Grey Wolf-Based Task Scheduling in Vehicular Fog Computing Systems

Document Type : Research Article

Authors

Department of Computer Engineering and Information Technology, Razi University, Iran.

10.22108/jcs.2024.142162.1145

Abstract

Vehicular fog computing (VFC) can be considered an important alternative to address the existing challenges in intelligent transportation systems (ITS). The main purpose of VFC is to perform computational tasks through various vehicles. At present, VFCs include powerful computing resources that bring the computational resources nearer to the requesting devices. This paper presents a new algorithm based on a meta-heuristic optimization method for task scheduling problem in VFC. The task scheduling in VFC is formulated as a multi-objective optimization problem, which aims to reduce makespan and monetary costs. The proposed method utilizes grey wolf optimization (GWO) and assigns different priorities to static and dynamic fog nodes. Dynamic fog nodes represent the parked or moving vehicles and static fog nodes show the stationary servers. Afterward, the tasks that require the most processing resources are chosen and allocated to fog nodes. The GWO-based method is extensively evaluated in more detail. Furthermore, the effectiveness of various parameters in the GWO algorithm is analyzed. We also assess the proposed algorithm on real applications and random data. The outcomes of our experiments confirm that, in comparison to previous works, our algorithm is capable of offering the lowest monetary cost.

Keywords

Main Subjects


[1] Jamil, Bushra and Ijaz, Humaira and Shojafar, Mohammad and Munir, Kashif. IRATS: A DRL-based intelligent priority and deadline-aware online resource allocation and task scheduling algorithm in a vehicular fog network. Ad hoc networks. 141: 103090, Elsevier. 2023. [DOI ]
[2] Jassbi, Sommayeh Jafarali and Teymori, Sahar. The improvement of wavefront cellular learning automata for task scheduling in fog computing. Transactions on Emerging Telecommunications Technologies. 34(8): e4803, Wiley Online Library. 2023. [DOI ]
[3] Pal, Souvik and Jhanjhi, NZ and Abdulbaqi, Azmi Shawkat and Akila, D and Alsubaei, Faisal S and Almazroi, Abdulaleem Ali. An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications. Sustainability. 15(6): 5104, MDPI. 2023. [DOI ]
[4] Xu, Jiuyun and Hao, Zhuangyuan and Zhang, Ruru and Sun, Xiaoting. A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access. 7: 116218--116226, IEEE. 2019. [DOI ]
[5] Abd Elaziz, Mohamed and Xiong, Shengwu and Jayasena, KPN and Li, Lin. Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowledge-Based Systems. 169: 39--52, Elsevier. 2019. [DOI ]
[6] Ghobaei-Arani, Mostafa and Souri, Alireza and Safara, Fatemeh and Norouzi, Monire. An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Transactions on Emerging Telecommunications Technologies. 31(2): e3770, Wiley Online Library. 2020. [DOI ]
[7] Rafique, Hina and Shah, Munam Ali and Islam, Saif Ul and Maqsood, Tahir and Khan, Suleman and Maple, Carsten. A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access. 7: 115760--115773, IEEE. 2019. [DOI ]
[8] Vemireddy, Satish and Rout, Rashmi Ranjan. Fuzzy Reinforcement Learning for energy efficient task offloading in Vehicular Fog Computing. Computer Networks. 199: 108463, Elsevier. 2021. [DOI ]
[9] Kumar, Mohit and Kishor, Avadh and Samariya, Jitendra Kumar and Zomaya, Albert Y. An autonomic workload prediction and resource allocation framework for fog enabled industrial IoT. IEEE Internet of Things Journal. IEEE. 2023. [DOI ]
[10] Movahedi, Zahra and Defude, Bruno and Hosseininia, Amir Mohammad. An efficient population-based multi-objective task scheduling approach in fog computing systems. Journal of Cloud Computing. 10: 1--31, Springer. 2021. [DOI ]
[11] Nguyen, Binh Minh and Thi Thanh Binh, Huynh and The Anh, Tran and Bao Son, Do. Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud--fog computing environment. Applied Sciences. 9(9): 1730, MDPI. 2019. [DOI ]
[12] Saif, Faten A and Latip, Rohaya and Hanapi, Zurina Mohd and Shafinah, Kamarudin. Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing. IEEE Access. 11: 20635--20646, IEEE. 2023. [DOI ]
[13] Yadav, Ashish Mohan and Tripathi, Kuldeep Narayan and Sharma, SC. A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm. The Journal of Supercomputing. 78(3): 4236--4260, Springer. 2022. [DOI ]
[14] Abd Elaziz, Mohamed and Abualigah, Laith and Attiya, Ibrahim. Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems. 124: 142--154, Elsevier. 2021. [DOI ]
[15] Tong, Wang and Hussain, Azhar and Bo, Wang Xi and Maharjan, Sabita. Artificial intelligence for vehicle-to-everything: A survey. IEEE Access. 7: 10823--10843, IEEE. 2019. [DOI ]
[16] Giang, Nam Ky and Leung, Victor CM and Lea, Rodger. On developing smart transportation applications in fog computing paradigm. Proceedings of the 6th ACM symposium on development and analysis of intelligent vehicular networks and applications. 91--98, 2016. [DOI ]
[17] Hakak, Saqib and Gadekallu, Thippa Reddy and Maddikunta, Praveen Kumar Reddy and Ramu, Swarna Priya and Parimala, M and De Alwis, Chamitha and Liyanage, Madhusanka. Autonomous Vehicles in 5G and beyond: A Survey. Vehicular Communications. 39: 100551, Elsevier. 2023. [DOI ]
[18] RAC Foundation. Keeping the Nation Moving: Facts on Parking. (October). https://www.racfoundation.org/assets/ rac\_foundation/content/ downloadables/facts\_on\_parking.pdf. 2012.
