DenoiseNet: An Efficient Image Denoising Using Convolutional Neural Networks Harsh Patel, Anamika Jain 2025 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2025, 2025 Image denoising is a crucial research area in image processing, with significant advancements made over the past decade. Recently, this field has gained renewed attention due to the advancement of deep learning method. With this work, we have proposed an optimized Convolutional Neural Network (CNN) architecture for image denoising (DenoiseNet). The model is designed to handle various types of noise, i.e. Gaussian, Poisson, and salt-and-pepper noise. To evaluate its performance, we tested DenoiseNet on the publicly available Flickr2k dataset, achieving high Peak Signal-to-Noise Ratio (PSNR) while preserving image quality. Moreover, DenoiseNet emphasizes computational efficiency, making it highly suitable for real-time applications where both speed and accuracy are critical. The results affirm that DenoiseNet is a robust solution for contemporary image denoising challenges.
Machine Learning-Based Diabetic Retinopathy Detection: A Comprehensive Study Using InceptionV3 Model Gautam Deshpande, Yash Govardhan, Anamika Jain 2024 Asu International Conference in Emerging Technologies for Sustainability and Intelligent Systems Icetsis 2024, 2024 Diabetic retinopathy is considered as a common eye disease that affects vision of people those have diabetes. Early diagnosis of diabetic retinopathy has become a crucial step to prevent vision loss. In this paper, we have proposed a method to detect diabetic retinopathy using machine learning based method. We used two publicly available datasets, EyePACS and APTOS 2019, for training and testing our model. We employed a pre-trained model, Inception V3,and fine-tuned it on our dataset. We achieved and accuracy and F1 score of 74.28% and 73.81 % respectively on the EyePACS dataset and on APTOS 19 dataset we obtained an accuracy and F1 score of 81.61 % and 80.21 % respectively. Our findings suggest that with machine learning, we can detect diabetic retinopathy in the early stages.
Fake Profile Detection Using Machine Learning Kunal Umbrani, Deven Shah, Amit Pile, Anamika Jain 2024 Asu International Conference in Emerging Technologies for Sustainability and Intelligent Systems Icetsis 2024, 2024 Nowadays, Social Media Platforms on behalf of entities or individuals, can harm (SMPs) are being utilized by an enormous number their reputations and reduce the number of likes of users to get connected with their friends and and followers they receive. Additionally, they family. There are sites like Facebook, Instagram, experience unnecessary confusion with other Twitter where people spend a significant amount of time to get updated about the world. The data uploaded on social media contains their personal information, thoughts on certain topics, news, etc. The social media platforms verify the authenticity of the registered user. However, some of the users hide their identities and these people are threats to the security of other users’ data. These bot accounts are used to scam, or purposefully cause harm to people. There is a need for detection techniques to find and eradicate these bots as quickly as possible. In this work, we have proposed a Machine learning based model that can identify fake or bot created accounts accurately. This paper is divided into multiple parts: Introduction, Literature Review, Methodology, Results and Discussion, Conclusion. To validate the authenticity of our work, we have experimented over the publicly available dataset TwiBot-20 and achieved accuracy of 87%.
Plant Disease Detection Using Machine Learning Anamika Jain, Anagha Langhe, Harsh Choudhary, Ashutosh Mishra 2024 Asu International Conference in Emerging Technologies for Sustainability and Intelligent Systems Icetsis 2024, 2024 In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their spread and minimize any damage. However, this task often requires a high amount of labor and experience. This project suggests using image processing techniques to extract characteristics from images of plant leaves and then utilizing machine learning algorithms to classify these leaves as either healthy or diseased. The evaluation of machine learning algorithms, such as Convolutional Neural Networks, was conducted to measure their performance, using their accuracy, precision, and recall. The proposed system displays a level of accuracy in detecting and classifying plant diseases. It focuses on categorizing images of types of plants into disease types as well as healthy ones. These findings highlight the potential benefits of employing machine learning techniques in the detection of plant diseases offering farmers a tool for managing and safeguarding their crops.
MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images Shajal Afaq, Anamika Jain Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2022, 2022 In today’s times, one of the most prevalent form of cancer in women is breast. Early detection of breast cancer improves patients’ chances of survival by allowing them to get the best treatment available. Convolutional Neural Networks has been very popular to extract the relevant features and classification. To develop a real time breast cancer model, we have developed a light weighted convolutional Neural Network (MAAMMO-Net). In this work we have proposed a convolutional Neural Network (MAMMO-Net) for automated diagnosis of breast cancer. We have also processed the mammographic images before giving input to the MAMMO-Net by applying Gabor filter. We experimented with the publicly accessible mammographic dataset CBISDDSM to verify the performance of the suggested model and obtained accuracy of 98%.
Feature Ensemble based method for verification of Offline Signature images Pooja Chaturvedi, Anamika Jain 2022 International Conference on Machine Learning Big Data Cloud and Parallel Computing Com IT Con 2022, 2022 Signature verification is the most researched area in computer vision as it has less intra-class and less inter-class similarity. Our objective is to reduce the intra-class variation and increase the inter-class similarity. This paper has proposed an ensemble based approach where method of ensemble of feature created by using geometrical and Mobile Net features along with the ensemble of classifier has utilized. The proposed method has been tested over the publicly available dataset BHSig260. This paper has achieved 99.4% and 99.3% accuracy over Bengali and Hindi dataset respectively.
Prediction of Heart Disease using Dense Neural Network Akansha Singh, Anamika Jain 2022 IEEE Global Conference on Computing Power and Communication Technologies Globconpt 2022, 2022 Heart disease are very common in humans because of their life style. It become life threatening when do not treated on time. So the early detection of heart disease has become very important. Many researchers have done a lot of research on the prediction of the heart disease. To minimize the risk of loosing life, we have proposed a Artificial neural network based method that can predict the heart disease in the early stages. The proposed method is tested over the publicly available UCI dataset. We have achieved 96.09% accuracy with the proposed method.
Improving Electricity Theft Detection with a Stacked Ensemble Model Anamika Jain, Awanti Karmarkar, Shejal Mete, Aryan Rai Communications on Applied Nonlinear Analysis 32 (10), 2862 - 2874 , 2025 2025.0
Prediction of Cardiovascular Disease using XGBoost with OPTUNA A Jain, A Singh, A Doherey SN Computer Science 6 (5), 1-7 , 2025 2025.0 Citations: 7
DenoiseNet: An Efficient Image Denoising Using Convolutional Neural Networks H Patel, A Jain 2025 International Conference on Recent Advances in Electrical, Electronics … , 2025 2025.0
Eye Detection for Attention-Based Video Playback S Bagade, M Wasnik, P Jadhav, A Jain, A Sakpal, S Thakur EPJ Web of Conferences 341, 01009 , 2025 2025.0
Fake Profile Detection Using Machine Learning K Umbrani, D Shah, A Pile, A Jain 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 16
Plant Disease Detection Using Machine Learning A Jain, A Langhe, H Choudhary, A Mishra 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 4
Machine Learning-Based Diabetic Retinopathy Detection: A Comprehensive Study Using InceptionV3 Model G Deshpande, Y Govardhan, A Jain 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 25
DenseMammoNet: An Approach for Breast Cancer Classification in Mammograms S Afaq, A Jain Congress on Intelligent Systems, 147-156 , 2023 2023.0 Citations: 3
MultiNet: A Multimodal Approach for Biometric Verification P Sagar, A Jain Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, 679-690 , 2023 2023.0 Citations: 4
Prediction of Heart Disease using Dense Neural Network A Singh, A Jain 2022 IEEE Global Conference on Computing, Power and Communication … , 2022 2022.0 Citations: 7
MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images S Afaq, A Jain 2022 International Conference on Computational Intelligence and Sustainable … , 2022 2022.0 Citations: 11
Feature Ensemble based method for verification of Offline Signature images P Chaturvedi, A Jain 2022 International Conference on Machine Learning, Big Data, Cloud and … , 2022 2022.0 Citations: 6
Signature based authentication: A multi-label classification approach to detect the language and forged sample in signature A Jain, SK Singh, KP Singh International Conference on Computer Vision and Image Processing, 198-208 , 2021 2021.0 Citations: 3
Signature verification using geometrical features and artificial neural network classifier A Jain, SK Singh, KP Singh Neural Computing and Applications 33, 6999-7010 , 2021 2021.0 Citations: 36
Multi‐task learning using GNet features and SVM classifier for signature identification A Jain, SK Singh, K Pratap Singh IET Biometrics 10 (2), 117-126 , 2021 2021.0 Citations: 26
Handwritten signature verification using shallow convolutional neural network A Jain, SK Singh, KP Singh Multimedia Tools and Applications 79, 19993-20018 , 2020 2020.0 Citations: 91
Reversible Watermarking based on Histogram Shifting Modification: A Review A Jain, N Tiwari
MOST CITED SCHOLAR PUBLICATIONS
Handwritten signature verification using shallow convolutional neural network A Jain, SK Singh, KP Singh Multimedia Tools and Applications 79, 19993-20018 , 2020 2020.0 Citations: 91
Signature verification using geometrical features and artificial neural network classifier A Jain, SK Singh, KP Singh Neural Computing and Applications 33, 6999-7010 , 2021 2021.0 Citations: 36
Multi‐task learning using GNet features and SVM classifier for signature identification A Jain, SK Singh, K Pratap Singh IET Biometrics 10 (2), 117-126 , 2021 2021.0 Citations: 26
Machine Learning-Based Diabetic Retinopathy Detection: A Comprehensive Study Using InceptionV3 Model G Deshpande, Y Govardhan, A Jain 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 25
Fake Profile Detection Using Machine Learning K Umbrani, D Shah, A Pile, A Jain 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 16
MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images S Afaq, A Jain 2022 International Conference on Computational Intelligence and Sustainable … , 2022 2022.0 Citations: 11
Prediction of Cardiovascular Disease using XGBoost with OPTUNA A Jain, A Singh, A Doherey SN Computer Science 6 (5), 1-7 , 2025 2025.0 Citations: 7
Prediction of Heart Disease using Dense Neural Network A Singh, A Jain 2022 IEEE Global Conference on Computing, Power and Communication … , 2022 2022.0 Citations: 7
Feature Ensemble based method for verification of Offline Signature images P Chaturvedi, A Jain 2022 International Conference on Machine Learning, Big Data, Cloud and … , 2022 2022.0 Citations: 6
Plant Disease Detection Using Machine Learning A Jain, A Langhe, H Choudhary, A Mishra 2024 ASU International Conference in Emerging Technologies for … , 2024 2024.0 Citations: 4
MultiNet: A Multimodal Approach for Biometric Verification P Sagar, A Jain Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, 679-690 , 2023 2023.0 Citations: 4
DenseMammoNet: An Approach for Breast Cancer Classification in Mammograms S Afaq, A Jain Congress on Intelligent Systems, 147-156 , 2023 2023.0 Citations: 3
Signature based authentication: A multi-label classification approach to detect the language and forged sample in signature A Jain, SK Singh, KP Singh International Conference on Computer Vision and Image Processing, 198-208 , 2021 2021.0 Citations: 3
Improving Electricity Theft Detection with a Stacked Ensemble Model Anamika Jain, Awanti Karmarkar, Shejal Mete, Aryan Rai Communications on Applied Nonlinear Analysis 32 (10), 2862 - 2874 , 2025 2025.0
DenoiseNet: An Efficient Image Denoising Using Convolutional Neural Networks H Patel, A Jain 2025 International Conference on Recent Advances in Electrical, Electronics … , 2025 2025.0
Eye Detection for Attention-Based Video Playback S Bagade, M Wasnik, P Jadhav, A Jain, A Sakpal, S Thakur EPJ Web of Conferences 341, 01009 , 2025 2025.0
Reversible Watermarking based on Histogram Shifting Modification: A Review A Jain, N Tiwari
