Current projects. Data Science, Bigdata, Machine Learning, Deep Learning, Computer Vision and its applications, some of the topics are:

Analysis and visualization of bigdata on Spark Vs BeeGFS filesystems in context of videos, IMAGEnet, MIT Places 365 and 205, Google Sports dataset, UCF 101 Video Action Recognition, Body Worn Camera Video Research, Surveillance Video Research, Deep Neural Network Optimization on bigdata, Sentiment Analysis on text, Stock prediction, Autism detection, Cancer imaging.

Past projects.Stanford 40 Action Recognition, Diabetic Retinopathy Recognition, Comprehensive Cars dataset, Plankton Recognition, Caltech 256 object recognition, Scene recognition, Texture recognition, Flower recognition, Biometrics Security.

Publications and Presentations

(student co-authors are underlined)

Journals and Conferences (Peer-Reviewed)

41. J. Eapen, A. Verma, and D. Bein, "Improved Big Data Stock Index Prediction Using Deep Learning with CNN and GRU," Int. Journal of Big Data Intelligence, 2021. (accepted)

40. J. V. Dirisam, D. Bein, and A. Verma, "Predictive Analytics of Donors in crowdfunding platforms: A case study on Donorschoose.org," the 11th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Jan. 27-30, 2021, Las Vegas, NV, USA.

39. Y. Lavinia, H. Vo, and A. Verma, "New Color Fusion Deep Learning Model for Large-Scale Action Recognition," Int. Journal of Computational Vision and Robotics, vol. 10, no. 1, pp. 41-60, 2020. [PDF]

38. H. Al-Barazanchi, H. Qassim, and A. Verma, "Large-Scale Scene Image Categorization with Deep Learning Based Model," Int. Journal of Computational Vision and Robotics, vol. 10, no. 3, pp. 185-201, 2020. [PDF]

37. C. Vielma, A. Verma, and D. Bein, "Single and Multibranch CNN-Bidirectional LSTM for IMDb Sentiment Analysis," the 17th International Conference on Information Technology: New Generations, Apr. 5-8, 2020, Las Vegas, NV, USA.

36. A. Quintanilla and A. Verma, "Novel Deep Learning Model with Fusion of Multiple Pipelines for Stock Market Prediction," Int. Journal of Advanced Intelligence Paradigms. 2020. (accepted) [PDF]

35. J. Eapen, A. Verma, and D. Bein, "Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction," the 9th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Jan. 7-9, 2019, Las Vegas, NV, USA. [PDF]

34. A. Jeerige, D. Bein, and A. Verma, "Comparison of Deep Reinforcement Learning Approaches for Intelligent Game Playing," the 9th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Jan. 7-9, 2019, Las Vegas, NV, USA. [PDF]

33. R. Jhangiani, D. Bein, and A. Verma, "Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions," the 10th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 10-12, 2019, New York, NY, USA. [PDF]

32. P. Mandal and A. Verma, "Novel Hash Based Radix Sorting Algorithm," the 10th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 10-12, 2019, New York, NY, USA. [PDF]

31. P. Ly, D. Bein, and A. Verma, "New Compact Deep Learning Model for Skin Cancer Recognition," the 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Nov. 8-10, 2018, New York, NY, USA. (Best paper award) [PDF]

30. H. Qassim, A. Verma, and D. Feinzimer, "Compressed Residual-VGG16 CNN Model for Big Data Places Image Recognition," the 8th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Jan. 8-10, 2018, Las Vegas, NV, USA. [PDF]

29. H. Al-Barazanchi, A. Verma, and S. Wang, "Intelligent Plankton Image Classification with Deep Learning," International Journal of Computational Vision and Robotics, vol. 8, no. 6, pp. 561-571, 2018. [PDF]

28. A. Yenter and A. Verma, "Deep CNN-LSTM with Combined Kernels from Multiple Branches for IMDb Review Sentiment Analysis," the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 19-21, 2017, New York, NY, USA. [PDF]

