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Your search keyword '"Reddy, V."' showing total 74 results
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74 results on '"Reddy, V."'

2. Emotion and Gender Classification Using Convolution Neural Networks

Author

Pandi, Chiranjeevi, Adi Narayana Reddy, K., Alladi, Ramesh, Chandra Sekhar Reddy, V., Sumithabhashini, 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, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2022
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3. Deep learning-based privacy-preserving recommendations in federated learning.

Author

Kolli, Chandra Sekhar, Krishna Reddy, V. V., Reddy, Tatireddy Subba, Chandol, Mohan Kumar, Dasari, Durga Bhavani, and Reddy, Mule RamaKrishna

Subjects
*FEDERATED learning, *DEEP learning, *CONVOLUTIONAL neural networks, *DATA privacy, *DATA security
Abstract

Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Literature Survey—Food Recognition and Calorie Measurement Using Image Processing and Machine Learning Techniques

Author

Hemalatha Reddy, V., Kumari, Soumya, Muralidharan, Vinitha, Gigoo, Karan, Thakare, Bhushan S., 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, Liang, Qilian, Series Editor, Martin, 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, 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, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, and Mozar, Stefan, editor

Published
2020
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5. An Efficient Intrusion Detection System with Convolutional Neural Network

Author

Maheshwar Reddy, V., Ravi Prakash Reddy, I., Adi Narayana Reddy, K., 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, Chillarige, Raghavendra Rao, editor, Distefano, Salvatore, editor, and Rawat, Sandeep Singh, editor

Published
2020
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6. ML prediction and ANN-PSO based optimization for compressive strength of blended concrete.

Author

Reddy V., Mallikarjuna, Lomada, Ramaprasad Reddy, Kumar C., Vivek, Karthikeyan R., Sergei, Solovev, Vatin, Nikolai Ivanovich, and Joshi, Abhishek

Subjects
*ARTIFICIAL neural networks, *PARTICLE swarm optimization, *COMPRESSIVE strength, *DEEP learning, *MACHINE learning
Abstract

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCM). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete that was developed for ground-granulated blast furnace slag (GGBS) along with Alccofine 1203 (AF). Boosting regressions of Gradient Boosting Regression (GBR), Extreme Gradient Boosting Regression (XGBR) and Light Gradient Boosting Regression (LGBR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 120 experimental conditions. Moreover, a comparative study was performed among GBR, XGBR and LGBR to benchmark the proposed model against a given combination of features (parameters/factors) and compressive strength. By using CPC-X Neural Power software, the experimental conditions were optimized using the Particle Swam Optimization (PSO) algorithm after the model was created by the Artificial Neural Network (ANN). Of the three boosting algorithms, the smallest errors along with the largest coefficient of determination were observed in the GBR algorithm and the optimized values of CS is 77.4 MPa and its optimized process factors (Alccofine, GGBS, Cement, C.A, F.A, SP dosage and days) were 24.77 kg/m³, 24.63 kg/m³, 534.92 kg/m³, 1100.08 kg/m³, 628.64 kg/m³, 4.20 lit/m³ and 29.03 days respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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7. An efficient novel approach for early detection of lung cancer through document image classification with distributed machine learning paradigms.

Author

Usha Priya, B. and Lokeswara Reddy, V.

Subjects
*DEEP learning, *IMAGE recognition (Computer vision), *LUNG cancer, *EARLY detection of cancer, *CONVOLUTIONAL neural networks, *MACHINE learning
Abstract

Recent advancements in deep learning have made it easier to identify and classify lung disorders in medical imaging. As a result, there are several works in the literature on the diagnosis of lung disorders using deep learning. The deep learning research for detecting lung illnesses in medical imaging is presented in this section. Only few research publications on deep learning for detecting lung disorders has been published in the recent five years. Early detection of coronavirus disease in 2019 (COVID-19) is crucial to control the pandemic. COVID-19 is spreading rapidly around the world. The use of computed tomography (CT) images enables rapid and accurate detection of COVID-19. The deep learning techniques of the proposed method are based on a Hadoop Distributed Deep Convolutional Neural Network (HdiDConvNNet) address the problem statement for early detection of lung diagnose. The purpose of our research is to use various distributed deep learning algorithms to distinguish CT scan images with COVID-19 and non-COVID 19. Clinicians can use automated COVID-19 diagnostics using CT scan images as a fast and efficient way to detect COVID - 19. Precision and Recall are both important factor for this research work, hence the results are analyzed in terms of F1-Score. The confusion matrix and results for the F1-Score, Precision, Recall, and overall Accuracy are also presented to provide a complete analysis of the Model performance. The proposed methods have had a significant impact in the country as a warning to society. Therefore, the existence of such a hypothesis for innovative research will significantly reduce the incidence of lung cancer deaths affected by Covid-19 and will be considered for prior public advice. [ABSTRACT FROM AUTHOR]

