Funding Agencies

OPTImization for MAchine Learning ( OPTIMAL )

Research Group

Funding Agencies

PUBLICATIONS

Under Revision

  1. Comprehensive Review on Twin SVM, ACM COMPUTING SURVEYS
    [SCI Indexed with Impact Factor: 6.13]

  2. A reduced universum twin support vector machine for class imbalance learning, Pattern Recognition, Elsevier
    [SCI Indexed with Impact Factor: 5.89]

  3. Minimum Variance-Embedded Deep Kernel Regularized Least Square Method for One-class Classification and Its Applications to Biomedical Data, Neural Networks, Elsevier.
    [SCI Indexed with Impact Factor: 5.78]

  4. A Semantic Collaboration Method Based on Uniform Knowledge Graph, IEEE Internet of Things Journal
    [SCI Indexed with Impact Factor: 9.51]

  5. Least squares projection twin support vector clustering (LSPTSVC), Information Sciences, Elsevier.
    [SCI Indexed with Impact Factor: 5.52]

  6. Least squares K-nearest neighbor-based weighted multi-class twin SVM, Neurocomputing, Elsevier.
    [SCI Indexed with Impact Factor: 4.072]

  7. Diagnosis of Alzheimer's disease using universum support vector machine based recursive featureelimination (USVM-RFE), Biomedical Signal Processing Control, Elsevier.
    [SCI Indexed with Impact Factor: 2.94]

  8. LSTSVM classifier with enhanced features from pre-trained functional link network, Applied Soft Computing, Elsevier.
    [SCI Indexed with Impact Factor: 4.87]

  9. Kernel ridge regression-based auto-encoder for one-class classification using privileged information, Cognitive Computation, Springer.
    [SCI Indexed with Impact Factor: 4.28]

  10. Multiobjective Genetic Algorithm and Non-Dominated Sorting Based Rank Assignment Method for Optimal Knowledge Set Generation A Classification Rule Mining System, Swarm and Evolutionary Computation, Elsevier
    [SCI Indexed with Impact Factor: 6.33]

  11. A novel method for the classification of Alzheimer's disease from normal controls using MRI images, Expert Systems, Wiley.
    [SCI Indexed with Impact Factor: 1.50]

  12. A novel approach for classification of mental tasks using multiview ensemble learning (MEL), Neurocomputing, Elsevier.
    [SCI Indexed with Impact Factor: 4.07]

YEAR - WISE PUBLICATIONS

    2019
  1. Q Li, Z Cao, M Tanveer, HM Pandey, C Wang (2019). An effective reliability evaluation method for power communication network based on community structure, IEEE Transactions on Industry Applications
    [SCI Indexed with Impact Factor: 3.34]

  2. M.A. Ganaie, M. Tanveer, P.N. Suganthan (2019). Oblique decision tree ensemble via twin bounded SVM, Expert Systems with Applications, Elsevier.
    [SCI Indexed with Impact Factor: 4.29]

  3. M Khan, T Hussain, M Tanveer, G Sannino, V Albuquerque (2019). Cost-Effective Video Summarization using Deep CNN with Hierarchical Weighted Fusion for IoT Surveillance Networks, IEEE Internet of Things Journal.
    [SCI Indexed with Impact Factor: 9.51]

  4. L Fang, Y Li, X Yun, Z Wen, S Ji, W Meng, Z Cao, M Tanveer (2019). THP: An SDN-based authentication scheme resistant to multiple attacks in IoT, IEEE Internet of Things Journal.
    [SCI Indexed with Impact Factor: 9.51]

  5. C. Gautam, A. Tiwari, M. Tanveer (2019). KOC+: Kernel ridge regression-based one-class classification using privileged information, Information Sciences, Elsevier.
    [SCI Indexed with Impact Factor: 5.52]

  6. M. Tanveer, B. Richhariya, R.U. Khan, A.H. Rashid, M. Prasad, P. Khanna, C.T. Lin (2019). Machine learning techniques for the diagnosis of Alzheimer's disease: A review, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).
    [SCI Indexed with Impact Factor: 2.87]

  7. M. Tanveer, A. Sharma, P.N. Suganthan (2019). General twin support vector machine with pinball loss function, Information Sciences, Elsevier.
    [SCI Indexed with Impact Factor: 5.52]

