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moviQuest-MAS: An Intelligent Platform for Ubiquitous Social Networking Business

moviQuest-MAS: An Intelligent Platform for Ubiquitous Social...

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12th international conference on Artificial Intelligence and Soft computing, ICAISC 2013

作者： Soto, Ramon Soto, Alexandro Camalich, Juan Univ Sonora Dept Accounting Hermosillo Sonora Mexico Univ Sonora Dept Math Hermosillo Sonora Mexico SA CV Dept Engn moviQuest Hermosillo Sonora Mexico

ISBN: (纸本)9783642386091

New communications technologies have revolutionized human interaction. One of the main features of this new order is an explosion of relationships a person can establish and the issues that she can start. However, keeping control of the course of these relationships has become a very complex issue. this paper presents a generic platform for implementing social networks which aim to establish serious relationships between people based on their membership to affinity groups. the proposed social network platform uses an artificial intelligence approach which has been designated as Ubiquitous Artificial Intelligence. the basis of this approach is a multi-agent architecture that integrates a variety of pattern recognition algorithms to determine the profiles of social network use along with mobile expert systems that assist the user in their social network communication from the mobile. this approach also uses a specific ontology of the social network that simplifies communication and makes it safer.

关键词： Social networks Mobile computing Intelligent computing Multiagent technologies

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the Study of PSO-SVM and PSO-GRNN Algorithm Used in the Fault pattern Classification of Transformer 2020

The Study of PSO-SVM and PSO-GRNN Algorithm Used in the Faul...

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12th international conference on Machine Learning and computing, ICMLC 2020

作者： Shiling, Zhang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing Yubei China

ISBN: (纸本)9781450376426

Support Vector Machine and GR neural network are widely used in the fault pattern classification. For large data samples and fault types, these methods have high classification accuracy. In order to further improve the practical range and operational efficiency of numerical classifier, the Particle Swarm Optimization Algorithm is used to the optimize penalty and kernel function parameters of SVM, weight and threshold parameter of GRNN. By numerical verification, it is found that the classifier is applied to multi-peak complex curved surface function, has nice classification ability. the classifier is used in classification of DGA data of actual oil immersed, and it is found: running time of PSO-SVM classifier increases with the increase of the fault mode, and the recognition accuracy shows the trend of the overall decline. the classification accuracy of the PSO-GRNN classifier is relatively stable, but the fault classification is more time-consuming. through the research and analysis of this paper, validity of optimization classification algorithm is verified by the simulation, compared performance of the PSO-GRNN and PSO-SVM classification algorithms, which provides ideas and methods for the fault classification method of oil immersed transformer. © 2020 ACM.

关键词： Support vector machines

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach 12

Road Condition Detection and Crowdsourced Data Collection fo...

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12th international conference on Image Processing theory, Tools and Applications, IPTA 2023

作者： Jahan, Md Saroar Islam, Mominul Hossain, Md Sanjid Kabir Mim, Jhuma Oussalah, Mourad Akter, Nasrin Oulu Finland Daffodil International University Dept. of Cse Dhaka Bangladesh Lut Univerity Computer Vision and Pattern Recognition Lappeenranta Finland University of Oulu Cmvs Faculty of Itee Oulu Finland

ISBN: (纸本)9798350325416

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. this results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. the application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study's findings, a mobile application has been developed. the app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy. © 2023 IEEE.

关键词： Safety factor

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Palm Vein Age and Gender Estimation Using Center Symmetric-Local Binary pattern 12th

Palm Vein Age and Gender Estimation Using Center Symmetric-L...

引用

international Joint conference: 12th international conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th international conference on European Transnational Education, ICEUTE 2019

作者： Damak, Wafa Trabelsi, Randa Boukhris Masmoudi, Alima Damak Sellami, Dorra University of Sfax Sfax Engineering School Sfax Tunisia

ISBN: (纸本)9783030200046

Many investigations on hand veins modality have been done in the literature for identification and recognition systems. However, researches on age and gender estimation by hand veins are very limited and very preliminary. Our contribution in this paper is to propose a system able to estimate the age and the gender of a person from its hand veins. Accordingly, we are interested in studying the discriminating features for the prediction of a person’s age and gender. In fact, hand vein images are very rich in orientation and contour characteristics and they are faced with poor quality and illumination variation. Hence, we investigate texture analysis invariant to illumination as well as venous pattern gradient information determination by Center Symmetric-Local Binary patterns (CSLBP) descriptor. Since Region Of Interest (ROI) extraction is important in a biometric system, we aim to cover the whole informative region of hand veins by our dynamic ROI extraction method. Our experimental study is based on palm vein VERA database. As considered database has a class imbalance problem, we remediate this problem by using Weighted K-Nearest Neighbor (WKNN). the obtained performance metrics demonstrate the effectiveness of our proposed system for gender classification and age estimation respectively: 95.8% and 94.2% for F-measure, 95.9% and 94.4% for G-mean. © 2020, Springer Nature Switzerland AG.

