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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是91-100 订阅

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A Forensic Method for DeepFake Image based on Face Recognition 20

A Forensic Method for DeepFake Image based on Face Recogniti...

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4th High Performance computing and Cluster Technologies conference, HPCCT 2020 and the 3rd international conference on Big data and Artificial Intelligence, BDAI 2020

作者： Wu, Jian Feng, Kai Chang, Xu Yang, Tongfeng School of Cyberspace Security Shandong University of Political Science and Law Jinan China

ISBN: (纸本)9781450375603

DeepFake digital images have serious negative impacts on news integrity, legal forensics, and social security. In order to detect the DeepFake digital images more accurately, a method based on face recognition is proposed. Face image feature vectors are extracted by Facenet, and the Euclidean distances among the vectors of different face images are calculated as classification principle. Then, the machine learning algorithms is trained to perform binary classification of real and fake face images. The experimental results on the Celeb-DF data set show that the proposed method has better detection effect than the existing detection methods. © 2020 ACM.

关键词： Face recognition

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Modified Bio-Inspired Algorithms for Intrusion Detection System 3rd

Modified Bio-Inspired Algorithms for Intrusion Detection Sys...

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3rd international conference on Innovative computing and Communication, ICICC 2020

作者： Sharma, Moolchand Saini, Shachi Bahl, Sneha Goyal, Rohit Deswal, Suman Maharaja Agrasen Institute of Technology Delhi India Deenbandhu Chhotu Ram University of Science & Technology University in Murthal SonipatHaryana India

ISBN: (纸本)9789811551123

In today’s time, the Internet has become a critical part of everyone’s life. It has helped make everything very easy. Be it paying bills, booking shows, or flights, and it has made it all a matter of few clicks. A network is a means to connect two people or organizations or within the organization. With the advancements in technology, even the attackers have become more advanced with their attacks, and hence network security has become a severe matter of concern for individuals and organizations. Intrusion detection system (IDS) is one of many solutions to strengthen the system. It is like a security alarm that goes off whenever it sniffs an attack. It keeps the network traffic under observation, looking for any suspicious activity and then warns when such an event is found. The primary task for an IDS is to know what kind of traffic represents which type of an attack. In this paper, we have proposed a novel approach to strengthening the IDS by using bio-inspired algorithms, namely binary firefly optimization (BFA), binary swarm particle optimization (BPSO), and cuttlefish optimization. We have introduced a unique approach, i.e., modified cuttlefish algorithm (MCA) and also compared it with the rest two algorithms mentioned above. Modified cuttlefish algorithm has shown a significant accuracy other than two bio-inspired algorithms as shown below in the paper. We have used the CICIDS2017 dataset which incorporates benign and the most recent attacks, which resemble the real-world data to a greater extent. It contains the labeled flows from the network traffic analysis based on the source and destination ports, source and destination IPs, protocols timestamp and attack. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Improving I/O performance for High Performance computing with Application Forwarding Layer 20

Improving I/O performance for High Performance Computing wit...

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4th High Performance computing and Cluster Technologies conference, HPCCT 2020 and the 3rd international conference on Big data and Artificial Intelligence, BDAI 2020

作者： Mingming, Hu Shen, Hong Benbo, Zha Hengwei, Liu Ruiqi, Zhuo School of Data and Computer Science Sun Yat-sen University China

ISBN: (纸本)9781450375603

In the use of High Performance computing platforms, I/O performance has become an important limiting factor. For regular users, after a supercomputer is put into use, I/O performance optimization based on the physical structure is usually difficult to achieve. In this paper, we show that this can be achieved through modifying the application layer. Considering that a supercomputer has many computing nodes, we present an approach that uses dedicated computing nodes to process the requests from different applications to optimize the I/O performance. The dedicated computing nodes perform aggregation and reorder the operations before sending the requests to the storage system. We call the role played by these computing nodes in this process the logical I/O forwarding layer which corresponds to the I/O forwarding layer of the physical structure. Our experiments show that this approach is feasible and provides room for further improvement. © 2020 ACM.

关键词： Supercomputers

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An Efficient and Privacy-Preserving Physiological Case Classification Scheme for E-healthcare System 3rd

An Efficient and Privacy-Preserving Physiological Case Class...