Published
2023
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8. A study of assistive technologies for visually impaired.

Author

Shanmukhi, K. Durga, Kumar, P. Naga Pavan, Anand, M. Viswanath, Ranjith Kumar, B., and Reddy, V. Siva

Subjects
DEEP learning, PEOPLE with visual disabilities, OBJECT recognition (Computer vision), COMPUTER vision, PATTERN recognition systems, ARTIFICIAL intelligence, ASSISTIVE technology
Abstract

From the past few years, The navigation systems are advancing with every possible technology. Researchers are do-ing there best to improve the navigation system and guidance systems. These advancement in the navigation systems with some modification can be helpful to the visually impaired people to navigate in there day to day life. This paper gives a review of the better technologies present in the society to help the visually impaired. This paper covers the use of different technologies like AI(artificial intelligence),Deep learning, IOT etc in creating useful gadgets for the visually impaired people. Traditional approaches such as: cane or guide dog, And there are gadgets such as infrared based cane or ultrasonic blind stick with are limited by the distance of identifying the objects. And there are some gadgets which are microcontroller based and microprocessor based. Some of the proposed systems uses the computer vision for object detection, character recognition, face detection etc. There are wide range of gadgets involving various technologies to help the visually impaired. The review covers various technologies used in different proposed systems for the visually impaired people and help to develop a better navigating device for visually impaired. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Abnormal Driving Detection In Video Using Alternative Wide Group Residual Densely.

Author

Reddy, A. Hari Prasad, Kumar, Peeroji Shiva, and Reddy, V. Shiva Narayana

Subjects
AUTOMOBILE driving, PASSENGERS, CONVOLUTIONAL neural networks, DEEP learning, GENERALIZATION
Abstract

Video-based abnormal driving behavior detection is becoming increasingly popular for the time being. It is highly important in ensuring the safety of drivers and passengers in the vehicle, and it is an essential step in realizing automatic driving at the current stage. Thanks to recent developments in deep learning techniques, this challenging detection task can be largely facilitated via the prominent generalization ability of advanced deep learning models and large volumes of video clips that are indispensable for thoroughly training these data-driven deep learning models. Deep ability combination strategies are careworn, and three precise deep gaining knowledge of-based combination variations are inspired by using the days proposed. Famous Alternative wide group residual densely and densely connected convolution network (DenseNet) is provided to meet the video-primarily based unusual riding movement's discovery activity for the first time. [ABSTRACT FROM AUTHOR]

Published
2022

10. Hybrid Adaptive Deep Learning Based Human Action Recognition.

Author

Kalli, Siva Nagi Reddy, Reddy, V. Sidda, and Leberu, Rangaiah

Subjects
*HUMAN behavior, *DEEP learning, *CONVOLUTIONAL neural networks, *COMPUTER algorithms, *ACCURACY
Abstract

Many Human Action Recognition (HAR) systems are used to recognize everyday human behaviors such as standing, walking, sitting, running, cooking, and exercising. The existing human action recognition technology is not perfect. It has poor adaptability and low accuracy. Deep learning is used to improve action recognition accuracy and motion detection. Deep neural network models can be difficult to train, such as gradient disappearance, gradient explosion, and over-fitting. This paper will help to reduce the difficulty in training the above deep neural model parameter initialization method based upon the activation function for the multi-layer networks. It also addresses the problem of model training deep neural networks. The presented hybrid algorithm Improved Spotted Hyena algorithm and Seagull Optimization Algorithm, (ISHOSOA), is used to solve optimization algorithms and obtain minimum optimum values. The segmented image is further classified using different inputs by Adaptive Deep Convolution Networks (ADCNN), with feature maps. The classification method provides performance metrics such as Accuracy and Precision, F1 measure, along with Recall that can be used to recognize the most accurate results and the best classifiers. This algorithm is highly efficient in improving classification accuracy and solving global optimization problems. Experimental results show that the proposed algorithm has a 98.21% higher accuracy than other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Published
2022