关键词： Extraction

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Analysis and modeling of contextual activity pattern for identifying lifestyle disorders 12

Analysis and modeling of contextual activity pattern for ide...

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12th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2021

作者： Sakhare, Apeksha V. Sharma, Ashish K. Computer Science and Engineering G H Raisoni University MH Amravati India Computer Science and Engineering G H Raisoni College of Engineering MH Nagpur India School of Engineering and Technology G H Raisoni University MH Amravati India

Nowadays with the help of various government health records and surveys, it has been found that the root cause of the various severe diseases (non-communicable) very frequently occur in the early age of humans and adolescents has not even aware of any symptoms or signs. this type of disease has a root cause of human irregular and very unhealthy, unorganized lifestyle and diet pattern. From the data analysis done on health information collected by various government organizations, it has been found that 309889 adolescents between ages 12-18 (53.8 female genders) from approx. 70 countries have been suffering from this non-communicable disease. Medical departments or health care organizations are producing a very wide amount of health-based data every day which be used for identifying or recognizing or predicting this disease which has no early signs or symptoms during some of its growing stages. this hidden data of healthcare organizations will be useful for taking decisions effectively for affected person treatment and recovery procedure. Here the main challenge is how to extract the hidden data from the collected health records, there some sort of data mining and machine learning technique is required. the expected outcome of our research work is that if the disease is detected and identified at its early stage it can be cured very effectively and easily with more accuracy and thus reducing the risk factor of life of a patient and the cost of treatment required to cure the disease. Early recognition of disease will also increase the percentage rate of cure from a disease of a patient and also decreases the risk of loss of life of a patient. this research paper provides a brief introduction of lifestyle Health problems and its cause. Our proposed research work includes a review of various techniques used by different research scholars, a working block diagram with a brief description of the diagram, and at the end, we have concluded with future work and the scope of

关键词： Diseases

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Engineering Applications of Neural Networks 1

引用

丛书名： IFIP Advances in Information and Communication Technology

1000年

作者： Lazaros Iliadis Chrisina Jayne

ISBN: (数字)9783642239571

ISBN: (纸本)9783642239564;9783642269547

the two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. the 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. the first volume includes the papers that were accepted for presentation at the EANN 2011 conference. they are organized in topical sections on computer vision and robotics, self organizing maps, classification/pattern recognition, financial and management applications of AI, fuzzy systems, support vector machines, learning and novel algorithms, reinforcement and radial basis function ANN, machine learning, evolutionary genetic algorithms optimization, Web applications of ANN, spiking ANN, feature extraction minimization, medical applications of AI, environmental and earth applications of AI, multi layer ANN, and bioinformatics. the volume also contains the accepted papers from the Workshop on Applications of Soft computing to Telecommunication (ASCOTE 2011), the Workshop on Computational Intelligence Applications in Bioinformatics (CIAB 2011), and the Second Workshop on Informatics and Intelligent Systems Applications for Quality of Life Information Services (ISQLIS 2011).

关键词： Computation by Abstract Devices pattern recognition Artificial Intelligence Simulation and Modeling Biometrics User Interfaces and Human Computer Interaction

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Enhancing Toxoplasmosis Chorioretinitis Detection: A Hybrid ResNet-YOLO Classifier with Fitness Sorted-Shark Smell Optimization 12

Enhancing Toxoplasmosis Chorioretinitis Detection: A Hybrid ...

引用

12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Gautam, Vinay Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350369861

Ophthalmologists often examine fundus pictures to detect toxoplasmosis chorioretinitis, a serious condition of the eye. Preventing blindness in people who may be impacted by this disorder requires prompt and correct diagnosis. In this study, we present a new method for improving Toxoplasmosis chorioretinitis diagnosis accuracy by using a hybrid deep learning model that fuses features from the ResNet and YOLO (You Only Look Once) architectures. the core difference in our method is that we use the YOLO classifier for illness identification instead of the ResNet model's fully connected layer. We use the Fitness Sorted-Shark Smell Optimization (FS-SSO) technique to fine-tune crucial model parameters to get a high recognition rate with this hybrid deep learning model. Using a wide range of performance indicators and accuracy measures, we rigorously assess the effectiveness of the suggested strategy in our experimental investigation. the findings show that our hybrid model outperformed the state-of-the-art methods for detecting Toxoplasmosis chorioretinitis with an accuracy of 99%. Using the FS-SSO approach for parameter optimization, this study demonstrates the promise of combining the strengths of the ResNet and YOLO models for medical picture analysis. Our method's excellent accuracy suggests that it might be used to enhance patient outcomes and minimize the incidence of blindness caused by Toxoplasmosis chorioretinitis by allowing for earlier diagnosis and treatments. © 2023 IEEE.