[19] Meng, Zeng and Qian, Qiaochu and Xu, Mengqiang and Yu, Bo and Y{\i}ld{\i}z, Ali R{\i}za and Mirjalili, Seyedali. PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation. Computer Methods in Applied Mechanics and Engineering. 414: 116172, Elsevier. 2023. [DOI ]
[20] Chen, Xiaozhi and Ma, Huimin and Wang, Xiang and Zhao, Zhichen. Improving object proposals with multi-thresholding straddling expansion. Proceedings of the IEEE conference on computer vision and pattern recognition. 2587--2595, 2015. [DOI ]
[21] Wang, Rujing and Jiao, Lin and Xie, Chengjun and Chen, Peng and Du, Jianming and Li, Rui. S-RPN: Sampling-balanced region proposal network for small crop pest detection. Computers and Electronics in Agriculture. 187: 106290, Elsevier. 2021. [DOI ]
[22] Wang, Qijin and Zhang, Shengyu and Qian, Yu and Zhang, Guangcai and Wang, Hongqiang. Enhancing representation learning by exploiting effective receptive fields for object detection. Neurocomputing. 481: 22--32, Elsevier. 2022. [DOI ]
[23] Nazir, Tahira and Nawaz, Marriam and Masood, Momina and Javed, Ali. Copy move forgery detection and segmentation using improved mask region-based convolution network (RCNN). Applied Soft Computing. 131: 109778, Elsevier. 2022. [DOI ]
[24] Zhou, Dingfu and Fang, Jin and Song, Xibin and Liu, Liu and Yin, Junbo and Dai, Yuchao and Li, Hongdong and Yang, Ruigang. Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1839--1849, 2020. [DOI ]
[25] Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 580--587, 2014. [DOI ]
[26] Hariharan, Bharath and Arbel{\'a}ez, Pablo and Girshick, Ross and Malik, Jitendra. Simultaneous detection and segmentation. European Conference on Computer Vision. 297--312, 2014. [DOI ]
[27] Gu, Chunhui and Lim, Joseph J and Arbel{\'a}ez, Pablo and Malik, Jitendra. Recognition using regions. 2009 IEEE Conference on Computer Vision and Pattern Recognition. 1030--1037, 2009.
[28] Arbel{\'a}ez, Pablo and Hariharan, Bharath and Gu, Chunhui and Gupta, Saurabh and Bourdev, Lubomir and Malik, Jitendra. Semantic segmentation using regions and parts. 2012 IEEE Conference on Computer Vision and Pattern Recognition. 3378--3385, 2012.
[29] Alexe, Bogdan and Deselaers, Thomas and Ferrari, Vittorio. What is an object?. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 73--80, 2010.
[30] Carreira, Joao and Sminchisescu, Cristian. CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(7): 1312--1328, IEEE. 2012.
[31] Pont-Tuset, Jordi and Arbelaez, Pablo and Barron, Jonathan T and Marques, Ferran and Malik, Jitendra. Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE transactions on pattern analysis and machine intelligence. 39(1): 128--140, IEEE. 2017.
[32] Uijlings, Jasper RR and Van De Sande, Koen EA and Gevers, Theo and Smeulders, Arnold WM. Selective search for object recognition. International journal of computer vision. 104(2): 154--171, Springer. 2013.
[33] Zhu, Hongyuan and Meng, Fanman and Cai, Jianfei and Lu, Shijian. Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. Journal of Visual Communication and Image Representation. 34: 12--27, Elsevier. 2016.
[34] Shotton, Jamie and Winn, John and Rother, Carsten and Criminisi, Antonio. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. European conference on computer vision. 1--15, 2006.
[35] Cheng, Ming-Ming and Zhang, Ziming and Lin, Wen-Yan and Torr, Philip. BING: Binarized normed gradients for objectness estimation at 300fps. Proceedings of the IEEE conference on computer vision and pattern recognition. 3286--3293, 2014.
