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Every Drop Counts: Unleashing the prospective locations for Water Harvesting using Geospatial Analytics 17

Every Drop Counts: Unleashing the prospective locations for ...

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international conference on Internet of Things and machine learning (IML)

作者： Gupta, Aparana Garg, Anshul Rawat, Namrata Chigurupati, Sandeep Kumar, Dinesh U. Indian Inst Management Bangalore Karnataka India

ISBN: (纸本)9781450352437

Water is at the heart of 'Sustainable Development Goals (SDGs)'set by United Nations- with an objective to balance the three dimensions of sustainable development: Environment, Social and Economic - and is indirectly associated with the success of all the other Goals. But, with changing climatic patterns, untimely rains, prolonged dry spells, depleting ground water and drought making every drop of water extremely precious, the need of the hour is to gauge and work towards the major aspects of water harvesting- 'Catchment'. Water Harvesting must be a key element of any strategy to bring an end to India's perennial swings between drought and flood and to meet the following SDGs for sustained development. This study presents a structured and meticulous approach, wielding `Geospatial Analytics'to identify the prospective locations for Water Harvesting in arid and semi-arid parts of the country for sustainable *** paper is structured as follows. Section 1 describes the background and motivation for this idea. Section 2 details out the objective. In section 3 we present the 'Literature Survey 'on the work that has already been carried out in this field. While section 4 discerns our area of study, Section 5 provides process flow starting from Data gathering, Data extraction,Data pre-processing, Model selection and Multi Criteria Decision Making (Model Application). In Section 6, we present and validate our experimental results achieved using the proposed methodology. Section 7 concludes our study followed by Section 8 on Recommendations for future enhancements and next steps.

关键词： Geospatial Analytics AHP Analytical Hierarchy Process LAND SAT-8 Digital Elevation Model image processing Rain Water Harvesting Water Tanks RWH optimum location selection GIS Sliding Window Algorithm Flood Fill Model Smart Water

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Hybrid Segmentation Technique Using Wavelet Packet and Watershed Transform for Medical images 1st

Hybrid Segmentation Technique Using Wavelet Packet and Water...

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1st international conference on Advances in Computing and Data Sciences (ICACDS)

作者： RajMohan, K. Thirugnanam, G. Mangaiyarkarasi, P. Annamalai Univ Dept Elect & Instrumentat Engn Chidambaram Tamil Nadu India

ISBN: (纸本)9789811054273;9789811054266

image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.

关键词： Magnetic resonance imaging Wavelet packet transform Watershed algorithm image segmentation

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Neuronal Texture Analysis in Murine Model of Down's Syndrome 16th

Neuronal Texture Analysis in Murine Model of Down's Syndrome

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14th international Work-conference on Artificial Neural Networks (IWANN)

作者： Sarmiento, Auxiliadora Angel Fernandez-Granero, Miguel Galan, Beatriz Luz Montesinos, Maria Fondon, Irene Univ Seville Dept Signal Theory & Commun C Descubrimientos S-N Seville 41092 Spain Univ Cadiz Dept Syst & Automat Engn Elect Technol & Elect Campus Univ Rio San Pedro S-N Cadiz 11510 Spain Univ Seville Dept Med Phisiol & Biophys Avda Dr Fedriani S-N E-41009 Seville Spain

ISBN: (纸本)9783319591476;9783319591469

An alteration of neuronal morphology is present in cognitive neurological diseases where learning or memory abilities are affected. The quantification of this alteration and its evolution by the study of microscopic images is essential. However, the use of advanced and automatic image processing techniques is currently very limited, focusing on the analysis of the morphology of isolated neurons. On this article we present a new methodology, based on texture analysis, to characterize the global distribution of different neural patterns in immunofluorescence images of brain tissue sections, where the neurons can be visualized as they are really distributed. We apply the technique to mice brain tissue section dividing them into two classes: Ts1Cje Down's syndrome model and wild type, free of this neurodegenerative disease. Taking into account CA1 region of the hippocampus, we calculate and compare several state of the art texture descriptors that are subsequently classified using machine learning techniques. Achieving a 95% of accuracy, the assumption that texture characterization is relevant to quantify globally morphological alterations in the neurons, seems to be demonstrated.