  8. M. Tanveer, S. Sharma, R. Rastogi, P. Anand (2019). Sparse support vector machine with pinball loss, Wiley Transactions on Emerging Telecommunications Technologies.
    [SCI Indexed with Impact Factor: 1.25]

  9. M. Tanveer, C. Gautam, P.N. Suganthan (2019). Comprehensive evaluation of twin SVM based classifiers on UCI datasets, Applied Soft Computing, Elsevier.
    [SCI Indexed with Impact Factor: 4.87]

  10. M. Tanveer, A. Tiwari, R. Choudhary, S. Jalan (2019). Sparse pinball twin support vector machines, Applied Soft Computing, Elsevier.
    [SCI Indexed with Impact Factor: 4.87]

  11. E.J. Cheng, K.P. Chou, S. Rajora, B.H. Jin, M. Tanveer, C.T. Lin, K.Y. Young, W.C. Lin, M. Prasad (2019). Deep sparse representation classifier for facial recognition and detection system, Pattern Recognition Letters, Elsevier.
    [SCI Indexed with Impact Factor: 2.81]

  12. B. Richhariya, M. Tanveer (2019) A fuzzy universum support vector machine based on information entropy. In: Tanveer M., Pachori R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore

  13. M. Dalal, M. Tanveer, R.B. Pachori (2019) Automated identification system for focal EEG signals using fractal dimension of FAWT-based sub-bands signals. In: Tanveer M., Pachori R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore

  14. M. Tanveer, T. Rajani, M.A. Ganaie (2019). Improved sparse pinball twin SVM, IEEE SMC 2019, Oct. 06-09, 2019, Bari, Italy.

  15. Z. Cao, M. Prasad,M. Tanveer, C.T. Lin (2019). Tensor Decomposition for EEG Signals Retrieval, IEEE SMC 2019, Oct. 06-09, 2019, Bari, Italy.

  16. 2018
  17. B. Richhariya, M. Tanveer (2018), A robust fuzzy least squares twin support vector machine for class imbalance learning. Applied Soft Computing, Elsevier, 71:418-432.
    [SCI Indexed with Impact Factor: 4.87]

  18. B. Richhariya, M. Tanveer (2018), EEG signal classification using universum support vector machine. Expert Systems with Applications, Elsevier, 106: 169-182.
    [SCI Indexed with Impact Factor: 4.29]

  19. S. Gupta, K.H. Krishna, R.B. Pachori, M. Tanveer (2018), Fourier-Bessel series expansion based technique for automated classification of focal and non-focal EEG signals. International Joint Conferences on Neural Network (IJCNN), July 08-13, 2018, Rio, Brazil [Core Rank: A].

  20. D. Badarinath, C. Siddu, N. Bharill, M. Tanveer, M. Prasad, A. Appaji, S. Vinekar and A. Ningappa (2018), Study of clinical staging and classification of retinal images for Retinopathy of Prematurity (ROP) screening, International Joint Conferences on Neural Network (IJCNN), July 08-13, 2018, Rio, Brazil [Core Rank: A].

  21. B. Richhariya, A. Sharma, M. Tanveer (2018), Improved universum twin support vector machines, 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE SSCI 2018), Nov. 2018

  22. M. Tanveer, R.B. Pachori, N.V. Angami (2018), Entropy based features in FAWT framework for automated detection of epileptic seizure EEG signals, 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE SSCI 2018), Nov. 2018

  23. M. Tanveer, A Gupta, D. Kumar, S. Priyadarshini, A. Chakraborti, R. Mallipeddi (2018), Cognitive task classification using fuzzy based empirical wavelet transform, IEEE International Conference on Systems, Man & Cybernetics (IEEE SMC2018), Oct. 2018, Japan.

  24. M. Tanveer, R.B. Pachori, N.V. Angami (2018), Classification of seizure and seizure-free EEG signals using Hjorth parameters, 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE SSCI 2018), Nov. 2018

  25. 2017
  26. M. Tanveer, K. Shubham (2017). A regularization on Lagrangian twin support vector regression, International Journal of Machine Learning and Cybernetics, Springer, USA 8(3): 807-821.
    [SCI Indexed with Impact Factor: 3.84]

  27. M. Tanveer, K. Shubham (2017). Smooth twin support vector machines via unconstrained convex minimization, Filomat, USA 31(8): 2195-2210.
    [SCI Indexed with Impact Factor: 0.695]

