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6,300 results on '"Reddy, N. A."'

3. EXB_RNN: A Hybrid Ensemble Approach for Enhanced Aspect-Based Sentiment Analysis

Author

Ganesh, Devadi, Rao, P. V. Venkateswara, Reddy, N. Sudhakar, Suneetha, Sampathirao, Lakineni, Prasanna Kumar, Yoganandh, Sampathirao, 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, Das, Asit Kumar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, Himabindu, M., editor, Vimal, S., editor, and Pelusi, Danilo, editor

Published
2025
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5. Design of a Wideband Metamaterial-Inspired Microwave Absorber Implementing Anti-symmetric Geometry for Radar Installation Applications

Author

Paik, Harikrishna, Venkatsena Reddy, N., Sainadh, Y., Ganga Purusotham, A., 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, Kumar, Adesh, editor, Pachauri, Rupendra Kumar, editor, Mishra, Ranjan, editor, and Kuchhal, Piyush, editor

Published
2025
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8. Exploring the Impact of Security, Confidentiality, and Related Factors on M-Banking Adoption in India: A Machine Learning Perspective

Author

Jahnavi, M., Bung, Purushottam, Nagasubba Reddy, N., Murugesan, T. K., Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, Hamdan, Allam, editor, and Braendle, Udo, editor

Published
2025
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9. Food Spoilage Detection System

Author

Anitha, Vulugundam, Harshavalli, W., Shreya, K., Reddy, N. Deetya, Varsha, M., 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, 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, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
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10. Exploring Cold Start Challenges in Recommender Systems Using Deep Learning Approaches

Author

Matam, Vinay Kumar, Reddy, N. Madhusudhana, 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, 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, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
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11. JWST FRESCO: a comprehensive census of H$\beta$+[OIII] emitters at 6.8<z<9.0 in the GOODS fields

Author

Meyer, R. A., Oesch, P. A., Giovinazzo, E., Weibel, A., Brammer, G., Matthee, J., Naidu, R. P., Bouwens, R. J., Chisholm, J., Covelo-Paz, A., Fudamoto, Y., Maseda, M., Nelson, E., Shivaei, I., Xiao, M., Herard-Demanche, T., Illingworth, G. D., Kerutt, J., Kramarenko, I., Labbe, I., Leonova, E., Magee, D., Matharu, J., Lyon, G. Prieto, Reddy, N., Schaerer, D., Shapley, A., Stefanon, M., Wozniak, M. A., and Wuyts, S.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present the census of H$\beta$+[OIII] 4960,5008 \AA emitters at 6.87$ galaxies. We report a rapid decline of the [OIII] luminosity density at $z\gtrsim 6-7$ which cannot be explained by the evolution of the cosmic star-formation rate density. Finally we find that FRESCO detects in only 2h galaxies likely accounting for $\sim 10-20\%$ of the ionising budget at $z=7-8$ (assuming an escape fraction of 10%), raising the prospect of directly detecting a significant fraction of the sources of reionisation with JWST., Comment: 20 pages + appendices. Accepted in MNRAS. Public catalogue release at https://github.com/rameyer/fresco. V3: matching accepted version

Published
2024
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13. WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion

Author

Vuong, Khiem, Reddy, N. Dinesh, Tamburo, Robert, and Narasimhan, Srinivasa G.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work, we introduce a novel framework for automatically generating a large, realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box, segmentation, keypoint) and 3D (pose, shape) predictions as pseudo-groundtruth, unoccluded 3D objects are identified automatically and composited into the background in a clip-art style, ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction, particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes., Comment: To appear in CVPR 2024. Homepage: https://www.cs.cmu.edu/~walt3d

Published
2024

14. GRITv2: Efficient and Light-weight Social Relation Recognition

Author

Reddy, N K Sagar, Kasera, Neeraj, and Thakur, Avinash

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Our research focuses on the analysis and improvement of the Graph-based Relation Inference Transformer (GRIT), which serves as an important benchmark in the field. We conduct a comprehensive ablation study using the PISC-fine dataset, to find and explore improvement in efficiency and performance of GRITv2. Our research has provided a new state-of-the-art relation recognition model on the PISC relation dataset. We introduce several features in the GRIT model and analyse our new benchmarks in two versions: GRITv2-L (large) and GRITv2-S (small). Our proposed GRITv2-L surpasses existing methods on relation recognition and the GRITv2-S is within 2% performance gap of GRITv2-L, which has only 0.0625x the model size and parameters of GRITv2-L. Furthermore, we also address the need for model compression, an area crucial for deploying efficient models on resource-constrained platforms. By applying quantization techniques, we efficiently reduced the GRITv2-S size to 22MB and deployed it on the flagship OnePlus 12 mobile which still surpasses the PISC-fine benchmarks in performance, highlighting the practical viability and improved efficiency of our model on mobile devices.

