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Your search keyword '"Rajitha, B."' showing total 111 results
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111 results on '"Rajitha, B."'

3. A Ensembled Model to Detect COVID-19 and Pneumonia Using Chest X-Ray Images

Author

Prince, Master, Verma, Pooja, Rajitha, B., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Venkata Rao, Ravipudi, editor, and Taler, Jan, editor

Published
2024
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5. A Novel Bayesian Fusion Model for IR and Visible Images

Author

Fahimuddin, S., Sree Keerthana Reddy, A., Rajitha, B., Sai Prasanth, K., Sai, U., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Senatore, Sabrina, editor, and Gunjan, Vinit Kumar, editor

Published
2023
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9. Weighted ensemble approach for smoke-like scene classification in remote sensing images.

Author

Verma, Pooja and Rajitha, B.

Abstract

Damage to the environment or the ecosystem because of natural lightning or human-induced circumstances can lead to losses that reverberate across decades. Harnessing satellite images can be an imperative change that can help avoid relentless and escalating fires. Continuous and comprehensive coverage by remote sensing enables the detection of small plumes of fire or smoke that can emerge as wildfire if not detected early. Mainly, research has been focused on only limited predefined classes. To overcome this limitation the dataset provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), referred as USTC_SmokeRS is utilized in this study. This dataset includes smoke and situations that resemble smoke, such as clouds, dust, and haze. USTC- SmokeRS dataset helps in developing a system that can adapt across diverse geographical regions. This article employs three different pre-trained models: MobilenetV2, InceptionV3, and DenseNet201, which are optimized and are used for the smoke classification task. Along with optimization of the models, an ensemble algorithm is also proposed to combine the capabilities of the three individual models. The proposed ensemble model achieved the highest accuracy of 95.73% on this dataset. Further, the Grad-CAM technique is adapted for localization task. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Automated Short Video Caption Generation Using Video Features and Audio Content

Author

Choudhary, Shubhra, Ahuja, Yogesh Kumar, Makhija, Nishkarsh, Tangudu, Srihitha, Rajitha, B., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Gupta, Mukesh Kumar, editor, Tomar, G. S., editor, and Lipo, Wang, editor

Published
2021
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11. Gradient Feature-Based Classification of Patterned Images

Author

Srivastava, Divya, Rajitha, B., Agarwal, Suneeta, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Singh, Pradeep Kumar, editor, Wierzchoń, Sławomir T., editor, Tanwar, Sudeep, editor, Ganzha, Maria, editor, and Rodrigues, Joel J. P. C., editor

Published
2021
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13. Survey on Soft Computing Methods for Accident Condition and Severity Predictions

Author

Kannojiya, Ashwani Kumar, Maurya, Richa, Rajitha, B., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pandian, A. Pasumpon, editor, Ntalianis, Klimis, editor, and Palanisamy, Ram, editor

Published
2020
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38. Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques

Author

Rajitha B.

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, 021101 geological & geomatics engineering
Abstract

Abnormal behavior detection from on-line/off-line videos is an emerging field in the area of computer vision. This plays a vital role in video surveillance-based applications to provide safety for humans at public places such as traffic signals, shopping malls, railway stations, etc. Surveillance cameras are meant to act as digital eyes (i.e., watching over activities at public places) and provide security. There are a number of cameras deployed at various public places to provide video surveillance, but in reality, they are used only after some incident has happened. Moreover, a human watch is needed in order to detect the person/cause of the incident. This makes surveillance cameras passive. Thus, there is a huge demand to develop an intelligent video surveillance system that can detect the abnormality/incident dynamically and accordingly raise an alarm to the nearest police stations or hospitals as per requirement. If AI-supported CCTV systems are deployed at commercial and traffic areas, then we can easily detect the incidents/crimes, and they can be traced in minimal time.

Published
2021

45. Evolving Deep Neural Networks for Efficient Image Classification

Author

Abhinav Agnihotri and Rajitha B

Subjects
Contextual image classification, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Cognitive neuroscience of visual object recognition, Word error rate, Pattern recognition, Image (mathematics), ComputingMethodologies_PATTERNRECOGNITION, Video tracking, Deep neural networks, Action recognition, Feature estimation, Artificial intelligence, business
Abstract

Image classification or recognition has become base for many real time applications like: object recognition, object tracking, action recognition, drug recovery, video tracking/recognition etc. These applications accuracy depends on the results of image classification system. In literature many researchers have used low level features such as Texture, Gabor, Gradient, SURF and high level features such as CNN for image classification. In recent years, Deep Neural Networks are widely used for image analysis due to its deep feature estimation procedure (i.e. the more no. of features leads to good classification). Thus, this paper proposes three different models based on deep neural networks for efficient Image Classification. The proposed methods are focused on reducing the error rate while increasing the classification accuracy (above 95%).

Published
2019

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