  28. M. Tanveer (2017). Linear programming twin support vector regression, Filomat, USA 31(7): 2123-2142 (Sole author).
    [SCI Indexed with Impact Factor: 0.695]

  29. 2016
  30. M. Tanveer, I. Ahmad, M. Mangal, Y.H. Shao (2016). One norm linear programming support vector regression, Neurocomputing, Elsevier, Netherlands 173: 1508-1518.
    [SCI Indexed with Impact Factor: 4.072]

  31. M. Tanveer, M.A. Khan, S.S. Ho (2016). Robust energy-based least squares twin support vector machines, Applied Intelligence, Springer, Netherlands 45(1): 174-186.
    [SCI Indexed with Impact Factor: 2.88]

  32. M. Tanveer, K. Shubham, M. Aldhaifallah, S.S. Ho (2016). An efficient regularized knn-based weighted twin support vector regression, Knowledge-Based Systems, Elsevier, Netherlands 94: 70-87.
    [SCI Indexed with Impact Factor: 5.10]

  33. M. Tanveer, K. Shubham, M. Al-Dhafallah, K.S. Nisar (2016). An efficient implicit regularized Lagrangian twin support vector machines, Applied Intelligence, Springer, Netherlands 44(4): 831-848.
    [SCI Indexed with Impact Factor: 2.88]

  34. 2015
  35. M. Tanveer (2015). Robust and sparse linear programming twin support vector machines, Cognitive Computation, Springer, USA 7: 137-149 (Sole author).
    [SCI Indexed with Impact Factor: 4.28]

  36. M. Tanveer (2015). Application of smoothing techniques for linear programming twin support vector machines, Knowledge and Information Systems, Springer, USA 45(1): 191-214 (Sole author).
    [SCI Indexed with Impact Factor: 2.39]

  37. M. Tanveer (2015). Newton method for implicit Lagrangian twin support vector machines, Journal of Machine Learning and Cybernetics, Springer, USA 6:1029-1040 (Sole author).
    [SCI Indexed with Impact Factor: 3.84]

  38. 2014
  39. M. Tanveer (2014). Note on some recent fixed point theorems in fuzzy metric spaces, Journal of Intelligent & Fuzzy Systems, IOS Press, USA 26: 811-814 (Sole author).
    [SCI Indexed with Impact Factor: 1.63]

  40. 2013
  41. S. Balasundaram, M. Tanveer (2013). On Lagrangian twin support vector regression, Neural Computing & Applications, Springer, UK 22(1): 257-267.
    [SCI Indexed with Impact Factor: 4.66]

  42. S. Balasundaram, M. Tanveer (2013). Smooth Newton method for implicit Lagrangian twin support vector regression, KES, IOP Press, 17(4), 267-278.

  43. 2012
  44. M. Tanveer, M. Imdad, D. Gopal, D.K. Patel (2012). Common fixed point theorems in modified intuitionistic fuzzy metric spaces with common property (E.A), Fixed Point Theory & Applications, SpringerOpen, USA 2012: 36.
    [SCI Indexed with Impact Factor: 2.5]

  45. S. Balasundaram, M. Tanveer (2012). On proximal bilateral-weighted fuzzy support vector machine classifiers, IJAIP, USA 4 (3/4), 199-210.

  46. 2011
  47. M. Imdad, J. Ali, M. Tanveer (2011). Remarks on some recent metrical common fixed point theorems, Applied Mathematics Letters, Elsevier, UK 24(7): 1165-1169.
    [SCI Indexed with Impact Factor: 3.48]

  48. J. Ali, M. Imdad, D. Mihet, M. Tanveer (2011). Common fixed points of strict contractions in Menger spaces, Acta Math. Hungarica, Springer, Hungary 132(4): 367-386.
    [SCI Indexed with Impact Factor: 0.583]

  49. 2010
  50. M. Imdad, M. Tanveer, M. Hasan (2010). Some common fixed point theorems in Menger spaces, Fixed Point Theory & Applications, Springer Open, USA 2010: 14 Pages. (Corresponding author).
    [SCI Indexed with Impact Factor: 2.5]

  51. 2009
  52. M. Imdad, J. Ali, M. Tanveer (2009). Coincidence and Common fixed point theorems for nonlinear contractions in Menger PM Spaces, Chaos, Solitons and Fractals, Elsevier, England 42: 3121-3129.
    [SCI Indexed with Impact Factor: 3.06]