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Music Tagging and Similarity Analysis for Recommendation System 1st

Music Tagging and Similarity Analysis for Recommendation Sys...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Kumar, Laxman Mitra, Anirban Mittal, Mamta Sanghvi, Vatsal Roy, Sudipta Setua, Sanjit Kumar Department of Computer Science and Engineering U.V. Patel College of Engineering Ganpat University Mehsana Kherva384012 India Department of Computer Science and Engineering Academy of Technology AdisaptagramWest Bengal712121 India Department of Computer Science and Engineering Calcutta University Technology Campus JD-2 Sector-III Salt Lake Kolkata700098 India Department of Computer Science & Engineering G. B. Pant Government Engineering College Okhla New Delhi110020 India Mallinckrodt Institute of Radiology Department Washington University in St. Louis 510 South Kingshighway Boulevard St. LouisMO63110-1076 United States

ISBN: (纸本)9789811390418

Many of the websites follow the system of retrieving and recommending music based on the metadata. Metadata is generally a text file that attached to the music file has title and genre. Without attached metadata, it is very difficult for such websites to recommend or retrieve music. A regularly utilized rundown of the fundamental components incorporates pitch, timber, surface, volume, span, and frame. In the proposed methodology to process such a vast amount of data, the distributed storage and data processing systems like Hadoop and Spark has been used. Hadoop Distributed File System has been used for storing the music files and extracting feature information. Kafka queues has been used for asynchronous feature extraction in the background and finally Spark has been used for feature analysis using machine learning algorithms. This Proposed automated system for assigning genres for music provides very promising accuracy with a high true positive value. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Metadata

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Flame Temperature Prediction Using machine learning Model 1

Flame Temperature Prediction Using Machine Learning Model

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1st IEEE India Council international Subsections conference, INDISCON 2020

作者： Agrawal, Goutam Mishra, Rutuparnna Ransingh, Anshit Chakravarty, S. Centurion University of Technology and Management Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781728187341

The intensity or amount of heat present in any material, substance, or object is known as temperature. The process of measuring temperature is a tiresome and complex task from any visible heat source. The process of measuring temperature is known as thermometry. It plays a vital role in various industrial and manufacturing processes. There are several devices or gadgets present which are used for measuring temperature like a thermistor, Resistance Temperature Detector (RTD), infrared thermometer, thermocouples, pyrometers, etc. Every temperature measuring instrument has its demerits. While measuring temperature in some devices, one must be very alert because it is a necessity to check that the temperature of the material or substance should be less than or equal to the instrument temperature. In some instruments, the high temperature reduces productivity, and the efficiency of the sensors present in it. Some devices face the drawback of difference in temperature because in such types of devices there is a threshold temperature. If the temperature exceeds the threshold temperature in such a case, the measured temperature will differ with the temperature of the system. Under such circumstances, it will deviate from the original heat transfer property. To overcome all these drawbacks a machine learning model is proposed to detect approx. temperature using the color-temperature correlation approach. In this proposed system, histogram backprojection is used for pre-processing of the input image to derive the color of the flame. To predict the temperature, Support Vector machine (SVM) and Artificial Neural Network (ANN) have been used and compared. Simulation results show that Support Vector machine outperforms Artificial Neural Network. © 2020 IEEE.

关键词： Neural networks

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Classifications of High-Resolution SAR and Optical images Using Supervised Algorithms 1st

Classifications of High-Resolution SAR and Optical Images Us...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Balnarsaiah, Battula Prasad, T.S. Parayitam, Laxminarayana Penta, Balakrishna Patibandla, Chandrasekhar Research and Training Unit for Navigational Electronics Osmania University HyderabadTelangana India HyderabadTelangana India Department of Geo-Engineering Andhra University VisakhapatnamAndhra Pradesh India