27. A. Verma, H. Qassim, and D. Feinzimer, "New Compressed Faster CNDS-Residual Squeeze Network for Large Scale Image Recognition," the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 19-21, 2017, New York, NY, USA. (Best paper award) [PDF]

26. A. Verma and Y. Liu, "Hybrid Deep Learning Ensemble Model for Improved Large Scale Car Recognition," the 3rd IEEE Smart World Congress, Aug 3-7, 2017, San Francisco, CA, USA. (Acceptance rate is 30%) [PDF]

25. S. Preetham, F. George, K. George, and A. Verma, "Deep Learning Based Video Recognition for Predicting Meltdown in Autistic Kids," the 5th IEEE International Conference on Healthcare Informatics, Aug 23-26, 2017, Park City, UT, USA. [PDF]

24. H. Vo and A. Verma, "New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space," the 12th IEEE International Symposium on Multimedia, Dec. 11-13, 2016, San Jose, CA, USA. (Acceptance rate is 26%) [PDF]

23. Y. Lavinia, H. Vo and A. Verma, "Fusion Based Deep CNN Architecture for Improved Large-Scale Image Action Recognition," the 12th IEEE International Workshop on Multimedia Information Processing and Retrieval, Dec. 11-13, 2016, San Jose, CA, USA. [PDF]

22. H. Al-Barazanchi, H. Qassim, and A. Verma, "Novel CNN Architecture with Residual Learning and Deep Supervision for Large-Scale Scene Image Categorization," the 7th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 20-22, 2016, New York, NY, USA. [PDF]

21. H. Vo and A. Verma, "Discriminant Color Texture Descriptors for Diabetic Retinopathy Recognition," the 12th IEEE International Conference on Intelligent Computer Communication and Processing (Track: Computer Vision) (ICCP), September 8-10, 2016, Cluj-Napoca, Romania. [PDF]

20. H. Al-Barazanchi, A. Verma, and S. Wang, "Performance Evaluation of Hybrid CNN for SIPPER Plankton Image Classification," the 3rd IEEE International Conference on Image Information Processing (ICIIP), Dec 21-24, 2015, Waknaghat, H.P., India. (Acceptance rate is 25.6%) [PDF]

19. H. Al-Barazanchi, A. Verma, and S. Wang, "Plankton Image Classification using Convolutional Neural Networks," the 19th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), July 27-30, 2015, Las Vegas, Nevada, USA. (Acceptance rate is 25%) [PDF]

18. A. Verma, C. Liu, and J. Jia, "Iris Recognition based on Robust Iris Segmentation and Image Enhancement," International Journal of Biometrics, vol. 4, no. 1, pp. 56-76, 2012. [PDF]

17. A. Verma and C. Liu, "Efficient Iris Identification with Improved Segmentation Techniques," in Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, V.K. Mago and N. Bhatia Eds., IGI Global, USA, pp. 148-164, 2012. [PDF]

16. A. Verma, C. Liu, and J. Jia, "New Color SIFT Descriptors for Image Classification with Applications to Biometrics," International Journal of Biometrics, vol. 3, no. 1, pp. 56–75, 2011. [PDF]

15. S. Banerji, A. Verma, and C. Liu, "Novel Color LBP Descriptors for Scene and Image Texture Classification," the 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), July 18-21, 2011, Las Vegas, Nevada, USA. [PDF]

14. A. Verma and C. Liu, "Fusion of Color SIFT Features for Image Classification with Applications to Biometrics," the 11th IAPR International Conference on Pattern Recognition and Information Processing (PRIP), May 18-20, 2011, Minsk, Belarus. [PDF]

13. A. Verma and C. Liu, "Novel EFM-KNN Classifier and a New Color Descriptor for Image Classification," the 20th IEEE Wireless and Optical Communications Conference (Track: Multimedia Services and Applications) (WOCC), April 15-16, 2011, Newark, New Jersey, USA. [PDF]

