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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是201-210 订阅

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Performance Comparison of Credit Card Fraud Detection System using machine learning

Performance Comparison of Credit Card Fraud Detection System...

引用

international conference on Signal Processing and Information Security (ICSPIS)

作者： Alavikunhu Panthakkan Najiya Valappil Majida Appathil Seema Verma Wathiq Mansoor Hussain Al-Ahmad University of Dubai Dubai United Arab Emirates Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665492669

Cyber security is becoming an integral part of modern life, tackling abnormal activities being the main challenge in the domain. To find and abort a fishy transaction is the procedure for maintain the security in credit cards. With the availability of past datasets, machine learning algorithms have made it possible to detect abnormal activities among the transactions. the aim of the paper is to create a balanced data from a pre-existing dataset and to apply six different machine learning algorithms like Decision tree, K-Nearest Neighbor, Logistic regression, SVM, Random Forest and XGBoost to detect fraud activities. Later, the results are compared and discussed on the basis of evaluation matrices which include precision, recall, f1-score and accuracy. All the algorithms are applied on three sets of data, where the ratio of fraud to valid transactions is varied. three cases are considered with ratios as 1:100, 1:10 and 1:1. A balanced dataset showed greater performance in the terms of all evaluation matrices.

关键词： Support vector machines machine learning algorithms Signal processing algorithms Information security Signal processing Credit cards Fraud

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Cross-Domain Sentiment Classification via Deep Reinforcement learning 22

Cross-Domain Sentiment Classification via Deep Reinforcement...

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Proceedings of the 2022 5th international conference on machine learning and Natural Language Processing

作者： Lintao Dou Jian Huang College of Intelligent Science National University of Defense Technology China

ISBN: (纸本)9781450399067

Cross-domain sentiment classification aims to transferring the labeled knowledge of the source domain to the target domain for sentiment classification. In the case of lacking labeled target domain data, it reduces manual marking cost of labeling data. Most existing works perform transfer learning by extracting pivot and non-pivot features between domains. these methods are susceptible to noise data and ignore important sentiment information. To solve the above problem, we propose the cross-domain sentiment classification via deep reinforcement learning. We propose a deep reinforcement learning framework, which formulates feature selection policy to solve the noise data problem and pay attention to important data features. the framework is integrated with a policy network and a classification network. the policy network is applied to the feature selection. the classification network makes predictions based on feature selection and calculate the reward of the policy network. Experiment results on the Amazon review datasets demonstrate that the proposed method considerably outperforms other state-of-the-art methods.

关键词： Cross-domain

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Analyses of Software data and their Interpretations: A Framework of Information Granules 22

Analyses of Software Data and Their Interpretations: A Frame...

引用

Proceedings of the 2022 5th international conference on Algorithms, Computing and Artificial Intelligence

作者： Ayomide Bakare Yegor Bugayenko Artem Kruglov Witold Pedrycz Giancarlo Succi Innopolis University Russia Huawei Technologies Russia University of Alberta Canada University of Bologna Italy

ISBN: (纸本)9781450398336

data collected from software applications such as issue management systems or version control systems are abstract and require their thorough and comprehensive analysis. Automated issue generation is an understudied area in automated software development despite its effectiveness, safety, and satisfaction which increases developer productivity. Analysis of software data from automated issue generation provides information which could be used by relevant tools or monitored as any other feature in the development process. In this paper, we systematically apply a suite of methods, including clustering algorithms, cluster validity indexes, and information granularity, to generate explainable prototypes using software data from generated Github Issues. Among other approaches of data analytics, we employ the principle of justifiable granularity and a method of optimal information allocation. these methods are applied to two dimensional synthetic Gaussian data to illustrate the performance of the methods. the study provides the experimental results using the methods applied to real industrial data coming from the 0pdd software. the resultant groups provide some insights into structure for organising puzzles with similar characteristics.