关键词： Texture analysis Down's syndrome pattern recognition machine learning

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IOP conference Series: Materials Science and Engineering

IOP Conference Series: Materials Science and Engineering

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1st international conference on Material Engineering and Advanced Manufacturing Technology, MEAMT 2017

The proceedings contain 20 papers. The topics discussed include: failure investigations of failed valve plug SS410 steel due to cracking;facial synthesis of nanostructured ZnCo2O4on carbon cloth for supercapacitor application;dual lead-crowning for helical gears with anti-twist tooth flanks on the internal gear honing machine;parameter optimization of electrochemical machining process using black hole algorithm;analysis on the cost structure of product recall for reverse supply chain;destructive behavior of iron oxide in projectile impact;the performance of double layer structure membrane prepared from flowing coagulant;digital image processing techniques for the analysis of fuel sprays global pattern;DECO FRECASE (drywall eco-friendly from eggshell and cane bagasse) as an innovation of eco-friendly interior construction;size effects in micro rolling of metals;a study of commuters' decision-making when delaying departure for work-home trips;thermodynamic analysis of steam-injected advanced gas turbine cycles;computer-aided solvent selection for multiple scenarios operation of limited-known properties solute;measurements of K x-ray fluorescence cross-sections, fluorescence yields, level widths and radiative vacancy transition probabilities for the elements Zr, Mo, Cd, Er at 59.54 keV;and enhanced definition and required examples of common datum imposed by ISO standard.

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Detection of Lung Nodules on Temporal Subtraction images Using 3D Sparse Coding 17

Detection of Lung Nodules on Temporal Subtraction Images Usi...

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17th international conference on Control, Automation and Systems (ICCAS)

作者： Tanaka, Takahisa Miyake, Noriaki Lu, Huimin Tan, Joo Kooi Kim, Hyoungseop Murakami, Seiichi Aoki, Takatoshi Hirano, Yasushi Kido, Shoji Kyushu Inst Technol 1-1 Sensui Kitakyushu Fukuoka 8048550 Japan Univ Occupat & Environm Hlth Dept Radiol Fukuoka Japan Yamaguchi Univ Grad Sch Sci & Technol Innovat Yamaguchi Japan

ISBN: (纸本)9788993215144

In recent years, the death rate caused by lung cancer is increasing. To detect the lung cancer, multi detector-row computed tomography (MDCT) images are used in visual screening. Lung cancer can be easily detected by using the chest MDCT images, however, it has enormous images and burden to radiologists. Research and development of the computer aided diagnosis (CAD) system have been assisted the diagnosis. As one of the CAD technologies, temporal subtraction technique is possible to emphasize the changing interval on the CT images. It uses subtraction operation between previous and current CT images of the same patient. On the other hand, pattern recognition using image reconstruction by sparse coding method has attracted attention. This technique is mathematically modeling the information processing by the primary visual cortex of human. It is the technique for representing images by the linear combination of a small number of basis. In this paper, candidate nodules under 20[ mm] were segmented from temporal subtraction images based on the 3D sparse coding technique. 3D sparse coding is three dimensional expansion of the sparse coding. Also, we classified the final candidate nodules using support vector machine (SVM) method based on coefficient matrix which are obtained by the 3D sparse coding. We applied proposed method to 31 cases of chest MDCT images in which the number of nodules was more than one. We achieved experimental result with true positive rates (TPR) of 70.2[[%], and false positive rates (FP) of 34.7[/scan], respectively.

关键词： Computer Aided Diagnosis Temporal Subtraction Technique Sparse Coding Support Vector machine OMP K-SVD

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Development of biometrie palm vein trait based person recognition system: Palm vein biometrics system

Development of biometrie palm vein trait based person recogn...

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international conference on Intelligent Systems and Information Management (ICISIM)

作者： Shriram D. Raut V. T. Humbe Arjun V. Mane Dept. of Computer Science Solapur University Solapur Solapur India School of Technology S. R. T. M. University Latur India Dep. of Digital and Cyber Forensic Govt. Institute of Forensic Science Nagpur India

ISBN: (纸本)9781509042654;9781509042647

Biometrie Authentication is the main stream to attract attention of researcher to develop algorithm for data and security concern. The palm vein biometric is emerging as the most promising physiological characteristic to develop efficient recognition system. This paper discuss about the new dimension to generate biometric trait key rather a template free key generation extracted by means of rigorous pattern recognition and information security tactics. The generation of key is exercised through mapping of certain digital image processing operation, distance metric computation and information security policies. The model of recognition system proposed that includes phases such as feature extraction and detection followed by the development of recognition technique based on unique and distinct detected palm vein feature characteristics. The proposed work gives novel and robust algorithm for the recognition of the subject. The experimental work gives result with 99.47% high rate of accuracy for the recognition of the subject.

关键词： Veins Feature extraction image edge detection Blood Biomedical imaging Gabor filters Euclidean distance

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Remote Sensing image Object recognition Based on Convolutional Neural Network 1

Remote Sensing Image Object Recognition Based on Convolution...

