[1] Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., ... & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex and intelligent systems, 5(4), 409-416.
[2] Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global. DOI: 10.4018/978-1-7998-1313-2.ch006
[3] Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems, 24, 134-149.
[4] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International journal of engineering and advanced technology (IJEAT), 8(3), 347-353.
[5] Kumar, R., Edaltpanah, S. A., Jha, S., Broumi, S., & Dey, A. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems, 23, 5-15.
[6] Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.
[7] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex and intelligent systems, 5(2), 255-263. https://doi.org/10.1007/s40747-019-0108-1
[8] Pratihar, J., Kumar, R., Edalatpanah, S. A., & Dey, A. (2021). Modified Vogel’s approximation method for transportation problem under uncertain environment.
Complex and intelligent systems,
7(1), 29-40.
https://doi.org/10.1007/s40747-020-00153-4
[9] Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology, 8(3), 385-390.
[10] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple t-norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.
[11] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.
[12] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Soft subring theory under interval-valued neutrosophic environment (Vol. 36). Neutrosophic sets and systems, 36, 193-219.
[13] Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Introduction to interval-valued neutrosophic subring (Vol. 36). Neutrosophic sets and systems, 36, 220-245.
[14] Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic hypersoft subgroup. Neutrosophic sets and systems, 33, 208-233.
[15] Kumar, R., Edalatpanah, S. A., & Mohapatra, H. (2020). Note on “optimal path selection approach for fuzzy reliable shortest path problem”. Journal of intelligent and fuzzy systems, 39(5), 7653- 7656.
[16] Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA), 9(2), 132-161. doi: 10.4018/IJFSA.2020040106
[17] Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering, 4(1), 1-7.
[18] Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering, 8(6), 724-731.
[19] Kumar, R., Edalatpanah, S. A., Gayen, S., & Broum, S. (2021). Answer note “a novel method for solving the fully neutrosophic linear programming problems: suggested modifications”. Neutrosophic sets and systems, 39(1), 12.
[20] Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research, 8(4), 975-982.
[21] Mohapatra, H., Rath, S., Panda, S., & Kumar, R. (2020). Handling of man-in-the-middle attack in wsn through intrusion detection system. International journal of emerging trends in engineering research, 8(5), 1503-1510.
[23] Mohapatra, H., RATH, A. K., Landge, P. B., Bhise, D. H. I. R. A. J., Panda, S., & Gayen, S. A. (2020). A comparative analysis of clustering protocols of wireless sensor network. International journal of mechanical and production engineering research and development (IJMPERD) ISSN (P), 2249-6890.
[24] Mohapatra, H., & Rath, A. K. (2020). Survey on fault tolerance-based clustering evolution in WSN.
IET networks,
9(4), 145-155.DOI:
10.1049/iet-net.2019.0155
[25] Mohapatra, H., Debnath, S., Rath, A. K., Landge, P. B., Gayen, S., & Kumar, R. (2020). An efficient energy saving scheme through sorting technique for wireless sensor network. International journal of emerging trends in engineering research, 8(8), 4278-4286.
[26] Mohapatra, H., & Rath, A. K. (2020). Fault tolerance in WSN through uniform load distribution function. International journal of sensors, wireless communications and control, 10(1), 1-10.
[27] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance through energy balanced cluster formation (EBCF) in WSN. In Smart innovations in communication and computational sciences, 851, 313-321, Singapore: Springer. https://doi.org/10.1007/978-981-13-2414-7_29
[28] Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol.
IET wireless sensor systems,
9(6), 358-365.DOI:
10.1049/iet-wss.2018.5229
[29] Mohapatra, H. (2018). C programming: practice. Independently published.
[30] Mohapatra, H., & Rath, A. K. (2020). Fundamentals of software engineering: designed to provide an insight into the software engineering concepts. BPB Publications.
[31] Mohapatra, H. (2009). HCR by using neural network (Master's thesis, College of Engineering and Technology, Bhubaneswar). https://www.researchgate.net/profile/Hitesh-Mohapatra/publication/323547763_Handwritten_Character_Recognition_HCR_Using_Neural_Network/links/5a9c3c340f7e9be379681552/Handwritten-Character-Recognition-HCR-Using-Neural-Network.pdf
[32] Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific and technology research, 8(8), 1009-1013.
[33] Furtado, F., & Singh, A. (2020). Movie recommendation system using machine learning. International journal of research in industrial engineering, 9(1), 84-98.
[34] Singh, A., Herunde, H., & Furtado, F. (2020). Modified haar-cascade model for face detection issues. International journal of research in industrial engineering, 9(2), 143-171.
[35] Herunde, H., Singh, A., Deshpande, H., & Shetty, P. (2020). Detection of pedestrian and different types of vehicles using image processing. International journal of research in industrial engineering, 9(2), 99-113.
