Sheikh Wakie Masood

@presidencyuniversity.in

Assistant Professor School of Information Science
Presidency University

Sheikh Wakie Masood


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https://researchid.co/masoodpu

EDUCATION

Ph.D. (Thesis Submitted)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Engineering, Artificial Intelligence, Computer Science Applications
6

Scopus Publications

30

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A review: Comprehensive framework for advanced deep learning-based image classification with pattern recognition and predictive visual analytics
    Munmi Gogoi, Sheikh Wakie Masood, Sachin Upadhyay, Nabanita Choudhury, Shahin Ara Begum
    International Journal of Wavelets Multiresolution and Information Processing, 2026
    Image classification is a fundamental task in computer vision that entails categorizing images into pre-defined classes according to their visual features. Traditional classification approaches mostly relied on handcrafted feature extraction techniques, which often struggled to handle complex patterns, high variability and large-scale diverse datasets. Recent advancements in deep learning have highly impacted image classification tasks by enabling automatic hierarchical feature learning directly from raw image data. This review presents a comprehensive analysis of advanced deep learning architectures designed for image classification, focusing on architectural innovations, feature representation, transfer learning strategies and model interpretability. Different network architectures, such as residual networks, recurrent attention-based networks, fully convolutional networks, capsule networks and region-based convolutional networks, are critically reviewed based on their design and performance across different application domains. The review further explores the role of pre-trained models and knowledge transfer techniques in addressing challenges, data scarcity and training complexity. In addition, it discusses widely used development frameworks that facilitate efficient model implementation and deployment. By integrating both theoretical knowledge and recent developments, this review provides systematic guidance for researchers and practitioners to develop efficient, scalable, and robust image classification systems for both scientific research and industrial applications.
  • Optimised SMOTE-based Imbalanced Learning for Student Dropout Prediction
    Sheikh Wakie Masood, Munmi Gogoi, Shahin Ara Begum
    Arabian Journal for Science and Engineering, 2025
  • An Explainable AI Approach to Predicting Radiation Pneumonia in Head & Neck Cancer
    Abhijit Nath, Sheikh Wakie Masood, Shahin Ara Begum, Ravi Kannan
    2025 International Conference on Sensors and Related Networks Sennet 2025 Special Focus on Digital Healthcare 64220, 2025
    Radiation pneumonia is a serious complication for head and neck cancer patients receiving radiation therapy, so it is important to estimate the risk accurately. In the study, a combination of XGBoost and LightGBM models is used, with XGBoost as the meta learner, to estimate radiation pneumonia. To manage the large difference between the number of pneumonia and non-pneumonia cases (568 pneumonia vs. 1914 non-pneumonia cases), the SMOTETomek resampling approach was used on the training data. Moreover, with SHAP analysis, the changes in how each feature affected the predictions between the original imbalanced and the balanced data were clearly seen. Based on the experimental evaluation over the considered dataset, the precision, recall, F1-Score, F2-Score, and ROC-AUC have improved significantly. The study reveals that the approach proposed can predict well while giving simple and meaningful insights to doctors. The developed model may enable oncologists to understand and provide insights into AI-driven decision-making to make informed decisions.
  • Data Collection and Pre-processing for Machine Learning-Based Student Dropout Prediction
    Sheikh Wakie Masood, Shahin Ara Begum
    Lecture Notes in Electrical Engineering, 2024
  • Data Collection and Feature Selection for Development of Machine Learning Prediction Application in Radiotherapy: An Institutional Experience
    Abhijit Nath, Sheikh Wakie Masood, Shahin Ara Begum, Ravi Kannan
    2024 International Conference on Computational Intelligence for Green and Sustainable Technologies Iccigst 2024 Proceedings, 2024
    Machine Learning (ML) prediction models show the potential to improve treatment results, and radiotherapy remains a crucial therapeutic modality in cancer management. This study provides insights into the institutional experience with the problem of data gathering and feature selection for an ML prediction application in radiotherapy. This study presents a systematic data-gathering strategy employing structured clinical data from radiation therapy patients with head and neck cancer. This present study examines methods such as feature engineering, normalisation, and data cleansing that ensure data integrity and comprehensiveness. The data collected consists of 29 clinical variables, which reflects the complexity of the data related to cancer therapy. The best features for treatment outcome prediction were extracted using a variety of feature selection approaches. The SelectKBest approach is particularly notable since it found ten crucial characteristics that affect treatment outcomes. The following parameters showed constant importance across a variety of selection techniques: stage, goal_of_treatment, total_no_of_fraction_advised, end_weight, start_weight, comorbidities, diagnosis, organ_system, site, and age. In oncological datasets with a broad range of modalities and characteristics, the importance of feature selection needs to be emphasised. In order to improve the interpretation, performance, and generalizability of the models, the selection of effective features ensures that machine learning models focus on the most relevant variables. The aim of this work is to ensure a high degree of data collection and methodological rigour in the development of Machine Learning Prediction Models for Radiotherapy, by emphasizing the important role of feature selection.
  • Comparison of Resampling Techniques for Imbalanced Datasets in Student Dropout Prediction
    Sheikh Wakie Masood, Shahin Ara Begum
    Proceedings 2022 IEEE Silchar Subsection Conference Silcon 2022, 2022
    One of the challenges in the Student Dropout Prediction (SDP) problem is imbalanced data, which reduces the efficiency of the Machine Learning (ML) classifier when predicting dropout students. The disproportionate distribution of samples between the majority class (more samples) and the minority class (fewer samples) causes the class imbalance problem, which is a significant challenge in classification problems. When a dataset is highly imbalanced, the ML classifiers give high accuracy as they learn mostly from the majority class. Hence, the accuracy may not always give correct insight about the trained model. In this paper, the findings of the study of several resampling techniques for handling imbalanced data at the data preprocessing level are presented. The Machine learning algorithms, viz. Logistic Regression and Support Vector Machine (SVM), over different performance evaluation metrics for binary classification problems, have been used in the present study to predict the minority class. It is found that the Area Under Curve (AUC) score gives the most reliable result amongst the other considered metrics for predicting the minority class, i.e., the dropout rate of the students.