[36] Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 91--99, 2015.
[37] He, Kaiming and Gkioxari, Georgia and Doll{\'a}r, Piotr and Girshick, Ross. Mask r-cnn. Proceedings of the IEEE international conference on computer vision. 2961--2969, 2017.
[38] Zhang, Ziming and Liu, Yun and Chen, Xi and Zhu, Yanjun and Cheng, Ming-Ming and Saligrama, Venkatesh and Torr, Philip HS. Sequential optimization for efficient high-quality object proposal generation. IEEE transactions on pattern analysis and machine intelligence. 40(5): 1209--1223, IEEE. 2018.
[39] Dai, Jifeng and He, Kaiming and Sun, Jian. Convolutional feature masking for joint object and stuff segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3992--4000, 2015.
[40] Achanta, Radhakrishna and Shaji, Appu and Smith, Kevin and Lucchi, Aurelien and Fua, Pascal and S{\"u}sstrunk, Sabine. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence. 34(11): 2274--2282, IEEE. 2012.
[41] Shi, Jianbo and Malik, Jitendra. Normalized cuts and image segmentation. Departmental Papers (CIS). 107, 2000.
[42] Vedaldi, Andrea and Soatto, Stefano. Quick shift and kernel methods for mode seeking. European conference on computer vision. 705--718, 2008.
[43] Jie, Zequn and Lu, Wen Feng and Sakhavi, Siavash and Wei, Yunchao and Tay, Eng Hock Francis and Yan, Shuicheng. Object proposal generation with fully convolutional networks. IEEE transactions on circuits and systems for video technology. 28(1): 62--75, IEEE. 2016.
[44] Kim, Jaechul and Grauman, Kristen. Shape Sharing for Object Segmentation.. ECCV (7). 444--458, 2012.
[45] Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z. Craft objects from images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6043--6051, 2016.
[46] Everingham, Mark and Eslami, SM Ali and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew. The pascal visual object classes challenge: A retrospective. International journal of computer vision. 111(1): 98--136, Springer. 2015.
[47] Tang, Kevin and Joulin, Armand and Li, Li-Jia and Fei-Fei, Li. Co-localization in real-world images. Proceedings of the IEEE conference on computer vision and pattern recognition. 1464--1471, 2014.
[48] Xu, Hang and Yao, Lewei and Zhang, Wei and Liang, Xiaodan and Li, Zhenguo. Auto-fpn: Automatic network architecture adaptation for object detection beyond classification. Proceedings of the IEEE International Conference on Computer Vision. 6649--6658, 2019.
[49] Li, Hongyang and Liu, Yu and Ouyang, Wanli and Wang, Xiaogang. Zoom out-and-in network with map attention decision for region proposal and object detection. International Journal of Computer Vision. 127(3): 225--238, Springer. 2019.
[50] Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and others. Hybrid task cascade for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4974--4983, 2019.
[51] Endres, Ian and Hoiem, Derek. Category independent object proposals. European Conference on Computer Vision. 575--588, 2010.
[52] Comaniciu, Dorin and Meer, Peter. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis \& Machine Intelligence. 603--619, IEEE. 2002.
[53] Ojala, Timo and Pietik{\"a}inen, Matti and M{\"a}enp{\"a}{\"a}, Topi. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis \& Machine Intelligence. 971--987, IEEE. 2002.
[54] Huang, Di and Shan, Caifeng and Ardabilian, Mohsen and Wang, Yunhong and Chen, Liming. Local binary patterns and its application to facial image analysis: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 41(6): 765--781, IEEE. 2011.
[55] Zhong, Zhuoyao and Sun, Lei and Huo, Qiang. An anchor-free region proposal network for Faster R-CNN-based text detection approaches. International Journal on Document Analysis and Recognition (IJDAR). 22(3): 315--327, Springer. 2019.
[56] Chen, Liang-Chieh and Hermans, Alexander and Papandreou, George and Schroff, Florian and Wang, Peng and Adam, Hartwig. Masklab: Instance segmentation by refining object detection with semantic and direction features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4013--4022, 2018.
[57] Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin. High Performance Visual Tracking With Siamese Region Proposal Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
[58] Lateef, Fahad and Ruichek, Yassine. Survey on semantic segmentation using deep learning techniques. Neurocomputing. 338: 321--348, Elsevier. 2019.
[59] Yu, Hongshan and Yang, Zhengeng and Tan, Lei and Wang, Yaonan and Sun, Wei and Sun, Mingui and Tang, Yandong. Methods and datasets on semantic segmentation: A review. Neurocomputing. 304: 82--103, Elsevier. 2018.
[60] Sultana, Farhana and Sufian, Abu and Dutta, Paramartha. Evolution of image segmentation using deep convolutional neural network: A survey. Knowledge-Based Systems. 106062, Elsevier. 2020.
