this paper proposes the use of artificial neural networks(ANNs) to classify human postures, using an invasive(intrusive) approach, into 6 categories namely standing, sitting, sleeping and bending - forward and backwar...
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ISBN:
(纸本)9781538666784
this paper proposes the use of artificial neural networks(ANNs) to classify human postures, using an invasive(intrusive) approach, into 6 categories namely standing, sitting, sleeping and bending - forward and backward. Human posture recognition has numerous applications in the field of healthcare analysis like patient monitoring, lifestyle analysis, elderly care etc. Most importantly, our solution is capable of classifying the aforementioned postures in real-time, by wirelessly(Wi-Fi) acquiring and processing the sensor data on a Raspberry-Pi device with minimal lag. A data-set of 44,800 samples was collected - from 3 subjects - which was used to train and test the neural *** experimenting and testing with a plethora of network architectures, an optimal neural network architecture(6-9-6) with suitable hyper-parameters was determined which gave an overall accuracy of 97.589%.
A large amount of modern healthcare data is generated through imaging, Electronic Health Report (EHR), sensor based technology and other various healthcare processes. An elaborative perspective in technological advanc...
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ISBN:
(纸本)9783319606187;9783319606170
A large amount of modern healthcare data is generated through imaging, Electronic Health Report (EHR), sensor based technology and other various healthcare processes. An elaborative perspective in technological advancement has enabled practitioners to answer questions for governance and future decision making. However, very few tools exist to critically analyze such big data for future knowledge discovery. We can further say that cloud computing technology can be a benchmark to substantiate big data which may lead to discover of hidden patterns and trends to enhance knowledge for progression of disease. this paper approached various aspects of cloud based services to enable big data analytic in healthcare data management system.
multiple fundamental frequency estimation, or Multiple-F0 estimation, is one of the most important problem on automatic music transcription, but it has not been well resolved up to now. this paper presents a supervise...
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ISBN:
(纸本)9781467313261
multiple fundamental frequency estimation, or Multiple-F0 estimation, is one of the most important problem on automatic music transcription, but it has not been well resolved up to now. this paper presents a supervised patternrecognition and machine learning methods for computer-synthesized music specifically to Multiple-F0 estimation. Computer-synthesized music is almost free from similar instruments of the differences between different individuals, so it is a good research object. It can be shown in this paper that the experimental results indicate that this method has very good recognition results.
Machine part cell formation is the group technology problem, in which the parts with near similar machining requirements are grouped into part families and the corresponding machines into machine cells. In this paper,...
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ISBN:
(纸本)9783319606187;9783319606170
Machine part cell formation is the group technology problem, in which the parts with near similar machining requirements are grouped into part families and the corresponding machines into machine cells. In this paper, a genetic algorithm with a fine tuning procedure is proposed to solve the group technology problem considering only one process plan for each part. the grouping efficacy achieved by the proposed method is comparable to the existing methods in general and better for 11.42% of the datasets.
Most of the people in the world rely on traditional medicine which is made from medicinal plants. However, very few works concentrate on automatic classification. therefore, the automatic classification of medicinal p...
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ISBN:
(纸本)9781728107882
Most of the people in the world rely on traditional medicine which is made from medicinal plants. However, very few works concentrate on automatic classification. therefore, the automatic classification of medicinal plants demands more investigation which is an important issue for conservation, authentication, and production of medicines. In this paper, for automatically classifying medicinal plants, we present a Multi-channel Modified Local Gradient pattern (MCMLGP), a new texture-based feature descriptor that uses different channels of color images for extracting more significant features to improve the performance of classification. We have trained our proposed approach using SVM classifier with various kernels such as linear, polynomial and HI. In addition, we have used different feature descriptors for comparative experimental analysis with MCMLGP by conducting the rigorous experiment on our own medicinal plants dataset. the proposed approach gain higher accuracy (96.11%) than other techniques, and significantly valuable for exploration and evolution of medicinal plants classification.
this book constitutes the refereed proceedings of the 8thinternationalconference on Intelligent computing, ICIC 2012, held in Huangshan, China, in July 2012. the 242 revised full papers presented in the three volume...
