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Age Estimation from Face Images: Challenging Problem for Audience Measurement Systems 16

Age Estimation from Face Images: Challenging Problem for Aud...

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16th FRUCT Conference of Open-Innovations-Association

作者： Khryashchev, Vladimir Ganin, Alexander Stepanova, Olga Lebedev, Anton Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9785757704890

The real-time audience measurement system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and in-cloud data statistics analysis. The challenging part of such system is age estimation algorithm on the basis of machine learning methods. The face aging process is determined by different factors: genetic, lifestyle, expression and environment. That is why same age people can have quite different rates of facial aging. We propose a novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification. Experimental results on the FG-NET, MORPH and our own database are presented. Human perception ability in age estimation is studied using crowdsourcing which allows a comparison of the ability of machines and humans.

关键词： face recognition feature extraction image classification learning (artificial intelligence) object detection object tracking statistical analysis support vector machines FG-NET database MORPH database adaptive feature extraction age classification stage age estimation audience measurement system crowdsourcing environment factor expression factor face detection stage face images face tracking stage facial aging gender recognition stage genetic factor in-cloud data statistics analysis stage lifestyle factor local binary pattern machine learning method support vector machine classification algorithm design and analysis Classification algorithms Databases Estimation Face Testing Training Accelerated aging Support vector machine classification Feature extraction algorithm design and analysis Face Face sorting algorithm Facial Recognition Genetic factors Object detection Image classification Support Vector Network Database Crowdsource binary pattern

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Automatic Design algorithm of a Robotic End-Effector for a set of Sheet-Metal Parts 7

Automatic Design Algorithm of a Robotic End-Effector for a s...

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Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) And Robotics, Automation and Mechatronics (RAM)

作者： Sintov, Avishai Shapiro, Amir Ben Gurion Univ Negev Dept Mech Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9781467373388

Robotic end-effectors in automated production lines are specially designed and built for a unique task and for a particular part. An end-effector capable of grasping different parts with different geometries will expand its benefit and reduce costs. This work focuses on the grasp of sheet metal parts for the automotive industry. In order to maximize the use of a single end-effector, this paper proposes a search algorithm for a simple common grasp configuration. Such configuration imply for an end-effector design capable of grasping a set of sheet metal parts. The algorithm maps possible grasps which are candidate to be common. We define a novel quality measure estimating the distribution of the contacts across the sheet metal part. Candidate grasps which have a sufficient quality are mapped into high-dimensional feature vectors. These feature vectors parameterize the geometry of the polyhedron formed by the contacts locations and the direction of the surface normals relative to the polyhedron. Thereby, they describe the design of the end-effector compatible to the grasp. A database is generated for all possible grasps for each part in the feature vector space. A similarity join based on nearest-neighbor search and classification algorithm are used for intersecting all possible feature vectors over all sheet metal parts and finding common ones. Simulations of a 3-finger grasp on four meshed sheet metal parts resulted in a common grasp. Results of the simulations validate the feasibility of the proposed algorithm.

关键词： Sheet metal components Grasp Eigenvector end effectors polyhedrons Nearest neighbor query surface normal SEARCHING algorithm Computer-Aided Design Design algorithm metal parts sorting algorithm

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New approach for feature selection based on rough set and bat algorithm 9

New approach for feature selection based on rough set and ba...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Emary, E. Yamany, Waleed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Fayoum Univ Fac Comp & Informat Al Fayyum Egypt SRGE Cairo Egypt

ISBN: (纸本)9781479965946

This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using VCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.

关键词： data reduction feature selection optimisation pattern classification rough set theory BA bat algorithm classification performance fitness function optimization reduction size rough set Accuracy algorithm design and analysis Classification algorithms Equations Indium phosphide Integrated circuits Feature extraction Rough sets algorithm design and analysis Indium phosphide sorting algorithm barium Rough set theory Pattern recognition Bats data reduction bat Chiroptera Dataset

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Classification of Leakage Current Waveforms using Wavelet Packet Transform on High Voltage Insulator 4

Classification of Leakage Current Waveforms using Wavelet Pa...

