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Convolutional Neural Networks for Placenta Cell Classification

Convolutional Neural Networks for Placenta Cell Classificati...

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： E Rohith V Sowmya K P Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

The placenta is a complicated organ that plays several roles during fetal evolution. There was a little awareness regarding the connection of anatomical placental disorders with fetal biology. In this particular work, we introduce an open source mathematically traceable deep learning pipeline to examine cell-level placenta histology using neural convolution networks. Also, we have a tendency to learn deep embedded encoding makeup information that's capable of each stratifying 5 distinct cell populations and learn interclass makeup variance. We anticipate that the automation of such a pipeline to population-scale placenta histology research does have the potential to benefit our understanding of the basic cellular placental biology, substantially its role in forecasting adverse birth results. The objective of the present work is to classify the cell populations into 5 classes using the convolution neural network.

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Influencing high frequency trading patterns in highway operator stock using efficient V2V V2I C4 solutions for vehicular networks 3

Influencing high frequency trading patterns in highway opera...

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3rd International Conference on Trends in Electronics and Informatics, ICOEI 2019

作者： Arul, Selvam S. Yashank, Fomra R. Aswin, K. Aswin, P. Shanmugha, Sundaram G.A. Department of Electronics and Communications Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India SIERS Research Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781538694398

Vehicular communication systems are networks in which vehicles and roadside units constitute the communicating nodes, providing each other with critical operational information such as safety warnings and traffic situations. They can prove effective in avoiding accidents and traffic congestion. An intelligent transport system will process the data from vehicle-to-vehicle (V2 V) and vehicle-to-infrastructure (V2I) communication networks in order to improve traffic management. This shall allow vehicles to also communicate with roadside infrastructure such as sensors, passengers, traffic databases, signaling systems, and among themselves. The tasks included in the study presented here consider a lot of real time data analysis, automated trade-off negotiations, algorithms for which are deployed on embedded computing platforms to perform tasks identified as part of efficient traffic reorganization and flow. The outcomes are destined to make a significant impact in the form of financial engineering by means of high frequency trading of the stake holders' stock. © 2019 IEEE.

关键词： Traffic congestion

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Investigating the Effectiveness of DMD and its Variants for Complex Data Analysis

Investigating the Effectiveness of DMD and its Variants for ...

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Intelligent Computing and Control Systems (ICCS), International Conference on

作者： S. Akshay E.A. Gopalakrishnan K.P. Soman Amrita School of Engineering Center for Computational Engineering & Networking (CEN) Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681145

The unprecedented availability of data in various fields reinforces the need for more comprehensive and advanced data-driven algorithms to deal with it. The data-driven techniques are able to handle the large volume of heterogeneous data and to extract more valuable information regarding the underlying system. This paper investigates the effectiveness of modern data-driven methods for data from non-linear systems. The effectiveness of dynamic mode decomposition (DMD) and its variants such as rSVD-DMD, randomized DMD (RDMD) and total DMD (TDMD) for dta from nonlinear system is investigated. The data considered for this study are cylindrical fluid flow and neuro recordings.

关键词： Heuristic algorithms Matrix decomposition Eigenvalues and eigenfunctions Sleep Data analysis Singular value decomposition Conferences

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Part-of-Speech Tagger for Biomedical Domain Using Deep Neural Network Architecture

Part-of-Speech Tagger for Biomedical Domain Using Deep Neura...

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International Conference on Computing and networking Technology (ICCNT)

作者： Athira Gopalakrishnan K.P Soman B Premjith Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

POS tagging is the process of classifying words into their parts of speech like noun, verb, preposition etc. to a word. It is the most important and basic process in NLP. It is acts as an essential preprocess for other applications in natural language processing (NLP) like sentiment analysis, NER, speech recognition and so on. POS tagging is treated as a sequence labeling problem in which it labels words with their appropriate Part-Of-Speech. This work implementing a POS tagger for biomedical domain using deep neural network architecture. The experiment is RNN, LSTM, and GRU will give better performance since they are able to access more context information and which we evaluated using publicly accessible dataset from GENIA. Most of the applications in NLP became solved due to the advancement of neural network or deep learning.

关键词： Tagging Logic gates Hidden Markov models Bidirectional control Recurrent neural networks Natural language processing

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Indian Car Number Plate Recognition using Deep Learning

Indian Car Number Plate Recognition using Deep Learning

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： R Naren Babu V Sowmya K P Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

Recognition of cars is very important for the control and surveillance systems. Automobiles can be recognized by number plates, which contains a unique combination of alphabets and numbers. However, it's a hard and intensive job for humans to manually recognize all the parked or passing car number plates. In this paper, we approach a training-based pathway for vehicle number plate recognition. Most of the previous works in automatic number plate recognition (ANPR) systems have limitations in their working conditions, like for example restricting them to stationary backgrounds, indoor area, restricted vehicle speeds, prescribed driveways, fixed illumination, or match the predefined distance between camera and vehicle. The main objective of our work is to create a robust number plate recognition model that works under different illuminations and angles. We created our recognition model by training on our manually collected car number plate dataset using YOLO V3. The algorithm has been tested over 640 images which are of different colours, and illuminations.

