638
20
16481
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Conference Papers
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Faculty Publications- Conference Papers
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Real-Time State of Charge Prediction Model for Electric Two-Wheeler
Subject
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E-2W; Neural Network; OCV; SoC
Description
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To maximise the efficiency and performance of electric vehicles, traction battery State of Charge (SoC) must be accurately predicted. In this work, a prediction model for traction battery State of Charge estimation is developed in real time. The traction battery powers an electric two-wheeler through a predetermined drive cycle. To produce accurate state-of-charge forecasts, the predictive model considers several input characteristics, such as temperature, voltage, and current. This research is crucial for fostering effective energy management and improving the safety and dependability of electric two-wheelers. Open-circuit voltage (OCV) and coulomb counting are two commonly utilised techniques used to evaluate the state of charge prediction model. These techniques act as standards for assessing the developed Neural Network model prediction, the model's dependability and accuracy. The model's usefulness and its potential to outperform the current State of Charge estimating techniques are demonstrated by comparing the state-of-charge predictions from the model with these standard methods. 2024 IEEE.
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Jose P.; Alex A.J.; Iyyanam A.G.; Varghese A.; Athiyalil A.A.; Raj A.
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2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/SEFET61574.2024.10717933" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SEFET61574.2024.10717933</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208980231&doi=10.1109%2FSEFET61574.2024.10717933&partnerID=40&md5=04c9d61546b136ace0176e52d9145225" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208980231&doi=10.1109%2fSEFET61574.2024.10717933&partnerID=40&md5=04c9d61546b136ace0176e52d9145225</a>
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ISBN: 979-835038399-7
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Online
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English
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Conference paper
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Jose P., Christ University, Department of Eee, Bengaluru, India; Alex A.J., Christ University, Department of Eee, Bengaluru, India; Iyyanam A.G., Christ University, Department of Eee, Bengaluru, India; Varghese A., Christ University, Department of Eee, Bengaluru, India; Athiyalil A.A., Christ University, Department of Eee, Bengaluru, India; Raj A., Christ University, Department of Eee, Bengaluru, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep
Title
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Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
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01-01-2025
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Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342
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<a href="https://doi.org/10.1007/978-981-97-7717-4_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000473335?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000473335?origin=resultslist</a>
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Probha A., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thind P.S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Gupta A.D., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India
Description
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This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An IoT Wearables Dataset for Womens Safety: Stress Detection and Analysis was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of stressed and unstressed based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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Accuracy; Internet of Things (IoT); Logistic regression; Machine learning; Mental health; Personalized stress detection; Physiological data; Random forest; Safety; Stress detection; Support vector machines (SVM); Wearable sensors; Womens health
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Springer Science and Business Media Deutschland GmbH
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ISSN: 21903018; ISBN: 978-981977716-7;
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English
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Conference paper
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Faculty Publications
Conference Paper
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Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep
Title
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Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
Date
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01-01-2025
Source
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Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342
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<a href="https://doi.org/10.1007/978-981-97-7717-4_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000473335?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000473335?origin=resultslist</a>
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Probha A., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thind P.S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Gupta A.D., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India
Description
An account of the resource
This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An IoT Wearables Dataset for Womens Safety: Stress Detection and Analysis was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of stressed and unstressed based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
The topic of the resource
Accuracy; Internet of Things (IoT); Logistic regression; Machine learning; Mental health; Personalized stress detection; Physiological data; Random forest; Safety; Stress detection; Support vector machines (SVM); Wearable sensors; Womens health
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Springer Science and Business Media Deutschland GmbH
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ISSN: 21903018; ISBN: 978-981977716-7;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
-
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Real-time Traffic Prediction in 5G Networks Using LSTM Networks
Subject
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5G networks; deep learning; LSTM networks; network management; real-time traffic prediction
Description
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This research explores the application of Long Short-Term Memory (LSTM) networks for real-time traffic prediction within 5G networks, aiming to address the critical need for accurate prediction models in dynamic network environments. Leveraging the sequential learning capabilities of LSTM networks, the proposed methodology encompasses dataset preparation, model architecture design, training, and evaluation. Experimental results demonstrate the effectiveness of the LSTM-based prediction model in capturing temporal dependencies and providing reliable predictions across various prediction horizons. While promising, further research is warranted to enhance the model's performance and address remaining challenges. This study contributes to advancing the state-of-the-art in traffic prediction methodologies, facilitating more efficient network management and optimization in 5G environments. 2024 IEEE.
