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检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

共 89 条记录，以下是41-50 订阅

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Critical review of applications of Artificial Neural Networks in groundwater hydrology

Critical review of applications of Artificial Neural Network...

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12th international conference on Computer methods and Advances in Geomechanics 2008

作者： Manisha, P.J. Rastogi, A.K. Mohan, B.K. Dept. of Civil Engineering IIT Bombay Mumbai India Centre for Studies in Resources Engg IIT Bombay Mumbai India

ISBN: (纸本)9781622761760

Artificial Neural Networks (ANNs) are massively parallel distributed processors made up of simple processing units which store knowledge of the phenomenon under consideration without explicit physical consideration, which can be used as and when required. Water resources are important asset of any country and are facing worldwide shortage. In such a situation planned use of groundwater resources can give sustained relief. To avoid over exploitation, an effective groundwater management policy aimed at promotion of efficiency, equity and sustainability is required. Efficient management of ground water resources requires a thorough understanding of the often complex confined, unconfined leaky aquifer systems and its adequate conceptualization for its assessment and predictions. Most of the ground water hydrology problems involve processes which are nonlinear, complex, multivariate with parameters having spatial and temporal variability. these are expressed by complex partial differential equations which are normally solved with approximations. ANNs do not require these governing equations which are constructed on simplifying assumptions. From the review of the literature authors consider that ANNs may provide better alternative to the conventional numerical computational techniques in groundwater hydrology. Main advantages of ANNs can be stated as ease in application, reduced data requirement in some cases lesser computational burden and improved result accuracy than the conventional methods (Jain and Deo, 2004). the technique can be used for function approximation, optimization, system modeling (prediction and forecasting) and pattern recognition (classification problems). this is an attempt to examine applications of ANNs in groundwater hydrology in various problems. Some practical guidelines regarding network design are put forth for new modelers. the technique is critically evaluated by studying its advantages along with demerits for applications in groundwater hyd

关键词： Neural networks

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Artificial Intelligence applications and Innovations 1

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丛书名： IFIP Advances in Information and Communication Technology

1000年

作者： Lazaros Iliadis Ilias Maglogiannis

ISBN: (数字)9783319449449

ISBN: (纸本)9783319449432;9783319831688

this book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in thessaloniki, Greece, in September 2016. the workshops are the third Workshop on New methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE *** 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. the 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. the papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery pattern recognition Computer applications Algorithm Analysis and Problem Complexity Computer Communication Networks

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Gender recognition Using Local Block Difference pattern 12th

Gender Recognition Using Local Block Difference Pattern

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12th international conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Lai, Chih-Chin Wu, Chih-Hung Pan, Shing-Tai Lee, Shie-Jue Lin, Bor-Haur Natl Univ Kaohsiung Dept Elect Engn Kaohsiung 81148 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 81148 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9783319502120;9783319502113

Determining the gender of a person in a given image or video by a machine is a challenging problem. It has been attracting research attention due to its many potential real-life applications. As the human face provides important visual information for gender perception, a large number of studies have investigated gender recognition from face perception. In this paper, we present a method which uses Local Block Difference pattern for feature extraction to identify the gender from the face images. the recognition is performed by using a support vector machine, which had been shown to be superior to traditional pattern classifiers in the gender recognition problem. Experimental results on the FERET database are provided to demonstrate the proposed approach is an effective method, compared to other similar methods.

关键词： Gender recognition Local Block Difference pattern Support Vector Machine

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MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other applications

MIPPR 2023: Remote Sensing Image Processing, Geographic Info...

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SPIE 12th international Symposium on Multispectral Image Processing and pattern recognition, MIPPR 2023

ISBN: (纸本)9781510674998

the proceedings contain 25 papers. the topics discussed include: a method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure;research on deep learning-based land cover extraction method using multi-source mixed samples;a lossless compression of remote sensing images based on ANS entropy coding algorithm;research on the impact of change features for remote sensing image change detection;supervised spiking neural network for SAR image recognition: analysis of encoding methods and performance under strong noise influence;an edge-guided network for building footprint extraction from optical remote sensing images;and planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images.

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Automated Machine Learning Evaluation of Squat Movement patterns Using Smartphone and CoreML 12

Automated Machine Learning Evaluation of Squat Movement Patt...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Sokolowska, Marta Podpora, Michal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland

ISBN: (纸本)9798350358056

there is a vast diversity of Machine Learning use cases, and both the hardware and the methodology itself are constantly getting better. this article presents a very specific use case - the recognising, classifying, and evaluation of human physical activity by using just a video stream from a smartphone and potentially instructing people how to exercise to get better results. the paper presents two key aspects of the challenge: (1) defining the problem in the physical activity domain and choosing a proper ML approach and tool, and (2) conducting the experiment using consumer-grade hardware (a smartphone) to evaluate if the proposed methodology is applicable using an easy-to-use tool and widely accessible hardware for users with limited (or without) Machine Learning experience. Chapter II presents the process of gathering and preparing the dataset, while the next chapter presents the creation of a Machine Learning model, model training using the acquired dataset, and the model performance. It was possible to obtain the result of 92.4% validation accuracy for the squat movement pattern classification model. the trained model can be used in an ML smartphone application for evaluation of user-provided examples of squats. © 2023 IEEE.

