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Nersisson Ruban

Bio: Nersisson Ruban is an academic researcher from VIT University. The author has contributed to research in topics: Sadness & Computer science. The author has an hindex of 4, co-authored 10 publications receiving 27 citations.

Papers
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Proceedings ArticleDOI
01 Oct 2018
TL;DR: Experimental results manifest that the proposed technique garners better accuracy by correctly identifying the emotions and these results were moreover compared to the other existing methods of speech emotion detection.
Abstract: The detection of emotions from the speech is one of the most stirring and intriguing research areas in the field of artificial intelligence. In this paper, the emotion identification from Hindi language speech which is a popular language of India is carried out in a noisy environment after which multifarious emotions are classified into 4 main groups of emotional states namely happiness, sadness, anger and neutral. The proposed technique involves extraction of prosodic and spectral features of an acoustic signal like pitch, energy, formant, Mel-frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstral Coefficient (LPCC) along with their classification using a cubic spine Support Vector Machine (SVM) classifier model. The system gave an overall accuracy of, 98.75% in male actor utterances and 95% in female actors. Experimental results manifest that the proposed technique garners better accuracy by correctly identifying the emotions and these results were moreover compared to the other existing methods of speech emotion detection. Furthermore, the extracted features along with, different classifier models were contrasted in this paper for better evaluation.

34 citations

Proceedings ArticleDOI
22 Mar 2019
TL;DR: This paper is utilizing MATLAB to pin the information from the crude MAT-documents from physio bank and Kubios HRV programming to dissect the information of ECG signals, and acknowledges distinctive info information groups for electrocardiogram (ECG) information and beat-to-beat RR interim information.
Abstract: Electrocardiogram (ECG) is a biological signal that plays a significant role in the detection of heart diseases. Translating the QRS complex is a standout amongst the most imperative parts of ECG signals and its processing and flag preparing. R wave is the real segment in the intricate, which has a basic job in the analysis of heart Rate abnormalities and furthermore in deciding pulse fluctuation (HRV). In this paper, we are utilizing MATLAB to pin the information from the crude MAT-documents from physio bank and Kubios HRV programming to dissect the information. Three unique methodologies were taken for consideration for the investigation, Time-area analysis, Frequency space analysis, and non-linear analysis methods. The correct utilization of MATLAB capacities (both implicit and user characterized), toolbox and Simulink software can lead us to work with raw ECG signals for preparing, separating and examine by simulation with extraordinary accuracy and ease. The product acknowledges distinctive info information groups for electrocardiogram (ECG) information and beat-to-beat RR interim information. The product figures all the normally utilized time-area and recurrence space HRV parameters and more than 40 investigation parameters. The examination results are spared as an ASCII content record, MATLAB MAT-document, or as a PDF report. The product is essentially a direct result of its reduced graphical UI. The product can be utilized uninhibitedly on Windows and Linux working frameworks.

12 citations

Proceedings ArticleDOI
26 May 2019
TL;DR: This paper has implemented a defect detection and rectification system using image processing on tin cans to check for any irregularities apart from the ones that are intended to be on it, provided by a classifier neural network.
Abstract: Packaging is one of the most important aspects in the food industry. The problems faced during packaging can classified into two categories, defects in the packaging before the substrate is filled and after the substrate is filled. There are methods to determine the defects in the food containers after the food has been packed by means of measuring the phenomenon caused by the defects such as use of moisture sensor for detecting any leaking or the use of pH indicators. But these methods, although cost effective, slows down the plant as well as increases the risk of damaging the system. In this paper, we have implemented a defect detection and rectification system using image processing on tin cans to check for any irregularities apart from the ones that are intended to be on it. The decision-making capability is provided by a classifier neural network. The case study takes up the problems faced in coconut oil industry. A similar set up can be used after the package to detect if any defect is present. For other fluids such as soft drinks and other drinks a pH indicator can be used for detecting defects.

