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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41-50 订阅

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Efficient Implementation to Reduce the data Size in Big data Using Classification Algorithm of machine learning 5th

Efficient Implementation to Reduce the Data Size in Big Data...

引用

5th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： RajKumar, V. Priyadharshini, G. Krishnasamy College of Engineering and Technology Cuddalore India

ISBN: (纸本)9783031283239

Big data play an enormous role in the real-world industry, such as financial, banking, government sector, and healthcare, to store the enormous amount of data stored and processed. Here, big data analytics processing develops many applications but also leads to some challenges: data security, data structure, data standardization, and data storage and transfer. Big data are working more number of data to process, and nowadays, the stored data are increased exponentially in terabytes to zeta bytes, so we will not handle those increases by filtering each data to stored classified data value, removing the replicated data and the unnecessary data stored in big data. In this research, we involved big data analytics in machine learning technique (BDML) to reduce the challenges mentioned above using a classification algorithm. they are many types of classification algorithms that are there to predict the outcome of unstructured data. the primary purpose of the research is to reduce the rising size of data stored and sent, improve data security, and create organized data using machine learning techniques. A graphical model shows our aim of research attained with exemplary efficient implementation. In future, we will improve this technique to work with various real-world applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data analytics

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data-Driven Approach to Network Intrusion Detection System Using Modified Artificial Bee Colony Algorithm for Nature-Inspired Cybersecurity 1

引用

6th international conference on applied machine learning and data analytics, amlda 2023

作者： Gupta, V.B. Shandilya, Shishir Kumar Ganguli, Chirag Choudhary, Gaurav School of Data Science and Forecasting Devi Ahilya University Madhya Pradesh Indore India VIT Bhopal University Bhopal India DTU Compute Technical University of Denmark Lyngby Kongens2800 Denmark

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

With ever-evolving cyberspace, adaptive defense is crucial. In this paper, we show the Adaptive Defense Mechanism to identify Anomalous hosts in a network using the Artificial Bees Colonization Algorithm. A self-driven metric has been defined to determine the performance of a network that would detect the behavior of its nodes. this algorithmic metric is inspired by the Nature-Inspired Artificial Bees Colonization Algorithm. the end result is randomly generated using a dimension index that gives the same result on the node’s behavior which is then used to determine the probabilistic parametric fitness of the individual nodes. this helps to determine which nodes are getting affected the most or are nearer to the attack surface. the defense mechanism is based on the Nature Inspired Artificial Bees Colonization Algorithm, which is able to detect the nearest point/s of attack on nodes based on the experimental simulation of attacked nodes. It also shows the impact of the defense mechanism on the various topologies of the nodes as predefined in the testbed implementing a Distributed Denial-of-Service attack on the nodes. the proposed algorithm showcases the nodes that are affected due to the attack, providing the nearest point of the breach, which can provide a comprehensive way of examining the intrusion point. this algorithm outperformed in terms of stability and early identification of the malicious nodes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intrusion detection

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ICAIW 2022 - Joint Proceedings of the 5th international conference on applied Informatics Workshops: 3rd international Workshop on applied Artificial Intelligence, WAAI 2022, 4th international Workshop on applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data Engineering and analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation Systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on Knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on Systems Modeling, WSSC 2022, co-located with the 5th international conference on applied Informatics, ICAI 2022

ICAIW 2022 - Joint Proceedings of the 5th International Conf...

引用

Joint 5th international conference on applied Informatics Workshops, ICAIW 2022: 3rd international Workshop on applied Artificial Intelligence, WAAI 2022, 4th international Workshop on applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data Engineering and analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation Systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on Knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on Systems Modeling, WSSC 2022

the proceedings contain 24 papers. the topics discussed include: analysis on early dropouts in engineering careers;depression diagnosis using text-based AI methods – a systematic review;satellite orbit prediction using a machine learning approach;bibliometric software: the most commonly used in research;bio-business of polymer derived from cassava waste for the industrial plastic sector;dynamic models to determine external factors and their impact on the shrimp price forecast;how to use Instagram to travel the world? an approach to discovering relevant insights from tourist media content;public safety perception in Ecuador: an approach from social networks over data analytics;relationship between corporate social responsibility, assets and income of companies in Ecuador;retrospective of scientific production on e-democracy;sticky floor and glass ceiling in Ecuador. the evolution of the gender wage gap, 2010-2021;and technical indicator for a better intraday understanding of uptrends or downtrends in the financial markets using volume transactions as a trigger.