关键词： Prototypes Unsupervised learning Principle of Justifiable Granularity Information Granules Cluster Validity Clustering

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Unified Analysis of Algorithms for Dictionary learning

Unified Analysis of Algorithms for Dictionary Learning

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Computing and Big data (ICCBD), international conference on

作者： Min Xiang School of Mathematical Sciences Zhejiang University Hangzhou China

the advantages of data sparse representation have generated a lot of interest in recent years. the goal of dictionary learning is to seek an over-complete dictionary adaptively by data, from which every signal can be represented approximately by a linear combination of only a few columns of the dictionary. this problem is often solved by solving two optimization processes iteratively: sparse approximation and dictionary update. We analyze these two processes respectively. the drawback of the greedy algorithm for sparse approximation is discussed in this paper, and we give a new forward-backward index updating strategy for sparse approximation, which makes use of previous sparse patterns in iterations. A unified analysis of the framework for dictionary update is given, and we conclude that the dictionary update of existing algorithms all can be regarded as the projected gradient algorithm on Euclidean space or Oblique manifold with different choices of step size. Based on our analysis, a new algorithm is proposed for dictionary learning with a convergent cost function sequence. Numerical simulations show that our algorithm can be applied to refine the solutions produced by existing dictionary learning algorithms.

关键词： Manifolds Greedy algorithms Dictionaries machine learning Big data Approximation algorithms Sparse representation

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Traffic Flow Prediction Using Deep learning Models

Traffic Flow Prediction Using Deep Learning Models

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Computing and Big data (ICCBD), international conference on

作者： China-Seh Wu Satya Pranavi Manthena Saurabh Kale Maithili Vinayak Kulkarni Department of Computer Science San Jose State University San Jose California USA

For the effective implementation of Intelligent Transportation Systems (ITS), accurate and timely traffic flow information is critical. Traffic data has exploded in recent years, ushering in the era of big data. the main issue of a traffic flow prediction system is determining how to build an adaptive model based on past data. Existing traffic flow forecast approaches rely on shallow learning models, which are unsatisfactory for many real-world applications. this situation prompts us to reconsider traffic flow prediction using deep learning models and large amounts of traffic data. In this paper, we present the Long Short-Term Memory (LSTM) model, Gated Recurrent Unit (GRU) model, Stacked Auto Encoder (SAE), and an Auto-Regressive Integrated Moving Average (ARIMA) model, all of which are deep learning methods. these techniques are applied to real-world traffic big data collected by performance measurement systems. Compared to other deep learning and shallow machine learning prediction networks, experimental results demonstrate that the gated recurrent unit model is more applicable and performs better.

关键词： Deep learning Adaptation models Training data Predictive models Big data Traffic control Logic gates

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Contingency Analysis of Power System using Big data Analytic Techniques

Contingency Analysis of Power System using Big Data Analytic...

引用

5th international conference on Computing, Communication and Security (ICCCS)

作者： Angadi, Ravi, V Daram, Suresh Babu Venkataramu, P. S. Presidency Univ Dept EEE Bengaluru Karantaka India Sree Vidyanikethan Engn Coll Ctr Energy Dept EEE Tirupati AP India Presidency Univ Bengaluru Karantaka India

ISBN: (纸本)9781728191805

this paper introduce the application of Big data Analytic Technique to predict the severity of various Single Transmission line outages. the severity of the outage is assessed by computing the Line Voltage Stability Index (LVSI) and is used for ranking purpose under different loading condition. this results in generation of large volume of data. the data obtained from the simulations for various scenarios is processed and applied to machine learning to predict the rank and severity condition of the line. the severity is predicted for various test systems to ascertain the suitability of the technique applied and the results of the study conducted on the IEEE 30 Bus system are presented with the analysis needed. the MATLAB and the WEKA software are used for simulation purpose.

关键词： Contingency Line Outage Line Voltage Stability Index Big data data analytics Classification and machine learning

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Aspect Analysis Based on Statistical Description and Visualization of data

Aspect Analysis Based on Statistical Description and Visuali...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Priyanka Tomar Nonita Sharma Shailesh D. Kamble Information Technology Department Indira gandhi Delhi Technical University For Women Delhi India Artificial Intelligence and Data Science Indira Gandhi Delhi University for Women Delhi India

the trending machine learning algorithms have been applied in almost all spheres of life and able to give accurate results. However, none of the algorithms perform aspect analysis beforehand to see the nature and inferences of data. this research work presents the aspect analysis of heart failure data. As per the day today life scenario where each one is living a busy life, it’s very important to know the health of our heart. this work will show case different types of models for cardiovascular diseases and its prediction through various heart patients’ data and detect various heart disease. this work is developed based on the data visualization approach using python by plotting graphs between different types of attributes in dataset and analyzing the graphs which are plotted between various attributes of our data like age, cholesterol, trestbps, sex and fbs. Research work would visualize the pattern of heart diseases according to the age and gender of a heart patient and would like to present a profound and relevant result which will be helpful for the doctors to analyze and provide proper treatment to heart patients.