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Land Cover Satellite Image Classification Using NDVI and SimpleCNN

Land Cover Satellite Image Classification Using NDVI and Sim...

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International Conference on Computing and networking Technology (ICCNT)

作者： T. Tulasi Sasidhar Sreelakshmi K. Vyshnav M.T. Sowmya V. Soman K.P. Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

Image classification and prediction is a task which is embedded with quite a lot of challenges. Introduction of deep learning gave a rapid rise in this area of research. The efficient and the simplest deep learning algorithm that has helped researchers to make immense contributions in the field of image classification is Convolutional Neural Network (CNN). One of the important applications of image classification is in remote sensing, where it is used for land cover classification. In this paper we developed a SimpleCNN architecture for the classification of multi-spectral images from SAT-4 and SAT-6 airborne datasets. Two sets of experiments are conducted using the model by feeding it with different features. First level of experiment is done by providing the model with Near-Infrared (NIR) band information as it can sense vegetation health. The domain knowledge of Normalized Difference Vegetation Index (NDVI) motivated us to utilize Red and NIR spectral bands together in the second level of experimentation for the classification. It is observed from the experiment that the two band information gave better results for land cover classification.

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Dynamic Mode Decomposition based feature for Image Classification

Dynamic Mode Decomposition based feature for Image Classific...

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IEEE Region 10 International Conference TENCON

作者： K. Rahul-Vigneswaran S. Sachin-Kumar Neethu Mohan K.P. Soman Amrita Vishwa Vidyapeetham Amritapuri India Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham University Coimbatore India

Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.

关键词： Feature extraction Kernel Heuristic algorithms Matrix decomposition Eigenvalues and eigenfunctions Support vector machines Data models

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Amrita-CEN-SentiDB 1: Improved Twitter Dataset for Sentimental Analysis and Application of Deep learning

Amrita-CEN-SentiDB 1: Improved Twitter Dataset for Sentiment...

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International Conference on Computing and networking Technology (ICCNT)

作者： K S Naveenkumar R Vinayakumar K P Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

Powerful weapon in today's world is ones emotion is social media. They have the power to make an individual trending overnight or even may pull down anyone reputation. In this paper, the sentiment analysis task has been performed by collecting the dataset from the publically available sources and by merging them together to form a new dataset for the sentiment analysis task that is positive or negative sentiment based on the context of the subject. A new reliable dataset is subjected to various pre-processing techniques and then the feature extraction techniques aftermath they are passed to the deep learning techniques out of which by using the text representation method, global vectors (glovec) with the long short-term memory (lstm) has the highest accuracy of 75%, which is the benchmark accuracy for this dataset. For the research purpose the dataset used in this paper is made available publically for research purpose.

关键词： Task analysis Feature extraction Twitter Deep learning Sentiment analysis Databases

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Amrita-CEN-SentiDB: Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Amrita-CEN-SentiDB: Twitter Dataset for Sentimental Analysis...

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Intelligent Computing and Control Systems (ICCS), International Conference on

作者： K.S. Naveenkumar R. Vinayakumar K.P. Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681145

Social media is a platform in which the data is generated each and every day in an abundance manner. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, we use the text data for classification of tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are common between humans which has a way to express it that decides the person's feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) that belongs to machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. The dataset is made publically available for research purpose.

关键词： Feature extraction Machine learning Social network services Support vector machines Databases Tagging Task analysis

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Convolutional Neural Networks for Fingerprint Liveness Detection System

Convolutional Neural Networks for Fingerprint Liveness Detec...

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Intelligent Computing and Control Systems (ICCS), International Conference on

作者： Arun Kumar T.K. R. Vinayakumar Sajith Variyar V.V. V. Sowmya K.P. Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681145

Fingerprint liveness detection is a challenge that possess tremendous security risks. The detection is a technique for determining the liveness of a given biometric system and to ensure it is not fake. Fingerprint attack deals with images coming from artificial replication of symptomatic fingerprints, which are made of wood glue, gelatin or latex. To obtain a better liveness detection system, a convolution neural network is used to develop a system in our work. Our system is evaluated with LivDet 2019 Database which consist of 2000 live and fake biometric fingerprint images. Within the small training set containing 1300 samples, the trained model achieved 100% training accuracy and testing accuracy of 95%. The features taken from the CNN were given to machine learning classifiers for a comparison study where K-Nearest Neighbors, Logistic Regression and Naïve- Bayes classifiers gave results as good as the CNN architecture.

关键词： Fingerprint recognition Feature extraction Machine learning Computer architecture Support vector machines Training Machine learning algorithms

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