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Browsing by Author "Maiti, J."

Browsing by Author "Maiti, J."

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  • Classification and pattern extraction of incidents: a deep learning-based approach 
    Sarkar, Sobhan.; Vinay, Sammangi.; Djeddi, Chawki.; Maiti, J. (Neural Computing & Applications, 2022-09)
    Classifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. ...
  • DiPSVM : polynomial kernel-free support vector machine 
    Saha, Soumadip.; Das, Meghashrita.; Mondal, Baishali Sow.; Sarkar, Sobhan.; Maiti, J. (2021 International Conference on Data Analytics for Business and Industry (ICDABI), Bahrain. IEEE, 2021-10-25)
    Support Vector Machine (SVM), is a popular and efficient classification algorithm in machine learning (ML) paradigm. However, the kernel-based dependency of the SVM algorithm requires a long time to compute the support ...
  • An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis 
    Sarkar, Sobhan.; Ejaz, Numan.; Maiti, J.; Pramanik, Anima. (Neural Computing & Applications, 2022-04-14)
    To prevent the occurrences of accidents at workplaces, accident data should be analyzed properly. However, handling such data of higher dimension is often a difficult task for analysis to achieve efficient decision making ...
  • An integrated approach using Rough Set Theory, ANFIS, and Z-number in occupational risk prediction 
    Sarkar, Sobhan.; Pramanik, Anima.; Maiti, J. (Engineering Applications of Artificial Intelligence, 2023)
    In recent years, machine learning (ML)-based approaches have gained increasing attention in occupational accident research. However, the challenges of data uncertainty, unstructured information handling, lower prediction ...
  • A kernel-free support vector machine with Q-margin 
    Pradhan, Smarak.; Kumar, Sagar.; Sarkar, Sobhan.; Maiti, J. (2021 International Conference on Data Analytics for Business and Industry (ICDABI), Bahrain. IEEE, 2021-10-25)
    Support vector machine (SVM) is one of the most well-known machine learning algorithms highly adept at handling both linear and non-linear binary classification problems. Linear separation margin deals easily with linearly ...
  • A Novel Optimized Method for Feature Selection Using Non-linear Kernel-Free Twin Quadratic Surface Support Vector Machine 
    Maity, Saptashwa.; Rastogi, Arjav.; Djeddi, Chawki.; Sarkar, Sobhan.; Maiti, J. (Springer, 2022-04-13)
    Feature Selection (FS) is an important topic in the domain of machine learning. Support Vector Machine (SVM) is one of the most popular ML models for classification tasks. Efficient feature selection may ensure enhanced ...
  • Real-Time Detection of Traffic Anomalies Near Roundabouts 
    Pramanik, Anima.; Sarkar, Sobhan.; Djeddi, Chawki.; Maiti, J. (Springer, 2022-04-13)
    Visual analytics can bridge the gap between computational and human approaches for detecting traffic anomalies near the round-about, making the data analysis process more transparent. The main problem with anomaly detection ...