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3rd international conference on Security and Privacy in New computing Environments, SPNCE 2020

作者： Shen, Gang Gui, Yumin Zhang, Mingwu Chen, Yu Gao, Hanjun Su, Yixin School of Computer Science Hubei University of Technology Wuhan430068 China Department of Ophthalmology Wuhan Puren Hospital Wuhan430080 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China China Nuclear Power Operation Technology Corporation LTD Wuhan430070 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9783030669218

In this work, an efficient and privacy-preserving physiological case classification scheme for e-healthcare system (EPPC) is proposed. Specifically, a homomorphic cryptosystem combined with a support vector machine (SVM) algorithm is applied to efficiently classify the physiological cases without compromising patients’ privacy. In terms of the EPPC, it has the capability of diagnosing the patient’s symptom in a timely manner. In addition, a signature authentication technology applied in EPPC can efficiently prevent data from being forged or modified. Security analysis result shows that the proposed EPPC scheme has the following advantages: protect the privacy of patients;ensure that the classification parameters of SVM are secured. Compared with the existing works, the proposed EPPC scheme shows significant advantages in terms of computational costs and communication overheads. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Support vector machines

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Fuzzy Particle Swarm Page Rank Clustering Algorithm 3rd

Fuzzy Particle Swarm Page Rank Clustering Algorithm

引用

3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Kushwaha, Neetu Pant, Millie Department of Applied Science and Engineering Indian Institute of Technology Roorkee247667 India

ISBN: (纸本)9789811507502

Fuzzy C-Means (FCM) is the most commonly used clustering algorithm despite being sensitive to initial choice of the cluster centroids. To overcome this shortcoming, the present study proposes a clustering algorithm called Fuzzy Particle Swarm Page Rank Clustering (Fuzzy-PSOPRC) which embeds the concept of fuzzy clustering into particle swarm optimization. Fuzzy-PSOPRC gives more precise cluster centroids which groups data objects into better clusters. The proposed algorithm has been tested and evaluated on benchmark datasets. Experimental results show that the proposed clustering algorithm achieves better results in comparison with other clustering techniques in terms of accuracy and F1 measure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Efficient VM Placement Policy for data Centre in Cloud Environment 3rd

Efficient VM Placement Policy for Data Centre in Cloud Envir...

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3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Dubey, Kalka Nasr, Aida A. Sharma, S.C. El-Bahnasawy, Nirmeen Attiya, Gamal El-Sayed, Ayman Cloud Computing and Wireless sensor Lab Indian Institute of Technology Roorkee RoorkeeUttarakhand247664 India Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9789811507502

Cloud computing now becomes the accomplished infrastructure model for delivering the cloud service to its user over the Internet. Virtualization is one of the key concepts, which makes the dynamic sharing of physical resources of cloud more effective. In virtualization, virtual machine is placed over the physical machine in data centre and VM placement is one of the challenging issues in cloud computing, so cloud service provider needs good VM placement policy to increase the utilization of physical resources as well as reduce the load-balancing problem. In this paper, we proposed a new VM placement policy called Efficient of service Best Fit Decreasing (EBFD). From the simulation experiments result, we see that the proposed policy performs much better performance compared with first-fit and best-fit policy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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AI-Based Recommendation System for Social Networking 3rd

AI-Based Recommendation System for Social Networking

引用

3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Chaudhry, Simran Dhawan, Sanjeev Department of Computer Science and Engineering U.I.E.T Kurukshetra University KurukshetraHaryana136119 India Kurukshetra University KurukshetraHaryana136119 India

ISBN: (纸本)9789811507502

It is being more than a decade that recommendation system is proposed as a key for solving the issue of information overload and number of algorithms and the system has been proposed for the same. In spite of these attempts, the recommendation system still has number of challenges like improvement in accuracy, cold start, and data sparsity issues. On the contrary, social networking emergence has provided huge amount of information for friend interactions and user behavior. This research work has proposed a novel mechanism of social networking based on recommendation system using genetic algorithm (GA) and artificial neural network (ANN). Parameters such as precision, recall, F-measure, and accuracy are considered to compute the effectiveness of the research work. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Establishment and application of big data processing platform 3

Establishment and application of big data processing platfor...

