCAT.2017.00035","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00035","url":null,"abstract":"Reconfigurable Manufacturing Systems (RMS) is a paradigm to flexibly deal with frequent changing demand and technologies. With the advancement of technology and more and more sensors and machines are connected, the world quickly enter the era of Internet of Things (IoT), which provides infrastructure for RMS. However existing studies lack a formalism that provides a framework for the development of RMS, from modeling, design to implementation. In particular, an important issue is design of dynamic process planner for RMS. This paper focuses on the development of a dynamic process planning method for the development of RMS. Modeling and managing RMS in manufacturing sector are challenging issues due to the complex workflows in the system. Recent progress in artificial intelligence and bio-inspired optimization technology provides a solid background to develop a framework to provide dynamic process planning for RMS in IoT-enabled manufacturing environment. In this paper, we propose a process planning method based on multi-agent systems (MAS) using Petri Nets to specify the workflows and capabilities of resources in the system and develop a solution algorithm based on a meta-heuristic method to solve the process planning problem based on discrete Particle swarm optimization (DPSO) approach The proposed method is illustrated by a several examples.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133022841","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00029
Mounira Hmayda, R. Ejbali, M. Zaied
Automatic identification of television programs in the TV stream is an important task for operating archives and represent a principal source of multimedia information.. The goal of the proposed approach is to enable a better exploitation of this source of video by multimedia services (i.e., TV-On-Demand, catch-up TV), social community, and video-sharing pla forms (Vimeo, Youtube, Facebook…) This paper presents a new spatio-temporal approach to identify the programs in TV stream using deep learning in two main steps. A database for video of visual jingles is constructed for training. In the test we use same jingles program type in order to identify the various program types in the TV stream. The main idea of identification process consists in using the principal of auto-encoder. After presenting the proposed approach, the paper overviews the encouraging experimental results on several streams extracted from different channels and composed of several programs. Comparison experiments to similar works have been carried out on the TRECVID 2017 database. We show significant improvements to TV programs identification exceed 95 %.
{"title":"Program Classification in a Stream TV Using Deep Learning","authors":"Mounira Hmayda, R. Ejbali, M. Zaied","doi":"10.1109/PDCAT.2017.00029","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00029","url":null,"abstract":"Automatic identification of television programs in the TV stream is an important task for operating archives and represent a principal source of multimedia information.. The goal of the proposed approach is to enable a better exploitation of this source of video by multimedia services (i.e., TV-On-Demand, catch-up TV), social community, and video-sharing pla forms (Vimeo, Youtube, Facebook…) This paper presents a new spatio-temporal approach to identify the programs in TV stream using deep learning in two main steps. A database for video of visual jingles is constructed for training. In the test we use same jingles program type in order to identify the various program types in the TV stream. The main idea of identification process consists in using the principal of auto-encoder. After presenting the proposed approach, the paper overviews the encouraging experimental results on several streams extracted from different channels and composed of several programs. Comparison experiments to similar works have been carried out on the TRECVID 2017 database. We show significant improvements to TV programs identification exceed 95 %.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133548557","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00056
Yong Zhang, Ruonan Li, Yanan Zhang, Mei Song
Energy efficiency is an important factor to optimize the multi-hop forwarding strategy. PD (Pairing-inspired Dijkstra) multi-hop data forwarding algorithm is proposed to share cellular spectrum resources with D2D (device-to-device) users. Candidate multiplexing channel model is established in our proposal. Based on this model, PD algorithm solves the issue on channel and path selection. PD algorithm includes two parts, KM dichotomous matching algorithm and multiple iterations for Dijkstra algorithm. Under energy efficiency and QoS (Quality of Service) constraint, PD algorithm selects optimal transmission path among D2D users. Furthermore, the energy efficiency and transmission delay are evaluated in simulation section under PD, Dijkstra and CD (Closest to Destination) algorithm. Simulation results indicate that PD has better performance on energy efficiency and E2E (End to End) delay.
