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共 182 条记录，以下是141-150 订阅

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Privacy preserving technique for euclidean distance based mining algorithms using a wavelet related transform

Privacy preserving technique for euclidean distance based mi...

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11th international conference on intelligent data engineering and automated learning, ideal 2010

作者： Kadampur, Mohammad Ali D.v.l.n., Somayajulu Warangal A.P. 506004 India

ISBN: (纸本)3642153801

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： data mining

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Fifth international conference on Artificial Intelligence and Computer Science, AICS 2023

Fifth International Conference on Artificial Intelligence an...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

ISBN: (纸本)9781510668621

the proceedings contain 143 papers. the topics discussed include: lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data;research on rock image classification algorithm based on multi-task learning and pseudo-labels;research on intelligent recognition algorithm for common roadbed diseases based on radar images;pre-planning of individualized talus implants based on computed tomography-automated segmentation;mining order-preserving submatrix based on charm for gene expression data;an improved watershed algorithm and its application in image edge extraction;research on augmented reality registration method based on SA-PSO algorithm;a personalized test paper generation model for English examination based on knowledge tracking;and improved ACLPSO algorithm for automatic parameter optimization.

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Vehicle Trajectory-Based Intersection Classification Using Deep Transfer learning 5

Vehicle Trajectory-Based Intersection Classification Using D...

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5th international conference on intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Kased, Abanoub Rabee, Rana Fahmy, Akram Mohamed, Hussein Yacoub, Marco Elhenawy, Mohammed Ashqar, Huthaifa I. Alsobeh, Anas Masoud, Mahmoud Hassan, Abdallah A. Minia University Faculty of Engineering Computers and Systems Department Minia Egypt Australia Arab American University Civil Engineering Department Jenin Palestine School of Computing Southern Illinois University Carbondale CarbondaleIL United States Center for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Department of Information Systems and Operations Management Dhahran Saudi Arabia

ISBN: (纸本)9798350354690

this document is a model and instructions for M-TEX. the accelerated expansion of global road networks necessitates the availability of accurate and up-to-date cartographic data for connected and automated vehicles. Conventional mapping techniques, such as satellite imagery and field surveys, are inher-ently time-consuming and labor-intensive. this study proposes a novel approach that makes use of the ubiquity of GPS-embedded devices to facilitate the efficient updating of road maps. We present a classifier that identifies the types of intersections from vehicle trajectories, which represents a crucial initial step in the generation of automated maps. the methodology employs five distinct convolutional deep neural network architectures for the detection and classification of road intersection types. the experimental results, which were obtained using three real-world vehicle trajectory datasets, demonstrate classification accuracy levels that range from 84 % to 91 %. It is noteworthy that these results were achieved using less than 10% of the data that is typically required for comparable accuracy levels, which highlights the efficiency of the feature extraction approach that was employed. this research contributes to the advancement of intelligent transportation systems by offering a more efficient and scalable method for road network mapping and updating. © 2024 IEEE.

关键词： Convolutional neural networks

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Automating stock prediction with neural network and evolutionary computation

Automating stock prediction with neural network and evolutio...

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4th international conference on intelligent data engineering and automated learning, ideal 2003

作者： Ao, Sio Iong Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)354040550X

In the previous studies [1, 2, 3], it has been found that there is strong correlation between the US market and the Asian markets in the long run. the VAR analysis shows that the US indices lead the Asian ones. But, such correlation is time-dependent and affects the performance of using the historical US data to predict the Asian markets by neural network. Here, a simplified automated system is outlined to overcome this difficulty by employing the evolutionary computation to simulate the markets interactive dynamics. the aim is to supplement the previous studies like [4, 5], which have focused more or less solely on the local stock market’s historical data, with additional information from other leading markets’ movements. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Automation

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automated Detection of Heart Disease Using PhysioNet ECG Signals and Machine learning Models 5

Automated Detection of Heart Disease Using PhysioNet ECG Sig...

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5th international conference on Machine learning and Computer Application, ICMLCA 2024

作者： Gadde, Nishant Kanaparti, Rishi Manukonda, Siddhardh Mohapatra, Avaneesh Venkatesh, Aniket Czech Technical University in Prague Laboratory of Intelligent Mobility and Energy Systems for Sustainability Prague Czech Republic Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering AtlantaGA United States Massachusetts Institute of Technology BostonMA United States Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798331530334

Heart diseases remain one of the leading causes of death and disability worldwide, even with the immense development of medical diagnosis and treatment. It is very important to diagnose cardiovascular conditions early using non-invasive methods, like the analysis of electrocardiogram (ECG) signals, for better prognosis and the reduction of mortality rates. In this regard, an automated system of heart disease detection is presented, based on the utilization of ECG data from the PhysioNet database in collaboration with a newly developed machine learning-based framework. It combines convolutional neural networks (CNNs) for automatic feature extraction and support vector machines (SVMs) for classification to take collective advantage of the strengths of both models. Besides this, the hybrid model utilizes temporal features like QRS-duration and RR intervals, as well as frequency-domain features derived using Fast Fourier Transform (FFT) for its enhanced predictive power. Results yielded a classification accuracy of 100% on both the training and validation sets, thereby showing the efficacy of the model proposed in this paper. However, the major limitation that was noticed from this model is the clear indication of overfitting-the model does not generalize well on the unseen test data. Future work for us includes working on cross-validation, augmentation techniques, and other regularization methods such as L2 regularization and dropout to generalize better and avoid overfitting. Despite such limitations, our study demonstrates that a hybrid approach using CNN and SVM using both time- and frequency-domain features has great potential in the development of real-time non-invasive diagnostic tools for the detection of heart disease. Once this method is further refined and validated in a larger patient cohort, it may easily be integrated into clinical settings for enabling early interventions and improving patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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learning negotiation policies using IB3 and bayesian networks

Learning negotiation policies using IB3 and bayesian network...

