Because image processing typically uses a lot of memory and computational time, a sophisticated platform is needed. As a result, it's critical to adopt cost-effective solutions to replace outdated ones. We chose t...
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ISBN:
(纸本)9781665476560
Because image processing typically uses a lot of memory and computational time, a sophisticated platform is needed. As a result, it's critical to adopt cost-effective solutions to replace outdated ones. We chose to use cloud computing to fulfill our needs for large-scale data processing as a result of these factors. In the meantime, this strategy offers quick access to on-demand services that are highly available and scalable. By outsourcing computations to a third party and utilizing cloud services rather than internal applications, healthcare organizations can reduce operating costs. However, robust data protection against both unauthorized users and unreliable clouds is required to avoid the disclosure of malicious data. Users can now assemble and execute their data using the cloud thanks to the development of numerous frameworks. distributed systems, cryptosystems, or a combination of the two are frequently used in their construction. Almost all current implementations use homomorphism cryptosystems, Service Oriented Architecture, secret share schemes, and secure multi-party computation as their primary security mechanisms. The main disadvantage of utilizing these techniques for large-scale data analysis on the cloud is the cost of computing image processing tasks. Keeping medical records & sensitive health-related information secure is a problem. We suggest a fresh method for protecting data processing in cloud environments that is based on machine learning methods. We typically employ Fuzzy C-means Clustering and Multi-Dense Convolution Neural Network to more accurately classify image pixels. To further reduce the danger of a possible leak of medical data, we add a third level, the CloudSaS-TPM module, to the conventional two-layered design. The results show some encouraging discoveries that provide new angles for creating cloud storage for the medical industry.
Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these...
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ISBN:
(纸本)9781728146768
Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these applications usually require huge processing or massive data handling which are normally performed at cloud but the application will suffer from latency. Edge computing offers solution for latency aware computations at edge of the network but with limited resources available at edge nodes. This problem can be resolved by leveraging edge resources in a group and executing resource intensive task. Most previous studies deploy centralized methods for clustering but comes with overhead of cluster formation and management. In this article, key idea is to group heterogeneous edge nodes on task arrival in a decentralize way, and handle task allocation and execution in parallel on group devices to achieve its deadline. Our methodology will help to reduce traffic amount travelling towards cloud in case of resource intensive big tasks of SCPS applications. We have proposed an algorithm for decentralized group formation and presented task division and allocation methodology for parallel execution. Our results show that our technique is working while providing desired goals of reducing overall latency, and limiting network traffic as well as achieving higher ratio for number of tasks meeting their deadline.
This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. This conference issue focuses on the breadth of...
This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. This conference issue focuses on the breadth of the data management field. The topics include view updates, query compilation, concurrency control, data parsing, data cleaning;tackle more recent problems in particular graph processing;on road networks;failure handling techniques from data processing to distributed application programming;breadth of technical problems as well as the breadth of solutions spanning the hardware-software stack and going from theoretical to systems-oriented are an important quality of the database;etc. The key terms of this proceedings include dynamic road networks, massively parallel parsing, contended main-memory multicore transactions, LSH framework, homogeneous network embedding, data cleaning, MDedup, debuggable dataflow system, dynamic speculative optimizations, imputation of missing values techniques.
Graph computing recently receives intensive interests due to a wide range of needs to analyse big graphs in real time. Many single-machine out-of-core graph computing systems have been developed to process large-scale...
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Graph computing recently receives intensive interests due to a wide range of needs to analyse big graphs in real time. Many single-machine out-of-core graph computing systems have been developed to process large-scale graphs by utilizing effective memory access techniques. The current systems either focus on graph compression or pay attention to disk scheduling strategies. In this paper, we propose an efficient and unified out-of-core graph computing model on a single machine named D 2 Graph which combines the differential storage strategy and the dynamic caching mechanism. D 2 Graph improves the spatial locality of graph data and the temporal locality of graph computing at the same time. The main contributions of our work lie in three aspects: firstly, we design a fine-grained differential storage(FGDS) strategy to reorganize graph data on disk; secondly, we propose a dynamic caching mechanism (DCM) to selectively load the necessary edge chunks; thirdly, we combine FGDS and DCM to construct D 2 Graph and evaluate it on five public graph data sets. What’s more, to optimize the performance of D 2 Graph, we also make use of the multi-threaded parallel technology. A series of results show that D 2 Graph outperforms state-of-the-art out-of-core graph computing systems by 1.3x-17.9x.
paralleldistributedprocessing approaches are relatively new. A distributed cloud is the application of cloud computing technologies to interconnect data and applications served from multiple geographic locations. A ...
ISBN:
(数字)9781728182315
ISBN:
(纸本)9781728182322
paralleldistributedprocessing approaches are relatively new. A distributed cloud is the application of cloud computing technologies to interconnect data and applications served from multiple geographic locations. A distributed information technology (IT) means that something is shared among multiple systems that may also be in the same or different locations. The amount of data and exhausted time to process them and regulate the expected results efficiently and in as lowest as possible time has been expanded enormous. Therefore, it is essential to prepare approaches to enhance parallelprocessingtechniques via cloud computing.