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Introduction of Optimization Algorithm for Adaptive Gradient 5

Introduction of Optimization Algorithm for Adaptive Gradient

引用

5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lamine, Mouna Kim, Sang-Chul Kookmin Univ Sch Comp Sci Seoul South Korea

ISBN: (纸本)9781665456456

machine learning is facing rapid development due to the almost unlimited amount of data available, and it is widely applied in diverse areas. Optimization is one of the core components in the machine learning which attracted more researcher's attention to it. In recent years there has been a great deal of work on improving optimisation methods in machine learning. In this paper we will introduce the Adaptive Gradient(Adapg), a new extension in the adaptive learning family Optimization algorithm.

关键词： machine learning Optimization Methods Gradient Decent Adaptive Algorithms AdaDelta Adam Adapg

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Predicting Depression History from a Short Reward/Aversion Task with Behavioral Economic Features 1

引用

5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Stefanopoulos, L. Lavlani, S. Kim, B. W. Vike, N. Bari, S. Azcona, E. Woodward, S. Block, M. Maglaveras, N. Katsaggelos, A. K. Breiter, H. Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Univ Cincinnati Dept Comp Sci & Biomed Engn Cincinnati OH USA Northwestern Univ Feinberg Sch Med Chicago IL USA Northwestern Univ Integrated Mkt Commun Evanston IL USA Aristotle Univ Thessaloniki Dept Med Thessaloniki Greece Massachusetts Gen Hosp Dept Psychiat Boston MA USA

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

this paper presents a novel example of depression prediction, merging cognitive science with data-driven machine learning. Behavioral economic features were engineered from a short picture rating task. Relative Preference theory was applied to rating data for quantifying the degree to which participants liked, disliked, or were neutral to several types of pictures;thus, behavioral economic variables including loss aversion, risk aversion, and 13 others that are amenable to psychological interpretation were mined. these variables were features of a logistic regression predictive model that targeted depression in a population-based sample (N = 281) with high test accuracy and no overfitting. Per our review of the literature, we cannot identify other papers that explicitly use behavioral economic features to predict depression with machine learning.

关键词： Depression prediction Behavioral Economic Features machine learning

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Classification of Daily Activities by Different machine learning Models Based on Characteristics in the Time Domain 1

引用

5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Ojeda Prado, Luis Antony Borja Inga, Rolando Samuel Ojeda Quispe, Fiorella Cristina Sifuentes Llatas, Mauricio Daniel Paredes Arellano, Alexander Pontificia Univ Catolica Peru Univ Peruana Cayetano Heredia Lima Peru

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

Portable physical activity monitors provide detailed, continuous and objective measurements of individual physical activity in the environment of daily activities. A major problem with wristbands, pedometers, and smartphones that use accelerometer technology is that they measure involuntary jerks as steps. therefore, they generate inaccurate values resulting in erroneous data. therefore, the purpose of this study is to determine and contrast the performance obtained for the classification of daily activities of different machine learning models based on characteristics in the time domain of signals obtained from accelerometry collected at various points of the body. the development of the activity identifier is based on models and characteristics of existing developments of calorie counters by accelerometric signals;these features are extracted in the time domain. the following classifiers were applied: Logistic Regression, K-Nearest Neighbors, Support Vector machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Light Gradient Boosting and Extreme Gradient Boosting. the performance of each model was measured by how accurately it emerged to classify 4 daily activities based on the test set. the results show that, to have an accuracy greater than 70% in most models, at least 2 accelerometers are required.

关键词： machine learning physical activity classification performance characteristics in the time domain signals from accelerometry

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Solution Approach for Detection of Stock Price Manipulation by Market Operators 5th

Solution Approach for Detection of Stock Price Manipulation ...