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Kumar H.; Al-Jawahry H.M.; Manohar M.; Karthika K.; Victor M.; Shetty A.D.
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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/IICCCS61609.2024.10763791" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IICCCS61609.2024.10763791</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214431884&doi=10.1109%2FIICCCS61609.2024.10763791&partnerID=40&md5=c05a3d9820768523968aebaaea9a75e0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214431884&doi=10.1109%2fIICCCS61609.2024.10763791&partnerID=40&md5=c05a3d9820768523968aebaaea9a75e0</a>
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ISBN: 979-835039075-9
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Online
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English
Type
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Conference paper
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Kumar H., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Mohali, India; Al-Jawahry H.M., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Manohar M., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Karthika K., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Shetty A.D., (Deemed to be University) Nmam Institute of Technology (NMAMIT), Department of Cse Nitte, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Real-Time Traffic Sign Detection Under Foggy Condition
Subject
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Accuracy; Convolution neural network; Machine learning; Traffic sign
Description
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Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Anthony R.; Biswas J.
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Lecture Notes in Electrical Engineering, Vol-840, pp. 137-144.
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Springer Science and Business Media Deutschland GmbH
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2022-01-01
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<a href="https://doi.org/10.1007/978-981-16-9012-9_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9012-9_12</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128641071&doi=10.1007%2F978-981-16-9012-9_12&partnerID=40&md5=9f0cdec375e5fb6fb46acc8ee9e0a73f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128641071&doi=10.1007%2f978-981-16-9012-9_12&partnerID=40&md5=9f0cdec375e5fb6fb46acc8ee9e0a73f</a>
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ISSN: 18761100; ISBN: 978-981169011-2
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Online
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English
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Conference paper
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Anthony R., Christ (Deemed to be University), Bangalore, India; Biswas J., Christ (Deemed to be University), Bangalore, India
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Book Chapter
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Real-time video segmentation using a vague adaptive threshold
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Content-based video retrieval; Real-time shot boundary analysis; Vague adaptive threshold; Video segmentation
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For the last two decades, video shot segmentation has been a widely researched topic in the field of content-based video analysis (CBVA). However, over the course of time, researchers have aimed to improve upon the existing methods of shot segmentation in order to gain accuracy. Video shot segmentation or shot boundary analysis is a basic and vital step in CBVA, since any error incurred in this step reduces the precision of the other steps. The shot segmentation problem assumes greater proportions when detection is preferred in real time. A spatiotemporal fuzzy hostility index (STFHI) is proposed in this work which is used for edge detection of objects occurring in the frames of a video. The edges present in the frames are treated as features. Correlation between these edge-detected frames is used as a similarity measure. In a real-time scenario, the incoming images are processed and the similarities are computed for successive frames of the video. These values are assumed to be normally distributed. The gradients of these correlation values are taken to be members of a vague set. In order to obtain a threshold after defuzzification, the true and false memberships of the elements are computed using a novel approach. The threshold is updated as new frames are buffered in and is referred to as the vague adaptive threshold (VAT). The shot boundaries are then detected based on the VAT. The VAT for detecting the shot boundaries is determined by using the three-sigma rule on the defuzzified membership values. The effectiveness of the real-time video segmentation method is established by an experimental evaluation on a heterogeneous test set, comprising videos with diverse characteristics. The test set consists of videos from sports, movie songs, music albums, and documentaries. The proposed method is seen to achieve an average F1 score of 0.992 over the test set consisting of 15 videos. Videos from the benchmark TRECVID 2001 are selected for comparison with other state-of-the-art-methods. The proposed method achieves very high precision and recall, with an average F1 score of 0.939 on the videos chosen from the TRECVID 2001 dataset. This is a substantial improvement over the other existing methods. 2020 Elsevier Inc.
