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14th international conference on engineering Applications of Neural Networks, EANN 2013

14th International Conference on Engineering Applications of...

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14th international conference on engineering Applications of Neural Networks, EANN 2013

ISBN: (纸本)9783642410123

the proceedings contain 51 papers. the special focus in this conference is on engineering Applications of Neural Networks. the topics include: Neural networks for digital media analysis and description;temperature forecasting in the concept of weather derivatives;improving the RACAI neural network MSD tagger;neural network simulation of photosynthetic production;thinking machines versus thinking organisms;prediction of foreign currency exchange rates using CGPANN;coastal hurricane inundation prediction for emergency response using artificial neural networks;crossroad detection using artificial neural networks;modeling spatiotemporal wild fire data with support vector machines and artificial neural networks;prediction of surface texture characteristics in turning of FRPs using ANN;a state space approach and Hurst exponent for ensemble predictors;3D molecular modelling of the helicase enzyme of the endemic, zoonotic Greek goat encephalitis virus;feature comparison and feature fusion for traditional dances recognition;intelligent chair sensor;detection of damage in composite materials using classification and novelty detection methods;impact of sampling on neural network classification performance in the context of repeat movie viewing;towards a wearable coach;real-time psychophysiological emotional state estimation in digital gameplay scenarios;probabilistic prediction for the detection of vesicoureteral reflux;an immune-inspired approach for breast cancer classification;classification of arrhythmia types using Cartesian genetic programming evolved artificial neural networks;novel neural architecture for air data angle estimation;audio data fuzzy fusion for source localization;boosting simplified fuzzy neural networks;a parallel and hierarchical Markovian RBF neural network;analysis of heating systems in buildings using self-organizing maps;local binary patterns and neural networks for no-reference image and video quality assessment;learning accurate active contours;p

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14th international conference on Informatics in Control, Automation and Robotics, ICINCO 2017

14th International Conference on Informatics in Control, Aut...

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14th international conference on Informatics in Control, Automation and Robotics, ICINCO 2017

ISBN: (纸本)9783030112912

the proceedings contain 39 papers. the special focus in this conference is on Informatics in Control, Automation and Robotics. the topics include: Application of a distributed containment algorithm: Trajectory tracking forâ mobile robots;Stochastic DP based on trained database for sub-optimal energy management of hybrid electric vehicles;modelling and minimum cost control of multiple tank pumping systems;reliable on-line re-optimization control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine;optimization algorithm for an exoskeleten robotic inverse kinematic application;a state dependent approach to resource allocation strategies;Auto-tuning of PID controllers for robotic manipulators using PSO and MOPSO;ATLASCAR: A sample of the quests and concerns for autonomous cars;intelligent labware transportation solution for the hierarchical life science automation;finite-Horizon H∞ Control Problem with Singular Control Cost;object detection and 6D pose estimation for precise robotic manipulation in unstructured environments;comprising of elastostatic and elastodynamic modeling for compliance error compensation in bipedal robot;autonomous navigation with open software platform for field robots;a multicopter design software tool for automated generation of simulation and visualization models;combining voronoi graph and spline-based approaches for a mobile robot path planning;geometrically finding best grasping points on single novel 3D point cloud;3D lidar data segmentation using a sequential hybrid method;ARTag, AprilTag and CALTag fiducial systems comparison in a presence of partial rotation: Manual and automated approaches;a simplified method to determine gravity vector through aerodynamic coefficients and its applications in attitude determination;sensor fusion approach for an autonomous shunting locomotive.

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Classification of the risk types of human papillomavirus by decision trees

Classification of the risk types of human papillomavirus by ...

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

作者： Park, Seong-Bae Hwang, Sohyun Zhang, Byoung-Tak School of Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)354040550X

the high-risk type of Human Papillomavirus (HPV) is the main etiologic factor of cervical cancer, which is a leading cause of cancer deaths in women worldwide. therefore, classifying the risk type of HPVs is very useful and necessary to the daignosis and remedy of cervical cancer. In this paper, we classify the risk type of 72 HPVs and predict the risk type of 4 HPVs of which type is unknown. As a machine learning method to classify them, we use decision trees. According to the experimental results, it shows about 81. 14% of accuracy. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Decision trees

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Multi-feature subspace learning via sparse correlation fusion and embedding

Multi-feature subspace learning via sparse correlation fusio...

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14th Pacific-Rim conference on Multimedia, PCM 2013

作者： Zhang, Hong Zhang, Yanpeng College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

ISBN: (纸本)9783319037301

Subspace learning is most traditional and important in multimedia analysis. Numerous researches have focused on how to introduce machine learning and statistical methods to multimedia subspace learning for semantic understanding and denoising, and have gained remarkable achievement in different multimedia applications, such as content-based retrieval, data clustering, face recognition, etc. However, most of these researches are based on multimedia data of single modality. Nowadays, with the rapid development of multimedia and information technology, multimedia data of different modalities often coexist, and the presence of one has a complementary effect on the other to some extent. Because different multimedia data are usually represented with heterogeneous low-level features and there exists the wellknown semantic gap, it is interesting and challenging to learn multimedia semantics by multi-feature subspace learning of different modalities. In this paper, we analyze sparse canonical correlation between feature matrices of different multimedia data, construct an isomorphic sparse multi-feature subspace;moreover, we propose subspace optimization strategy with correlation fusion, which explores both geometrical-based content correlation and graph-based semantic correlation. Our algorithm has been applied to content-based multimodal retrieval and data classification. Comprehensive experiments have demonstrated the superiority of our method over several existing algorithms. © Springer international Publishing Switzerland 2013.