关键词： Heart Visualization machine learning algorithms Statistical analysis Pain Computational modeling data visualization

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Knowledge discovery and framework for purchase behavior analysis in mobile gaming applications 14

Knowledge discovery and framework for purchase behavior anal...

引用

14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and 5th IADIS international conference Big data analytics, data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020, Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020

作者： Jansevskis, Martins Osis, Kaspars Faculty of Engineering Vidzeme University of Applied Sciences Valmiera Latvia

ISBN: (纸本)9789898704214

the gaming industry is a fast expanding industry with a large global market and is projected to hit $300 billion by 2025. the total number of players in 2019 was projected to hit 2.4 billion (Taylor, 2019). Knowledge discovery is achieved by collecting and analyzing data from within the gaming applications. the data collected is used to understand players, gain insights and to improve products for the gaming community based on feedback and user interaction. And as a result, obtained knowledge contributes to monetization in a way that is especially interesting in the e-sports and streaming space. machine learning is one of the technologies that can assist in knowledge discovery as it provides potential to obtain insights into previously overlooked data. this paper provides insight in a work of how machine learning algorithms are applied to gain behavior understanding whithin mobile gaming applications in a way compliant with European General data Protection Regulation (GDPR). the paper is a part of a larger research work and contributes to the domain of knowledge discovery within in-app purchase behavior data and serves as a step towards further research in this area. © Proceedings of the 14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS international conference Big data analytics, data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： data mining

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Administration of Digital Identities Using Blockchain

Administration of Digital Identities Using Blockchain

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Shipra Ravi Kumar Mukta Goyal Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Since the last two decades, the problem of check theft and the data breaches from centralized systems has gotten worse. In this study, we looked at how blockchain technology is employed in identification systems and how it may be applied to verify an identity’s veracity and legitimacy. the drawbacks of the conventional identification systems were examined in depth, and the advantages of using a Blockchain-based identity system were discussed, including the removal of the middleman and the need for long wait times for verification, authorization, and attestation. Nobody can alter their identification data because the nodes in a blockchain are supposed to be immutable, and because asymmetric cryptography is utilized while sharing data, personal information is kept secure. We were able to effectively establish an identity wallet-a node in a chain on the Ethereum platform-and test a self-sovereign identification system for a single person.

关键词： Terrorism Prototypes Authentication Information sharing Organizations machine learning Information age

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SmartMeter: An Automatic Water Metering System using Computer Vision and ARIMA-based machine learning 22

SmartMeter: An Automatic Water Metering System using Compute...

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Proceedings of the 2022 5th international conference on machine learning and Natural Language Processing

作者： Felix Fl Lin Yu Ys Sun Arnold O. Beckman High School USA Computer Science Department California State Polytechnic University USA

ISBN: (纸本)9781450399067

Having access to sufficient water usage data real-time is crucial not only to finding water leaks and preventing water bills (which can cost up to thousands of dollars) but also to tracking water usage and savings. However, to record a California household's water usage, a worker from the respective water district must personally check the water meter in order to update the data at a monthly frequency. this is because the current water infrastructure in California is outdated while also being expensive to replace. Additionally, while certain cities such as San Jose has Advanced Metering Systems for water installed, due to a lack of budget, it can be difficult for government agencies to develop a solution that can be implemented statewide, then nationwide, in an estimated 2-3 years from now. this paper proposes an IoT-based smart water monitor that utilizes computer vision to record water data, then compares the data's deviance from the predicted usage to check for water leaks. We applied our application to monitoring the water usage of my own household and conducted a qualitative evaluation of the approach. the results show that the water monitor is an effective and affordable way to water management.

关键词： Automated metering infrastructure

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