11. OHGCNet: Optimal feature selection-based hybrid graph convolutional network model for joint DR-DME classification.

Author

Purna Chandra Reddy, V. and Gurrala, Kiran Kumar

Subjects
DEEP learning, FUNDUS oculi, FEATURE selection, DIABETIC retinopathy, MACULAR edema, DEER hunting, MACHINE learning
Abstract

• Proposing HGCN-RACSA for extracting the individual DME, DR and joint DR-DME features. • Adopting optimal feature selection i.e., MDHOA for improving the classification accuracy. • Using the HGCN classifier for multi-grade classification of DME, DR and joint DR-DME. • The proposed OHGCNet obtained an improved accuracy as compared to state-of-art methods. Diabetic retinopathy (DR) is the crucial eye disease, which effects the blood vessels of the patient suffering with diabetes. The Diabetic macular edema (DME) is another crucial disease, that arises when DR reaches and damages the macula, resulting in fluid buildup in the retina. Individual, and joint screening methods of these DME, and DR require experts to manually analyze color eye fundus images. However, developing an efficient screening-oriented therapy is a time-consuming and costly endeavor because of the difficult nature of the screening approach and a scarcity of qualified human resources. In addition, automated systems are attempting to deal with these issues, and standard machine learning and deep learning processes have failed to fulfil the required criterion of performance and accuracy. Thus, this article focuses on the implementation of graph learning-based graph convolutional network (GCN) for the classification of joint DR-DME with enhanced accuracy. Initially, hybrid GCN (HGCN) with relation aware channel-spatial attention (RACSA) model is developed for extracting the deep features of individual DME, DR, and joint DR-DME. Further, a novel bio-optimization approach named modified deer hunting optimization algorithm (MDHOA) is employed as an optimal feature selection technique for the extraction of salient features. Finally, HGCN is utilized as a classifier for the classification of individual DME, DR, and joint DR-DME diseases. The extensive simulations conducted on IDRiD dataset shows that proposed OHGCNet performed superior as compared to the conventional methods with the improvement in classification accuracy as 5.11%, 3.88%, and 5.47% for DME, DR, and joint DR-DME. Furthermore, the performance of proposed OHGCNet also compared with the ISBI-sub challenge 2 and resulted in superior position as compared with leadership contenders. [ABSTRACT FROM AUTHOR]

Published
2022
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12. Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights.

Author

Purna Chandra Reddy, V. and Gurrala, Kiran Kumar

Subjects
DEEP learning, DIABETIC retinopathy, MACULAR edema, FEATURE selection, RETINAL imaging, CONVOLUTIONAL neural networks
Abstract

• To enhance the grading of the diseases, DLCNN-MGWO-VW method is introduced. • DSAM and DDAM are used for analysing the precise relationship of each disease. • MGWO-VW is implemented as an optimal feature selection algorithm. • DLCNN-MGWO-VW results are compared with ISBI 2018 sub- challenge 2. This paper aims to develop a computer-based diagnostic system to assist ophthalmologists in screening for diabetic retinopathy and diabetic macular edema by identifying the early signs of DR in retinal fundus images. The main objective of this work is to detect and grade DR by the severity of its classes using a hybrid deep-learning convolutional neural network-based modified grey-wolf optimizer with variable weights, which is termed as DLCNN-MGWO-VW. Initially, ResNet50 is used to extract the combined features of DR and DME diseases. Then, a disease-specific attention module is used to extract the disease-specific features, which differentiates the basic features of two considered diseases. These data are later fed to the MGWO-VW for the optimal selection of individual disease-specific features from DR and DME, respectively. Finally, the disease-dependent attention module is used to identify the exact internal relationship between the two diseases with the aid of disease-dependent features that are also used for joint detection and classification of DR and DME, respectively. The simulations are performed on a publicly available IDRiD dataset, according to the ISBI-2018 challenge on DR segmentation and grading sub-challenge-2, where proposed hybrid DLCNN-MGWO-VW architecture maximizes the overall performance jointly for grading DR and DME, as compared to the state-of-art approaches from the literature. The proposed DLCNN-MGWO-VW method outperforms all the ISBI-2018 subchallenge-2 teams, with accuracy rates of 96.0%, 93.2%, and 92.23% for the detection and classification of DR, DME, and joint DR-DME, respectively. [ABSTRACT FROM AUTHOR]