OODIDA (On-board/Off-board distributed Data Analytics) is a platform for distributing and executing concurrent data analytics tasks. It targets fleets of reference vehicles in the automotive industry and has a particu...
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ISBN:
(纸本)9783030483401;9783030483395
OODIDA (On-board/Off-board distributed Data Analytics) is a platform for distributing and executing concurrent data analytics tasks. It targets fleets of reference vehicles in the automotive industry and has a particular focus on rapid prototyping. Its underlying message-passing infrastructure has been implemented in Erlang/OTP. External Python applications perform data analytics tasks. Most work is performed by clients (on-board). A central cloud server performs supplementary tasks (off-board). OODIDA can be automatically packaged and deployed, which necessitates restarting parts of the system, or all of it. This is potentially disruptive. To address this issue, we added the ability to execute user-defined Python modules on clients as well as the server. These modules can be replaced without restarting any part of the system and they can even be replaced between iterations of an ongoing assignment. This facilitates use cases such as iterative A/B testing of machine learning algorithms or modifying experimental algorithms on-the-fly.
Counterfactual regret minimization (CFR) is one of the most widely used algorithms in iterative optimization algorithms. It is used to solve complex imperfect-information game problems. This paper introduced the Globa...
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Counterfactual regret minimization (CFR) is one of the most widely used algorithms in iterative optimization algorithms. It is used to solve complex imperfect-information game problems. This paper introduced the Global Counterfactual Regret Minimization Local Update (GCFR+) to solve task planning problems in a crowdsourcing environment. We designed a parallel mechanism to alleviate possible parallel conflicts in actual crowdsourcing scenarios and increase personal rewards. First of all, we chose to test the performance of GCFR+ on data sets with different scales. Then we compared the result with the result of the decision model with a parallel mechanism. It can be seen that the parallel mechanism has significantly improved the efficiency of the decision model. Finally, unlike general CFR, we proved that GCFR+ is applicable to decision tree pruning of imperfect-information games.
Genomics and bioinformatics have grown as an independent field and are an area of active research currently. In this work, we first discuss the idea of the genome, its importance and wide-ranging applications in healt...
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The proceedings contain 60 papers. The special focus in this conference is on Advances in Visual Informatics. The topics include: Interaction Design for Digital Saron Musical Instruments Using Call and Response System...
ISBN:
(纸本)9783030902346
The proceedings contain 60 papers. The special focus in this conference is on Advances in Visual Informatics. The topics include: Interaction Design for Digital Saron Musical Instruments Using Call and Response System and Rhythmic Emphasis Weighting Methods;a User Experience Model for Designing Educational Mobile Application;evidence-Based of Interactive Multimedia-Based Nutrition Education Package Efficacy on Obesity Outcomes Through Game and Video Intervention;evidence-Based of Improved Electron Tomogram Segmentation and Visualization Through High-Pass Domain Kernel in Bilateral Filter;VR-Based Relaxation Therapy for Customer Service Staff: A Pilot Study;Fusion Technology and Visualisation to Share STEM Data Using PETS Robots (i-COMEL) for Open Data Readiness Amongst Primary School Children;interactive Multimedia Kolb Experiential Learning Model Using Logistic Regression Algorithm to Improve Student Cognitive;performance Analysis of Machine Learning techniques for Sentiment Analysis;national Sport Institute Case: Automated Data Migration Using Talend Open Studio with ‘Trickle Approach’;color Aesthetic Enhancement for Categorical Data Visualization;Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach;software Redocumentation Using distributed Data processing Technique to Support Program Understanding for Legacy System: A Proposed Approach;system Design and Usability Evaluation of Ghana Music Documentation System Using the System Usability Scale;static Indoor Pathfinding with Explicit Group Two-Parameter Over Relaxation Iterative Technique;use of Faceted Search: The Effect on Researchers;Sustainable Product Innovation Using Patent Mining and TRIZ;personalised Smart Mobility Model for Smart Movement During Pandemic Covid-19;intelligent Multi-cellular Network Connectivity for Internet of Things applications;ioT-Based System for Real-Time Swimming Pool Water Quality Monitoring.
Ensemble learning techniques adopt comprehensive learning methodologies that produce optimized predictions with reduced variance and bias. The structured Random Forest ensemble learning technique equips a set of weak ...
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ISBN:
(纸本)9781665430869
Ensemble learning techniques adopt comprehensive learning methodologies that produce optimized predictions with reduced variance and bias. The structured Random Forest ensemble learning technique equips a set of weak and diverse decision trees, resulting in an active hybrid learning ensemble. Plagued with high computational complexity, Random Forest Ensemble continues to be the preferred technique when accuracy is of primary importance for learners. Efforts to accelerate the Random Forest Ensembles are in place, however failing to efficiently utilize the data transmission bandwidth between the host and the accelerator hardware. This paper provides an architectural overview of a reconfigurable accelerator based architecture of the Random Forest Ensemble with an efficient data path model for data streaming. The paper also derives the need for an accelerated parallel ensemble method by deriving the results from equivalent sequential software implementations of the algorithm. The validation of the results have been done on healthcare application involving breast cancer classification and environmental applications involving temperature prediction and fuel consumption.
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