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11th international conference on intelligent data engineering and automated learning, ideal 2010

作者： Nalepa, Gislaine M. Ávila, Bráulio C. Enembreck, Fabrício Scalabrin, Edson E. Graduate Program on Applied Computer Science PUCPR Pontifical Catholic University of Paraná R. Imaculada Conceição Curitiba PR 1155 Brazil

ISBN: (纸本)3642153801

this paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Bayesian networks

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An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble learning 5

An Effective Approach for Sonar Image Recognition with Impro...

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2022 5th international conference on Advanced Algorithms and Control engineering, ICAACE 2022

作者： Du, Yunben Lin, Wei Zhong, Weibo Yuan, Yu. College of Electronics and Information Jiangsu University of Science and Technology Zhenjiang212000 China

Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. the feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. this method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. the experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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5th international conference on Computing in engineering and Technology, ICCET 2020

5th International Conference on Computing in Engineering and...

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5th international conference on Computing in engineering and Technology, ICCET 2020

ISBN: (纸本)9789811548505

the proceedings contain 35 papers. the special focus in this conference is on Computing in engineering and Technology. the topics include: Dynamic virtual machine provisioning in cloud computing using knowledge-based reduction method;early detection of grape stem borer using IoT;Controlled Privacy-Aware (CPA) protocol for machine-to-machine communication in internet of things;Performance analysis of wireless sensor network (WSN);verification of 32-bit memory using layered testbench with optimum functional coverage and constrained randomization;stochastic model of a sensor node;prevention of replay attack for isolated smart grid;dangers of bias in data-intensive information systems;Performance of routing protocols using mobility models in VANET;Entity-Centric Combined Trust (ECT) algorithm to detect packet dropping attack in vehicular Ad Hoc Networks (VANETs);graph partitioning using heuristic kernighan-lin algorithm for parallel computing;experimenting with reordering model of phrase-based machine translation system for english to Hindi;investigation of imbalanced big data set classification: Clustering minority samples over sampling technique;Blended learning and analysis of factors affecting the Use of ICT in education;automation in hydroponics farming ecosystem;low power, low voltage, low drop-out on-chip voltage regulator;optimized low-energy adaptive clustering hierarchy in wireless sensor network;qoS in vertical handoff for wireless network using hungarian model and data parceling technique;Study of three-way solution for node movement in mobile Ad Hoc Networks (MANETS);ioT capable mechanism for crowd analysis;probability analysis of vehicular traffic at city intersection;automated malware identifier and analyzer;preface;UAV communication in FANETs with metaheuristic techniques;animal repellents from agricultural fields.

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5th international conference on Recent Innovations in Computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

ISBN: (纸本)9789819906000

the proceedings contain 66 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: the Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer Vision Applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised Machine learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with Machine learning Techniques;machine learning Techniques for Image Manipulation Detection: A Review and Analysis;improving Real-Time intelligent Transportation Systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive Computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;intelligent Communication for Internet of things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;Time Series-Based IDS for Detecting Botnet Attacks in IoT and Embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural Machine Translation from English to Marathi Using Various Techniques.

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An IVIS Typical Scene Generation Algorithm Based on Traffic Big data 21

An IVIS Typical Scene Generation Algorithm Based on Traffic ...

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5th international conference on Computer Science and Application engineering, CSAE 2021

作者： Wang, Xianghe Hu, Jianming Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781450389853

With the increasing development of automatic driving technology, the construction of virtual simulation scenes libraries for automatic driving vehicles, as well as the optimization of coverage of functions and redundancies based on the current scenes libraries, has become a problem that needs to be solved in process of the establishing the intelligent Vehicle-Infrastructure System (IVIS) test and evaluation system, especially facing the uncountable inexhaustible library of actual traffic scenes. the construction of a standardized general test scene library covering typical scene applications, to provide a complete closed loop for automated driving vehicle testing also becomes a necessity. Based on the traffic big data, this paper takes the intelligent vehicle road system IVIS as the background, and aims to use scene essential factors, which are indecomposable factors obtained by scene decomposition, to describe the traffic scenes data, with feature extraction algorithm notion of unsupervised learning and nonlinear dimensionality reduction as a reference. Choosing traffic cell modelling, this paper adopts the idea of essential factors as the core and raises a vectorization process of scenes data as a foundation of subsequent research of the generation algorithms. On the basis of primitive scene decomposition, two core goals are set: study the typical test scenes of IVIS High-fidelity and flexible reconstruction technology and research on the scalable and easy-totest generation technology of IVIS extreme test scenes. With scene vectorization process as the fundament, this paper attempts to select and optimize a suitable clustering algorithm, to use an improved density clustering algorithm called OIR-DBSCAN, to generate IVIS typical scenes, and accordingly ensure the generalizability and timeliness of the IVIS test.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Clustering algorithms

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