[61] Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.
[62] He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778, 2016.
[63] Dai, Jifeng and He, Kaiming and Sun, Jian. Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. Proceedings of the IEEE international conference on computer vision. 1635--1643, 2015.
[64] Zhou, Bolei and Zhao, Hang and Puig, Xavier and Xiao, Tete and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision. 127(3): 302--321, Springer. 2019.
[65] He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence. 37(9): 1904--1916, IEEE. 2015.
[66] Pinheiro, Pedro O and Lin, Tsung-Yi and Collobert, Ronan and Doll{\'a}r, Piotr. Learning to refine object segments. European conference on computer vision. 75--91, 2016.
[67] Pinheiro, Pedro OO and Collobert, Ronan and Doll{\'a}r, Piotr. Learning to segment object candidates. Advances in Neural Information Processing Systems. 1990--1998, 2015.
[68] Liu, Shu and Qi, Lu and Qin, Haifang and Shi, Jianping and Jia, Jiaya. Path aggregation network for instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 8759--8768, 2018.
[69] Lin, Tsung-Yi and Doll{\'a}r, Piotr and Girshick, Ross and He, Kaiming and Hariharan, Bharath and Belongie, Serge. Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2117--2125, 2017.
[70] Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 1097--1105, 2012.
[71] Zeiler, Matthew D and Fergus, Rob. Visualizing and understanding convolutional networks. European conference on computer vision. 818--833, 2014.
[72] Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9, 2015.
[73] Mottaghi, Roozbeh and Chen, Xianjie and Liu, Xiaobai and Cho, Nam-Gyu and Lee, Seong-Whan and Fidler, Sanja and Urtasun, Raquel and Yuille, Alan. The role of context for object detection and semantic segmentation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 891--898, 2014.
[74] Hariharan, Bharath and Arbel{\'a}ez, Pablo and Bourdev, Lubomir and Maji, Subhransu and Malik, Jitendra. Semantic contours from inverse detectors. 2011 International Conference on Computer Vision. 991--998, 2011.
[75] Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio. Coco-stuff: Thing and stuff classes in context. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1209--1218, 2018.
[76] Fu, Kun and Chang, Zhonghan and Zhang, Yue and Xu, Guangluan and Zhang, Keshu and Sun, Xian. Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images. ISP RS Journal of Photogrammetry and Remote Sensing. 161: 294--308, Elsevier. 2020.
[77] Long, Jonathan and Shelhamer, Evan and Darrell, Trevor. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 3431--3440, 2015.
[78] Qi, Xiaojuan and Liu, Zhengzhe and Shi, Jianping and Zhao, Hengshuang and Jia, Jiaya. Augmented feedback in semantic segmentation under image level supervision. European conference on computer vision. 90--105, 2016.
[79] Wei, Yunchao and Liang, Xiaodan and Chen, Yunpeng and Jie, Zequn and Xiao, Yanhui and Zhao, Yao and Yan, Shuicheng. Learning to segment with image-level annotations. Pattern Recognition. 59: 234--244, Elsevier. 2016.
[80] Ahn, Jiwoon and Cho, Sunghyun and Kwak, Suha. Weakly supervised learning of instance segmentation with inter-pixel relations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2209--2218, 2019.
[81] Kolesnikov, Alexander and Lampert, Christoph H. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. European conference on computer vision. 695--711, 2016.
[82] Pandey, Gaurav and Dukkipati, Ambedkar. Learning to segment with image-level supervision. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). 1856--1865, 2019.
[83] Wei, Yunchao and Liang, Xiaodan and Chen, Yunpeng and Shen, Xiaohui and Cheng, Ming-Ming and Feng, Jiashi and Zhao, Yao and Yan, Shuicheng. Stc: A simple to complex framework for weakly-supervised semantic segmentation. IEEE transactions on pattern analysis and machine intelligence. 39(11): 2314--2320, IEEE. 2016.
[84] Zhang, Bingfeng and Xiao, Jimin and Wei, Yunchao and Sun, Mingjie and Huang, Kaizhu. Reliability does matter: An end-to-end weakly supervised semantic segmentation approach. Proceedings of the AAAI Conference on Artificial Intelligence. 34(07): 12765--12772, 2020.
[85] Fan, Junsong and Zhang, Zhaoxiang and Tan, Tieniu and Song, Chunfeng and Xiao, Jun. Cian: Cross-image affinity net for weakly supervised semantic segmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 34(07): 10762--10769, 2020.
[86] Zhou, Lei and Gong, Chen and Liu, Zhi and Fu, Keren. SAL: Selection and Attention Losses for Weakly Supervised Semantic Segmentation. IEEE Transactions on Multimedia. IEEE. 2020.
