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Author

Shridevi S. Krishnakumar

Bio: Shridevi S. Krishnakumar is an academic researcher. The author has contributed to research in topics: Artificial intelligence & Computer science. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: A hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks is discussed, which proved to be more promising and efficient than its counters.
Abstract: Coronavirus has become one of the most deadly pandemics in 2021. Starting in 2019, this virus is now a significant medical issue all over the world. It is spreading extensively because of its modes of transmission. The virus spreads directly, indirectly, or through close contact with infected people. It is proclaimed that people should wear a mask in public areas as a counteraction measure, as it helps in suppressing transmission. A portion of the spaces, where the virus has broadly fanned out, is because of inappropriate wearing of facial cover. In crowded areas, keeping a check on facial masks manually is difficult. To automate this process, an effective and robust face mask detector is required. This paper discusses a hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks. The accuracy was compared for three different values of principal components. The test accuracy achieved was 0.87 for 64 components, 0.987 for 512 components, and 0.989 for 1,000 components. Hence, this approach proved to be more promising and efficient than its counters.

1 citations

Journal ArticleDOI
TL;DR: The proposed methodologies are able to handle a diverse variety of images that include labyrinthine backgrounds, user-specific distinctions, minuscule discrepancies between classes and image alterations as well as producing accuracies comparable with state-of-the-art literature.
Abstract: The proposed research deals with constructing a sign gesture recognition system to enable improved interaction between sign and non-sign users. With respect to this goal, five types of features are utilized—hand coordinates, convolutional features, convolutional features with finger angles, convolutional features on hand edges and convolutional features on binary robust invariant scalable keypoints—and trained on ensemble classifiers to accurately predict the label of the sign image provided as input. In addition, a hybrid artificial neural network is also fabricated that takes two of the aforementioned features, namely convolutional features and convolutional features on hand edges to precisely locate the hand region of the sign gesture under consideration in an attempt for classification. Experiments are also performed with convolutional neural networks on those benchmark datasets which are not accurately classified by the previous two methods. Overall, the proposed methodologies are able to handle a diverse variety of images that include labyrinthine backgrounds, user-specific distinctions, minuscule discrepancies between classes and image alterations. As a result, they are able to produce accuracies comparable with state-of-the-art literature.

1 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, a deep learning approach has been proposed for the classification of cluster instances as being intrusive or not intrusive, and a mini-batch Adam optimizer was used due to a large number of hidden layers in the model.
Abstract: A deep learning approach has been proposed for the classification of cluster instances as being intrusive or not intrusive. Mini-batch Adam optimizer was used due to a large number of hidden layers in the model. Massive amounts of data accumulated for training prevented the model from overfitting. After extensive testing of data with various algorithms, it was found that deep learning model with Adam optimizer outperformed others.
Journal ArticleDOI
TL;DR: In this article , a quantum machine learning model was proposed to classify images using a quantum classifier, which uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset.
Abstract: Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.

Cited by
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Journal Article
TL;DR: In this paper , supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset and the results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient.
Abstract: Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence on the effects of hazardous compounds on a certain quantity of water used. Classification technique of machine learning can be employed to under-stand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. Artificial neural net-work model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our re-search we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights about the significant factors of poor water quality and how to overcome that.
TL;DR: A hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks is discussed, which proved to be more promising and efficient than its counters.
Abstract: Received Apr 11, 2022 Revised Sep 16, 2022 Accepted Sep 30, 2022 Indigital circuits, energy reduction is the most important parameter in the design of handy and battery-operated devices. Flipflop is an important component in any digital system. By improving the performance of flip-flop, complete system performance is better. This paper addresses the design of D flip-flop using direct current diode-based positive feedback adiabatic logic (DC-DB PFAL) at various frequencies at 45nm technology node. Further, the layout for the proposed design is also presented. The performance analysis is carried out for delay, power dissipation, power delay product and transistor count. Circuit simulation is done by using cadence virtuoso tool at 10 MHz and 100 MHz frequencies. The results show an improvement in power dissipation of 18% with less transistors count compared to exiting designs in the literature.