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International Conference on High Voltage Engineering and Application (ICHVE)

作者： Chaou, A. K. Mekhaldi, A. Moula, B. Teguar, M. Ecole Natl Polytech Alger Lab Rech Electrotech 10 Ave Hassen BadiBP 182 Algiers 16200 Algeria

ISBN: (纸本)9781479966134

In this paper, the Wavelet Packet Transform (WPT) for Leakage Current (LC) examination on high voltage insulators under pollution conditions is exposed. Based on laboratory experiments under various artificial solution natures (consisting in a mixture of distilled water with NaCl, Kaolin or Kieselguhr), LC acquisition is firstly carried out. After a careful examination, three groups of LC waveforms are constituted depending on their peak values. Then, WPT is used to decompose LC waveforms. From this decomposition, feature extraction by energy calculation is processed. Hence, a feature vector, composed of wavelet coefficients energies values, is used as input for three classification algorithms consisting in K-Nearest Neighbors, Naive Bayes and Support Vector Machine, to distinguish between three LC groups. Indeed, this paper introduces WPT for LC investigation and classification.

关键词： Bayes methods feature extraction insulators leakage currents power engineering computing support vector machines wavelet transforms K-nearest neighbor algorithm LC acquisition LC examination Naive Bayes algorithm WPT energy calculation feature vector high voltage insulator leakage current waveform classification pollution condition support vector machine wavelet packet transform Classification algorithms Discrete wavelet transforms Feature extraction Insulators Pollution Wavelet packets Feature extraction Wavelet packets insulators Support Vector Network sorting algorithm K-nearest neighbor algorithm High Voltage Eigenvector discrete wavelet transform Power engineering computing Bayes methods wavelet transforms leakage currents line controller

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A Novel Parallel Approach of Radix Sort with Bucket Partition Preprocess

A Novel Parallel Approach of Radix Sort with Bucket Partitio...

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14th IEEE International Conference on High Performance Computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS)

作者： Zhang, Keliang Wu, Baifeng Fudan Univ Sch Comp Sci Shanghai 201203 Peoples R China

ISBN: (纸本)9780769547497

Radix sort is an important sorting algorithm which is widely used in applications such as binary search and database. The most important advantage of radix sort is its time complexity is O(n), lower than other sorting algorithms based on comparison operation. However, a factor that hampers its application is long execution time of its loop body. In this paper, based on data level parallelism idea, we present a new parallel radix sort algorithm. In this algorithm, each iteration of loop can be fully parallelized via thounds of concurrent threads, eliminating the original performance bottleneck. Furthermore, it is a scalable algoirthm, which means the performance of algorithm can be linearly improved with the increase of core number of modern many-core processors. Experiment results show the efficiency of the algorihtm.

关键词： radix sorting sorting algorithm caliber number Binary search algorithm efficiency Loops execution time complexity classes algorithms ALEMBIC buckets

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Atrial Fibrillation Classification Using QRS Complex Features and LSTM 44

Atrial Fibrillation Classification Using QRS Complex Feature...

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44th Computing in Cardiology Conference (CinC)

作者： Maknickas, Vykintas Maknickas, Algirdas Tesonet LLC Vilnius Lithuania Vilnius Gediminas Tech Univ Inst Mech Sci Fac Mech Vilnius Lithuania Vilnius Gediminas Tech Univ Fac Fundamental Sci Dept Informat Technol Vilnius Lithuania

ISBN: (纸本)9781538645550;9781538666302

Classification of Atrial Fibrillation from diverse electro-cardiographic (ECG) signals is the challenging objective of the 2017 Physionet Challenge. We suggest a Long Short Term Memory (LSTM) network, which learns patterns directly from pre-computed QRS complex features that classifies ECG signals. Although our architecture is considered deep, it only consists of 1791 parameters. The result is an accurate, lightweight solution that classifies ECG records as Normal, Atrial fibrillation, Other or Too noisy with final challenge score of 0.78.

关键词： Electrocardiography Feature extraction Computer architecture Atrial fibrillation Classification algorithms Noise measurement Heart atrial fibrillation Electrocardiography Noise measurement sorting algorithm Computer Architecture Feature extraction QRS complex Heart electrochemical grinding

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Shot Classification for Association Football from TV Broadcasting

Shot Classification for Association Football from TV Broadca...