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Bhaumik H.; Bhattacharyya S.; Chakraborty S.
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Hybrid Computational Intelligence: Challenges and Applications, pp. 191-220.
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Elsevier
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2020-01-01
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<a href="https://doi.org/10.1016/B978-0-12-818699-2.00010-X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-12-818699-2.00010-X</a>
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ISBN: 978-012818699-2
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Online
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English
Type
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Book chapter
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Bhaumik H., Department of Information Technology, RCC Institute of Information Technology, Kolkata, India; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Chakraborty S., Department of Computer Science & Technology, Indian Institute of Engineering Science and Technology, Howrah, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Mary, Sahaaya Arul; Dakshinamurthi, Vidyabharathi; Tamilselvi, M.; Seshaiah, M.A.; Abinaya, A.; Syed Husain, S.
Title
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Real-Time Video Text Spotting with OpenCV and OCR Powered by Deep Learning
Date
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01-01-2025
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2025 IEEE 6th Global Conference for Advancement in Technology, GCAT 2025;
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<a href="https://doi.org/10.1109/GCAT66372.2025.11368436" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/GCAT66372.2025.11368436</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034262457?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034262457?origin=resultslist</a>
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Mary S.A., Christ University, School of Engineering and Technology, Department of Aiml & Data Science, Karnataka, Bangalore, India; Dakshinamurthi V., Sona College of Technology, Department of Computer Science and Engineering, Salem, India; Tamilselvi M., Roever Engineering College, Department of Computer Science and Engineering, Tamilnadu, Perambalur, India; Seshaiah M.A., Velammal Engineering College, Department of Ece, Surapet, Chennai, India; Abinaya A., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Ece, Avadi, Tamilnadu, Chennai, India; Syed Husain S., K. Ramakrishnan College of Engineering, Dept. of Ece, Tamil Nadu, Tiruchirappalli, 621112, India
Description
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The identification of text in video places huge challenges, and translating them into target form demands high-level expert skills in computer vision and deep learning. This system can grab and supervise the real-time video process of text elements by utilizing OpenCV for text recognition and extraction. The proposed model was employed diverse machine translation models to guarantee high-quality results for translation. Based on wide testing and assessment, the goal is to make the apporach fast and precise, offering valuable tool for instructors, content creators, and overall users. This novel approach solves problems relating to language difficulty problems. Key elements include video processing, text detection and recognition, and machine translation. In addition to these essential functionalities, sophisticated preprocessing methods are applied to make text stand out from diverse backgrounds to render high performance in diverse environments. Deep learning algorithms improves the accuracy of text detection, especially for occluded or distorted characters. Finally, the cloud-based translation service offers real-time multilingual support to enable maximal adaptability to user needs. For the first time, this innovative technology finally enables streamlining access to the content and facilitates cross-cultural communication in multimedia contexts with nigh-guaranteed linguistic barriers broken down. 2025 IEEE.
Subject
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OpenCV; Real-Time Processing; Video Processing Text Detection
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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A related resource
ISBN: 979-833151458-7;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Conference Papers
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Real-World Application of Machine Learning and Deep Learning
Subject
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Artificial intelligence; Decision Tree; Deep Learning; Healthcare; Machine Learning
Description
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The world today is running on the latest computer technologies and one of those is machine learning. The real life example that most of us know is speech recognition. Google Assistant is the common example for this Speech recognition. This google assistant is not only limited till 'Ok Google', but it responds to all your questions in a smart way. It can manage all your calls or can book appointments. Imagine you fell down while de-boarding a bus. So, Next time you take care so that you don't fall that is something that your brain has interpreted from your past experience. This is what exactly deep learning is, it imitates human brain works. Deep learning is sub-branch of machine learning. It is able to build all new things based on its previous experiences. Many of us have heard about driverless cars and medical diagnosis. Recently google has developed a new technology where all your cardiovascular events can be predicted by eye scan so, that doctors can get a clear view of what is inside the body of a patient. These all are developed using machine learning. It has a capability to change the human world into a complete robotic world. Anyways, it also has its own disadvantages. This article discusses about those, Scope of machine learning, its Market potential, financial growth and Current applications of machine learning. 2019 IEEE.
