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3rd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Swiecicki, Mariusz Cracow Univ Technol Warszawska 24 PL-31155 Krakow Poland

ISBN: (纸本)9783031640667;9783031640674

Currently, one of the important problems related to data mining is the processing of large data sets. The article presents an algorithm that can be used in issues related to the classification of large-volume data sets. The motivation to define this type of algorithm was the fact that currently the methods used to process this type of data are subject to several significant limitations. The first significant limitation in the use of classical classification methods is the need to ensure a constant data size. The second type of limitation is related to the dimension of the data. The last type of limitation that occurs when using classic classification algorithms is related to the situation that a given input vector may contain data belonging to many classes at the same time, then we are talking about the so-called multiclass *** work attempts to define a data classification algorithm inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. The article presents the results of calculations for sample data sets and discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class, multi-class and variable-length data vectors.

关键词： natural computing algorithms chromatographic separation chromatographic data classification signal processing data mining big data sets

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Access Control based on CRDTs for Collaborative Distributed Applications 22

Access Control based on CRDTs for Collaborative Distributed ...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Rault, Pierre-Antoine Ignat, Claudia-Lavinia Perrin, Olivier Univ Lorraine CNRS INRIA LORIA F-54000 Nancy France

ISBN: (纸本)9798350381993;9798350382006

A key feature for most collaborative applications is their ability to distinguish access rights to shared documents in a dynamic group of collaborators. To achieve high availability and avoid single points of failure, systems can replicate this access control policy across sites. Increasingly, Conflict-free Replicated data Types (CRDTs) are used to this end. They embed conflict resolution strategies that take into account concurrent modifications to the policy but also to the document that it manages. However, few systems in the literature support multiple administrators, and those which do fall short of considering distributed applications where no node or group of nodes is central to its operation. To allow multiple concurrent edits of the policy in massively collaborative settings with no central server, we devise a specialized causal model that minimizes concurrency. Crucially our model allows to correct the document state in case operations have become unauthorized following a policy change. We apply our model to a CRDT replicating an access control policy with read and write rights on a collaborative document and admin rights on the policy. We end by studying flexible conflict resolution strategies, providing our underlying algorithms.

关键词： distributed algorithms access control CRDT (Conflict-free Replicated data Type) eventual consistency real-time collaborative editing

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Genetic Signal processing for Categorizing Genomic data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Pulicherla, Padmaja Aluvala, Srinivas Premchander, Munimanda Sai Sudha, Poranki V L R N Revanasiddappa Institute of Technology and Management Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95). © 2023 IEEE.

关键词： Convolutional layers Convolutional neural networks genetic signal processing Genetic signals Genomic data

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Research on the Load_breast_cancer data set under Multiple Machine Learning algorithms 24

Research on the Load_breast_cancer data set under Multiple M...

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international conference on Modeling, Natural Language processing and Machine Learning (CMNM)

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest Algorithm Support Vector Machine Algorithm Logistic Regression Algorithm Gaussian Naive Bayes Algorithm BP Neural Network Algorithm k-Neighbor Algorithm XGBoost Algorithm

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Optimizing Task Scheduling algorithms for Efficient CPU Utilization in Enhancing Server Availability 27

Optimizing Task Scheduling Algorithms for Efficient CPU Util...

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27th international Computer Science and Engineering conference, ICSEC 2023

作者： Changsan, Ukid Boonyopakorn, Pongsarun King Mongkut's University of Technology North Bangkok Department of Network and Information Security Bangkok Thailand

ISBN: (纸本)9798350342109

As modern computing continues to develop, server technology must adapt to handle the increasing demands, particularly in the context of big data processing. This paper focuses on addressing the challenges in allocating and scheduling server CPU resources for efficient big data processing. In the event that the CPU has processed large volumes of big data until the CPU usage reaches 100%, if it is interrupted by another process, such as updating the operating system software, it will be affected, leading to a potential impact on server availability. The study proposes employing Task Scheduling algorithms such as Earliest Due Date (EDD), Longest processing Time (LTP), and Round-Robin (RR) to optimize CPU usage. Additionally, through the execution of operating system commands, CPU cores are efficiently allocated to tasks according to their priority and processing time. An experimental application of these techniques in processing 75 jobs showed successful report generation while maintaining CPU utilization below 100%. This paper concludes that capping CPU usage at approximately 80-90% allows for enhanced system stability and an improved ability to manage interrupts, thus optimizing server performance for big data processing. © 2023 IEEE.

