Neeru Rathee

Bio: Neeru Rathee is an academic researcher from Maharaja Surajmal Institute of Technology. The author has contributed to research in topics: Feature extraction & Support vector machine. The author has an hindex of 5, co-authored 20 publications receiving 52 citations.

Feature fusion for fake Indian currency detection

Abstract: Fake currency detection is a serious issue worldwide, affecting the economy of almost every country including India. The possible solutions are to use either chemical properties of the currency or to use its physical appearance. The approach presented in this paper is based upon physical appearance of the Indian currency. Image processing algorithms have been adopted to extract the features such as security thread, intaglio printing(RBI logo) and identification mark, which have been adopted as security features of Indian currency. To make the system more robust and accurate, the decisive score of all the three features has been fused to differentiate between real and fake currencies. The fake currency detection accuracy of the proposed system is 100%. Another parameter used to measure the performance of the proposed system is mean square error, which is approximately 1%. It may be adopted by the common people as well, who quite often face the problem of differentiating between real and fake currencies.

Contactless and Hassle Free Real Time Heart Rate Measurement with Facial Video

Abstract: In the era of automation, an automatic and noninvasive tool is required for day-to-day health monitoring. Cardiac pulse being one of the vital physiological parameters has drawn attention of researchers for its measurement. In this article, the authors present an approach for automatic cardiac pulse measurement, using a typical webcam. In the presented approach, face region is detected and RGB traces are retrieved for the face region. FFT is applied on the extracted traces to compute power spectrum of the respective traces. The peaks of the power spectrum limited in the band region (0.75–4 Hz) are investigated to provide cardiac pulse measurement. The presented approach is hassle free in nature, and hence, can be adopted even in home environment. Experimental results represent the efficacy of the proposed approach.

An efficient approach for facial action unit intensity detection using distance metric learning based on cosine similarity

Abstract: Emotions of human beings are largely represented by facial expressions. Facial expressions, simple as well as complex, are well decoded by facial action units. Any facial expression can be detected and analyzed if facial action units are decoded well. In the presented work, an attempt has been made to detect facial action unit intensity by mapping the features based on their cosine similarity. Distance metric learning based on cosine similarity maps the data by learning a metric that measures orientation rather than magnitude. The motivation behind using cosine similarity is that change in facial expressions can be better represented by changes in orientation as compared to the magnitude. The features are applied to support vector machine for classification of various intensities of action units. Experimental results on the popularly accepted database such as DISFA database and UNBC McMaster shoulder pain database confirm the efficacy of the proposed approach.

Satellite Image Analysis: A Review

Abstract: Satellite image processing is an important area of research now a days due to its wide range of applications. Researchers and scientists have paid attention to satellite image processing so as to capture information from them. Satellite image analysis poses a great challenge to the researchers due to high variability, low resolution and big data of the satellite images. A lot of work has been done for satellite image analysis that covers the research from classification of hand crafted features to applying high performance computing on satellite images. The researchers have achieved great success in satellite image analysis. But a systematic review, which will lead researchers to identify the problem and to contribute in this field, is missing. In the presented chapter, an attempt has been made to present a detailed review of the various steps of satellite image processing, classification and available databases. This chapter will give an impetus towards further research in this field and will provide a baseline to research in the field of satellite image processing.

Comparative Study on Machine Learning Classifiers for Epileptic Seizure Detection in Reference to EEG Signals

Abstract: Epilepsy is the key concern of medical practitioners and machine learning researchers since last decade EEG signals play a very crucial role in early detection of epilepsy as well as cure of epilepsy The traditional approach to analyze EEG signals includes two main steps: feature extraction and classification Since multi-channel EEG data is chaotic data, selecting optimal features and classifying them are major challenges There exist a number of feature extraction and classification techniques proposed by researchers which perform well For feature extraction, wavelets have been proved to perform state-of-the-art performance, but no such state-of-the art performance exists for classification techniques The classifiers explored in the presented work include random forest classifier, support vector machine, Naive Bayes, k-nearest neighbor, decision trees, artificial neural network, and logistic regression Experimental results on the UCI dataset represent that random forest is performing best with 9978% accuracy

DistancePPG: Robust non-contact vital signs monitoring using a camera

Abstract: Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

Review of emerging concepts in nanotoxicology: opportunities and challenges for safer nanomaterial design.

Abstract: Nanotoxicology and nanosafety has been a topic of intensive research for about more than 20 years. Nearly 10 000 research papers have been published on the topic, yet there exists a gap in ...

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Abstract: The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

Redox metals homeostasis in multiple sclerosis and amyotrophic lateral sclerosis: a review.

Abstract: The effect of redox metals such as iron and copper on multiple sclerosis and amyotrophic lateral sclerosis has been intensively studied. However, the origin of these disorders remains uncertain. This review article critically describes the physiology of redox metals that produce oxidative stress, which in turn leads to cascades of immunomodulatory alteration of neurons in multiple sclerosis and amyotrophic lateral sclerosis. Iron and copper overload has been well established in motor neurons of these diseases’ lesions. On the other hand, the role of other metals like cadmium participating indirectly in the redox cascade of neurobiological mechanism is less studied. In the second part of this review, we focus on this less conspicuous correlation between cadmium as an inactive-redox metal and multiple sclerosis and amyotrophic lateral sclerosis, providing novel treatment modalities and approaches as future prospects.

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction

Abstract: Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine.