Anand Kumar

Bio: Anand Kumar is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Multi-mode optical fiber & Flyback converter. The author has an hindex of 3, co-authored 12 publications receiving 54 citations. Previous affiliations of Anand Kumar include Birla Institute of Technology, Mesra.

Sentiment Classification System of Twitter Data for US Airline Service Analysis

Abstract: The airline industry is a very competitive market which has grown rapidly in the past 2 decades. Airline companies resort to traditional customer feedback forms which in turn are very tedious and time consuming. This is where Twitter data serves as a good source to gather customer feedback tweets and perform a sentiment analysis. In this paper, we worked on a dataset comprising of tweets for 6 major US Airlines and performed a multi-class sentiment analysis. This approach starts off with pre-processing techniques used to clean the tweets and then representing these tweets as vectors using a deep learning concept (Doc2vec) to do a phrase-level analysis. The analysis was carried out using 7 different classification strategies: Decision Tree, Random Forest, SVM, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes and AdaBoost. The classifiers were trained using 80% of the data and tested using the remaining 20% data. The outcome of the test set is the tweet sentiment (positive/negative/neutral). Based on the results obtained, the accuracies were calculated to draw a comparison between each classification approach and the overall sentiment count was visualized combining all six airlines.

Monte Carlo analysis of the static noise margins for CMOS gates in predictive technology models

Abstract: The latest results on biasing (upsizing) the gate lengths of CMOS transistors implemented in advanced technologies have identified optimum lengths which allow maximizing the static noise margins (SNMs) The optimum lengths for nMOS and pMOS transistors were determined analytically, based on BSIM4v470 equations for the threshold voltage Further, it has been shown through simulations that designs using such optimum lengths exhibit better performances than those obtained using classical design methods (minimum length transistors) In this paper, we will present for the first time detailed Monte Carlo (MC) simulations for estimating the SNMs of XOR-2 and MAJ-3 CMOS gates when implemented in 22nm predictive technology models (PTM) These support the claim that the SNMs of CMOS gates using optimally sized transistors are significantly (about twice) better than the SNMs of classically sized CMOS gates, for any input combinations

An optimized piezoresistive microcantilever arsenic(III) sensor with CMOS-compatible active readout: Towards in-situ subsurface characterization using microsystems technology

Abstract: Arsenic (As) currently ranks as the number one substance in the most recent (ATDSR, 2007a) Comprehensive, Environmental, Response, Compensation and Liability Act (CERCLA) Priority List of Hazardous Substances published by the Agency for Toxic Substances and Disease Registry (ATSDR). The chemistry of this metalloid, emerging from various natural and anthropogenic sources, poses serious toxicological concern. Existing procedures for subsurface characterization rely primarily on direct sampling & off-site analysis techniques viz. liquid scintillation counting and ICP-MS. Not only do they impose severe limitations on readout-integration & packaging, but also lack precision. In this context, low-cost real-time in-situ diagnostic measurements for arsenic contamination are vital. This paper proposes a novel MEMS based low-power piezoresistive microcantilever sensor system, that can be effectively implanted in an on-site monitoring cavity to detect contamination. The design is based on a single crystal silicon substrate exploiting the benefits of anisotropic chemical etching. The transduction is based on the binding of the contaminant to the Al2O3 self-assembled-monolayer (SAM), and the adsorbtion-induced stress change is monitored via the bending of the beam and thus piezoresistivity. The device uses a readout interface consisting of a common-gate MOS transistor configuration unlike the conventionally used Wheatstone bridge, which allows significant reduction in power consumption as compared to its counterparts. The results strongly indicate the realizability of low-cost in-situ arsenic detection and monitoring. The working of the sensor is simulated using the Finite Element Analysis (FEA) module in COMSOL Multiphysics.

Hardware development and implementation of single phase matrix converter as a cycloconverter and as an inverter

Abstract: In this paper, an algorithm is developed that enable a single phase matrix converter (SPMC) to perform a function of cycloconverter and of inverter i.e. act as frequency changer and also convert DC to AC. The algorithm is first implemented on Matlab simulink software. Simulation results are presented for SPMC as a cycloconverter (at different output frequency) and as an inverter (DC to AC). Simulated results are verified with experimental result. Also a laboratory model test rig of the SPMC as a cycloconverter and inverter has been developed using microcontroller to experimentally verify the result. Good result was obtained between simulation and experiments.

A machine learning approach to analyze customer satisfaction from airline tweets

Abstract: Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.

A Novel Deep Learning based Sentiment Analysis of Twitter Data for US Airline Service

Abstract: The airline industry has evolved quite dynamically over the last two decades. Airline firms use traditional customer feedback types that are very routine and time-intensive. Sentiment analysis may be a crucial approach to the analysis of input in order to minimize the problem. Twitter data acts as a valuable method for gathering user tweets and viewpoint analyzes. This paper proposed a novel deep learning model that effectively combines different word embedding with deep learning methods to evaluate a dataset made up of tweets for six major US Airlines and multi-class sentiment analysis. System selections integrate these features with different deep-learning approaches for term embedding and classify sentimental documents. This methodology starts with raw DNN data extraction and tweet-cleaning pre-processing methods for CNN. The test set product is a positive/negative/neutral tweet interpretation with a 3-class data set and data set precision assessment. Finally, we understand the findings obtained from the models presented by various researchers and prove that our model is more reliable than the previous frameworks.

An efficient approach for sentiment analysis using machine learning algorithm

Abstract: Sentimental analysis determines the views of the user from the social media. It is used to classify the content of the text into neutral, negative and positive classes. Various researchers have used different methods to train and classify twitter dataset with different results. Particularly when time is taken as constraint in some applications like airline and sales, the algorithm plays a major role. In this paper an optimization based machine learning algorithm is proposed to classify the twitter data. The process was done in three stages. In the first stage data is collected and preprocessed, in the second stage the data is optimized by extracting necessary features and in the third stage the updated training set is classified into different classes by applying different machine learning algorithms. Each algorithm gives different results. It is observed that the proposed method i.e., sequential minimal optimization with decision tree gives good accuracy of 89.47% compared to other machine learning algorithms.

Hybrid Deep Learning Models for Sentiment Analysis

Abstract: Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

Sentiment Analysis of US Airlines Tweets Using LSTM/RNN

Abstract: Nowadays, a million users use social networking services such as Twitter to tweet their products and services by placing the reviews based on their opinions. Sentiment analysis has emerged to analyze the twitter data automatically. Sentiment classification techniques used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations done on six different US airlines. To detect sentiment polarity, we explored word embedding models (Word2Vec, Glove) in tweets using deep learning methods. Here, we investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units can deal with long term dependencies by introducing memory in a network model for prediction and visualization. The results showed better significant classification accuracy trained 80% for training set and 20% for testing set which shows that our models are reliable for future prediction. To improve this performance, the Bidirectional LSTM Model (Bi-LSTM) is used for further investigation studies.