关键词： Big data

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A Multi-Label Classification Algorithm Based on Self-paced Learning and LDA 8

A Multi-Label Classification Algorithm Based on Self-paced L...

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8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Wang, Qiangqiang Gu, Shenming Zhejiang Ocean University School of Information Engineering Zhoushan China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350302455

To address the problem that existing multi-label learning algorithms treat all samples equally during model training and ignore inter-sample variability, this paper proposes a multi-label classification algorithm based on self-paced learning and LDA (SPLDA for short). SPLDA first constructs an adaptive self-paced function based on the label prediction loss values in multi-label data, and introduces a self-paced learning framework. Secondly, the linear discriminant analysis regularization term in the low-dimensional space of the samples is constructed by combining the principles of minimizing the within-class distance and maximizing the between-class distance in this framework. Next, a multi-label classification model is trained using alternating optimization methods. Finally, the experimental results on 5 real datasets and 3 evaluation metrics show that SPLDA outperforms the existing multi-label classification algorithms. © 2023 IEEE.

关键词： Learning algorithms

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Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs 23

Fast tree-based algorithms for DBSCAN for low-dimensional da...

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52nd international conference on Parallel processing (ICPP)

作者： Prokopenko, Andrey Lebrun-Grandie, Damien Arndt, Daniel Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798400708435

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.

关键词： DBSCAN bounding volume hierarchy parallel algorithm GPU

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SuperCut: Communication-Aware Partitioning for Near-Memory Graph processing 23

SuperCut: Communication-Aware Partitioning for Near-Memory G...

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20th ACM international conference on computing Frontiers (CF)

作者： Zhao, Chenfeng Chamberlain, Roger D. Zhang, Xuan Washington Univ McKelvey Sch Engn St Louis MO 63110 USA

ISBN: (纸本)9798400701405

The parallel execution of many graph algorithms is frequently dominated by data communication overheads between compute nodes. This bottleneck becomes even more pronounced in Near-Memory processing (NMP) architectures with multiple memory cubes as local memory accesses are less expensive. Existing near-memory architectures typically use graph partitioning methods with a fixed vertex assignment, which limits their potential to improve performance and reduce energy consumption. Here, we argue that an NMP-based graph processing system should also consider the distribution of vertices onto memory cubes. We propose SuperCut, a framework for near-memory architectures to effectively reduce communication overheads while maintaining computational balance. We evaluate SuperCut via architectural simulation with 6 real-world datasets and 4 representative applications. The results show that it provides up to 1.8x total energy reduction and 2.6x speedup relative to current state-of-the-art approaches.

关键词： near-data processing 3D-stacked memory graph processing

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Enabling Scalable Applications with Intelligent Distributed data processing 2

Enabling Scalable Applications with Intelligent Distributed ...

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2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Mandela, Nelson Alam, Intekhab Amudha, A. Sirija Priyanka, D. Singh, Dhiraj Kumar Department of Animation and Virtual Reality Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350308846

This paper discusses the enabling of scalable programs with smart disbursed information processing. Scaling programs can be challenging, mainly when dealing with massive datasets. Existing scaling answers often rely on conventional database-reliant architectures, which usually require massive manual attempts and significant investments in hardware and software. Clever dispensed information processing offers an alternative technique to scalability. With this technique, facts can be processed successfully and with accelerated degrees of automation using intelligent algorithms and distributed computing. This paper presents a top-level view of the ideas in the back of slick disbursed information processing and examines the benefits and challenges associated with this technique. Key ideas addressed in this paper include using utility-precise AI algorithms, dispensed computing, and data correlations. Sooner or later, the report highlights the critical trade-off between scalability and cost regarding the usage of wise disbursed statistics processing. © 2023 IEEE.

关键词： computing Conventional Information Scalability Technique

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Efficient distributed algorithms for distance join queries in spark-based spatial analytics systems

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international JOURNAL OF GENERAL SYSTEMS 2023年第3期52卷 206-250页

作者： Garcia-Garcia, Francisco Corral, Antonio Iribarne, Luis Vassilakopoulos, Michael Univ Almeria Dept Informat Almeria Spain Univ Thessaly Dept Elect & Comp Engn Volos Greece

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ.

关键词： k nearest neighbor join query k closest pairs query distributed spatial data processing apache sedona locationspark simba spatial query evaluation

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