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Reema Sree S.; Vyshnavi S.B.; Jayapandian N.
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Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019, pp. 1069-1073.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2019-01-01
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<a href="https://doi.org/10.1109/ICSSIT46314.2019.8987844" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSIT46314.2019.8987844</a>
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ISBN: 978-172812119-2
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Online
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English
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Conference paper
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Reema Sree S., CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Vyshnavi S.B., CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Jayapandian N., CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Shrotriya, Shobhit; Banu, P. K. Nizar; Kulkarni, Avi; Kumar, Vinod G.
Title
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Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation
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01-01-2026
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Smart Innovation, Systems and Technologies;Volume;448 SIST;pp.17-28
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<a href="https://doi.org/10.1007/978-981-96-8126-6_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8126-6_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028362364?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028362364?origin=resultslist</a>
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Shrotriya S., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Banu P.K.N., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Kulkarni A., ThoughtSphere, CA, United States; Kumar V.G., Accenture, Bengaluru, India
Description
An account of the resource
Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, affecting individuals across all ages and sexes. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the field of medical diagnostics, offering the potential to enhance the detection, diagnosis, and prediction of breast cancer. Despite these advancements, challenges remain, including the need for large, diverse datasets to train robust models, the integration of AI tools into clinical workflows, and addressing ethical concerns related to AI in healthcare. This paper explores the application of Large Language Models (LLMs) using embeddings in breast cancer management, focusing on its ability to analyze medical data, including imaging, histopathology datasets to identify patterns that may be imperceptible to human experts. Datasets from real-world setting have been secured for analysis across multiple models. Convolutional Neural Network (CNN) model and custom-built large language model are employed to demonstrate the precision and accuracy of Generative AI techniques and observed that custom-built LLM with 98.44% outperforms the traditional AI approaches such as CNN with 61.72%. Future studies can further establish how these models can assist in stratifying patients based on risk, thereby enabling personalized treatment plans that can reduce overtreatment and improve quality of life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
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Artificial intelligence (AI); Breast cancer; Embeddings; Generative AI; Large language models (LLM); Machine learning (ML)
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Springer Science and Business Media Deutschland GmbH
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ISSN: 21903018; ISBN: 978-981968125-9;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
-
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Title
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Letters
Letter
Faculty Publications-Letters
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Title
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Realization of Green 5G Cellular Network Role in Medical Applications: Use of ChatGPT-AI
Subject
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5G cellular networks; ChatGPT; Green technologies; Medical research
Description
An account of the resource
Wireless communication in medical applications improves patient monitoring, care coordination, early disease detection, and patient empowerment. It improves healthcare and patient outcomes. The design and configuration of a solar-powered emergency battery backup system for 5G telecommunication base stations, including medical applications, may vary depending on local climate, power requirements, and resources. In this connection, uninterrupted power supply to the base stations become crucial. The author utilizes the ChatGPT-AI features and prepared this comprehensive letter for realizing the role of sustainable practices towards climatic changes. 2023, The Author(s) under exclusive licence to Biomedical Engineering Society.
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Janamala V.; Ram I.S.; Daram S.B.
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Annals of Biomedical Engineering, Vol-51, No. 11, pp. 2337-2339.