RECENT SCHOLAR PUBLICATIONS

  • A Review: Comprehensive Framework for Advanced Deep Learning Based Image Classification with Pattern Recognition and Predictive Visual Analytics
    M Gogoi, SW Masood, S Upadhyay, N Choudhury, SA Begum
    International Journal of Wavelets, Multiresolution and Information Processing , 2026
    2026
  • An Explainable AI Approach to Predicting Radiation Pneumonia in Head & Neck Cancer
    A Nath, SW Masood, SA Begum, R Kannan
    2025 International Conference on Sensors and Related Networks (SENNET … , 2025
    2025
  • Optimised SMOTE-based imbalanced learning for student dropout prediction
    SW Masood, M Gogoi, SA Begum
    Arabian Journal for Science and Engineering 50 (10), 7165-7179 , 2025
    2025
    Citations: 12
  • Data Collection and Feature Selection for Development of Machine Learning Prediction Application in Radiotherapy: An Institutional Experience
    A Nath, SW Masood, SA Begum, R Kannan
    2024 International Conference on Computational Intelligence for Green and … , 2024
    2024
    Citations: 1
  • Comparison of resampling techniques for imbalanced datasets in student dropout prediction
    SW Masood, SA Begum
    2022 IEEE Silchar Subsection Conference (SILCON), 1-7 , 2022
    2022
    Citations: 12
  • Data collection and pre-processing for machine learning-based student dropout prediction
    SW Masood, SA Begum
    International Conference on Big Data, Machine Learning, and Applications … , 2021
    2021
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Optimised SMOTE-based imbalanced learning for student dropout prediction
    SW Masood, M Gogoi, SA Begum
    Arabian Journal for Science and Engineering 50 (10), 7165-7179 , 2025
    2025
    Citations: 12
  • Comparison of resampling techniques for imbalanced datasets in student dropout prediction
    SW Masood, SA Begum
    2022 IEEE Silchar Subsection Conference (SILCON), 1-7 , 2022
    2022
    Citations: 12
  • Data collection and pre-processing for machine learning-based student dropout prediction
    SW Masood, SA Begum
    International Conference on Big Data, Machine Learning, and Applications … , 2021
    2021
    Citations: 5
  • Data Collection and Feature Selection for Development of Machine Learning Prediction Application in Radiotherapy: An Institutional Experience
    A Nath, SW Masood, SA Begum, R Kannan
    2024 International Conference on Computational Intelligence for Green and … , 2024
    2024
    Citations: 1
  • A Review: Comprehensive Framework for Advanced Deep Learning Based Image Classification with Pattern Recognition and Predictive Visual Analytics
    M Gogoi, SW Masood, S Upadhyay, N Choudhury, SA Begum
    International Journal of Wavelets, Multiresolution and Information Processing , 2026
    2026
  • An Explainable AI Approach to Predicting Radiation Pneumonia in Head & Neck Cancer
    A Nath, SW Masood, SA Begum, R Kannan
    2025 International Conference on Sensors and Related Networks (SENNET … , 2025
    2025