[36] Mohapatra, H. (2020). Offline drone instrumentalized ambulance for emergency situations. International journal of robotics and automation (IJRA), 9(4), 251-255.
[37] Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT.
IET wireless sensor systems,
9(6), 447-457.DOI:
10.1049/iet-wss.2019.0081
[38] Panda, H., Mohapatra, H., & Rath, A. K. (2020). WSN-based water channelization: an approach of smart water. In Smart cities—opportunities and challenges, 58, (pp. 157-166). Singapore: Springer. https://doi.org/10.1007/978-981-15-2545-2_15
[39] Rout, S. S., Mohapatra, H., Nayak, R. K., Tripathy, R., Bhise, D., Patil, S. P., & Rath, A. K. (2020). Smart water solution for monitoring of water usage based on weather condition. International journal of emerging trends in engineering research, 8(9), 5335-5343.
[40] Kelly, M. D. (1970). Visual identification of people by computer (No. 130). Department of Computer Science, Stanford University
[41] Kanade, T. (1974). Picture processing system by computer complex and recognition of human faces. Dept. of Science, Kyoto University
[42] Delac, K., & Grgic, M. (2004, June). A survey of biometric recognition methods. Proceedings. elmar-2004. 46th international symposium on electronics in marine (pp. 184-193). IEEE. Zadar, Croatia.
[43] Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features.
Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.DOI:
10.1109/CVPR.2001.990517
[44] Li, S. Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., & Shum, H. (2002, May). Statistical learning of multi-view face detection. European conference on computer vision, 2353, (pp. 67-81). Berlin, Heidelberg: Springer. https://doi.org/10.1007/3-540-47979-1_5
[45] Wu, B., Ai, H., Huang, C., & Lao, S. (2004, May). Fast rotation invariant multi-view face detection based on real adaboost.
Sixth IEEE international conference on automatic face and gesture recognition, 2004. Proceedings. (pp. 79-84). IEEE.DOI:
10.1109/AFGR.2004.1301512
[46] Mathias, M., Benenson, R., Pedersoli, M., & Van Gool, L. (2014, September). Face detection without bells and whistles. European conference on computer vision, 8692, (pp. 720-735). Cham: Springer. https://doi.org/10.1007/978-3-319-10593-2_47
[47] Jones, M., & Viola, P. (2003). Fast multi-view face detection. MERL - mitsubishi electric research laboratories. https://www.merl.com/publications/TR2003-96
[48]
Huang, C., Ai, H., Li, Y., & Lao, S. (2005, October). Vector boosting for rotation invariant multi-view face detection.
Tenth IEEE international conference on computer vision (ICCV'05) Volume 1 (Vol. 1, pp. 446-453). IEEE.DOI:
10.1109/ICCV.2005.246
[49] Huang, C., Ai, H., Li, Y., & Lao, S. (2007). High-performance rotation invariant multiview face detection.
IEEE transactions on pattern analysis and machine intelligence,
29(4), 671-686.DOI:
10.1109/TPAMI.2007.1011
[50] Park, U., Tong, Y., & Jain, A. K. (2010). Age-invariant face recognition.
IEEE transactions on pattern analysis and machine intelligence,
32(5), 947-954.DOI:
10.1109/TPAMI.2010.14
[51] Li, Z., Park, U., & Jain, A. K. (2011). A discriminative model for age invariant face recognition.
IEEE transactions on information forensics and security,
6(3), 1028-1037.DOI:
10.1109/TIFS.2011.2156787
[52] Ding, C., & Tao, D. (2016). A comprehensive survey on pose-invariant face recognition.
ACM transactions on intelligent systems and technology (TIST),
7(3), 1-42.
https://doi.org/10.1145/2845089
[54] Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions.
IEEE transactions on image processing,
19(6), 1635-1650.DOI:
10.1109/TIP.2010.2042645
[55] Zheng, W., Tang, H., Lin, Z., & Huang, T. S. (2009, September). A novel approach to expression recognition from non-frontal face images.
2009 IEEE 12th international conference on computer vision (pp. 1901-1908). IEEE.DOI:
10.1109/ICCV.2009.5459421
[56] https://www. biography.com/actor/matthew-perry
[57] M. Vulpo. (2019, June). Retrieved from https://www.eonline.com
[58] Wikimedia Commons contributors. (2020, November). The free encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Shah_Rukh_Khan&oldid=991433963
[59] M. Bleby. (2019, August). Financial review. Retrieved from https://www.afr.com/life-and-luxury/arts-and-culture/bollywood-king-shah-rukh-khan-talks-power-politics-and-metoo-20190801-p52d3h
[60] The Free Encyclopedia Wikipedia. (2020, November). The free encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Halsey_(singer)&oldid=991181483
[61] https://pngimg.com/download/30244
[62] Wikimedia Commons contributors. (2020, October). Retrieved from https://commons.wikimedia.org/w/index.php?title=File:Mr._Bean_2011.jpg&oldid=490772443
[63] Brunelli, R., & Poggio, T. (1993). Face recognition: features versus templates.