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IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Siles, Francisco Univ Costa Rica Fac Engn Sch Elect Engn PRIS LAB Pattern Recognit & Intelligent Syst Lab San Jose Costa Rica

ISBN: (纸本)9781479934423

The paper presents the shot classification algorithm used to classify the scenes from football TV broadcasting, to extract only the far view scenes. The far-view scenes are important for the segmentation and tracking of the players during the game, required for devising an abstract model for the interpretation of the game. The system for developing such a platform is named ACE-Football, and is currently being developed at the PRIS-LAB. Results tested using whole games of the latest FIFA's World Cup (FWC) are presented, and the classification algorithm validated using standard metrics, such as precision, recall and F1-score.

关键词： television broadcasting Footballs sorting algorithm Game theory shot Game animals Players Hunting

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Entropy Chain Multi-Label Classifiers for Traditional Medicine Diagnosing Parkinson's Disease

Entropy Chain Multi-Label Classifiers for Traditional Medici...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Peng, Yue Fang, Ming Wang, Chongjun Xie, Junyuan Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

ISBN: (纸本)9781467367981

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi -label dataset. Classifier Chains(CC) is a popular multi -label classified algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi -label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

关键词： Biology Biomedical imaging Integrated circuits Senior citizens aged person senior citizen Biomedical imaging Parkinson Disease Degenerative Diseases, Central Nervous System Chinese medicine Biological Science Disciplines biology Integrated circuits Electronic circuits Classifiers sorting algorithm

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An Improved RRT algorithm Incorporating Obstacle Boundary Information

An Improved RRT Algorithm Incorporating Obstacle Boundary In...

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IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Wang, Jiankun Li, Xintong Meng, Max Q. -H. Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509043644

Rapidly-Exploring Random Trees (RRT) is a sampling-based path planning method, which aims at finding the feasible path using a randomized data structure. But RRT usually can not find the optimal path. Although RRT* is an asymptotically optimal algorithm, its planning time scales poorly. In this paper, we propose an improved RRT algorithm incorporating obstacle boundary information to deal with these two problems. We classify obstacles in the environment into valuable and valueless obstacles. For the obstacle placed between the start and goal positions, it is regarded as valuable and we use its boundary information to make the path planner sample points around it to avoid other bad sampling points. Then the random tree will grow more intentionally to the goal position. By applying Partially Observed Markov Decision Process (POMDP) in our algorithm, we also can prove in theory that obstacle boundary information really improves RRT algorithm. In our experiments, we show that our algorithm can find a better feasible path faster than RRT.

关键词： Robot sensing systems Planning Path planning Service robots Classification algorithms Uncertainty REGIONAL RESPONSE TEAMS Manipulator sensing systems Service robots sorting algorithm Path planning algorithms barriers Uncertainty

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Comparing classification models in the final exam performance prediction

Comparing classification models in the final exam performanc...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Gamulin, Jasna Gamulin, Ozren Kermek, Dragutin Univ Zagreb Sch Med Salata 3 Zagreb 41001 Croatia Univ Zagreb Sch Med Dept Phys & Biophys Salata Zagreb Croatia Univ Zagreb Fac Org & Informat Dept Comp & Technol Dept Theoret & Appl Foundat Informat Sci Varazhdin Croatia

ISBN: (纸本)9789532330816

The use of Learning Management Systems (LMS) and the web-based formative and summative assessments during the traditional teaching in classroom provides the huge amount of data on students' behavior and results at the point of time when the course is still in progress. This data could be used for the final exam performance prediction so that the excellent as well as the students requiring help could be detected. The data on 302 students enrolled into the first year Physics course of a biomedical university study program in 2011/2012 have been used. The data were preprocessed by dividing Croatian grading system which comprises 5 grades (range 1-5;1=fail, 5=excellent) into 2 categorical classes in one version of the experiment and into 3 categorical classes in the second version. Several up to date algorithms for classification were applied without and with attributes optimization by a genetic algorithm. Also, since students have 4 chances at 5 exam terms during an academic year, 3 different dependent variables were constructed. In order to evaluate the performance that each of the classification models had the following performance criteria were used: accuracy for all models and sensitivity and area under curve (AUC) for binary classifier systems.

关键词： Accuracy Classification algorithms Data models Education Genetic algorithms Predictive models Support vector machines sorting algorithm data models Genetic algorithm (GA) Prediction models Support Vector Network Education performance prediction Students Performance metrics

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