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Credit Card Fraud Detection Using Symbolic Aggregate Approximation

Credit Card Fraud Detection Using Symbolic Aggregate Approxi...

引用

Blockchain computing and Applications (BCCA), international conference on

作者： Snit D. Zerea Belsabel T. Woldemichael Feven B. Araya Richard A. Ikuesan College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

In the current digital age, credit card fraud is a cybercrime that costs billions of dollars every year through fraudulent transactions. This study aims to identify fraudulent credit card transactions using Symbolic Aggregate Approximation (SAX) and anomaly discovery through motifs and discords. To do this, credit card data is used as time series data and then divided into equal-length segments. Each segment is then converted into a symbolic representation using a pre-defined threshold. A web-based interface is further developed to load a collection of credit card transaction data, based on the proposed SAX method. The preliminary results show that the proposed approach is capable of identifying fraudulent transactions within a set of transactions. By integrating this approach with other fraud detection mechanisms, a near real-time fraud detection process can be developed. This approach therefore provides a potential for context-independent fraud detection which is a major challenge in the fraud detection domain.

关键词： Machine learning algorithms Costs Aggregates Time series analysis Credit cards Information age Real-time systems Fraud pattern recognition Security

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Impression-Based Automatic pattern Generation for Product Design Support

Impression-Based Automatic Pattern Generation for Product De...

引用

international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Ryunosuke Ota Yuichiro Kinoshita Kentaro Go Department of Computer Science and Engineering University of Yamanashi Kofu Yamanashi Japan

This research proposed a method for automatically creating patterns that match the impression desired by users in product design support. An impression rating experiment clarified the relationship between pattern physical aspects and impressions. Impression evaluation models that estimate the impression of patterns are implemented by training convolutional neural networks using experimental scores. patterns are generated by evolutionary computation with impression evaluation models serving as evaluation functions. The validity of the automatically generated patterns is examined by the generation of evolutionary computation and the final generation patterns.

关键词： Training Computational modeling Training data Evolutionary computation Product design Data models Convolutional neural networks

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Image Texture Analysis with Local Binary patterns: A Review 3

Image Texture Analysis with Local Binary Patterns: A Review

引用

3rd IEEE international Virtual conference on Innovations in Power and Advanced computing Technologies, i-PACT 2021

作者： Srilatha, N. Reddy, V. Lokeswara JNT University Anantapauramu CSE Department Anantapuramu India KSRM College of Engineering CSE Department Kadapa India

ISBN: (纸本)9781665426916

Local binary pattern (LBP) and its changed forms have been widely used in the many applications of different fields such as image processing, computer vision, model recognition and texture analysis etc. In this paper, we did extensive research-based study on LBP and its forms. The pros and cons of standalone LBP methods which used in many applications were evaluated in this study. Finally, we interpret based on their defects and focused the gapes of recent forms of LBP model signifier. © 2021 IEEE.

关键词： Local binary pattern

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Feature extraction technique for vision-based indian sign language recognition system: A review

Feature extraction technique for vision-based indian sign la...

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international conference on Computational Methods and Data Engineering, ICMDE 2020

作者： Tyagi, Akansha Bansal, Sandhya AmbalaHaryana India

ISBN: (纸本)9789811568756

Vision-based sign language recognition system is an emerging research area in human-computer interaction (HCI). It has been proved as a powerful communication tool for deaf and mute society, irrespective of geographical differences. Automatic sign language recognition system works broadly in three phases namely image pre-processing, feature extraction and classification of gestures. The output of the feature extraction phase is crucial for various classifiers. This paper presents a comprehensive review of feature extraction techniques used in vision-based sign language recognition system. A taxonomy of currently used techniques for feature extraction has been presented. The paper concludes by presenting future direction in feature extraction technique for Indian sign language (ISL). © Springer Nature Singapore Pte Ltd. 2021.

关键词： soft computing

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Recursive Descent Parser and Context Free Grammar in Examining Indonesian Sentence patterns

Recursive Descent Parser and Context Free Grammar in Examini...

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Smart computing, IoT and Machine Learning (SIML), international conference on

作者： Ega Citra Meilandri Husni Thamrin informatics department universitas muhammadiyah surakarta Surakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

Language is the most important supporting tool in communication between humans which aims to convey ideas, thoughts, emotions, feelings or messages to other people. Language can be conveyed orally or in writing. Both forms of language have the same goal, namely to facilitate the delivery of messages clearly and effectively to the recipient of the message. In writing effective sentences, the sentences are built according to correct grammatical rules because they will influence the delivery of the message. Therefore, the author proposes research related to checking sentence patterns which is carried out automatically whether the sentence is in accordance with grammar which has the elements of subject, predicate, object, information (SPOK). The aim of this research is to make it easier to examine sentence patterns. This research uses a recursive descent parser algorithm and context free grammar. The context free grammar algorithm is used to group the types of words and sentence patterns to be examined. Meanwhile, the recursive descent parser algorithm is used to examine Indonesian sentence patterns. The results of this research produced a success rate (%) of 67.2% of the 500 sentences that had been entered.

关键词： Triples (Data structure) Production Machine learning Grammar pattern recognition

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Dynamic Edge/Cloud Resource Allocation for Distributed Computation Under Semi-Static Demands

Dynamic Edge/Cloud Resource Allocation for Distributed Compu...