Published
2024

15. Lyman Continuum Leaker Candidates at $z\sim3-4$ in the HDUV Based on a Spectroscopic Sample of MUSE LAEs

Author

Kerutt, J., Oesch, P. A., Wisotzki, L., Verhamme, A., Atek, H., Herenz, E. C., Illingworth, G. D., Kusakabe, H., Matthee, J., Mauerhofer, V., Montes, M., Naidu, R. P., Nelson, E., Reddy, N., Schaye, J., Simmonds, C., Urrutia, T., and Vitte, E.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

In recent years, a number of Lyman continuum (LyC) leaker candidates at intermediate redshifts have been found, providing insight into how the Universe was reionised at early cosmic times. Here we identify new LyC leaker candidates at $z\approx 3-4.5$ and compare them to objects from the literature to get an overview of the different observed escape fractions and their relation to the properties of the Lyman $\alpha$ (Ly$\alpha$) emission line. The aim of this work is to test indicators for LyC leakage and to improve our understanding of the kind of galaxies from which LyC radiation can escape. We use data from the Hubble Deep Ultraviolet (HDUV) legacy survey to search for LyC emission based on a sample of $\approx 2000$ Ly$\alpha$ emitters (LAEs) detected previously in two surveys with the Multi-Unit Spectroscopic Explorer (MUSE), MUSE-Deep and MUSE-Wide. Based on their known redshifts and positions, we look for potential LyC leakage in the WFC3/UVIS F336W band of the HDUV. The escape fractions are measured and compared based on spectral energy distribution (SED) fitting performed using the CIGALE software. We add twelve objects to the sample of known LyC leaker candidates, one of which was previously known, and compare their Ly$\alpha$ properties to their escape fractions. We find escape fractions between $\sim 20\%$ and $\sim 90\%$, assuming a high transmission in the intergalactic medium (IGM). We show a method to use the number of LyC leaker candidates we find to infer the underlying average escape fraction of galaxies, which is $\approx 12\%$. Based on their Ly$\alpha$ properties, we conclude that LyC leakers are not very different from other high-z LAEs and suggest that most LAEs could be leaking LyC even if this can not always be detected due to the direction of emission and the transmission properties of the IGM.

Published
2023

22. MOSAIC: Multi-Object Segmented Arbitrary Stylization Using CLIP

Author

Ganugula, Prajwal, Kumar, Y S S S Santosh, Reddy, N K Sagar, Chellingi, Prabhath, Thakur, Avinash, Kasera, Neeraj, and Anand, C Shyam

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Style transfer driven by text prompts paved a new path for creatively stylizing the images without collecting an actual style image. Despite having promising results, with text-driven stylization, the user has no control over the stylization. If a user wants to create an artistic image, the user requires fine control over the stylization of various entities individually in the content image, which is not addressed by the current state-of-the-art approaches. On the other hand, diffusion style transfer methods also suffer from the same issue because the regional stylization control over the stylized output is ineffective. To address this problem, We propose a new method Multi-Object Segmented Arbitrary Stylization Using CLIP (MOSAIC), that can apply styles to different objects in the image based on the context extracted from the input prompt. Text-based segmentation and stylization modules which are based on vision transformer architecture, were used to segment and stylize the objects. Our method can extend to any arbitrary objects, styles and produce high-quality images compared to the current state of art methods. To our knowledge, this is the first attempt to perform text-guided arbitrary object-wise stylization. We demonstrate the effectiveness of our approach through qualitative and quantitative analysis, showing that it can generate visually appealing stylized images with enhanced control over stylization and the ability to generalize to unseen object classes., Comment: Camera ready, New Ideas in Vision Transformers workshop, ICCV 2023

Published
2023

27. Office-like Test Chambers to Measure Cool Roof Energy Savings in Four Indian Climates

Author

Arumugam, R, B, Sasank, T, Rajappa, N, Vinay, Garg, V, Reddy, N, and Levinson, R

Abstract

Selecting a high albedo (solar reflectance) waterproofing layer on the top of a roof helps lower the roof’s surface temperature and reduce the air conditioning energy consumption in the top floor of a building. The annual energy savings depend on factors including weather, internal loads, and building operation schedule. To demonstrate the energy saving potential of high albedo roofs, an apparatus consisting of two nearly identical test chambers (A and B) has been built in four Indian climates: Chennai (hot & humid), Bangalore (temperate), Jhagadia (Hot & dry) and Delhi (composite). Each chamber has well-insulated walls to mimic the core of an office building. Both chambers have the same construction, equipment, and operating schedule, differing only in roof surface. The reinforced cement concrete roof of Chamber A is surfaced with a low-albedo cement layer, while that of Chamber B is surfaced with a high-albedo water proof membrane (change in solar reflectance of 0.28). The experiment will be carried out for one year to explore seasonal variations in energy savings. Initial results in the month of July (post summer) shows that savings from high albedo roof ranges from 0.04 kWh/m2/day in temperate climates, to 0.08 kWh/m2/day in hot & dry climate.

