A fine-tuned adaptive weight deep dense meta stacked transfer learning model for effective cervical cancer prediction Baijnath Kaushik, Abhigya Mahajan, Akshma Chadha, Yusera Farooq Khan, Shashwat Sharma Physica Scripta, 2025 In the digital world of remarkable technological advancements, the detection of cervical cancer at early stages is of important clinical significance as it can vastly improve the survival rate during treatment. Cervical cytopathology, often known as a Pap test is the frequently adopted screening method for cervical cancer. However, the test seems to be effective but investigation of images containing Pap smear with the help of a microscope is a difficult as well as laborious exercise. The procedure for the same demands an expert in the area and is often time-consuming. The serious pitfalls in subjective clinical evaluation evoke the need of developing an automated system for more reliable cervical cancer diagnosis. Therefore, the goal of this study primarily focuses on designing a Deep learning model to process the Pap smear images and correctly classify the cervical cells. For this purpose, firstly, a publically available dataset namely SIPaKMeD is utilized. Then, different data pre-processing methods are applied to intensify the data quality for effective analysis. Next, a novel stacking model is proposed that leverages a Support Vector Classifier (SVC) as a Meta model over a combination of different Transfer Learning Models including VGG16, ResNet101, InceptionV3, Xception, DenseNet169, and Inception ResNet. Furthermore, the dense layers are added to tune the underlying base transfer learning models to learn fine-tuned adaptive weights. The results obtained from experimental evaluation demonstrate the efficacy of the proposed stacking model by yielding the highest accuracy rate of 95.66% in comparison to other employed methods and existing state-of-the-art techniques.
Deep Learning for Breast Cancer Detection: A Review of Current Techniques and Emerging Trends 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
A Hybrid Feature Selection and Ensemble Stacked Learning Models on Multi-Variant CVD Datasets for Effective Classification Abhigya Mahajan, Baijnath Kaushik, Mohammad Khalid Imam Rahmani, Abdulbasid S. Banga IEEE Access, 2024 Predicting cardiac or heart disease has emerged as a formidable challenge in the medical domain recently. It is recognized as a major global health concern, and stands as one of the primary causes of mortality, posing a significant threat to human life. Early detection of heart disease helps to reduce mortality. This study has experimented with three benchmark datasets such as UCI Heart Disease, Framingham, and Z-Alizadeh Saini containing important clinical information for cardiac vascular disease (CVD). These three datasets’ multi-variant (categorical and continuous) features, variable dimensions, and multicollinearity characteristics provide substantial challenges for machine learning (ML) and other models aiming to achieve the desired results. This study proposes a statistical feature selection (SFS) stacking framework using four feature engineering techniques, Chi-Square, Gini Index, Information Gain, and ANOVA F-test, to select the optimal features from the datasets. Further, the likelihood of developing CVD based on characteristics extracted from the three benchmark datasets using a reduced set of optimized features from the initial feature set is fed to ensemble stacked learning models: stacking using Support Vector Machine (SFS-SVM) and stacking using Cross-Validation Classifier (SFS-SCVC). The SFS-SCVC model has achieved significant performance metrics and outperformed the SFS-SVM and traditional ML models on all three datasets.