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IEEE international conference on Communications (ICC)

作者： Ippokratis Sartzetakis Panagiotis Pantazopoulos Konstantinos V. Katsaros Vasilis Sourlas Emmanouel Varvarigos

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Edge computing is a recent paradigm where the processing takes place close to the data sources. It therefore reduces latency and saves bandwidth compared to traditional cloud computing. The latter can continue to play a supportive role. Edge-cloud computing provides benefits in many use cases including distributed computation algorithms, where the processing is divided into a number of tasks that are executed in parallel on different equipment. An important relevant challenge is to allocate the appropriate resources to process the data that are continuously generated from user devices. The issue becomes more complicated when we take into account the variations in the volume of the generated data as a function of time. In this paper we present a resource allocation algorithm for distributed computation with emphasis on machine learning algorithms. We consider that the resource requirements vary with time in a semi-static way that exhibits some daily pattern. We distinguish between periodic (expected) variations that occur during the day, and sporadic variations due to unexpected events. We propose an Integer Linear Programming algorithm to allocate the periodic resource requirements. To handle the non-periodic requirements, we consider a suitable prediction algorithm coupled with a reconfiguration algorithm that allocates the predicted required resources. Our results indicate that our proposal outperforms traditional allocation algorithms in terms of resource utilization, monetary cost and achieved accuracy.

关键词： Machine learning algorithms Costs Heuristic algorithms soft sensors Prediction algorithms Approximation algorithms Planning

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Proceedings - 2021 IEEE international conference on Dependable, Autonomic and Secure computing, international conference on Pervasive Intelligence and computing, international conference on Cloud and Big Data computing and international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

Proceedings - 2021 IEEE International Conference on Dependab...

引用

19th IEEE international conference on Dependable, Autonomic and Secure computing, 19th IEEE international conference on Pervasive Intelligence and computing, 7th IEEE international conference on Cloud and Big Data computing and 2021 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

ISBN: (纸本)9781665421744

The proceedings contain 148 papers. The topics discussed include: design and implementation of face recognition system based on raspberry pie;a privacy-preserving revocable framework in the deep-learning-as-a-service platform system based on non-software as a service;beyond homeostasis: a novel approach for assessing the stability and coherence of self-adaptive systems;a meta multi-agent reinforcement learning algorithm for multi-intersection traffic signal control;reducing model complexity and cost in the generation of efficient error detection mechanisms;quantitative security assurance case for in-vehicle embedded systems;chaos engineering for understanding consensus algorithms performance in permissioned blockchains;rethinking of reentrancy on the ethereum;self-attention based text matching model with generative pre-training;role-based approach as support for safety analysis of collaborative systems;optimizing dynamic timing analysis and reinforcement learning;impact of selective implementation on soft error detection through low-level re-execution;and applying neural networks for plant model simulation in embedded control system.

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Ambient Intelligence Ecosystem for Elderly pattern Detection and Care Using Social Robots

Ambient Intelligence Ecosystem for Elderly Pattern Detection...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Raúl Gómez-Ramos Jaime Duque-Domingo Eduardo Zalama Jaime Gómez-García-Bermejo CARTIF Technological Center ITAP-DISA University of Valladolid Valladolid Spain ITAP-DISA University of Valladolid Valladolid Spain

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

This article presents an ambient intelligence system to provide support and security to residents living alone, particularly in supervised domiciles. It confronts the challenges arising from the burgeoning elderly population in Europe, many of whom opt for independent living. The system combines non-intrusive sensors, a social robot, and algorithms geared towards activity monitoring and anomaly detection. Central to its functionality is the monitoring of activities, employing a hierarchical Hidden Markov Model (HMM)-based Human Activity recognition (HAR) system. This system adeptly discerns the Activities of Daily Living (ADLs) performed by users, enabling swift interventions or alerts in instances of emergencies. Furthermore, anomalies such as prolonged periods of inactivity or nocturnal wanderings are promptly identified, triggering requisite responses to ensure user safety and well-being. A key component is the cognitive system, which establishes rules for actions predicated on data gleaned from the HAR module and anomaly detection. This enables interactions with the social robot and interventions within the environment. Validation and performance evaluation in real-world scenarios underscore the system’s efficacy, with high precision, recall, and F1Score metrics reported for the HAR module.

关键词： Performance evaluation Social robots Hidden Markov models Sensor systems Ambient intelligence Human activity recognition Security Older adults Monitoring Anomaly detection

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Exploring Time Series Clustering with Head Based Aggregation

Exploring Time Series Clustering with Head Based Aggregation

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international conference on computing and Networking Technology (ICCNT)

作者： R Manjunatha Jyotirmaya Sahoo Abhinav Department of Data analytics and Mathematical Sciences School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Time series clustering is a technique of facts analysis that seeks to group hard and fast temporal statistics information into meaningful clusters based on their similarities. The goal of time collection clustering is to become aware of common styles or developments over time. Head primarily based aggregation (HBA) is an alternative approach to time collection clustering that makes use of a hierarchical clustering set of rules to without delay cluster time series records without guide intervention. This approach has been proven to produce significant results for brief temporal datasets. In this paper, we discover time collection clustering with head primarily based aggregation. We begin by discussing the fundamentals of HBA and the way it differs from conventional types of clustering. We then present outcomes from experiments with simulated temporal datasets and speak about the strengths and weaknesses of HBA. Sooner or later, we examine the consequences of making use of HBA to actual international time series datasets. Our outcomes display that HBA is a viable technique for time series clustering in some contexts but that its effectiveness varies depending on the complexity and dynamics of the information.

关键词： Time series analysis Noise Magnetic heads pattern recognition Delays Complexity theory

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE international conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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