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3. Real Time Traffic Management by Optimizing Level of Service

Author

Garg, Tanya, Kaur, Gurjinder, Rana, Prashant Singh, Wani, Niyaz Ahmad, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, K, Hemachandran, editor, Rodriguez, Raul Villamarin, editor, Rege, Manjeet, editor, Ade-Ibijola, Abejide, editor, Ong, Kok-Leong, editor, and Piuri, Vincenzo, editor

Published
2025
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4. Comparative Analysis of Pretrained Models for Text Classification, Generation and Summarization: A Detailed Analysis

Author

Pathak, Prakrit, Rana, Prashant Singh, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor

Published
2025
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9. Predictive Maintenance of Armoured Vehicles using Machine Learning Approaches

Author

Sengupta, Prajit, Mehta, Anant, and Rana, Prashant Singh

Subjects
Computer Science - Machine Learning
Abstract

Armoured vehicles are specialized and complex pieces of machinery designed to operate in high-stress environments, often in combat or tactical situations. This study proposes a predictive maintenance-based ensemble system that aids in predicting potential maintenance needs based on sensor data collected from these vehicles. The proposed model's architecture involves various models such as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier and Gradient Boosting to predict the maintenance requirements of the vehicles accurately. In addition, K-fold cross validation, along with TOPSIS analysis, is employed to evaluate the proposed ensemble model's stability. The results indicate that the proposed system achieves an accuracy of 98.93%, precision of 99.80% and recall of 99.03%. The algorithm can effectively predict maintenance needs, thereby reducing vehicle downtime and improving operational efficiency. Through comparisons between various algorithms and the suggested ensemble, this study highlights the potential of machine learning-based predictive maintenance solutions., Comment: In Conference Proceedings of INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE (pg:25-31) - (New Delhi, 2023)

Published
2023

12. Automated Event Detection and Extraction from E-Mails

Author

Singhal, Kriti, Rana, Prashant Singh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Shukla, Balvinder, editor, Murthy, B. K., editor, Hasteer, Nitasha, editor, Kaur, Harpreet, editor, and Van Belle, Jean-Paul, editor

Published
2024
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13. Analysis of Effectiveness of Indian Political Campaigns on Twitter

Author

Singhal, Kriti, Sood, Kartik, Kaushal, Akshat, Gehlot, Vansh, Rana, Prashant Singh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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14. A Multi-layered Approach to Brain Tumor Classification Using VDC-12

Author

Mehta, Anant, Sengupta, Prajit, Rana, Prashant Singh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Aurelia, Sagaya, editor, J., Chandra, editor, Immanuel, Ashok, editor, Mani, Joseph, editor, and Padmanabha, Vijaya, editor

Published
2024
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15. A Smart Technique of Gearbox Fault Diagnosis Based on Advanced Signal Processing and Machine Learning

Author

Mukherjee, Subrata, Kumar, Vikash, Chopra, Hetarth, Rohit, Jino, Hooda, Nishtha, Rana, Prashant Singh, Sarangi, Somnath, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Ghoshal, Sanjoy K., editor, Samantaray, Arun K., editor, and Bandyopadhyay, Sandipan, editor

Published
2024
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16. Protocol Security in 6th Generation (6G) Networks

Author

Garg, Tanya, Kaur, Gurjinder, Rana, Prashant Singh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Muthalagu, Raja, editor, P S, Tamizharasan, editor, Pawar, Pranav M., editor, R, Elakkiya, editor, Prasad, Neeli Rashmi, editor, and Fiorentino, Michele, editor

Published
2024
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19. RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

Author

Rana, Ashish, Khanna, Deepanshu, Ghosal, Tirthankar, Singh, Muskaan, Singh, Harpreet, and Rana, Prashant Singh

Subjects
Computer Science - Computation and Language
Abstract

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. We make our codebase available at https://github.com/ashishrana160796/RerrFact., Comment: Accepted in the AAAI-22 Workshop on Scientific Document Understanding at the Thirty-Sixth AAAI Conference on Artificial Intelligence (SDU@AAAI-22)

Published
2022

28. Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units

Author

Rana, Ashish, Singh, Taranveer, Singh, Harpreet, Kumar, Neeraj, and Rana, Prashant Singh