关键词： Computer aided diagnosis

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A Framework for the Automatic Combination and Evaluation of Gene Selection Methods 12th

A Framework for the Automatic Combination and Evaluation of ...

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12th international conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Grasnick, Bastien Perscheid, Cindy Uflacker, Matthias Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783319987026;9783319987019

High-throughput RNA-Sequencing technologies produce large gene expression datasets whose analysis leads to a better understanding and treatment of diseases like cancer. the data's high dimensionality poses challenges to its computational analysis, which is addressed by applying gene selection. Traditional gene selection methods are based on the data only. In turn, integrative approaches include curated biological information from external knowledge bases in the gene selection process, which improves result accuracy and computational complexity. this paper presents a framework for comparing knowledge based and computational gene selection. Moreover, a novel integrative method of the automatic combination of both approaches is presented. Results on a cancer dataset show that simple computational methods enriched by external knowledge can compete with complex computational techniques.

关键词： Gene expression data analysis Gene selection pattern recognition

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An Initial Parameter Search for Rapid Concept Drift Adaptation in Deep Neural Networks 1

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12th international conference on Soft computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Shah, Muhammad Zafran Bin Muhammad Zaly Zainal, Anazida Binti School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

ISBN: (数字)9783030736897

ISBN: (纸本)9783030736880

Concept drift is a common issue in data stream mining algorithms that causes prediction models to lose its original performance gradually or abruptly due to the non-stationarity of the data distribution and decision boundaries. To combat concept drifts, prediction models need to be updated periodically or when concept drifts occur to adapt to the current concept. Unfortunately, training deep neural networks often require a large amount of data samples and high computational resource consumption, making adaptation slow when concept drifts occur. this paper proposes an approach by searching for an optimum initial parameter that could be adapted quickly to all possible concept drift situations. the initial parameter search is based on the Reptile [1] algorithm, which had been successfully applied in image classification, which allows a neural network model to learn from a few samples and minimal gradient steps. We argue that using an optimum initial parameter allows prior information to be embedded and makes the prediction model less reliant on training exclusively from new data when concept drift occurs. Experimental results show that this approach performs at least as well as current data streaming algorithms but with the lowest computational overhead. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Global optimization

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12th international Symposium on Visual computing, ISVC 2016

12th International Symposium on Visual Computing, ISVC 2016

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12th international Symposium on Visual computing, ISVC 2016

ISBN: (纸本)9783319508344

the proceedings contain 77 papers. the special focus in this conference is on Computational Bioimaging, Computer Graphics, Motion, Tracking, Segmentation, pattern recognition, Visualization, 3D Mapping, Modeling, Surface Reconstruction, Visual Perception and Robotic Systems. the topics include: Similarity metric learning for 2D to 3D registration of brain vasculature;automatic optic disk segmentation in presence of disk blurring;tensor voting extraction of vessel centerlines from cerebral angiograms;stacked autoencoders for medical image search;an interactive exploration tool for cancer biomarker cutpoint optimization;adding turbulence based on low-resolution cascade ratios;creating feasible reflectance data for synthetic optical flow datasets;automatic web page coloring;automatic content-aware non-photorealistic rendering of images;detecting tracking failures from correlation response maps;camera motion estimation with known vertical direction in unstructured environments;reliability-based local features aggregation for image segmentation;image enhancement by volume limitation in binary tomography;optimizing intersection-over-union in deep neural networks for image segmentation;a mobile recognition system for analog energy meter scanning;towards landmine detection using ubiquitous satellite imaging;robustness of rotation invariant descriptors for texture classification;pollen grain recognition using deep learning;complementary keypoint descriptors;interpolation-based extraction of representative isosurfaces;spatiotemporal LOD-Blending for artifact reduction in multi-resolution volume rendering;visual analytics using graph sampling and summarization on multitouch displays;an efficient algorithm for feature-based 3D point cloud correspondence search;combining visual tracking and person detection for long term tracking on a UAV and change detection and object recognition using aerial robots.

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