Browsing by Author "Meghana, N.P."

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    A Multi-Protocol Home Automation System Using Smart Gateway
    (Springer, 2021) Chaudhary, S.K.; Yousuff, S.; Meghana, N.P.; Ashwin, T.S.; Guddeti, R.M.R.
    Smart Home is one of the most established applications of the Internet of Things. Almost every equipment we use in our daily life—appliances, electric lights, electrical outlets, heating, and cooling systems-connected to a remotely controllable network, giving the user’s ability to remotely control and monitor the house, save energy without compromising on comfort and ultimately improve the quality of experience of staying in the house. We present a cost-effective system and address a major challenge that the industry faces today-Protocol Compatibility. To address the challenge, we make use of separate gateways/bridges for each network and an open-source home automation framework called OpenHAB, where each bridge links with a single master Wi-Fi gateway, providing a single window of control through an Application or a web interface for an integrated Smart Home. We integrate an elderly health monitoring device-Beehealth with OpenHAB; addressing the paramount need of a portable, accurate, and efficient health monitoring and fall detection device. We present two methods for fall detection, namely: threshold-based and Neural Network-based, with the latter resulting in 94% accuracy for fall detection. We evaluate the Smart Home devices on parameters like syncing time, battery life, recharge time, deployability, and cost. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
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    A semantic approach to text steganography in sanskrit using numerical encoding
    (Springer Verlag service@springer.de, 2019) Keshava, K.; Pravalika, A.; Abhishek, D.V.; Meghana, N.P.; Prasad, G.
    Steganography is the art of hiding a message within another so that the presence of the hidden message is indiscernible. People who are not intended to be the recipients of the message should not even suspect that a hidden message exists. Text steganography is challenging as it is difficult to hide data in text without affecting the semantics. Retention of the semantics in the generated stego-text is crucial to minimize suspicion.This paper proposes a technique for text steganography using classical language Sanskrit. As Sanskrit is morphologically rich with a very large vocabulary, it is possible to modify the cover text without affecting the semantics. In addition numerical encoding is used to map a Sanskrit character to a numerical value. This helps in hiding the message effectively. Moreover, in this technique, a key is used for additional security. The key is generated dynamically and is appended to the final message to further add security to the proposed method. The proposed method generated stego-texts with syntactic correctness of 96.7%, semantic correctness of 86.6%, and with a suspicion factor of just 23.4% upon evaluation. © Springer Nature Singapore Pte Ltd. 2019
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    Domain-specific sentiment analysis approaches for code-mixed social network data
    (2017) Pravalika, A.; Oza, V.; Meghana, N.P.; Sowmya, Kamath S.
    Sentiment Analysis is one of the prominent research fields in Natural Language Processing because of its widespread real-world applications. Customer preferences, options and experiences can be analyzed through social media, reviews, blogs and other online social networking site data. However, due to increasing informal usage of local languages in social media platforms, multi-lingual or code-mixed data is fast becoming a common occurrence. Mixed code is generated when users use more than a single language in social network comments. Such data presents a significant challenge for applications using sentiment analysis and is yet to be fully explored by researchers. Existing sentiment analysis methods applied to monolingual social data are not suitable for code-mixed data due to the inconsistency in the grammatical structure in these sentences. In this paper, a novel method focused on performing effective sentiment analysis of bilingual sentences written in Hindi and English is proposed, that takes into account linguistic code switching and the grammatical transitions between the two considered languages. Experimental evaluation using real-world, code-mixed datasets obtained from Facebook showed that the proposed approach achieved very good accuracy and was also efficient performance-wise. � 2017 IEEE.
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    Domain-specific sentiment analysis approaches for code-mixed social network data
    (Institute of Electrical and Electronics Engineers Inc., 2017) Pravalika, A.; Oza, V.; Meghana, N.P.; Kamath S․, S.
    Sentiment Analysis is one of the prominent research fields in Natural Language Processing because of its widespread real-world applications. Customer preferences, options and experiences can be analyzed through social media, reviews, blogs and other online social networking site data. However, due to increasing informal usage of local languages in social media platforms, multi-lingual or code-mixed data is fast becoming a common occurrence. Mixed code is generated when users use more than a single language in social network comments. Such data presents a significant challenge for applications using sentiment analysis and is yet to be fully explored by researchers. Existing sentiment analysis methods applied to monolingual social data are not suitable for code-mixed data due to the inconsistency in the grammatical structure in these sentences. In this paper, a novel method focused on performing effective sentiment analysis of bilingual sentences written in Hindi and English is proposed, that takes into account linguistic code switching and the grammatical transitions between the two considered languages. Experimental evaluation using real-world, code-mixed datasets obtained from Facebook showed that the proposed approach achieved very good accuracy and was also efficient performance-wise. © 2017 IEEE.
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    Zigbee-based wearable device for elderly health monitoring with fall detection
    (2018) Yousuff, S.; Chaudary, S.K.; Meghana, N.P.; Ashwin, T.S.; Ram Mohana Reddy, Guddeti
    Health monitoring devices have flooded the market. But there are very few that cater specifically to the needs of elderly people. Continuously monitoring some critical health parameters like heart rate, body temperature can be lifesaving when the elderly is not physically monitored by a caretaker. An important difference between a general health tracking device and one meant specifically for the elderly is the pressing need in the latter to be able to detect a fall. In case of an elderly person or a critical patient, an unexpected fall, if not attended to within a very short time span, can lead to disastrous consequences including death. We present a solution in the form of a wearable device which, along with monitoring the critical health parameters of the elderly person, can also detect an event of a fall and alert the caretaker. We make use of a 3-axis accelerometer embedded into the wearable to collect acceleration data from the movements of the elderly. We have presented two algorithms for fall detections�one based on a threshold and the other based on a neural network and provided a detailed comparison of the two in terms of accuracy, performance, and robustness. � Springer Nature Singapore Pte Ltd. 2018.
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    Zigbee-based wearable device for elderly health monitoring with fall detection
    (Springer Verlag, 2018) Yousuff, S.; Chaudary, S.K.; Meghana, N.P.; Ashwin, T.S.; Guddeti, G.
    Health monitoring devices have flooded the market. But there are very few that cater specifically to the needs of elderly people. Continuously monitoring some critical health parameters like heart rate, body temperature can be lifesaving when the elderly is not physically monitored by a caretaker. An important difference between a general health tracking device and one meant specifically for the elderly is the pressing need in the latter to be able to detect a fall. In case of an elderly person or a critical patient, an unexpected fall, if not attended to within a very short time span, can lead to disastrous consequences including death. We present a solution in the form of a wearable device which, along with monitoring the critical health parameters of the elderly person, can also detect an event of a fall and alert the caretaker. We make use of a 3-axis accelerometer embedded into the wearable to collect acceleration data from the movements of the elderly. We have presented two algorithms for fall detections—one based on a threshold and the other based on a neural network and provided a detailed comparison of the two in terms of accuracy, performance, and robustness. © Springer Nature Singapore Pte Ltd. 2018.