能量效率是优化多跳转发策略的一个重要因素。为了与D2D (device-to-device)用户共享蜂窝频谱资源，提出了PD (pair -inspired Dijkstra)多跳数据转发算法。本文建立了候选复用信道模型。基于该模型，PD算法解决了信道和路径的选择问题。PD算法包括KM二分类匹配算法和多次迭代Dijkstra算法两部分。PD算法在能量效率和QoS (Quality Service)约束下，在D2D用户之间选择最优传输路径。在仿真部分对PD、Dijkstra和CD (nearest to Destination)算法下的能量效率和传输延迟进行了评估。仿真结果表明，PD在能效和端到端延迟方面具有较好的性能。
{"title":"Data Forwarding Algorithm Based on Energy Efficiency in Multi-Hop Device to Device Network","authors":"Yong Zhang, Ruonan Li, Yanan Zhang, Mei Song","doi":"10.1109/PDCAT.2017.00056","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00056","url":null,"abstract":"Energy efficiency is an important factor to optimize the multi-hop forwarding strategy. PD (Pairing-inspired Dijkstra) multi-hop data forwarding algorithm is proposed to share cellular spectrum resources with D2D (device-to-device) users. Candidate multiplexing channel model is established in our proposal. Based on this model, PD algorithm solves the issue on channel and path selection. PD algorithm includes two parts, KM dichotomous matching algorithm and multiple iterations for Dijkstra algorithm. Under energy efficiency and QoS (Quality of Service) constraint, PD algorithm selects optimal transmission path among D2D users. Furthermore, the energy efficiency and transmission delay are evaluated in simulation section under PD, Dijkstra and CD (Closest to Destination) algorithm. Simulation results indicate that PD has better performance on energy efficiency and E2E (End to End) delay.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122737040","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00036
Yukai Huang, Chia-Ping Huang
when undergoing sickness, the human body sends out warning signals from different parts, especially the ones which are directly connected with outside world, such as fever, tonsillitis, and otitis media. Our topic is aimed to discover otitis media at home using plug-in otoscope to exhibit visual image from the inside and design a system following Depth-First Search Algorithm to analyze these images as real-time otitis media image interpretation for parents, clinics, and pediatrician.
{"title":"A Depth-First Search Algorithm Based Otoscope Application for Real-Time Otitis Media Image Interpretation","authors":"Yukai Huang, Chia-Ping Huang","doi":"10.1109/PDCAT.2017.00036","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00036","url":null,"abstract":"when undergoing sickness, the human body sends out warning signals from different parts, especially the ones which are directly connected with outside world, such as fever, tonsillitis, and otitis media. Our topic is aimed to discover otitis media at home using plug-in otoscope to exhibit visual image from the inside and design a system following Depth-First Search Algorithm to analyze these images as real-time otitis media image interpretation for parents, clinics, and pediatrician.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123800799","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00044
C. Hlaing, Sai Maung Maung Zaw
We introduce a set of statistical features and propose the SIFT texture features descriptor model on statistical image processing. The proposed feature is applied to plant disease classification with PlantVillage image dataset. The input is plant leaf image taken by phone camera whereas the output is the plant disease name. The input image is preprocessed to remove background. The SIFT features are extracted from the preprocessed image. As a main contribution, the extracted SIFT features are model by Generalized Extreme Value (GEV) Distribution to represent an image information in a small number of dimensions. We focus on the statistical feature and model-based texture features to minimize the computational time and complexity of phone image processing. The propose features aim to be significantly reduced in computational time for plant disease recognition for mobile phone. The experimental result shows that the proposed features can compare with other previous statistical features and can also distinguish between six tomato diseases, including Leaf Mold, Septoria Leaf Spot, Two Spotted Spider Mite, Late Blight, Bacterial Spot and Target Spot.
{"title":"Model-Based Statistical Features for Mobile Phone Image of Tomato Plant Disease Classification","authors":"C. Hlaing, Sai Maung Maung Zaw","doi":"10.1109/PDCAT.2017.00044","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00044","url":null,"abstract":"We introduce a set of statistical features and propose the SIFT texture features descriptor model on statistical image processing. The proposed feature is applied to plant disease classification with PlantVillage image dataset. The input is plant leaf image taken by phone camera whereas the output is the plant disease name. The input image is preprocessed to remove background. The SIFT features are extracted from the preprocessed image. As a main contribution, the extracted SIFT features are model by Generalized Extreme Value (GEV) Distribution to represent an image information in a small number of dimensions. We focus on the statistical feature and model-based texture features to minimize the computational time and complexity of phone image processing. The propose features aim to be significantly reduced in computational time for plant disease recognition for mobile phone. The experimental result shows that the proposed features can compare with other previous statistical features and can also distinguish between six tomato diseases, including Leaf Mold, Septoria Leaf Spot, Two Spotted Spider Mite, Late Blight, Bacterial Spot and Target Spot.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311661","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00068
Zhansheng Chen, Hong Shen
Topology control based on appropriate cluster head election can drastically reduce energy consumption, balance traf?c load on sensor nodes and extends the lifetime of the network. In this paper, an ef?cient data gathering multihop routing approach based on ?xed-group for wireless sensor networks is proposed. Our proposed protocol, FGMRP (Fixed-Group based Multi-hop Routing Protocol), divides the monitoring area into several groups according to node intimacy, optimizes energy consumption among nodes in each group by performing adaptive cluster head round-robin rotations based on residual energy, concentration and centrality, and balances energy consumption among groups through a ?tness routing algorithm which considers node residual energy, forwarding distance and radial angle. Simulation results show that the FGMRP protocol effectively balances the energy consumption among nodes, achieves better monitoring performance and signi?cantly increases network lifetime as compared to the existing routing protocols, taking monitoring quality, the amount of data acquisition and network lifetime as evaluation indices.