引用

5th international conference on Smart Technologies in data Science and Communication, SMART-DSC 2022

作者： Kakde, Yogesh Chavan, Ganesh Sah, Basant Sen, Apoorva KL University AP Guntur India Medi-Caps University MP Indore India

ISBN: (纸本)9789811968792

Nowadays, many so-called stock analyst people are now sending tips via SMS, e-mails, and social media giving targets to stocks that are of very bad quality. In market, these are called pump and dump schemes, where "operators" or "manipulators" increase the price of a stock by various strategies. the increase in price attracts retail investor to purchase that stock. When the stock price crosses the required targets, set by the manipulators who sell it out and public is left holding stock whose price gets decreased suddenly. In this paper, we present a solution approach which can be implemented to detect such manipulation of stock price and avoid such malicious activity. In our solution approach, we suggest ideas, criteria which may be used to build a model based on data analytics, machine learning which can return us the list of stocks that is expected to be manipulated by the operator. this paper also proposes the data analytic models, machine learning models which may be used while implementing the solution approach suggested in this paper. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data analytics machine learning Pumps and dumps Stock market Stock price manipulation

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Comparison of Regressors applied to 3D Motion Marker data of a Historic Building Prototype Subjected to Shake Table Tests 5th

Comparison of Regressors Applied to 3D Motion Marker Data of...

引用

5th international conference on Protection of Historical Constructions-PROHITECH

作者： Palumbo, Domenico Ormando, Chiara Roselli, Ivan ENEA Casaccia Res Ctr Rome Italy

ISBN: (纸本)9783031873188;9783031873164;9783031873157

In the present study the performance of several machine learning regression techniques for the analysis of displacement data acquired in dynamic identification shaking table tests to assess a building's prototype state of damage were compared. the displacement data of a typical Italian historic masonry building prototype were recorded by a passive 3D motion capture system. these recorded displacement data were analyzed by calculating a widely accepted Damage Index (DI) based on the decay of the first modal frequency of the tested structure. To such purpose, a conventional Frequency Response Function (FRF) algorithm for modal analysis was adopted. then, several regressors, commonly used in machine learning applications, were applied to cepstral coefficients extracted from time-series segmentation of the 3D motion markers signals to predict the DI values. To assess the quality of the tested regressors, Coefficient of determination (R-2) and Relative Percent Deviation (RPD) parameters were evaluated. Different lengths of time-series segments were also explored and the results were statistically compared to highlight the differences between regressors. Excellent results were obtained in terms of R-2 (0.98) and RPD (6.87) with a weak dependence on the time partitioning performed on the data with optimal results for 4 or more seconds. Moreover, Stacked Regressor, Decision Tree Bagging Regressor and XGB Regressor were found as the best overall regressors. the results obtained demonstrated that machine learning methods can be effectively utilized to predict the level of damage of historic masonry buildings with very high accuracy.

关键词： machine learning regression historic buildings

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Appraisal of Artificial Intelligence for fall prevention & fall risk assessment 5

Appraisal of Artificial Intelligence for fall prevention & f...

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5th international conference on Informatics and data-Driven Medicine (IDDM)

作者： Campos, Jaime Linnaeus Univ Dept Informat PG Vejdes 6 & 7 S-35195 Vaxjo Sweden

the current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored.

关键词： Fall prevention fall risk assessment artificial intelligence A.I machine learning sensors eHealth

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5th international conference on Big data and Artificial Intelligence, BDAI 2022

5th International Conference on Big Data and Artificial Inte...

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5th international conference on Big data and Artificial Intelligence, BDAI 2022

ISBN: (纸本)9781665470810

the proceedings contain 37 papers. the topics discussed include: classification of human emotion from speech data using deep learning;a Chinese-Malay neural machine translation model based on ca-transformer and transfer learning;fixed-wing stalled maneuver control technology based on deep reinforcement learning;PhysioVec: a multi-stage deep-learning framework for searching online health information with breath sound;automatic machine learning classification algorithms for stability detection of smart grid;short-term power load forecasting technology based on bi-directional LSTM quantile regression;detecting anomalies in satellite telemetry data based on causal multivariate temporal convolutional network;regularized minimum maximum probability SVM based on radius-margin ratio;event classification with imbalanced and missing data for an air-handling unit;and a dimensionality reduction and reconstruction method for data with multiple connected components.

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