Suicidal Tendency on Social Media: A Case Study Priyanka Gupta, Baijnath Kaushik Proceedings of the International Conference on Machine Learning Big Data Cloud and Parallel Computing Trends Prespectives and Prospects Comitcon 2019, 2019
A novel computational intelligence model using improvised feature selection for Parkinson’s disease classification B Kaushik, R Sharma, YF Khan, A Chadha, Niharika Multimedia Tools and Applications 84 (36), 44997-45016 , 2025 2025
A three layer stacked multimodel transfer learning approach for deep feature extraction from Chest Radiographic images for the classification of COVID-19 B Kaushik, A Chadha, A Mahajan, M Ashok Engineering Applications of Artificial Intelligence 147, 110241 , 2025 2025 Citations: 8
A fine-tuned adaptive weight deep dense meta stacked transfer learning model for effective cervical cancer prediction B Kaushik, A Mahajan, A Chadha, YF Khan, S Sharma Physica Scripta 100 (3), 036002 , 2025 2025 Citations: 4
Handwritten north Indian script recognition using machine learning: a survey R Sharma, B Kaushik, NK Gondhi International Journal of Advanced Intelligence Paradigms 30 (5), 379-395 , 2025 2025
Lightweight group authentication protocol for secure RFID system S Kumar, H Banka, B Kaushik Multimedia Tools and Applications 83 (41), 89249-89277 , 2024 2024 Citations: 5
An ultra-lightweight secure rfid authentication protocol for low-cost tags S Kumar, H Banka, B Kaushik, S Sharma Journal of Computer Virology and Hacking Techniques 20 (4), 803-818 , 2024 2024 Citations: 7
A hybrid feature selection and ensemble stacked learning models on multi-variant CVD datasets for effective classification A Mahajan, B Kaushik, MKI Rahmani, AS Banga IEEE Access 12, 87023-87038 , 2024 2024 Citations: 16
An Ensemble Approach for Multiclass N Gupta, B Kaushik, A Chadha, YF Khan Proceedings of the Second International Conference on Computing … , 2024 2024
Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection. V Priyadarshni, SK Sharma, MKI Rahmani, B Kaushik, R Almajalid Computers, Materials & Continua 78 (2), 2242 , 2024 2024 Citations: 9
FeaTrim-ViT: Vision Transformer Trimming with One Shot Neural Architecture Search in Continuous Optimisation Space and Efficient Feature Selection S Sharma, B Kaushik International Conference on Cognitive Computing and Cyber Physical Systems … , 2023 2023
Emerging Trends of Artificial Intelligence in Detecting Neurodegeneration DP Singh, B Kaushik, YF Khan, A Chadha, A Mahajan, A Jandwani, ... International Conference on Cognitive Computing and Cyber Physical Systems … , 2023 2023 Citations: 3
Performance evaluation of learning models for intrusion detection system using feature selection B Kaushik, R Sharma, K Dhama, A Chadha, S Sharma Journal of Computer Virology and Hacking Techniques 19 (4), 529-548 , 2023 2023 Citations: 48
Performance evaluation of learning models for the prognosis of COVID-19 B Kaushik, A Chadha, R Sharma New Generation Computing 41 (3), 533-551 , 2023 2023 Citations: 17
A review of machine learning algorithms and feature selection techniques for cardiovascular disease prediction: Insights and implications A Mahajan, B Kaushik 2023 7th International Conference On Computing, Communication, Control And … , 2023 2023 Citations: 6
A data preprocessing and stacking ensemble learning model for improved CHD prediction A Mahajan, B Kaushik International Conference on Mathematical Modelling, Applied Analysis and … , 2023 2023 Citations: 2
CTDN (convolutional temporal based deep‐neural network): an improvised stacked hybrid computational approach for anticancer drug response prediction DP Singh, B Kaushik Computational Biology and Chemistry 105, 107868 , 2023 2023 Citations: 18
Ultra-lightweight blockchain-enabled RFID authentication protocol for supply chain in the domain of 5G mobile edge computing S Kumar, H Banka, B Kaushik Wireless Networks 29 (5), 2105-2126 , 2023 2023 Citations: 38
Takri touching text segmentation using statistical approach S Magotra, B Kaushik, A Kaul Sādhanā 48 (3), 104 , 2023 2023 Citations: 4
Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction SAL Niharika Gupta1 , Baijnath Kaushik1 , Mohammad Khalid Imam Rahmani2 Computers, Materials & Continua 76 (1), 347-366 , 2023 2023 Citations: 12