[87] Liu, Yun and Wu, Yu-Huan and Wen, Pei-Song and Shi, Yu-Jun and Qiu, Yu and Cheng, Ming-Ming. Leveraging instance-, image-and dataset-level information for weakly supervised instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 2020.
[88] Fortunato, S.. Community detection in graphs. Phys. Rep.-Rev. Sec. Phys. Lett.. 486: 75-174, 2010.
[89] Newman, M. E. J. and Girvan, M.. Finding and evaluating community structure in networks. Phys. Rev. E.. 69: 026113, 2004.
[90] Vehlow, C. and Reinhardt, T. and Weiskopf, D.. Visualizing Fuzzy Overlapping Communities in Networks. IEEE Trans. Vis. Comput. Graph.. 19: 2486-2495, 2013.
[91] Raghavan, U. and Albert, R. and Kumara, S.. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev E.. 76: 036106, 2007.
[92] \v{S}ubelj, L. and Bajec, M.. Robust network community detection using balanced propagation. Eur. Phys. J. B.. 81: 353-362, 2011.
[93] Lou, H. and Li, S. and Zhao, Y.. Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Physica A.. 392: 3095-3105, 2013.
[94] Clauset, A. and Newman, M. E. J. and Moore, C.. Finding community structure in very large networks. Phys. Rev. E.. 70: 066111, 2004.
[95] Blondel, V. D. and Guillaume, J. L. and Lambiotte, R. and Lefebvre, E.. Fast unfolding of communities in large networks. J. Stat. Mech.-Theory Exp.. 2008: P10008, 2008.
[96] Sobolevsky, S. and Campari, R.. General optimization technique for high-quality community detection in complex networks. Phys. Rev. E.. 90: 012811, 2014.
[97] Fortunato, S. and Barthelemy, M.. Resolution limit in community detection. Proc. Natl. Acad. Sci. U. S. A.. 104: 36-41, 2007.
[98] \v{S}ubelj, L. and Bajec, M.. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Phys. Rev. E.. 83: 036103, 2011.
[99] Wang, X. and Li, J.. Detecting communities by the core-vertex and intimate degree in complex networks. Physica A.. 392: 2555-2563, 2013.
[100] Li, J. and Wang, X. and Eustace, J.. Detecting overlapping communities by seed community in weighted complex networks. Physica A.. 392: 6125-6134, 2013.
[101] Fabio, D. R. and Fabio, D. and Carlo, P.. Profiling core-periphery network structure by random walkers. Sci. Rep.. 3: 1467, 2013.
[102] Chen, Q. and Wu, T. T. and Fang, M.. Detecting local community structure in complex networks based on local degree central nodes. Physica A.. 392: 529-537, 2013.
[103] Zhang, S. and Wang, R. and Zhang, X.. Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A.. 374: 483-490, 2007.
[104] Nepusz, T. and Petr\'oczi, A. and N\'egyessy, L. and Bazs\'o, F.. Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E.. 77: 016107, 2008.
[105] Fabricio, B. and Liang, Z.. Fuzzy community structure detection by particle competition and cooperation. Soft Comput.. 17: 659-673, 2013.
[106] Sun, P. and Gao, L. and Han, S.. Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Inf. Sci.. 181: 1060-1071, 2011.
[107] Havens, T. C. and Bezdek, J. C. and Leckie, C., Ramamohanarao, K. and Palaniswami, M.. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks. IEEE Trans. Fuzzy Syst.. 21: 1170-1175, 2013.
[108] Garcia-Garcia, Alberto and Orts-Escolano, Sergio and Oprea, Sergiu and Villena-Martinez, Victor and Martinez-Gonzalez, Pablo and Garcia-Rodriguez, Jose. A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing. 70: 41--65, Elsevier. 2018.
[109] Guo, Yanming and Liu, Yu and Georgiou, Theodoros and Lew, Michael S. A review of semantic segmentation using deep neural networks. International journal of multimedia information retrieval. 7(2): 87--93, Springer. 2018.
[110] Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 4700--4708, 2017.
[111] Huang, Zhaojin and Huang, Lichao and Gong, Yongchao and Huang, Chang and Wang, Xinggang. Mask scoring r-cnn. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6409--6418, 2019.
[112] Dai, Jifeng and He, Kaiming and Sun, Jian. Instance-aware semantic segmentation via multi-task network cascades. Proceedings of the IEEE conference on computer vision and pattern recognition. 3150--3158, 2016.
[113] Neuhold, Gerhard and Ollmann, Tobias and Rota Bulo, Samuel and Kontschieder, Peter. The mapillary vistas dataset for semantic understanding of street scenes. Proceedings of the IEEE International Conference on Computer Vision. 4990--4999, 2017.
[114] Le, Hoang-An and Mensink, Thomas and Das, Partha and Karaoglu, Sezer and Gevers, Theo. EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1579--1589, 2021.
[115] Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt. The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE conference on computer vision and pattern recognition. 3213--3223, 2016.