IEEE transactions on pattern analysis and machine intelligence,
15(10), 1042-1052.DOI:
10.1109/34.254061
[64] Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3), 519-524.
[65] Kirby, M., & Sirovich, L. (1990). Application of the Karhunen-Loeve procedure for the characterization of human faces.
IEEE transactions on pattern analysis and machine intelligence,
12(1), 103-108.DOI:
10.1109/34.41390
[66] Moghaddam, B., Wahid, W., & Pentland, A. (1998, April). Beyond eigenfaces: probabilistic matching for face recognition.
Proceedings third IEEE international conference on automatic face and gesture recognition (pp. 30-35). IEEE.DOi:
10.1109/AFGR.1998.670921
[67] Schölkopf, B., Smola, A., & Müller, K. R. (1997, October). Kernel principal component analysis.
International conference on artificial neural networks,
1327, (pp. 583-588). Berlin, Heidelberg: Springer.
https://doi.org/10.1007/BFb0020217
[68] Bartlett, M. S. (2001). Independent component representations for face recognition. In
face image analysis by unsupervised learning, (Vol. 612), (pp. 39-67). Boston, MA: Springer.
https://doi.org/10.1007/978-1-4615-1637-8_3
[69] Yang, J., Zhang, D., Frangi, A. F., & Yang, J. Y. (2004). Two-dimensional PCA: a new approach to appearance-based face representation and recognition.
IEEE transactions on pattern analysis and machine intelligence,
26(1), 131-137.DOI:
10.1109/TPAMI.2004.1261097
[70] Shi, J., Samal, A., & Marx, D. (2006). How effective are landmarks and their geometry for face recognition?.
Computer vision and image understanding,
102(2), 117-133.
https://doi.org/10.1016/j.cviu.2005.10.002
[71] Daniyal, F., Nair, P., & Cavallaro, A. (2009, September). Compact signatures for 3D face recognition under varying expressions.
2009 sixth IEEE international conference on advanced video and signal based surveillance (pp. 302-307). IEEE.DOI:
10.1109/AVSS.2009.71
[73] Dryden, I. L., & Mardia, K. V. (2016). Statistical shape analysis: with applications in R (Vol. 995). John Wiley & Sons. DOI:10.1002/9781119072492
[74] Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance.
IEEE transactions on pattern analysis and machine intelligence,
15(9), 850-863.DOI:
10.1109/34.232073
[75] Wiskott, L., Krüger, N., Kuiger, N., & Von Der Malsburg, C. (1997). Face recognition by elastic bunch graph matching.
IEEE transactions on pattern analysis and machine intelligence,
19(7), 775-779.DOI:
10.1109/34.598235
[77] Liu, C., & Wechsler, H. (2000). Robust coding schemes for indexing and retrieval from large face databases.
IEEE transactions on image processing,
9(1), 132-137.DOI:
10.1109/83.817604
[78] Gao, Y., & Leung, M. K. (2002). Face recognition using line edge map.
IEEE transactions on pattern analysis and machine intelligence,
24(6), 764-779.DOI:
10.1109/TPAMI.2002.1008383
[79] Albiol, A., Monzo, D., Martin, A., Sastre, J., & Albiol, A. (2008). Face recognition using HOG–EBGM.
Pattern recognition letters,
29(10), 1537-1543.
https://doi.org/10.1016/j.patrec.2008.03.017
[80] Chen, D., Cao, X., Wen, F., & Sun, J. (2013). Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3025-3032).
[81] Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a" siamese" time delay neural network. Advances in neural information processing systems, 6, 737-744
[82] Lin, S. H., Kung, S. Y., & Lin, L. J. (1997). Face recognition/detection by probabilistic decision-based neural network.
IEEE transactions on neural networks,
8(1), 114-132.doi:
10.1109/72.554196
[83] Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: closing the gap to human-level performance in face verification. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701-1708).
[84] Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).
[85] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks.
Communications of the ACM,
60(6), 84-90.
https://doi.org/10.1145/3065386
[86] https://p0.pikist.com/photos/861/709/person-man-male-portrait-head-face-side-face-hairstyle-looking-thumbnail.jpg
[87] Wikimedia Commons contributors. (2020, October). Wikimedia commons, the free media repository. Retrieved from https://commons.wikimedia.org/w/index.php?title=File:Ston e_Creek_teachers.jpg&oldid=506079995"
W. Champion, G. Berryman, C. Martin, & J. Buckland. (2011) Retrieved from https://www.youtube.com/watch?v=3Tu7dW9nzXg.