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1st international conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Zhen, Yumei Liu, Huanyu Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

The development of space remote sensing technology brings a lot of remote sensing image data. The traditional target detection method is difficult to adapt to the large amount of high-resolution remote sensing image data. It is necessary to find a way to automatically learn the most effective features from the image data, and to fully recover the correlation between the data. Based on the recognition of the typical targets in remote sensing image data, this paper proposes a method of remote sensing target recognition based on deep learning. In this paper, the pre-train method is introduced to improve the simulation of the model. The experiment of the test set proves the validity of the method.

关键词： deep learning object recognition pre-train

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image Segmentation and Object recognition Using machine learning 1st

Image Segmentation and Object Recognition Using Machine Lear...

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1st international conference on Smart Trends for Information Technology and Computer Communications (SmartCom)

作者： Sood, Ashima Sharma, Sahil Punjabi Univ Reg Ctr Mohali India Thapar Univ Patiala Punjab India

ISBN: (纸本)9789811034336;9789811034329

The digital image processing is a fast pace growing field which requires pre-processing of the images before their actual usage and experimentation. image Segmentation and Object recognition is one of the phase which requires the prior information about the object to give the extracted attributes as output of the images. Here, in the present research work we have already some outdoor segmented images which have been identified as to which category of classification they belong. This work has been accelerated with the help of machine learning that is a novel technique to identify the objects with outstanding accuracy. The machine learning algorithms have aided in this process of identifying the objects like sky, brick, cement, grass etc. This collaboration of image segmentation with machine learning has proved to be accessible in large datasets where after segmentation images can classify themselves into a category provided it has attributes of the images.

关键词： image segmentation Object recognition machine learning Classification Ensemble learning

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Comprative study with Analysis of OCR Algorithms and Invention Analysis of Character recognition Approched Methodologies 1

Comprative Study with Analysis of OCR Algorithms and Inventi...

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1st IEEE international conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Henge, Santosh Kumar Rama, B. Kakatiya Univ Dept Comp Sci Warangal Telangana State India

ISBN: (纸本)9781467385879

OCR is the most active, interesting evaluation invention of text cum character processing recognition and pattern based image recognition. In present life OCR has been successfully using in finance, legal, banking, health care and home need appliances. The OCR consists the different levels of processing methods like as image Pre Acquisition, Classification, Post-Acquisition, Pre-Level processing, Segmented processing, Post-Level processing, Feature Extraction. The many researchers are proposed various levels of different methodologies and approaches in different versions of languages with help of modern and traditional technologies. This paper expressed the detail study and analysis of various character recognition methods and approaches: in details like as flow and type of approached methodology was used, type of algorithm has built with support of technology has implemented background of the proposed methodology and invention best outcomes flow for the each methodology. This paper and also expressed the main objectives and ideology of various OCR algorithms, like as neural networks algorithm, structural algorithm, support vector algorithm, statistical algorithm, template matching algorithm along with how they classified, identified, rule formed, inferred for recognition of characters and symbols.

关键词： Support Vector machine (SVM) Optical Character recognition (OCR) Artificial Neural Network (ANN) pattern Matching Algorithms (PMA) stastical Algorithm (SstA) strctural Algorithm (SA) Freeman Chain Code (FCC) Digitized-Retrievable Rorm (DRF) Feedforwardneural Network (FINN) Feedback-neural Network (FbNN) Top-down-hierarchical-histogram (THH) Mirror image learning (MIL)

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Orange Sorting by Applying pattern recognition on Colour image 1

Orange Sorting by Applying Pattern Recognition on Colour Ima...

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1st international conference on Information Security and Privacy

作者： Jhawar, Jyoti JD Coll Engn & Management Nagpur Maharashtra India

Manual sorting/grading of oranges is done at wholesale markets/food processing factories based upon its maturity, size, quality and breeds. With an aim to replace the manual sorting system, this paper proposes the research work for automated grading of Oranges using pattern recognition techniques applied on a single color image of the fruit. This research is carried out on 160 Orange fruits collected from varied geographical locations in Vidarbha Region of Maharashtra. System designed can automatically classify an Orange fruit from this region, given its single color image of 640 x 480 pixel resolution, taken inside a special box designed with 430 lux intensity light inside it, by a digital camera. Only 4 features are used to classify oranges into 4 different classes according to the maturity level and 3 different classes as per size of oranges. In this paper two novel techniques based on pattern recognition are proposed Edited Multi Seed Nearest Neighbor Technique and Linear Regression based technique;although Nearest Neighbor Prototype technique is also deployed. Linear Regression based technique can explicitly predict the maturity of the unknown orange fruit, enabling classification into multiple classes with desired lifespan. Experimental results indicate success rate up to 90 % and 98 %. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Orange Sorting pattern recognition Color image processing

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