Published
2022
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18. Smoke Detection in Forest Using Deep Learning

Author

Sankara Narayanan, G., Sabarish, B. A., Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2023
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33. Brain Tumour Detection Using Convolutional Neural Networks in MRI Images

Author

Pavani, Dontha, Durgalaxmi, Kavali, Datta, Bingi Sai, Nagajyothi, D., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K.T.V., editor

Published
2022
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34. Heart Disease Prediction Using Deep Learning Algorithm

Author

Velakanti, Gouthami, Jarathi, Shivani, Harshini, Malladi, Ankam, Praveen, Vuppu, Shankar, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K.T.V., editor

Published
2022
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35. A Quantitative Analysis for Breast Cancer Prediction Using Artificial Neural Network and Support Vector Machine

Author

Walia, Harnehmat, Kaur, Prabhpreet, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K.T.V., editor

Published
2022
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36. Lung Disease Detection and Classification from Chest X-Ray Images Using Adaptive Segmentation and Deep Learning

Author

Goyal, Shimpy, Singh, Rajiv, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K.T.V., editor

Published
2022
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37. Evaluation of Machine Learning Algorithms for Electroencephalography-Based Epileptic Seizure State Recognition

Author

Patel, Vibha, Tailor, Jaishree, Ganatra, Amit, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K.T.V., editor

Published
2022
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38. Neural Abstractive Text Summarizer for Telugu Language

Author

Mohan Bharath, B., Aravindh Gowtham, B., Akhil, M., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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39. Mobile Application-Based Virtual Assistant Using Deep Learning

Author

Vishnu, R., Krishna Prakash, N., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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40. Naturalistic Driving Simulation Using Automation

Author

Subhashini, J., Khanna, Gaurav, Arya, Rohan, Kompalli, Leela Guna Krishna, Gupta, Rahul, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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41. A Novel Approach to Detect Anomalies in Business Process Event Logs Using Deep Learning Algorithm

Author

Vijayakamal, M., Vasumathi, D., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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42. Ensemble of Deep Transfer Learning Models for Parkinson's Disease Classification

Author

Rajanbabu, Kiranbabu, Veetil, Iswarya Kannoth, Sowmya, V., Gopalakrishnan, E. A., Soman, K. P., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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43. Multi-scale Convolutional Neural Network for Automatic Identification of Glaucoma in Fundus Image

Author

Devi, O. Roopa, Bindu, C. Shoba, Kumar, E. Sudheer, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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44. A Convolutional Neural Network Approach for Detecting Malignancy of Ovarian Cancer

Author

Mathur, Mansi, Jindal, Vikas, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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45. Various Image Modalities Used in Computer-Aided Diagnosis System for Detection of Breast Cancer Using Machine Learning Techniques: A Systematic Review

Author

Wadhwa, Gitanjali, Kaur, Amandeep, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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46. Detecting Surface Cracks on Buildings Using Computer Vision: An Experimental Comparison of Digital Image Processing and Deep Learning

Author

Yadhunath, Ramshankar, Srikanth, Srivenkata, Sudheer, Arvind, Jyotsna, C., Amudha, J., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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47. Deep Learning-Based Approach for Human Activity Recognition

Author

Kaur, Chanpreet, Kaur, Prabhpreet, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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48. Modified UNet Architecture with Less Number of Learnable Parameters for Nuclei Segmentation

Author

Vishnu Priyal, S., Vyshnav, M. T., Sowmya, V., Soman, K. P., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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49. An Approach Toward Deep Learning-Based Facial Expression Recognition in Wavelet Domain

Author

Sharma, Pooja, Singh, Rajiv, 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
Full Text
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50. Post-earthquake Building Damage Detection Using Deep Learning

Author

Mangalraj, P., Duddela, Sai Prashanth, Kirubanantham, P., Iniyan, S., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
Full Text
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