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Springer
Date
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2023-01-01
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<a href="https://doi.org/10.1007/s10439-023-03257-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10439-023-03257-3</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160822125&doi=10.1007%2Fs10439-023-03257-3&partnerID=40&md5=f6fce6dbf9bb9f6dfde2d525f948c2dc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160822125&doi=10.1007%2fs10439-023-03257-3&partnerID=40&md5=f6fce6dbf9bb9f6dfde2d525f948c2dc</a>
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All Open Access; Bronze Open Access; Green Open Access
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ISSN: 906964; PubMed ID: 37261590; CODEN: ABMEC
Format
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Online
Language
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English
Type
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Letter
Coverage
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Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Ram I.S., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering & Technology, Andhra Pradesh, Vijayawada, 521139, India; Daram S.B., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati, 517102, India
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Title
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Letters
Letter
Faculty Publications-Letters
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Title
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Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT
Subject
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ChatGPT; Disease detection; Edge computing; Humanoid; Internet of Things
Description
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Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized. The Author(s) under exclusive licence to Biomedical Engineering Society 2023.
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Venkataswamy R.; Janamala V.; Cherukuri R.C.
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Annals of Biomedical Engineering, Vol-52, No. 4, pp. 738-740.
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Springer
Date
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2024-01-01
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<a href="https://doi.org/10.1007/s10439-023-03316-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10439-023-03316-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164813620&doi=10.1007%2Fs10439-023-03316-9&partnerID=40&md5=598cdf7db1ebaeb7f675f7755ba34065" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164813620&doi=10.1007%2fs10439-023-03316-9&partnerID=40&md5=598cdf7db1ebaeb7f675f7755ba34065</a>
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ISSN: 906964; PubMed ID: 37453975; CODEN: ABMEC
Format
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Online
Language
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English
Type
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Letter
Coverage
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Venkataswamy R., Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Kanminike, Karnataka, Bangalore, 560074, India; Janamala V., Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Kanminike, Karnataka, Bangalore, 560074, India; Cherukuri R.C., Department of Computer Science and Engineering, Christ (Deemed to be University), Kanminike, Karnataka, Bangalore, 560074, India
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https://archives.christuniversity.in/files/original/9fae2d26b2f92d17883802a219f0e31c.pdf
033b8d3923c4818d45809a340e8ed34f
Dublin Core
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Title
A name given to the resource
MPHIL
Mphil
Mphil Thesis
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Reason and Belief in God in the Nietzschean Postmodern Philosophy: A Critical Study in the Light of Thomistic Philosophy
Creator
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D. PRINCE
Date
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2013
Source
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philosophy
-
https://archives.christuniversity.in/files/original/723cc42b61f6c527d06658bf93394a48.pdf
ccbebd697387df684776a3fa29b6669e
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Title
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MPHIL
Mphil
Mphil Thesis
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Reassertion of Dalit Identity Through Self- Narratives: An Analysis of Bamas Karukku and Sharankumar Limbales The Outcaste
Creator
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Sarkar Ipshita
Date
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2012
Source
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English Studies
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Faculty Publications
Article
Faculty Publications -Articles
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Jain, Prateek; Srivastava, Manish Kumar; Podilapu, Mounica Kavya
Title
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Rebel Foods: Pioneer in Indian internet restaurant and cloud kitchen
Date
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01-01-2025
Source
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Emerald Emerging Markets Case Studies;Volume;15;Issue;4;pp.1-12
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<a href="https://doi.org/10.1108/EEMCS-09-2023-0361" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/EEMCS-09-2023-0361</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025157931?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025157931?origin=resultslist</a>
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Jain P., Department of Strategy and Entrepreneurship, Birla Institute of Management Technology, Greater Noida, India; Srivastava M.K., School of Business and Management, Christ University, Bangalore, India; Podilapu M.K., Indian Institute of Management, Jammu, India
Description