ISBN:
(数字)9783642318375
ISBN:
(纸本)9783642318368
this book constitutes the refereed proceedings of the 8thinternationalconference on Intelligent computing, ICIC 2012, held in Huangshan, China, in July 2012. the 242 revised full papers presented in the three volumes LNCS 7389, LNAI 7390, and CCIS 304 were carefully reviewed and selected from 753 submissions. the papers in this volume (CCIS 304) are organized in topical sections on Neural Networks; Particle Swarm Optimization and Niche Technology; Kernel Methods and Supporting Vector Machines; Biology Inspired computing and Optimization; Knowledge Discovery and Data Mining; Intelligent computing in Bioinformatics; Intelligent computing in patternrecognition; Intelligent computing in Image Processing; Intelligent computing in Computer Vision; Intelligent Control and Automation; Knowledge Representation/Reasoning and Expert Systems; Advances in Information Security; Protein and Gene Bioinformatics; Soft computing and Bio-Inspired Techiques in Real-World Applications; Bio-Inspired computing and Applications.
One of the fundamental tasks of patternrecognition is pattern matching. It is the act of checking for presence of a pattern's constituents within token image to have exact match. For that, most distinctive fiduci...
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ISBN:
(纸本)9781509030385
One of the fundamental tasks of patternrecognition is pattern matching. It is the act of checking for presence of a pattern's constituents within token image to have exact match. For that, most distinctive fiducial features of pattern have to be assessed and searched in the sliding windows of same pattern size formed by logically dividing the token scene image. As huge numbers of sliding windows are to be checked withpattern, pattern matching process should be time efficient and to increase pattern matching accuracy impacts due to illumination, resolution, occlusion and pose variation must be reduced. For pattern matching, this paper presents a novel local feature descriptor, multi variant symmetric local graph structure (MVSLGS) taking into account symmetric local graph structure (SLGS) as precedent approach. the computational adequacy of the proposed approach is tested on two publicly available databases with high matching accuracy, showing its proficiency over the process of pattern matching.
Image matching is a key component in computer vision and has many applications. In this work, we propose a circular shifting binary descriptor to increase the speed of rotation invariant image matching algorithms. We ...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Image matching is a key component in computer vision and has many applications. In this work, we propose a circular shifting binary descriptor to increase the speed of rotation invariant image matching algorithms. We can compute the descriptors of the rotated image patches without rotating either the sample points or the image patch by circularly shifting the binary descriptor. thus, operations such as multiplications and divisions from the orientation estimation step are eliminated from the image matching process which significantly reduces the number of operations for computingthe descriptor. In addition, our experiments illustrate that the circular shifting binary descriptor shows limited rotation error in comparison with other descriptors such as ORB while attaining comparable mean average precision.
In spite of many years of work by scientists and specialists on various software qualities, testing stays one of the most broadly honed and concentrated on methodologies for evaluating and improving software quality. ...
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ISBN:
(纸本)9783319606187;9783319606170
In spite of many years of work by scientists and specialists on various software qualities, testing stays one of the most broadly honed and concentrated on methodologies for evaluating and improving software quality. Our objective, in this paper, is to present how optimization techniques provide solutions to different and difficult issues in different areas of software engineering. Optimization algorithms are mathematical procedures, which intends to best optimal results for the defect, fault, failure to accomplish tractability, strength, and low arrangement cost. In this paper, a comprehensive overview of software testing and metrics based on soft computing and optimization techniques is presented. In this survey, we try to explain some major problems like defect prediction, software fault prediction and their solutions by soft computing and optimization algorithms. the paper presents an overview of the usage of Mathematical optimization Algorithms and soft computing approaches.
Biometrics is a method to identify a person. However, there are several biometric techniques including face recognition, palm recognition, iris recognition, fingerprint recognition, and so on. there is a new approach ...
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ISBN:
(纸本)9781538663240
Biometrics is a method to identify a person. However, there are several biometric techniques including face recognition, palm recognition, iris recognition, fingerprint recognition, and so on. there is a new approach in biometrics, i.e., identification using full-body movement. In this paper, we introduce a full-body movement in human identification using three Kinects. In particular, we utilize the string grammar fuzzy-possibilistic C-medians (sgFPCNIed) to group string sequences from skeleton frames into pose string, then group the pose string sequences of each person into multi-group to create multi prototypes for each person. the K-nearest neighbor is used to identify the person in the test process on 27 subjects. the system yields 73.33% correct classification on the best validation set of four-fold cross validation.
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