Predicting anxiety, depression and stress in modern life using machine learning algorithms A Priya, S Garg, NP Tigga Procedia Computer Science 167, 1258-1267, 2020 | 428 | 2020 |
Prediction of type 2 diabetes using machine learning classification methods NP Tigga, S Garg Procedia Computer Science 167, 706-716, 2020 | 395 | 2020 |
Breast cancer prediction using varying parameters of machine learning models P Gupta, S Garg Procedia Computer Science 171, 593-601, 2020 | 151 | 2020 |
Assessment of anxiety, depression and stress using machine learning models P Kumar, S Garg, A Garg Procedia Computer Science 171, 1989-1998, 2020 | 119 | 2020 |
Ai-based resource allocation techniques in wireless sensor internet of things networks in energy efficiency with data optimization QW Ahmed, S Garg, A Rai, M Ramachandran, NZ Jhanjhi, M Masud, ... Electronics 11 (13), 2071, 2022 | 53 | 2022 |
Multilevel medical image fusion using segmented image by level set evolution with region competition S Garg, KU Kiran, R Mohan, US Tiwary 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 7680-7683, 2006 | 49 | 2006 |
Optimizing cellulase production from Aspergillus flavus using response surface methodology and machine learning models A Singhal, N Kumari, P Ghosh, Y Singh, S Garg, MP Shah, PK Jha, ... Environmental Technology & Innovation 27, 102805, 2022 | 40 | 2022 |
Autoregressive integrated moving average model based prediction of bitcoin close price S Garg 2018 international conference on smart systems and inventive technology …, 2018 | 33 | 2018 |
Comparison of content based image retrieval systems using wavelet and curvelet transform S Das, S Garg, G Sahoo The International Journal of Multimedia & Its Applications 4 (4), 137, 2012 | 33 | 2012 |
Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals NP Tigga, S Garg Health Information Science and Systems 11 (1), 1, 2022 | 22 | 2022 |
Comparison of machine learning algorithms for content based personality resolution of tweets S Garg, A Garg Social Sciences & Humanities Open 4 (1), 100178, 2021 | 22 | 2021 |
Predicting type 2 diabetes using logistic regression NP Tigga, S Garg Proceedings of the Fourth International Conference on Microelectronics …, 2021 | 19 | 2021 |
An overlapping sliding window and combined features based emotion recognition system for EEG signals S Garg, RK Patro, S Behera, NP Tigga, R Pandey Applied Computing and Informatics 21 (1/2), 114-130, 2025 | 18 | 2025 |
PV-based DC-DC buck-boost converter for LED driver KAM Junaid, Y Sukhi, N Anjum, Y Jeyashree, AF Ahamed, S Debbarma, ... e-Prime-Advances in Electrical Engineering, Electronics and Energy 5, 100271, 2023 | 18 | 2023 |
S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram A Abgeena, S Garg Health Information Science and Systems 11 (1), 40, 2023 | 10 | 2023 |
A comparison of prediction capabilities of Bayesian regularization and Levenberg–Marquardt training algorithms for cryptocurrencies A Priya, S Garg Smart Intelligent Computing and Applications: Proceedings of the Third …, 2019 | 10 | 2019 |
Human crowd behaviour analysis based on video segmentation and classification using expectation–maximization with deep learning architectures S Garg, S Sharma, S Dhariwal, WD Priya, M Singh, S Ramesh Multimedia Tools and Applications, 1-23, 2024 | 9 | 2024 |
Optimizing pretreatment of Leucaena leucocephala using artificial neural networks (ANNs) N Kumari, S Garg, A Singhal, M Kumar, M Bhattacharya, PK Jha, ... Bioresource Technology Reports 7, 100289, 2019 | 9 | 2019 |
Automatic text summarization of video lectures using subtitles S Garg Recent Developments in Intelligent Computing, Communication and Devices …, 2017 | 9 | 2017 |
Identification of Internet of Things (Iot) attacks using gradient boosting: A cross dataset approach S Garg, V Kumar, SR Payyavula Telematique 21 (1), 6982-7012, 2022 | 7 | 2022 |