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Learning outcomes The learning outcomes are as follows: to identify the potential for disruptive innovation in the changing dynamics of the online food industry with the emergence of technology; to examine appropriate responses to emerging disruptive (environmental) threats in developing a sustainable food retail business; to evaluate the process of executing change management within the organisation, as per the dynamic external business environment; and to develop marketing and distribution strategies that can be created based on a competitive business environment by applying disruptive technological initiatives. Case overview/synopsis Rebel Foods Private Limited was one of the largest internet restaurant and cloud kitchen companies, founded by Jaydeep Barman and Kallol Banerjee in 2016. Rebel Foods had established its presence in 70 cities owing to its extensive network of 450 cloud kitchens with more than 4, 000 internet restaurants. It was involved in all three steps of the food on demand industry: ordering, distribution and order fulfillment. Faasos (earlier name of Rebel Foods) had invested around four years in the restaurant industry using the brick-and-mortar format, slowly shifting to the online business model. Due to the spread of the COVID-19 virus in March 2020, Rebel Foods closed 70% of its kitchens and began selling do-it-yourself meal kits. After the pandemic, the kitchens at Rebel Foods used cutting-edge technology such as robotics, drum machines, automatic Tawas (pans) and auto fryers. The chefs at Rebel Foods had automated the cooking process using modern technology like computer vision and artificial intelligence. Despite all efforts, the companys operating revenue had dropped by 27.5% during FY21 to reach 405.1 crore INR, down from 558.7 crore INR in FY20. Sales have also been declining in the past few years. Complexity academic level The learners can be early-career entrepreneurs operating in the food industry and/or enrolled in postgraduate or short-term management programs. The case can also be used in Executive MBA courses in Strategic Management, Marketing Management and Entrepreneurship Management. Supplementary material Teaching notes are available for educators only. Subject code CSS 8: Marketing. 2025 Emerald Publishing Limited
Subject
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Customer relationship management; Digital Marketing; Food marketing; Internet marketing
Publisher
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Emerald Publishing
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ISSN: 20450621;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Rebuilding the Capabilities for Post COVID-19 Pandemic: Issues and Challenges of Bangalore Model of Development
Subject
The topic of the resource
Bangalore Model of Development; COVID-19 Pandemic; Inward Migration; IT/ITES Sector
Description
An account of the resource
The pace of urbanization has achieved considerable momentum in recent years with 34.93 per cent of India's population living in urban areas. However, the COVID - 19 pandemic has severely affected urban development with adverse effects on people's mobility, consumption level, health and poverty. Bangalore, the capital of Karnataka and the third largest city in India, has a population of 11 million and contributes more than one third of the state's GDP. The expansion of certain sectors including Information Technology, infrastructure and spread of educational institutions has fueled Bangalore's rapid growth in the past three decades which has made it a regional superpower in India, if not South Asia. This paper explores the unique features of the 'Bangalore Model of Development' as a regional development model and provides a systematic introspection of its capabilities. It discusses the impact of the pandemic on the key driving forces of Bangalore Model and assesses the current government measures. The situation analysis with the policy prescriptions would help to strengthen and sustain the urban system during the postpandemic times. 2022 IEEE.
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Rassendren M.J.G.; Karakunnel J.J.; Kureethara J.V.; Loganathan S.; Korrapati A.
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2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2022-01-01
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<a href="https://doi.org/10.1109/TQCEBT54229.2022.10041628" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT54229.2022.10041628</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149168001&doi=10.1109%2FTQCEBT54229.2022.10041628&partnerID=40&md5=f26194baace46724361cd609bfd7bdf7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149168001&doi=10.1109%2fTQCEBT54229.2022.10041628&partnerID=40&md5=f26194baace46724361cd609bfd7bdf7</a>
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ISBN: 978-166545361-5
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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Rassendren M.J.G., Christ University, Department of Economics, Bangalore, India; Karakunnel J.J., Christ University, Department of Economics, Bangalore, India; Kureethara J.V., Christ University, Department of Mathematics, Bangalore, India; Loganathan S., Christ University, Department of Economics, Bangalore, India; Korrapati A., Christ University, Department of Economics, Bangalore, India
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Newspaper Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Rebuilding Trust In Public Education: How To Stop Drift To Private Schools
Subject
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Samagra Shiksha, Government School, Government and Private Schools
Description
An account of the resource
NA
Creator
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John J Kennedy
Source
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Deccan Herald, 15-June-2025
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Deccan Herald
Date
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2025-June-15
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<a href="https://www.deccanchronicle.com/opinion/columnists/john-j-kennedy-rebuilding-trust-in-public-education-how-to-stop-drift-to-private-schools-1885305" target="_blank" rel="noreferrer noopener">https://www.deccanchronicle.com/opinion/columnists/john-j-kennedy-rebuilding-trust-in-public-education-how-to-stop-drift-to-private-schools-1885305</a>
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NA
Format
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Online
Language
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English
Type
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Newspaper Article
Coverage
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India
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https://archives.christuniversity.in/files/original/093631935feb8d9097722cad4806166b.pdf
ffa0e288976bfd220c4a08677c59f57e
Dublin Core
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Title
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MASTERS
Masters' Dissertation
Masters Dissertation
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Title
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Recall of advertisements placed during Indian crime shows /
Subject
The topic of the resource
Media Studies
Description
An account of the resource
Brad J. Bushman conducted a study in 1998 that studied the effects of watching televised violence on memory. Bushman conducted experiments to find out if the viewers remembered the advertisements after watching violence on television. Results showed that violence had a negative effect on recall of the brand and the advertisement message. The present research sought to study the effect of violence in Indian crime shows on the recall of the brand, the product category and the tag lines used in the advertisements.