Machine learning models for alzheimer’s disease detection using medical images YF Khan, B Kaushik, D Koundal Data analysis for neurodegenerative disorders, 165-182 , 2023 2023 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
A survey on internet of vehicles: Applications, security issues & solutions S Sharma, B Kaushik Vehicular Communications 20, 100182 , 2019 2019 Citations: 506
Ensemble model for diagnostic classification of Alzheimer’s disease based on brain anatomical magnetic resonance imaging YF Khan, B Kaushik, CL Chowdhary, G Srivastava Diagnostics 12 (12), 3193 , 2022 2022 Citations: 66
A hybrid deep learning model using grid search and cross-validation for effective classification and prediction of suicidal ideation from social network data A Chadha, B Kaushik New Generation Computing 40 (4), 889-914 , 2022 2022 Citations: 54
Stacked deep dense neural network model to predict Alzheimer’s dementia using audio transcript data YF Khan, B Kaushik, MKI Rahmani, ME Ahmed Ieee Access 10, 32750-32765 , 2022 2022 Citations: 53
Performance evaluation of learning models for intrusion detection system using feature selection B Kaushik, R Sharma, K Dhama, A Chadha, S Sharma Journal of Computer Virology and Hacking Techniques 19 (4), 529-548 , 2023 2023 Citations: 48
A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques DP Singh, B Kaushik Chemical Biology & Drug Design 101 (1), 175-194 , 2023 2023 Citations: 46
A survey on prediction of suicidal ideation using machine and ensemble learning A Chadha, B Kaushik The Computer Journal 64 (11), 1617-1632 , 2021 2021 Citations: 45
Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review DP Singh, B Kaushik Chemometrics and Intelligent Laboratory Systems 229, 104637 , 2022 2022 Citations: 40
Offline recognition of handwritten Indic scripts: A state-of-the-art survey and future perspectives R Sharma, B Kaushik Computer Science Review 38, 100302 , 2020 2020 Citations: 40
Ultra-lightweight blockchain-enabled RFID authentication protocol for supply chain in the domain of 5G mobile edge computing S Kumar, H Banka, B Kaushik Wireless Networks 29 (5), 2105-2126 , 2023 2023 Citations: 38
Quantitative analysis of stock market prediction for accurate investment decisions in future S Sharma, B Kaushik Journal of Artificial Intelligence 11 (1), 48-54 , 2018 2018 Citations: 37
Character recognition using machine learning and deep learning-a survey R Sharma, B Kaushik, N Gondhi 2020 International Conference on Emerging Smart Computing and Informatics … , 2020 2020 Citations: 33
A review and analysis of secure and lightweight ECC‐based RFID authentication protocol for Internet of Vehicles S Kumar, H Banka, B Kaushik, S Sharma Transactions on Emerging Telecommunications Technologies 32 (11), e4354 , 2021 2021 Citations: 31
HSI-LFS-BERT: novel hybrid swarm intelligence based linguistics feature selection and computational intelligent model for Alzheimer’s prediction using audio transcript YF Khan, B Kaushik, MKI Rahmani, ME Ahmed IEEE Access 10, 126990-127004 , 2022 2022 Citations: 29
Cryptographic Solution Based Secure Elliptic Curve Cryptography Enabled Radio Frequency Identification Mutual Authentication Protocol for Internet of Vehicles S Sharma, MEA Baijnath Kaushik, Mohammad Khalid Imam Rahmani IEEE ACCESS , 2021 2021 Citations: 27
Performance evaluation of approximated artificial neural network (AANN) algorithm for reliability improvement B Kaushik, H Banka Applied Soft Computing 26, 303-314 , 2015 2015 Citations: 27
DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques PS Davinder, G Abhishek, K Baijnath Chemometrics and Intelligent Laboratory Systems 225 , 2022 2022 Citations: 22
Feature selection from biological database for breast cancer prediction and detection using machine learning classifier A Gupta, BN Kaushik Journal of Artificial Intelligence 11 (2), 55-64 , 2018 2018 Citations: 21
A sentiment analysis of food review using logistic regression M Wankhade, ACS Rao, S Dara, B Kaushik Int J Sci Res Comput Sci Eng InformTechnol 2 (7), 251-260 , 2017 2017 Citations: 20
Performance evaluation of learning models for identification of suicidal thoughts A Chadha, B Kaushik The Computer Journal 65 (1), 139-154 , 2022 2022 Citations: 19