引用

3rd international conference on Computer Network, Electronic and Automation, ICCNEA 2020

作者： Liu, Ruixia Zhang, Qizhi Xi'An Shiyou University School of Electrical Engineering China Shaanxi Provincial Key Lab of Oil and Gas Well Measurement and Control Technology Xi an China

ISBN: (纸本)9781728170831

With the advent of the era of big data, more and more enterprises begin to use big data technology to deal with related analysis work. Hadoop is the dominant processing platform in the field of big data, an ecosystem that integrates distributed computing, storage, and management. The Spark framework, on the other hand, is a faster, more versatile distributed computing platform. However, it is only a computing platform and does not provide distributed storage and management per se, and computing remains dependent on distributed file system HDFS and cluster Resource Manager Yarn in the Hadoop ecosystem. Therefore, the combination of Spark and Hadoop to build a big data processing platform can better improve the algorithm efficiency and processing scale. This article explains the setup process and running state of Hadoop and Spark in detail, and verifies its feasibility through several ways. © 2020 IEEE.

关键词： Big data

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Big Health data Resource Integration Method Based on Hybrid Cloud and Fog computing 2020

Big Health Data Resource Integration Method Based on Hybrid ...

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3rd international conference on Electronics, Communications and Control Engineering, ICECC 2020

作者： Zhang, Xiaodong Xia, Xiaojun Leng, Miao Shenyang Institute of Computing Technology Chinese Academy of Science No.16NanPing RoadHunNan New Zone Shen YangLiao Ning Prov China University of Chinese Academy of Sciences No.16NanPing RoadHunNan New Zone Shen YangLiao Ning Prov China Shenyang Institute of Computing Technology Chinese Academy of Science HunNan New Zone Shen YangLiao Ning Prov China Shenyang Urban Construction University Shenyang No.380 Baita street Dongling District ShenyangLiao Ning Prov China

ISBN: (纸本)9781450374996

Aiming at the problems of high latency and poor accuracy of retrieval response in current health data integration related research, this paper presents a health data resource integration method based on Hybrid Cloud and Fog computing. Based on the general model of health data resource service platform, the framework of large health data resource integration is constructed. Sample reduction and dimension reduction are used to reduce big health data. The field matching method based on participle and weight is used to clean data resources and calculate the field matching degree. When the matching degree is larger than a threshold value, the fields to be matched are similar duplicate records, and the redundant data is removed. The weight of resource data is calculated by Hybrid Cloud and Fog computing, and the big health data is arranged according to the weight value, so as to realize the classification and integration of health *** experimental results show that the proposed method has good performance, low data redundancy, low retrieval response delay and high classification integration accuracy. © 2020 ACM.

关键词： Fog

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3rd international conference on Applied Informatics, ICAI 2020

3rd International Conference on Applied Informatics, ICAI 20...

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3rd international conference on Applied Informatics, ICAI 2020

ISBN: (纸本)9783030617011

The proceedings contain 35 papers. The special focus in this conference is on Applied Informatics. The topics include: A Linux Scheduler to Limit the Slowdown Generated by Volunteer computing Systems;reliability Analysis in Desktop Cloud Systems;a Case-Based Approach to Assess Employees’ Satisfaction with Work Guidelines in Times of the Pandemic;a data-Driven Approach for Automatic Classification of Extreme Precipitation Events: Preliminary Results;A data-Driven Method for Measuring the Negative Impact of Sentiment Towards China in the Context of COVID-19;a Virtual Wallet Product Recommender System Based on Collaborative Filtering;evaluating Models for a Higher Education Course Recommender System Using State Exam Results;future Scenarios of Water Security: A Case of Bogotá River Basin, Colombia;Inverse data Visualization Framework (IDVF): Towards a Prior-Knowledge-Driven data Visualization;a Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation;The Application of DBSCAN Algorithm to Improve Variogram Estimation and Interpretation in Irregularly-Sampled Fields;a Multi-objective Evolutionary Algorithms Approach to Optimize a Task Scheduling Problem;diabetes Classification Techniques: A Brief State-of-the-Art Literature Review;learning from Students’ Perception on Professors Through Opinion Mining;A Framework for BYOD Continuous Authentication: Case Study with soft-Keyboard Metrics for Healthcare Environment;diabetes Link: Platform for Self-control and Monitoring People with Diabetes;design and Prototyping of a Wearable Kinesthetic Haptic Feedback System to Support Mid-Air Interactions in Virtual Environments;Comparison of Gabor Filters and LBP Descriptors Applied to Spoofing Attack Detection in Facial Images;deep Transfer Learning Model for Automated Screening of Cervical Cancer Cells Using Multi-cell Images.

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