ISBN: (纸本)9789811390418

Synthetic aperture radar (SAR) images (microwave images) and optical ones have been recognized as important sources to study land use and land cover. The aim of this study is to create land use/cover classification using the maximum likelihood (ML) and support vector machines (SVM) algorithms. Essential geo corrections were applied to the images at the pre-processing stage. To evaluate both the classified images, the metrics of overall accuracy and kappa coefficient were used. The so-evaluated accuracy assessment results demonstrated that the SVM algorithm gave an accuracy of 88.94 and 77.89% in optical and SAR images, respectively, and the kappa coefficients in the same order being 0.87 and 0.75 approximately. The kappa coefficient of the SVM is higher than that of the ML algorithm, both in the case of optical and microwave classified data. Therefore, the SVM algorithm is suggested to be used as an image classifier for both optical and SAR (microwave) high-resolution images. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image classification

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M-unet: Modified u-net segmentation framework with satellite imagery 1

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1st international conference on Global AI Congress, GAIC 2019

作者： Soni, Ashish Koner, Radhakanta Villuri, Vasanta Govind Kumar Dhanbad Dhanbad826004 India Dhanbad Dhanbad826004 India

ISBN: (数字)9789811521881

ISBN: (纸本)9789811521874

In recent years, convolution neural network (CNN) has emerged as a dominant paradigm in machine learning for image processing application. In remote sensing, image segmentation is a very challenging task, and CNN has shown its worth over traditional image segmentation methods. U-Net structure is one of the simplified architectures used for image segmentation. However, it cannot extract promising spatial information from satellite data due to insufficient numbers of layers. In this study, a modified U-Net architecture has designed and proposed. This new architecture is deep enough to extract contextual information from satellite imagery. The study has formulated the downsampling part by introducing the DenseNet architecture, encourages the use of repetitive feature map, and reinforces the information propagation throughout the network. The long-range skip connections implemented between downsampling and upsampling. The quantitative comparison has performed using intersection over union (IOU) and overall accuracy (OA). The proposed architecture has shown 73.02% and 96.02% IOU and OA, respectively. © Springer Nature Singapore Pte Ltd. 2020.

关键词： image segmentation

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Virtual learning surveillance processing 1

Virtual learning surveillance processing

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1st international conference on Advances in Physical Sciences and Materials 2020, ICAPSM 2020

作者： Jacintha, V. Murugan, K H Shakthi Gomathi, M. Gency, T. Jenifer, J. Jesy, J. Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Jeppiaar Maamallan Engineering College Sriperumpudur Tamil Nadu India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India

Virtual learning is an environment where trainees make use of computerized technologies for accessing educational modules exterior to conventional classrooms consigned completely through online. Though it takes over a huge benefit today, learners could lose their concentration easily. To overcome this major drawback, we put forward the idea of surveillance of eye gaze. This uses the technique of Feature extraction method called Kalman filter for gaze movement process. First the eye area is located by Haar's cascade classifier and once found, the classification is done with the trained datasets which is done through support vector machine (SVM). The salient patches on the image of the face which detects the state of emotions facial landmarks using automatic follow free facial landmark detectioing technique. This gives the result of the attentiveness of the learner by moving to the next webpage if they losses their concentration over it. This technique gives rise to better performance in an e-learning process by almost 3% when compared to the existing system. Thus the eye gazing process increases a better environment for virtual learning process. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Smiling and non-smiling emotion recognition based on lower-half face using deep-learning as convolutional neural network

Smiling and non-smiling emotion recognition based on lower-h...