关键词： Face recognition

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Multi-graph learning with positive and unlabeled bags 14

Multi-graph learning with positive and unlabeled bags

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14th SIAM international conference on data Mining, SDM 2014

作者： Wu, Jia Hong, Zhibin Pan, Shirui Zhu, Xingquan Zhang, Chengqi Cai, Zhihua Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Australia Dept. of Computer and Electrical Engineering and Computer Science Florida Atlantic University United States Dept. of Computer Science China University of Geosciences Wuhan430073 China

ISBN: (纸本)9781510811515

In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. this problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning;and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. the iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance. Copyright © SIAM.

关键词： Directed graphs

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iTB-test: An intelligent Image-enabled Diagnostic System for In Vitro Screening of Infectious Diseases 14

iTB-test: An Intelligent Image-enabled Diagnostic System for...

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14th international conference on Software, Knowledge, Information Management and Applications, SKIMA 2022

作者： Tania, Marzia Hoque Kaiser, M. Shamim Shabut, Antesar M. Abu-Hassan, Kamal Mahmud, Mufti Hossain, M.A. Institute of Biomedical Engineering University of Oxford Department of Engineering Science Oxford United Kingdom Institute of Information Technology Jahangirnagar University Savar Bangladesh Computing and Digital Industries Leeds Trinity University Faculty of Business Leeds United Kingdom School of Computing and Engineering University of West London London United Kingdom Nottingham Trent University Department of Computer Science Nottingham United Kingdom Cambodia University of Technology and Science Phnom Penh Cambodia

ISBN: (纸本)9781665493345

this paper performs an investigation into the development of an intelligent image-based automatic in vitro diagnostic system for infectious diseases using personal devices. the proposed framework of the image-based diagnostic system is demonstrated using the case study of Tuberculosis (TB)-specific antibody detection. the developed system, denoted as the iTB-test, is an intelligent bio-sensing system, comprised of a plasmonic Enzyme-Linked Immunosorbent Assay based colourimetric test in combination with an artificial intelligence-enabled image-based system. the presented system can separate the region of interest with 99.62% accuracy using clustering-based hybrid image processing algorithms, whereas the classification accuracy of antibody detection using a supervised machine learning technique is 100% based on the experiments conducted for the case study. © 2022 IEEE.

关键词： Chemical detection

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Efficient Binary Static Code data Flow Analysis Using Unsupervised learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

the ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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Cooperative learning Strategy for Human Behavior Prediction Using Multi-Modal data

Cooperative Learning Strategy for Human Behavior Prediction ...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Doyun Lee Hoon Lee Department of Intelligent Robot Engineering Pukyong National University Busan Korea Department of Electrical Engineering UNIST Ulsan South Korea

Understanding human behaviors leads to fully-automated systems in the near future. this paper investigates a deep learning solution that forecasts human activity patterns based on sensing signals measured by internet-of-things devices. Practical limitations on these small-form-factor sensors request remote deep learning services at a distant edge computing server. therefore, we need to involve impairments in sensor-server communication phases, such as random packet loss, resource constraint, and propagation noise, in the design of the remote learning architecture. To address these challenges, we propose a collaborative learning strategy among the sensors and server. Each sensor is equipped with its own encoding neural network that compresses high-dimensional sensing signals to communication messages. these are forwarded to the server through imperfect backhaul channels. then, a classifier at the server infers desired labels. A joint training mechanism of the encoders and classifier is developed along with the channel impairment. By doing so, we can obtain a robust prediction model for arbitrary communication noises. Numerical results demonstrate the viability of the proposed methods.

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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LLM-Project: automated engineering Task Planning via Generative AI and WBS Integration

LLM-Project: Automated Engineering Task Planning via Generat...

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IEEE international conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Yue Zhen Sheng Bi Shuo Tang Xing-tong Lu Wei-qin Pan Hai-peng Shi Zi-rui Chen Yi-shu Fang Xin-meng Wang South China University of Technology School of Computer Science and Engineering Guang Zhou Human centered design and engineering University of Washington Seattle Northeastern University Khoury College of Computer Sciences Boston

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

We propose and implement a task planning system that integrates a series of validated robotic instructions, deep learning models, and demonstration cases, into a larger structure using the Work Breakdown Structure (WBS) which is commonly used in engineering project management. By organizing these small-scale solutions in a sequence based on temporal and resource dependencies, we use simulations and evaluation functions to select the optimal structure as the Standard Operating Procedure (SOP). these SOPs are then used to train a generative AI, enabling it to mimic human experts in generating high-level structures according to real-world needs and perform minor parameter generalizations to address issues involving temporal dependencies, spatial relationships, and resource allocation. the generated high-level structures are gradually transformed into low-level operations to execute actual tasks. the system records the execution results for automatic or manual analysis, and the feedback is used to continuously improve the generative AI's output. Our work lays the foundation for further research on the innovative application of combining generative AI with WBS data in robotic task planning. https://***/NOMIzy/LLM-Project

关键词： Automation Generative AI Service robots Robot sensing systems Encoding Real-time systems Planning Resource management Reliability Standards

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