12. A. Verma, S. Banerji, and C. Liu, "A New Color SIFT Descriptor and Methods for Image Category Classification," the 2010 International Congress on Computer Applications and Computational Science (Track: Image, Speech and Signal Analysis) (CACS), December 4-6, 2010, Singapore. [PDF]

Book Chapters

Non Peer-Reviewed

11. A. Verma and C. Liu, "SIFT Features in Multiple Color Spaces for Improved Image Classification," in Recent Advances in Intelligent Image Search and Video Retrieval, C. Liu Ed., Springer, USA, pp. 141-162, 2017. [PDF]

10. Q Liu, Y. Lavinia, A. Verma, et al., "Feature Representation and Extraction for Image Search and Video Retrieval," in Recent Advances in Intelligent Image Search and Video Retrieval, C. Liu Ed., Springer, USA, pp. 1-18, 2017. [PDF]

9. M. Villa and A. Verma, "Fingerprint Recognition," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 265-281, 2017. [PDF]

8. A. Gubenko and A. Verma, "Face Recognition," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 283-290, 2017. [PDF]

7. R. Martinez and A. Verma, "Voice Recognition," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 291-298, 2017. [PDF]

6. A. Yenter and A. Verma, "Iris Recognition in Mobile Devices," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 299-307, 2017. [PDF]

5. M. Villa and A. Verma, "Biometric Signature for Mobile Devices," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 309-319, 2017. [PDF]

4. Y. Lavinia and A. Verma, "Hand Biometric Case Study," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 321-328, 2017. [PDF]

3. J. Ligon and A. Verma, "Keystroke Dynamics," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 329-336, 2017. [PDF]

2. Y. Liu and A. Verma, "Gait Recognition," in Biometrics in a Data Driven World: Trends, Technologies, and Challenges, M. Gofman and S. Mitra Eds., CRC Press, USA, pp. 337-343, 2017. [PDF]

1. S. Banerji, A. Verma, and C. Liu, "LBP and Color Descriptors for Image Classification," in Cross Disciplinary Biometric Systems, C. Liu and V.K. Mago Eds., Springer, pp. 205-225, 2012. [PDF]

Doctoral Dissertation

A. Verma, Title: Investigation on Advanced Image Search Techniques, 2011
Advisor: Dr. Chengjun Liu, Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
Description: Develop new color descriptors and classification methodology for color image search and classification. Propose novel segmentation techniques for iris recognition.

Presentation at Conferences

1. A. Verma, "New Compact Deep Learning Model for Skin Cancer Recognition," the 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Nov. 8-10, 2018, New York, NY, USA.

2. A. Verma, "Deep CNN-LSTM with Combined Kernels from Multiple Branches for IMDb Review Sentiment Analysis," the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 19-21, 2017, New York, NY, USA.

3. A. Verma, "New Compressed Faster CNDS-Residual Squeeze Network for Large Scale Image Recognition," the 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 19-21, 2017, New York, NY, USA.

4. A. Verma, "New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space," Oral Presentation at the 12th IEEE International Symposium on Multimedia, Dec. 11-13, 2016, San Jose, CA, USA.

5. A. Verma, "Fusion Based Deep CNN Architecture for Improved Large-Scale Image Action Recognition," Oral Presentation at the 12th IEEE International Workshop on Multimedia Information Processing and Retrieval, Dec. 11-13, 2016, San Jose, CA, USA.

6. A. Verma, "Novel CNN Architecture with Residual Learning and Deep Supervision for Large-Scale Scene Image Categorization," Oral Presentation at the 7th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), Oct. 20-22, 2016, New York, NY, USA.

7. A. Verma, "Novel EFM-KNN Classifier and a New Color Descriptor for Image Classification," Oral Presentation at the 20th IEEE Wireless and Optical Communications Conference, Newark, New Jersey, USA, April 15-16, 2011.

8. A. Verma, "New SIFT Descriptors for Image Category Classification," Poster Presentation at the 6th Annual Graduate Student Research Day, New Jersey Institute of Technology, Nov 4, 2010, Newark, New Jersey, USA.