Creator
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Rego, Liza. - 1424034
Source
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Master of Arts in Media and Communication Studies
Publisher
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CHRIST (Deemed To Be University)
Date
A point or period of time associated with an event in the lifecycle of the resource
2016-02-03
Contributor
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Department of Media Studies
Format
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PDF
Language
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English
Type
The nature or genre of the resource
Masters Dissertation
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Mukhopadhyay, Debarka; Bhattacharyya, Siddhartha; Platos, Jan; Zelinka, Ivan; Snasel, Vaclav; Palodhi, Kanik
Title
A name given to the resource
Recent Advancements inPhotonic Quantum Computation
Date
A point or period of time associated with an event in the lifecycle of the resource
01-01-2026
Source
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Lecture Notes on Data Engineering and Communications Technologies;Volume;274;pp.73-84
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<a href="https://doi.org/10.1007/978-3-032-07326-6_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07326-6_7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020388868?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020388868?origin=resultslist</a>
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Mukhopadhyay D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic; Platos J., VSB Technical University of Ostrava, Ostrava, Czech Republic; Zelinka I., VSB Technical University of Ostrava, Ostrava, Czech Republic; Snasel V., VSB Technical University of Ostrava, Ostrava, Czech Republic; Palodhi K., Department of of Applied Optics and Photonics, University of Calcutta, Kolkata, India
Description
An account of the resource
Photonic quantum computation has emerged as a promising paradigm for scalable and fault-tolerant quantum computing, leveraging the unique properties of photons, such as low decoherence and ease of manipulation. Recent advancements in integrated photonics, reduction of noise and error correction, and entanglement distribution have significantly enhanced the feasibility of large-scale photonic quantum processors. This article comprehensively reviews the latest developments in single-photon qubits, photonic quantum gates, photonic chips capable of performing quantum operations and communications, and hybrid quantum architectures. We also discuss breakthroughs in optimizing fidelity in quantum gates, reducing error rates, and chip-based quantum circuits that contribute to the rapid progress in this field. Furthermore, we analyze key challenges, including loss mitigation, ensuring the sustained preservation of quantum coherence across long distances, and the effect of temperature fluctuations and coupling between adjacent photonic waveguides while exploring potential solutions. By synthesizing recent research trends, this review aims to offer insights into the future trajectory of photonic quantum computation and its role in advancing quantum technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Subject
The topic of the resource
Linear and Non-linear optical devices; Photonic Quantum gates; Photonic qubits; Photons; Quantum computing
Publisher
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Springer Science and Business Media Deutschland GmbH
Relation
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ISSN: 23674512;
Language
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English
Type
The nature or genre of the resource
Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
-
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Title
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Reviews
Review
Faculty Publications- Reviews
Dublin Core
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Title
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Recent Advances in Analytical Techniques for Antidepressants Determination in Complex Biological Matrices: A Review
Subject
The topic of the resource
analytical; antidepressant; biological matrices; chromatography; determination
Description
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Depression is one of the most prevalent but severe of mental disorders, affecting thousands of individuals across the globe. Depression, in its most extreme form, may result in self-harm and an increased likelihood of suicide. Antidepressant drugs are first-line medications to treat mental disorders. Unfortunately, these medications are also prescribed for other in- and off-label conditions, such as deficit/hyperactivity disorders, attention disorders, migraine, smoking cessation, eating disorders, fibromyalgia, pain, and insomnia. This results in an increase in the use of antidepressant medications, leading to clinical and forensic overdose cases that could be either accidental or deliberate. The findings revealed that people who used antidepressants had a 33% greater chance of dying sooner than expected, compared to those who did not take the medications. Analytical techniques for precisely identifying and detecting antidepressants and their metabolic products in a variety of biological matrices are greatly needed to be developed and made available. Hence, this study attempts to discuss various analytical techniques used to identify and determine antidepressants in various biological matrices, which include urine, blood, oral fluid (saliva), and tissues, which are commonly encountered in clinical and forensic science laboratories. The Author(s) 2023.