[116] Yang, Zhixian and Dong, Ruixia and Xu, Hao and Gu, Jinan. Instance Segmentation Method Based on Improved Mask R-CNN for the Stacked Electronic Components. Electronics. 9(6): 886, Multidisciplinary Digital Publishing Institute. 2020.
[117] Dai, Zhenzhen and Carver, Eric and Liu, Chang and Lee, Joon and Feldman, Aharon and Zong, Weiwei and Pantelic, Milan and Elshaikh, Mohamed and Wen, Ning. Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks. Advances in Radiation Oncology. 5(3): 473--481, Elsevier. 2020.
[118] Chiao, Jui-Ying and Chen, Kuan-Yung and Liao, Ken Ying-Kai and Hsieh, Po-Hsin and Zhang, Geoffrey and Huang, Tzung-Chi. Detection and classification the breast tumors using mask R-CNN on sonograms. Medicine. 98(19): Wolters Kluwer Health. 2019.
[119] Min, Hang and Wilson, Devin and Huang, Yinhuang and Liu, Siyu and Crozier, Stuart and Bradley, Andrew P and Chandra, Shekhar S. Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 1111--1115, 2020.
[120] Zhang, Hang and Xue, Jia and Dana, Kristin. Deep ten: Texture encoding network. Proceedings of the IEEE conference on computer vision and pattern recognition. 708--717, 2017.
[121] Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit. Context encoding for semantic segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 7151--7160, 2018.
[122] Song, Chunfeng and Huang, Yan and Ouyang, Wanli and Wang, Liang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3136--3145, 2019.
[123] Papandreou, George and Chen, Liang-Chieh and Murphy, Kevin P and Yuille, Alan L. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. Proceedings of the IEEE international conference on computer vision. 1742--1750, 2015.
[124] Khoreva, Anna and Benenson, Rodrigo and Hosang, Jan and Hein, Matthias and Schiele, Bernt. Simple does it: Weakly supervised instance and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 876--885, 2017.
[125] Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew. " GrabCut" interactive foreground extraction using iterated graph cuts. ACM transactions on graphics (TOG). 23(3): 309--314, ACM New York, NY, USA. 2004.
[126] Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin. Weakly supervised instance segmentation using class peak response. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3791--3800, 2018.
[127] Zhu, Hongyuan and Meng, Fanman and Cai, Jianfei and Lu, Shijian. Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. Journal of Visual Communication and Image Representation. 34: 12--27, Elsevier. 2016.
[128] Hu, Zheng and Liu, Zhi and Li, Gongyang and Ye, Linwei and Zhou, Lei and Wang, Yang. Weakly supervised instance segmentation using multi-stage erasing refinement and saliency-guided proposals ordering. Journal of Visual Communication and Image Representation. 73: 102957, Elsevier. 2020.
[129] Bonechi, Simone and Bianchini, Monica and Scarselli, Franco and Andreini, Paolo. Weak supervision for generating pixel--level annotations in scene text segmentation. Pattern Recognition Letters. 138: 1--7, Elsevier. 2020.
[130] Zhang, Wenwen and Wang, Kunfeng and Wang, Yutong and Yan, Lan and Wang, Fei-Yue. A loss-balanced multi-task model for simultaneous detection and segmentation. Neurocomputing. 428: 65--78, Elsevier. 2021.
[131] Liu, Xiaolong and Deng, Zhidong and Yang, Yuhan. Recent progress in semantic image segmentation. Artificial Intelligence Review. 52(2): 1089--1106, Springer. 2019.
[132] Taghanaki, Saeid Asgari and Abhishek, Kumar and Cohen, Joseph Paul and Cohen-Adad, Julien and Hamarneh, Ghassan. Deep semantic segmentation of natural and medical images: A review. Artificial Intelligence Review. 54(1): 137--178, Springer. 2021.
[133] Neuhold, Gerhard and Ollmann, Tobias and Rota Bulo, Samuel and Kontschieder, Peter. The mapillary vistas dataset for semantic understanding of street scenes. Proceedings of the IEEE international conference on computer vision. 4990--4999, 2017.
[134] Gong, Ke and Liang, Xiaodan and Zhang, Dongyu and Shen, Xiaohui and Lin, Liang. Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. Proceedings of the IEEE conference on computer vision and pattern recognition. 932--940, 2017.
[135] Cheng, Ho Kei and Chung, Jihoon and Tai, Yu-Wing and Tang, Chi-Keung. Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8890--8899, 2020.
[136] Liu, Ce and Yuen, Jenny and Torralba, Antonio. Nonparametric scene parsing via label transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(12): 2368--2382, IEEE. 2011.
[137] Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt. The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE conference on computer vision and pattern recognition. 3213--3223, 2016.
[138] Geiger, Andreas and Lenz, Philip and Stiller, Christoph and Urtasun, Raquel. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research. 32(11): 1231--1237, Sage Publications Sage UK: London, England. 2013.