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1st international Multi-Disciplinary conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020

作者： Malallah, Fahad L. Al-Jubouri, Ahmed A. Sabaawi, Abdulbasit M. Shareef, Baraa T. Saeed, Mustafa G. Yasen, Khaled N. Computer and Information College of Electronics Engineering Ninevah University Mosul Iraq Department of Information Technology College of Information Technology Ahlia University Manama Bahrain Cihan-Slemani University College of Science Department of Computer Science Iraq Department of Computer Science Cihan University-Erbil Erbil44001 Iraq

ISBN: (纸本)9781631902611

image understanding is considered important among researchers. In this paper, a new technique is proposed to classify a detected face into two classes as a smile or non-smile category. First, the system detects and segments only the face. Then, it converts the image from RGB to Gray-Scale and enhances the image via an equalization technique. Where the contribution of this research is depending only on the lower half of the face since most of the smiling information can be perceived from the mouth and its perimeter. Then, a convolutional neural network (CNN) is applied to generate two output nodes. Public GENKI-4K database is used for the experiments, which contains 4000 challenged face images. The results demonstrate that the accuracy with 4-Fold cross-validation is 91%. This approach achieves a promising performance as compared with the state-of-the-art techniques in both accuracy and processing time. © 2020 EAI.

关键词： Deep learning

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Word-Spotting approach using transfer deep learning of a CNN network

Word-Spotting approach using transfer deep learning of a CNN...

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Communications, Control Systems and Signal processing (CCSSP), 2020 1st international conference on

作者： Ryma Benabdelaziz Djamel Gaceb Mohammed Haddad Computer Science Modeling Optimization and Electronic Systems Laboratory (LIMOSE) UMBB Boumerdes Algeria Lab LIRIS UMR CNRS 5205 University of Claude Bernard Lyon 1 Villeurbanne Algeria

ISBN: (数字)9781728158358

ISBN: (纸本)9781728158365

Convolutional Neural Networks (CNNs) are deep learning models that are trained to automatically extract the most discriminating features directly from an input image to be used for visual classification tasks. Recently, CNNs attracted a lot of interest thanks to their effectiveness in many computer vision applications (medical imaging, video surveillance, biometrics, pattern recognition, OCR, etc.). Transfer learning is an optimization method that uses a pretrained network to speed up the training of another related task or application. This helps speed up and improve the training process on a new dataset. In this paper, we propose a new approach of handwritten word retrieval based on deep learning and transfer learning. We compared the performance between two types of extracted features based on transfer learning: from a pre-trained model and a fine-tuned network. Experiments are performed using six different CNN architectures and three similarity measures on the presegmented Bentham dataset of the ICDAR competition. The obtained results demonstrate the effectiveness of our proposed approach compared to existing methods, evaluated in this competition.

关键词： Feature extraction machine learning Computer architecture Computational modeling Visualization Task analysis Training

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A Comparative Analysis by KNN, SVM & ELM Classification to Detect Sickle Cell Anemia 1

A Comparative Analysis by KNN, SVM & ELM Classification to D...

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1st international conference on Robotics, Electrical and Signal processing Techniques (ICREst)

作者： Chy, Tajkia Saima Rahaman, Mohammad Anisur Chittagong Univ Engn & Technol Dept Elect & Telecommun Engn Chittagong Bangladesh

ISBN: (纸本)9781538680124

Red blood cell abnormalities involve erythrocytes that supply oxygen to all body tissues. Sometimes the formation and role of erythrocytes are hindered. Sickle cell anemia (SCA) is one kind of red blood cell disease. People carrying sickle cell anemia are increasing day by day. Sickle cell anemia shortens life expectancy. But life expectancy can be extended by diagnosing it an early stage. To identify the existence of sickle cells, an image processing procedure is developed. Blood samples are collected in the form of image format. The conversion of gray image, noise filtering and enhancement of image is done in image pre-processing. Fuzzy C means clustering is applied to determine the normal and sickle cells. Morphological operations are also applied to images. The geometrical and statistical features are used for extraction. Lastly, k nearest neighbor (km), support vector machine (svm) & extreme learning machine (elm) classifiers are implemented to test images. Comparisons among the classifiers with reliable results are presented by this system.