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Kumar S.; Darshan S.; Baggi T.R.
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International Journal of Toxicology, Vol-42, No. 4, pp. 352-364.
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SAGE Publications Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1177/10915818221150779" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/10915818221150779</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146626681&doi=10.1177%2F10915818221150779&partnerID=40&md5=5243cfd15f15404064500477e52444ad" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146626681&doi=10.1177%2f10915818221150779&partnerID=40&md5=5243cfd15f15404064500477e52444ad</a>
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Restricted Access
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ISSN: 10915818; PubMed ID: 36630687; CODEN: IJTOF
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Online
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English
Type
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Review
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Kumar S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Darshan S., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Baggi T.R., Central Forensic Science Laboratory, Ministry of Home Affairs, Govt. of India, Ramanthapur, Hyderabad, India, University College of Science, Osmania University, Hyderabad, India
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Reviews
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Faculty Publications- Reviews
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
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Recent advances in bimetallic based nanostructures: Synthesis and electrochemical sensing applications
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Bimetallic nanoparticles; Biomolecules; Detection limit; Electrochemical sensors; Sensitivity
Description
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Detection of biomolecules is paramount for the diagnosis of various health conditions in humans for improving the quality as well as the standard of living. Many of the existing analytical detection methods being used have one or the other drawbacks. This has led to revolutionary paradigms in research and development in the electrochemical sensing of biomolecules. Nanoparticles have enhanced surface area to volume ratio and are highly effective as sensors due to their improved electrocatalytic properties. Bimetallic nanoparticles such as platinum, nickel, iron, palladium, and gold-based nanoparticles in combination with other metals are being extensively used for electrode modification owing to their unique electrocatalytic characteristics. The synergistic effect of two metals provides us with highly sensitive electrodes. Over a while, there has been a significant increase in research carried out in this field, which encouraged us to carry out a thorough survey of the development of bimetallic nanoparticle-based electrochemical sensors for the sensing of glucose, hydrogen peroxide, ascorbic acid, dopamine, and uric acid. We have also mentioned the different types of electrode modifications for developing electrochemical sensors. This review provides readers a comparative study of the analytical parameters of the various electrochemical sensors for linear range, detection limit, and sensitivity. 2020 Elsevier B.V.
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Rajeev R.; Datta R.; Varghese A.; Sudhakar Y.N.; George L.
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Microchemical Journal, Vol-163
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Elsevier Inc.
Date
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2021-01-01
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<a href="https://doi.org/10.1016/j.microc.2020.105910" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.microc.2020.105910</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098940263&doi=10.1016%2Fj.microc.2020.105910&partnerID=40&md5=d22e1d63e69baa91c5cf828e244973b4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098940263&doi=10.1016%2fj.microc.2020.105910&partnerID=40&md5=d22e1d63e69baa91c5cf828e244973b4</a>
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Restricted Access
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ISSN: 0026265X; CODEN: MICJA
Format
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Online
Language
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English
Type
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Review
Coverage
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Rajeev R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Datta R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Sudhakar Y.N., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; George L., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India