[139] Le, Hoang-An and Mensink, Thomas and Das, Partha and Karaoglu, Sezer and Gevers, Theo. Eden: Multimodal synthetic dataset of enclosed garden scenes. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1579--1589, 2021.
[140] Ranjan, Rajeev and Patel, Vishal M and Chellappa, Rama. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE transactions on pattern analysis and machine intelligence. 41(1): 121--135, IEEE. 2017.
[141] Minaee, Shervin and Boykov, Yuri Y and Porikli, Fatih and Plaza, Antonio J and Kehtarnavaz, Nasser and Terzopoulos, Demetri. Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence. IEEE. 2021.
[142] Ghosh, Swarnendu and Das, Nibaran and Das, Ishita and Maulik, Ujjwal. Understanding deep learning techniques for image segmentation. ACM Computing Surveys (CSUR). 52(4): 1--35, ACM New York, NY, USA. 2019.
[143] Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang. Cascade rpn: Delving into high-quality region proposal network with adaptive convolution. Advances in neural information processing systems. 32: 2019.
[144] Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. 234--241, 2015.
[145] Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya. Pyramid scene parsing network. Proceedings of the IEEE conference on computer vision and pattern recognition. 2881--2890, 2017.
[146] Izadi, Saadat and Ahmadi, Mahmood and Rajabzadeh, Amir. Network Traffic Classification Using Deep Learning Networks and Bayesian Data Fusion. Journal of Network and Systems Management. 30(2): 1--21, Springer. 2022.
[147] Heidari, Hadis and Chalechale, Abdolah. Biometric authentication using a deep learning approach based on different level fusion of finger knuckle print and fingernail. Expert Systems with Applications. 191: 116278, Elsevier. 2022.
[148] Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence. 40(4): 834--848, IEEE. 2017.
[149] Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence. 39(12): 2481--2495, IEEE. 2017.
[150] Paszke, Adam and Chaurasia, Abhishek and Kim, Sangpil and Culurciello, Eugenio. Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147. 2016.
[151] Chen, Liang-Chieh and Zhu, Yukun and Papandreou, George and Schroff, Florian and Adam, Hartwig. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV). 801--818, 2018.
[152] Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio. Learning deep features for discriminative localization. Proceedings of the IEEE conference on computer vision and pattern recognition. 2921--2929, 2016.
[153] Zhang, Hang and Xue, Jia and Dana, Kristin. Deep ten: Texture encoding network. Proceedings of the IEEE conference on computer vision and pattern recognition. 708--717, 2017.
[154] Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit. Context encoding for semantic segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 7151--7160, 2018.
[155] Taghizadeh, Maryam and Chalechale, Abdolah. A comprehensive and systematic review on classical and deep learning based region proposal algorithms. Expert Systems with Applications. 189: 116105, Elsevier. 2022.
[156] Lin, Min and Chen, Qiang and Yan, Shuicheng. Network in network. arXiv preprint arXiv:1312.4400. 2013.
[157] Tian, Yunong and Yang, Guodong and Wang, Zhe and Li, En and Liang, Zize. Instance segmentation of apple flowers using the improved mask R--CNN model. Biosystems Engineering. 193: 264--278, Elsevier. 2020.
[158] Deng, Li and Liu, Yang. Deep learning in natural language processing. Springer. 2018.
[159] Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio. Region-based semantic segmentation with end-to-end training. European Conference on Computer Vision. 381--397, 2016.
[160] Shuhan, Chen and Ben, Wang and Jindong, Li and Xuelong, Hu. Semantic image segmentation using region-based object detector. 2017 13th IEEE International Conference on Electronic Measurement \& Instruments (ICEMI). 505--510, 2017.
[161] Wang, Yuhang and Liu, Jing and Li, Yong and Yan, Junjie and Lu, Hanqing. Objectness-aware semantic segmentation. Proceedings of the 24th ACM international conference on Multimedia. 307--311, 2016.
[162] Wei, Yunchao and Liang, Xiaodan and Chen, Yunpeng and Jie, Zequn and Xiao, Yanhui and Zhao, Yao and Yan, Shuicheng. Learning to segment with image-level annotations. Pattern Recognition. 59: 234--244, Elsevier. 2016.
[163] Noh, Hyeonwoo and Hong, Seunghoon and Han, Bohyung. Learning deconvolution network for semantic segmentation. Proceedings of the IEEE international conference on computer vision. 1520--1528, 2015.
[164] Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew. " GrabCut" interactive foreground extraction using iterated graph cuts. ACM transactions on graphics (TOG). 23(3): 309--314, ACM New York, NY, USA. 2004.
[165] Song, Chunfeng and Huang, Yan and Ouyang, Wanli and Wang, Liang. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3136--3145, 2019.