关键词： action classiication Artiicial Intelligence Machine Learning movement patterns pattern recognition video analysis

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DBGAN: A Data Balancing Generative Adversarial Network for Mobility pattern recognition 25th

DBGAN: A Data Balancing Generative Adversarial Network for M...

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25th international conference on Big Data Analytics and Knowledge Discovery (DaWaK)

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity Coll Dublin Dublin D02 R123 Ireland Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783031398308;9783031398315

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel Data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. the results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. these results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

关键词： Mobility pattern recognition Data imbalance Kernel embedding Sequence to image Generative adversarial networks Self-attention

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A Learning Framework to Improve Unsupervised Gene Network Inference 1

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12th international conference on Machine Learning and Data Mining (MLDM)

作者： Turki, Turki Bassett, William Wang, Jason T. L. King Abdulaziz Univ Dept Comp Sci POB 80221 Jeddah 21589 Saudi Arabia New Jersey Inst Technol Bioinformat Program Newark NJ 07102 USA Dept Comp Sci Newark NJ 07102 USA

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs;however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. the three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

关键词： Feature selection Graph mining Network analysis applications in biology and medicine

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12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

12th IFIP WG 12.5 International Conference and Workshops on ...

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12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

ISBN: (纸本)9783319449432

the proceedings contain 62 papers. the special focus in this conference is on Medical Artificial Intelligence Modeling, Classification - pattern recognition, Ontology-Web and Social Media AI Modeling, Environmental AI Modeling, Machine Learning-Learning, Optimization-SVM, Agents-Robotics-Control, Artificial Neural Network Modeling and New methods and Tools for Big Data wokshop. the topics include: A cumulative training approach to schistosomiasis vector density prediction;mobile and evolving tool to predict colorectal cancer survivability;machine learning preprocessing method for suicide prediction;increasing diversity in random forests using naive bayes;identifying asperity patterns via machine learning algorithms;combining prototype selection with local boosting;modeling economic open data;web image indexing using wice and a learning-free language model;an intelligent internet search assistant based on the random neural network;deep neural networks for web page information extraction;environmental impact on predicting olive fruit fly population using trap measurements;a hybrid soft computing approach producing robust forest fire risk indices;applying artificial neural networks to short-term pm2.5 forecasting modeling;convolutive audio source separation using robust ica and reduced likelihood ratio jump;use of flight simulators in analyzing pilot behavior;active learning algorithms for multi-label data;design of an advanced smart forum for tesys e-learning platform;efficient support vector machine classification using prototype selection and generation;genetic search of pickup and delivery problem solutions for self-driving taxi routing and introducing chemotaxis to a mobile robot.

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Experimental of Circular Patch Microstrip Antenna Using Defected Ground Structure (DGS) methods in DVB-T2 applications 12

Experimental of Circular Patch Microstrip Antenna Using Defe...

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12th IEEE international conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Ashari, Sofian Dwi Praja, Muhammad Panji Kusuma Wicaksono, Agung Institut Teknologi Telkom Purwokerto Central Java Telecommunication Enginering Indonesia

ISBN: (纸本)9798350341102

In this digital era, television cannot be separated, as it has changed from analog to digital, as stated in the Ministry of Communication and Informatics regulations. Digital TV broadcasting in Indonesia uses the digital video broadcasting second-generation terrestrial (DVB-T2) standard, which operates in the frequency range of 478 MHz - 694 MHz. Switching from analog to digital TV requires a suitable antenna that can detect the signal. the microstrip antenna used has the advantage of being compact and easy to fabricate, but has weaknesses including the resulting narrow bandwidth;therefore, in this study, using a circular patch and using the defective ground structure (DGS) method, this method is used to widen the bandwidth. A simulation software was used in this design. From the simulation results that have been obtained, the value of the return loss at a frequency of 586 MHz -39.32, a bandwidth of 365 MHz, the lowest VSWR is 0.19, the gain at a frequency of 586 MHz is 1.18 dBi and the radiation pattern is bidirectional;then, on antenna measurements, there is a slight difference, where the value of the return loss at a frequency of 586 MHz -35.95, bandwidth of 2SS MHz, lowest VSWR 1.032, gain at 586 MHz 0.55 dBi and omnidirectional radiation pattern. © 2023 IEEE.

关键词： Microstrip antennas

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Pose-Based Temporal-Spatial Network (PTSN) for Gait recognition with Carrying and Clothing Variations 12th

Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognit...

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12th Chinese conference on Biometric recognition, CCBR 2017

作者： Liao, Rijun Cao, Chunshui Garcia, Edel B. Yu, Shiqi Huang, Yongzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China 7ma A 21406 PlayaHavana Cuba

ISBN: (纸本)9783319699226

One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. the body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions. © 2017, Springer international Publishing AG.

关键词： Gesture recognition

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