6 citations

Book ChapterDOI
27 Sep 2021
TL;DR: This article examined the features of cross-cultural recognition of four basic emotions (joy, neutral (calm state) - sadness - anger) in the spontaneous and acting speech of Indian and Russian children across Russian and Tamil languages.
Abstract: We examined the features of cross-cultural recognition of four basic emotions “joy – neutral (calm state) - sadness - anger” in the spontaneous and acting speech of Indian and Russian children across Russian and Tamil languages. Cross-cultural studies point that although basic emotion recognition is universal; emotion recognition is more accurate when speakers and receivers come from the same culture than the other cultures. The results showed that Russian and Indian experts recognized correctly the emotional states of children by their speech, but with different accuracy. Both groups of experts agreed on the state of sadness via spontaneous and acting speech of Russian children and the neutral state in spontaneous speech and anger state in the acting speech of Indian children. The importance of cultural recognition are that Indian experts classify more speech samples of spontaneous and acting speech from Russian children as reflecting a state of anger, Russian experts - a state of joy and a neutral state in the acting speech of Tamil children. Differences were revealed in the acoustic characteristics of the speech of Russian and Indian children, reflecting the basic emotions. Experts, when recognizing emotions in spontaneous speech, rely on the pitch values, in acting speech - on the intensity. The novelty of our finding lies in the cross-cultural recognition of emotions from the speech of children and the comparison of two distant languages - Russian and Tamil.

5 citations

Journal ArticleDOI
TL;DR: The results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks.
Abstract: This paper introduces the extended description of a database that contains emotional speech in the Russian language of younger school age (8–12-year-old) children and describes the results of validation of the database based on classical machine learning algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The validation is performed using standard procedures and scenarios of the validation similar to other well-known databases of children’s emotional acting speech. Performance evaluation of automatic multiclass recognition on four emotion classes “Neutral (Calm)—Joy—Sadness—Anger” shows the superiority of SVM performance and also MLP performance over the results of perceptual tests. Moreover, the results of automatic recognition on the test dataset which was used in the perceptual test are even better. These results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks. The results also confirm that this database can be a valuable resource for researchers studying affective reactions in speech communication during child-computer interactions in the Russian language and can be used to develop various edutainment, health care, etc. applications.

4 citations


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Journal ArticleDOI
01 Jan 2021
TL;DR: In this article, the authors provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing.
Abstract: The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

69 citations

Journal ArticleDOI
TL;DR: An automated EEG based emotion recognition method with a novel fractal pattern feature extraction approach is presented and has been tested on emotional EEG signals with 14 channels using linear discriminant, k-nearest neighborhood, support vector machine, and SVM.
Abstract: Electroencephalogram (EEG) signal analysis is one of the mostly studied research areas in biomedical signal processing, and machine learning. Emotion recognition through machine intelligence plays critical role in understanding the brain activities as well as in developing decision-making systems. In this research, an automated EEG based emotion recognition method with a novel fractal pattern feature extraction approach is presented. The presented fractal pattern is inspired by Firat University Logo and named fractal Firat pattern (FFP). By using FFP and Tunable Q-factor Wavelet Transform (TQWT) signal decomposition technique, a multilevel feature generator is presented. In the feature selection phase, an improved iterative selector is utilized. The shallow classifiers have been considered to denote the success of the presented TQWT and FFP based feature generation. This model has been tested on emotional EEG signals with 14 channels using linear discriminant (LDA), k-nearest neighborhood (k-NN), support vector machine (SVM). The proposed framework achieved 99.82% with SVM classifier.

65 citations

Journal ArticleDOI
TL;DR: A unique taxonomy from an IoT-ML–based healthcare perspective where key steps in developing healthcare systems are highlighted and an ML pipeline centering on healthcare application is shown and discussed every step of it is proposed.

34 citations

Journal ArticleDOI
TL;DR: A Collaborative RNN Inference Mapping Engine (CRIME), which automatically selects the best inference device for each input, and can reduce the execution time by more than 25% compared to any single-device approach.
Abstract: The excellent accuracy of Recurrent Neural Networks (RNNs) for time-series and natural language processing comes at the cost of computational complexity. Therefore, the choice between edge and cloud computing for RNN inference, with the goal of minimizing response time or energy consumption, is not trivial. An edge approach must deal with the aforementioned complexity, while a cloud solution pays large time and energy costs for data transmission. Collaborative inference is a technique that tries to obtain the best of both worlds, by splitting the inference task among a network of collaborating devices. While already investigated for other types of neural networks, collaborative inference for RNNs poses completely new challenges, such as the strong influence of input length on processing time and energy, and is greatly unexplored. In this article, we introduce a Collaborative RNN Inference Mapping Engine (CRIME), which automatically selects the best inference device for each input. CRIME is flexible with respect to the connection topology among collaborating devices, and adapts to changes in the connections statuses and in the devices loads. With experiments on several RNNs and datasets, we show that CRIME can reduce the execution time (or end-node energy) by more than 25 percent compared to any single-device approach.

18 citations

Journal ArticleDOI
TL;DR: This study combines advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended, and implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method.
Abstract: Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the best-performing model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA.

16 citations