关键词： Sickle cell anemia gray scale image conversion median filter fuzzy c means clustering morphological operations feature extraction k nearest neighbor support vector machine extreme learning machine

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Classification of Landsat 8 imagery Using Kohonen’s Self Organizing Maps and learning Vector Quantization

Classification of Landsat 8 Imagery Using Kohonen’s Self Or...

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international conference on VLSI, Signal processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems, VSPICE 2019

作者： Shivakumar, B.R. Rajashekararadhya, S.V. Department of E&C Engineering N.M.A.M. Institute of Technology Nitte Karkala India Department of E&C Engineering Kalpataru Institute of Technology Tiptur India

ISBN: (纸本)9789811506253

image classification is the most commonly employed technique for extricating land cover report from remotely sensed images. In the last two decades, advanced image classifiers have been extensively applied in remotely sensed (RS) image classification studies. Kohonen’s Self Organizing Maps (SOM) and its supervised version learning Vector Quantization (LVQ) algorithms have been applied in a variety of machine learning and pattern recognition studies as they are derived from neural networks. In this paper, the classification ability of SOM, LVQ-1 and LVQ-2 techniques are investigated using medium resolution Landsat 8 OLI/TIRS RS imagery. The study presents a detailed analysis of SOM and LVQ algorithms in RS data classification based on the LULC separability. The study uses Divergence (Div) and Transformed Divergence (TD) as metrics for measuring LULC separability. Results of SOM and LVQs were compared with conventional maximum likelihood and minimum-distance- to-means classifiers. Supervised Kohonen’s LVQ algorithms produced better accuracies than conventional classifiers. Also, LVQ algorithms showed great efficiency in avoiding overfitting of the dominant classes and separating spectrally overlapping classes. The study is indication of the applicability of Kohonen’s algorithms for RS data classification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Self organizing maps

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image Analysis Based on Heterogeneous Architectures for Precision Agriculture: A Systematic Literature Review 1st

Image Analysis Based on Heterogeneous Architectures for Prec...

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1st international conference on Computer Science, Electronics and Industrial Engineering, CSEI 2019

作者： Pusdá-Chulde, Marco R. Salazar-Fierro, Fausto A. Sandoval-Pillajo, Lucía Herrera-Granda, Erick P. García-Santillán, Iván D. De Giusti, Armando Faculty of Engineering in Applied Sciences Universidad Técnica del Norte Ibarra Ecuador Universidad Nacional de La Plata La Plata Argentina Carrera de Software Universidad Regional Autónoma de Los Andes Ibarra Ecuador

ISBN: (纸本)9783030336134

Precision agriculture (AP) is a management strategy that uses ICT (Information and Communication Technologies) to obtain information from different sources in order to support decision-making, considering environmental and economic aspects to optimize the Farmer’s tasks and provide quality products to the costumer. The application of AP in agriculture can reduce time spent in manual activities, avoid the indiscriminate use of chemicals, increase production costs, soil deterioration and environmental pollution. Nowadays, AP is a booming area that, taking advantage of technological advances, in computer vision, heterogeneous architectures (Multicore, GPU, FGPA) and artificial intelligence techniques (machine learning, Deep learning), has allowed to systematize a variety of agricultural activities, such as disease detection, plant counting, and identification of weed, pests and insects in different crops. This paper presents a systematic review of literature (SRL) of image analysis and processing techniques applied in precision agriculture using heterogeneous technologies. Therefore, 32 scientific articles of the last five years from four relevant bibliographic databases (Scopus, ScienceDirect, IEEE Xplore, SpringerLink) were analyzed and synthesized. The selected publications answer to four research questions proposed in this study. From the obtained results, great opportunities for image analysis (segmentation), machine learning and the use of graphic accelerators (GPU) were identified, which stand out as promising techniques and tools for the development of efficient and precise automatic systems, with the perspective to its application in real time for many agricultural tasks. © Springer Nature Switzerland AG 2020.

关键词： Field programmable gate arrays (FPGA)

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