Published
2023

28. Reconstructing Animatable Categories from Videos

Author

Yang, Gengshan, Wang, Chaoyang, Reddy, N Dinesh, and Ramanan, Deva

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid categories or single instances. We present RAC that builds category 3D models from monocular videos while disentangling variations over instances and motion over time. Three key ideas are introduced to solve this problem: (1) specializing a skeleton to instances via optimization, (2) a method for latent space regularization that encourages shared structure across a category while maintaining instance details, and (3) using 3D background models to disentangle objects from the background. We show that 3D models of humans, cats, and dogs can be learned from 50-100 internet videos., Comment: Project page: https://gengshan-y.github.io/rac-www/

Published
2023

29. The JWST FRESCO Survey: Legacy NIRCam/Grism Spectroscopy and Imaging in the two GOODS Fields

Author

Oesch, P. A., Brammer, G., Naidu, R. P., Bouwens, R. J., Chisholm, J., Illingworth, G. D., Matthee, J., Nelson, E., Qin, Y., Reddy, N., Shapley, A., Shivaei, I., van Dokkum, P., Weibel, A., Whitaker, K., Wuyts, S., Covelo-Paz, A., Endsley, R., Fudamoto, Y., Giovinazzo, E., Herard-Demanche, T., Kerutt, J., Kramarenko, I., Labbe, I., Leonova, E., Lin, J., Magee, D., Marchesini, D., Maseda, M., Mason, C., Matharu, J., Meyer, R. A., Neufeld, C., Lyon, G. Prieto, Schaerer, D., Sharma, R., Shuntov, M., Smit, R., Stefanon, M., Wyithe, J. S. B., and Xiao, M.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present the JWST Cycle 1 53.8hr medium program FRESCO, short for "First Reionization Epoch Spectroscopically Complete Observations". FRESCO covers 62 arcmin$^2$ in each of the two GOODS/CANDELS fields for a total area of 124 arcmin$^2$ exploiting JWST's powerful new grism spectroscopic capabilities at near-infrared wavelengths. By obtaining ~2 hr deep NIRCam/grism observations with the F444W filter, FRESCO yields unprecedented spectra at R~1600 covering 3.8 to 5.0 $\mu$m for most galaxies in the NIRCam field-of-view. This setup enables emission line measurements over most of cosmic history, from strong PAH lines at z~0.2-0.5, to Pa$\alpha$ and Pa$\beta$ at z~1-3, HeI and [SIII] at z~2.5-4.5, H$\alpha$ and [NII] at z~5-6.5, up to [OIII] and H$\beta$ for z~7-9 galaxies, and possibly even [OII] at z~10-12. FRESCO's grism observations provide total line fluxes for accurately estimating galaxy stellar masses and calibrating slit-loss corrections of NIRSpec/MSA spectra in the same field. Additionally, FRESCO results in a mosaic of F182M, F210M, and F444W imaging in the same fields to a depth of ~28.2 mag (5 $\sigma$ in 0.32" diameter apertures). Together with this publication, the v1 imaging mosaics are released as high-level science products via MAST. Here, we describe the overall survey design and the key science goals that can be addressed with FRESCO. We also highlight several, early science results, including: spectroscopic redshifts of Lyman break galaxies that were identified almost 20 years ago, the discovery of broad-line active galactic nuclei at z>4, and resolved Pa$\alpha$ maps of galaxies at z~1.4. These results demonstrate the enormous power for serendipitous discovery of NIRCam/grism observations., Comment: 12 pages, 11 figures; MNRAS in press; for more information on the survey and data releases, see http://jwst-fresco.astro.unige.ch/ and https://archive.stsci.edu/hlsp/fresco

Published
2023

30. Fundamentals of Computational Design in Nanomaterials

Author

Sreejith, S., Ajayan, J., Uma Reddy, N. V., Radhika, J. M., Mathew, Jimsha K., Sivasankari, B., Raghavendra Reddy, N. V., Lockwood, David J., Series Editor, Naik, Ramachandra, editor, Nagaswarupa, H. P., editor, Ananda Murthy, H. C., editor, and Sillanpää, Mika E. T., editor

Published
2024
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31. Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection

Author

Ghosh, Anurag, Reddy, N. Dinesh, Mertz, Christoph, and Narasimhan, Srinivasa G.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to architectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time detection performance. In this work, we propose a learnable geometry-guided prior that incorporates rough geometry of the 3D scene (a ground plane and a plane above) to resample images for efficient object detection. This significantly improves small and far-away object detection performance while also being more efficient both in terms of latency and memory. For autonomous navigation, using the same detector and scale, our approach improves detection rate by +4.1 $AP_{S}$ or +39% and in real-time performance by +5.3 $sAP_{S}$ or +63% for small objects over state-of-the-art (SOTA). For fixed traffic cameras, our approach detects small objects at image scales other methods cannot. At the same scale, our approach improves detection of small objects by 195% (+12.5 $AP_{S}$) over naive-downsampling and 63% (+4.2 $AP_{S}$) over SOTA., Comment: CVPR 2023 Accepted Paper, 21 pages, 16 Figures

Published
2023

37. Assessment of Enhanced Email Spam Detection System Through Machine Learning Algorithms

Author

Sreenivasulu, Meruva, Ramesh Babu, Ch., Ramanjaneya Reddy, N., Reddy, Sowmya Kethi, 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, Simic, Milan, editor, Bhateja, Vikrant, editor, Murty, M. Ramakrishna, editor, and Panda, Sandeep Kumar, editor

Published
2024
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38. Detecting Fake Face Images in Biometric Systems Using LBP Feature Extraction and CNN

Author

Gowri Sree Lakshmi, N., Chaturya Sri, K., Kusuma Kumari, R., Sobha Rani, K., Chandra Sekhar Reddy, N., 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, Simic, Milan, editor, Bhateja, Vikrant, editor, Murty, M. Ramakrishna, editor, and Panda, Sandeep Kumar, editor

Published
2024
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39. Fast Iterative Filtering-Based Deep Belief Network for Accurate Short-term Electric Load Forecasting

Author

Sai Satwik Reddy, N., Venkata Siva Manoj, A., Mohan, Neethu, Sachin Kumar, S., Soman, K. P., 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, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, Jaiswal, Ajay, editor, and Kumar, Prabhat, editor

Published
2024
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40. Cottonseed Biodiesel and Butanol Blends as a Fuel for Diesel Engine

Author

Ramachandruni, Madhur, Reddy, N. Sreekara, Vamsi, Madu, Dharanidharareddy, Kotebavi, Vinod, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Singh, Achhaibar, editor, Mishra, Debi Prasad, editor, and Bhat, Ganapathi, editor

Published
2024
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41. Case Study on Smart Irrigation Using Internet of Things and XAI Techniques

Author

Vandana, Konduru, Sravya, Singampalli, Supriya, Mucherla, Reddy, N. Gunavardhan, Bale, John Babu, Sarma, I. V. Satyanarayana, Manitha, P. V., Kacprzyk, Janusz, Series Editor, Dorigo, Marco, Editorial Board Member, Engelbrecht, Andries, Editorial Board Member, Kreinovich, Vladik, Editorial Board Member, Morabito, Francesco Carlo, Editorial Board Member, Slowinski, Roman, Editorial Board Member, Wang, Yingxu, Editorial Board Member, Jin, Yaochu, Editorial Board Member, Sumithra, M. G., editor, Sathyamoorthy, Malathy, editor, Manikandan, M., editor, Dhanaraj, Rajesh Kumar, editor, and Ouaissa, Mariya, editor

Published
2024
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43. Development and Performance Evaluation of NavIC-Based Reefer Monitoring System

Author

Praneeth Reddy, N., Sumanth Reddy, B., Supraja Reddy, A., Satyanarayana, K., Dileep Reddy, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Kumar Jain, Pradip, editor, Nath Singh, Yatindra, editor, Gollapalli, Ravi Paul, editor, and Singh, S. P., editor

Published
2024
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45. Cloud Based Attendance Management System Using Face Recognition with Haar Cascade Classifier and CNN

Author

Reddy, N. V. V. Ramana, Reddy, Vijaya Bhasker, Reddy, K. Manohar, Pyla, Sai Karthika, Reddy, R. Deepak, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Satheeskumaran, S., editor, Zhang, Yudong, editor, Balas, Valentina Emilia, editor, Hong, Tzung-pei, editor, and Pelusi, Danilo, editor

Published
2024
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46. Classification of Colorectal Cancer Tissue Utilizing Machine Learning Algorithms

Author

Reddy, N. Sai Satwik, Manoj, A. Venkata Siva, Sowmya, V., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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47. Transfer Learning Approach for Differentiating Parkinson’s Syndromes Using Voice Recordings

Author

Reddy, N. Sai Satwik, Manoj, A. Venkata Siva, Reddy, V. Poorna Muni Sasidhar, Aadhithya, Aadharsh, Sowmya, V., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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