{"title":"Efficient Data Gathering in Wireless Sensor Networks with Fixed-Group Method","authors":"Zhansheng Chen, Hong Shen","doi":"10.1109/PDCAT.2017.00068","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00068","url":null,"abstract":"Topology control based on appropriate cluster head election can drastically reduce energy consumption, balance traf?c load on sensor nodes and extends the lifetime of the network. In this paper, an ef?cient data gathering multihop routing approach based on ?xed-group for wireless sensor networks is proposed. Our proposed protocol, FGMRP (Fixed-Group based Multi-hop Routing Protocol), divides the monitoring area into several groups according to node intimacy, optimizes energy consumption among nodes in each group by performing adaptive cluster head round-robin rotations based on residual energy, concentration and centrality, and balances energy consumption among groups through a ?tness routing algorithm which considers node residual energy, forwarding distance and radial angle. Simulation results show that the FGMRP protocol effectively balances the energy consumption among nodes, achieves better monitoring performance and signi?cantly increases network lifetime as compared to the existing routing protocols, taking monitoring quality, the amount of data acquisition and network lifetime as evaluation indices.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115318965","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00037
Shumin Qiao, Binbin Zhang, Weiyi Liu
Different applications have different preferences for resource requirements. In virtualization environment, if multiple virtual machines hosted on the same server have the same resource requirement preference, performance can be greatly affected for the resource competition between virtual machines. In this paper, we propose an approach to use a feature weighting naive Bayes classifier with Laplacian correction model to classify the applications according to the characteristics of application accessing to CPU, memory, hard disk, and the L2 cache collected using profiling. Based on the application classification, the virtual machines running applications of different types can be deployed on the same physical host. The experiments show that this method can achieve high classification accuracy. And this methods avoid the performance bottleneck due to resource competition to a certain extent.
{"title":"Application Classification Based on Preference for Resource Requirements in Virtualization Environment","authors":"Shumin Qiao, Binbin Zhang, Weiyi Liu","doi":"10.1109/PDCAT.2017.00037","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00037","url":null,"abstract":"Different applications have different preferences for resource requirements. In virtualization environment, if multiple virtual machines hosted on the same server have the same resource requirement preference, performance can be greatly affected for the resource competition between virtual machines. In this paper, we propose an approach to use a feature weighting naive Bayes classifier with Laplacian correction model to classify the applications according to the characteristics of application accessing to CPU, memory, hard disk, and the L2 cache collected using profiling. Based on the application classification, the virtual machines running applications of different types can be deployed on the same physical host. The experiments show that this method can achieve high classification accuracy. And this methods avoid the performance bottleneck due to resource competition to a certain extent.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944410","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00076
Jing Wang, Wei Liu, Shuang Li
Agent collaboration is a fundamental part of multi-agent systems when an agent cannot accomplish a goal by itself. Such collaborations are usually regulated by commitment protocols, which are typically defined at design-time. However, in many situations a protocol may not exist or be predefined at design-time which may not fit the needs of the agents when environment changes. In order to deal with such situations, agents should be able to generate protocols at run-time. In this paper, we combined commitment with capability. Firstly, we proposed the capability matching method to generate commitment protocols dynamically at run-time. Secondly, we combined capability with commitment and extended its traditional definition. Thirdly, we compared the forms, generation time and execution time of typical and extended commitments. At last, we introduced the criteria of profit to select the optimal protocol. The application of our approach will be demonstrated in intelligent parking lot systems.
{"title":"Agent Collaboration in Intelligent Parking Lot Systems: Dynamic Generation of Commitment Protocol","authors":"Jing Wang, Wei Liu, Shuang Li","doi":"10.1109/PDCAT.2017.00076","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00076","url":null,"abstract":"Agent collaboration is a fundamental part of multi-agent systems when an agent cannot accomplish a goal by itself. Such collaborations are usually regulated by commitment protocols, which are typically defined at design-time. However, in many situations a protocol may not exist or be predefined at design-time which may not fit the needs of the agents when environment changes. In order to deal with such situations, agents should be able to generate protocols at run-time. In this paper, we combined commitment with capability. Firstly, we proposed the capability matching method to generate commitment protocols dynamically at run-time. Secondly, we combined capability with commitment and extended its traditional definition. Thirdly, we compared the forms, generation time and execution time of typical and extended commitments. At last, we introduced the criteria of profit to select the optimal protocol. The application of our approach will be demonstrated in intelligent parking lot systems.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"57 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002448","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00048
Amik Singh, M. Miśra
Prediction of ribonucleic acid (RNA) secondary structure is one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. We present a novel algorithm to optimize Zuker Algorithm on CUDA GPUs and achieve a speedup of ∼10x for certain viruses.
{"title":"Optimization of Zuker Algorithm on GPUs","authors":"Amik Singh, M. Miśra","doi":"10.1109/PDCAT.2017.00048","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00048","url":null,"abstract":"Prediction of ribonucleic acid (RNA) secondary structure is one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. We present a novel algorithm to optimize Zuker Algorithm on CUDA GPUs and achieve a speedup of ∼10x for certain viruses.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125177488","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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