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization error increases i.e. they are not good generalizers outside training range. Consider the case of automating cell counting process where more dense images with higher cell counts are commonly encountered as compared to images used in training data. By making better predictions for higher ranges of cell count we are aiming to create better generalization systems for cell counting. With architecture proposal of neural arithmetic logic units (NALU) for arithmetic operations, task of counting has become feasible for higher numeric ranges which were not included in training data with better accuracy. As a part of our study we used these units and different other activation functions for learning cell counting task with two different architectures namely Fully Convolutional Regression Network and U-Net. These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes. This comparative study is described in terms of optimizing regression loss problem from these models trained with extensive data augmentation techniques. We were able to achieve better results in our experiments of cell counting tasks with introduction of these numerically biased units to already existing architectures in the form of residual layer concatenation connections. Our results confirm that above stated numerically biased units does help models to learn numeric quantities for better generalization results., Comment: * code repository for project: https://github.com/ashishrana160796/nalu-cell-counting

Published
2020

29. Ensemble Model for Music Genre Classification

Author

Singhal, Kriti, Chawla, Shubham, Agarwal, Arnav, Goyal, Pranshu, Agarwal, Abhiroop, Rana, Prashant Singh, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, Dutta, Paramartha, editor, and Piuri, Vincenzo, editor

Published
2023
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30. A Review: The Study and Analysis of Neural Style Transfer in Image

Author

Bagwari, Shubham, Choudhary, Kanika, Raikwar, Suresh, Rana, Prashant Singh, Mighlani, Sumit, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tistarelli, Massimo, editor, Dubey, Shiv Ram, editor, Singh, Satish Kumar, editor, and Jiang, Xiaoyi, editor

Published
2023
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39. Enhancing Road Traffic Prediction Using Data Preprocessing Optimization.

Author

Garg, Tanya, Kaur, Gurjinder, Rana, Prashant Singh, and Cheng, Xiaochun

Subjects
MACHINE learning, STANDARD deviations, TRANSPORTATION planning, TRANSPORTATION management, TRAFFIC estimation
Abstract

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm's variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications. [ABSTRACT FROM AUTHOR]

Published
2025
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42. ARGUABLY @ AI Debater-NLPCC 2021 Task 3: Argument Pair Extraction from Peer Review and Rebuttals

Author

Kohli, Guneet Singh, Kaur, Prabsimran, Singh, Muskaan, Ghosal, Tirthankar, Rana, Prashant Singh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lu, editor, Feng, Yansong, editor, Hong, Yu, editor, and He, Ruifang, editor

Published
2021
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49. MULTILEVEL ENSEMBLE MODEL FOR LOAD PREDICTION ON HOSTS IN FOG COMPUTING ENVIRONMENT.

Author

BAWA, Shabnam, RANA, Prashant Singh, and TEKCHANDANI, Rajkumar

Subjects
VIRTUAL machine systems, DECISION trees, RANDOM forest algorithms, MACHINE learning, PREDICTION models
Abstract

With the growing demand for various IoT applications, fog nodes frequently become overloaded. Fog computing requires effective load balancing to maximize resource utilization. It is essential to determine the load on host to obtain workload consolidation. Various random parameters, including CPU utilization, the number of CPU cores, RAM, memory allocated, memory available, disk I/O, and network I/O are employed to better comprehend host workloads. In the proposed work, the host's load status is detected using an ensemble approach into three categories: under-loaded, balanced and overloaded. Further, the proposed work considers three case studies and varying numbers of virtual machines (VMs) are executed with various parameter combinations. In each case study, a different number of VMs are executed in parallel on two different platforms. In the proposed study, we predicted the load on multiple hosts by employing a variety of advanced machine-learning models. To construct an ensemble model, we selected models with higher accuracy based on retrieved performance evaluation criteria. The ensemble method is applied to deal with the worst-case scenario of the model prediction. For a selected number of case studies, the Random Forest model, Ada Boost Classifier, Gradient Boost and Decision Tree models perform better than other models. These state-of-the-art predictive models are outperformed by our proposed ensemble model and achieves an improved accuracy of nearly 82% by correctly classifying hosts as overloaded, underloaded and balanced. [ABSTRACT FROM AUTHOR]

Published
2024
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