[166] Kulharia, Viveka and Chandra, Siddhartha and Agrawal, Amit and Torr, Philip and Tyagi, Ambrish. Box2seg: Attention weighted loss and discriminative feature learning for weakly supervised segmentation. European Conference on Computer Vision. 290--308, 2020.
[167] O Pinheiro, Pedro O and Collobert, Ronan and Doll{\'a}r, Piotr. Learning to segment object candidates. Advances in neural information processing systems. 28: 2015.
[168] Dai, Jifeng and He, Kaiming and Sun, Jian. Instance-aware semantic segmentation via multi-task network cascades. Proceedings of the IEEE conference on computer vision and pattern recognition. 3150--3158, 2016.
[169] Dai, Jifeng and He, Kaiming and Li, Yi and Ren, Shaoqing and Sun, Jian. Instance-sensitive fully convolutional networks. European Conference on Computer Vision. 534--549, 2016.
[170] Huang, Zhaojin and Huang, Lichao and Gong, Yongchao and Huang, Chang and Wang, Xinggang. Mask scoring r-cnn. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6409--6418, 2019.
[171] Hayder, Zeeshan and He, Xuming and Salzmann, Mathieu. Boundary-aware instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 5696--5704, 2017.
[172] Chen, Liang-Chieh and Hermans, Alexander and Papandreou, George and Schroff, Florian and Wang, Peng and Adam, Hartwig. Masklab: Instance segmentation by refining object detection with semantic and direction features. Proceedings of the IEEE conference on computer vision and pattern recognition. 4013--4022, 2018.
[173] Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr. Tensormask: A foundation for dense object segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2061--2069, 2019.
[174] Hu, Ronghang and Doll{\'a}r, Piotr and He, Kaiming and Darrell, Trevor and Girshick, Ross. Learning to segment every thing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4233--4241, 2018.
[175] Hsu, Cheng-Chun and Hsu, Kuang-Jui and Tsai, Chung-Chi and Lin, Yen-Yu and Chuang, Yung-Yu. Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems. 32: 2019.
[176] Li, Qizhu and Arnab, Anurag and Torr, Philip HS. Weakly-and semi-supervised panoptic segmentation. Proceedings of the European conference on computer vision (ECCV). 102--118, 2018.
[177] Ardila, Diego and Kiraly, Atilla P and Bharadwaj, Sujeeth and Choi, Bokyung and Reicher, Joshua J and Peng, Lily and Tse, Daniel and Etemadi, Mozziyar and Ye, Wenxing and Corrado, Greg and others. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature medicine. 25(6): 954--961, Nature Publishing Group. 2019.
[178] Proen{\c{c}}a, Pedro F and Sim{\~o}es, Pedro. TACO: Trash annotations in context for litter detection. arXiv preprint arXiv:2003.06975. 2020.
[179] Tsai, Hsieh-Fu and Gajda, Joanna and Sloan, Tyler FW and Rares, Andrei and Shen, Amy Q. Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning. SoftwareX. 9: 230--237, Elsevier. 2019.
[180] Kaldera, HNTK and Gunasekara, Shanaka Ramesh and Dissanayake, Maheshi B. Brain tumor classification and segmentation using faster R-CNN. 2019 Advances in Science and Engineering Technology International Conferences (ASET). 1--6, 2019.
[181] Liu, Tianrui and Stathaki, Tania. Faster R-CNN for robust pedestrian detection using semantic segmentation network. Frontiers in neurorobotics. 64, Frontiers. 2018.
[182] Tang, Wei and Zou, Dongsheng and Yang, Su and Shi, Jing and Dan, Jingpei and Song, Guowu. A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Computing and Applications. 32(11): 6769--6778, Springer. 2020.
[183] Anand, Tanmay and Sinha, Soumendu and Mandal, Murari and Chamola, Vinay and Yu, Fei Richard. AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensors Journal. 21(16): 17581--17590, IEEE. 2021.
[184] Dai, Jifeng and Li, Yi and He, Kaiming and Sun, Jian. R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems. 29: 2016.
[185] Pinheiro, Pedro O and Collobert, Ronan. From image-level to pixel-level labeling with convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 1713--1721, 2015.
[186] Sermanet, Pierre and Eigen, David and Zhang, Xiang and Mathieu, Micha{\"e}l and Fergus, Rob and LeCun, Yann. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229. 2013.
[187] Hariharan, Bharath and Arbel{\'a}ez, Pablo and Girshick, Ross and Malik, Jitendra. Hypercolumns for object segmentation and fine-grained localization. Proceedings of the IEEE conference on computer vision and pattern recognition. 447--456, 2015.
[188] Mostajabi, Mohammadreza and Yadollahpour, Payman and Shakhnarovich, Gregory. Feedforward semantic segmentation with zoom-out features. Proceedings of the IEEE conference on computer vision and pattern recognition. 3376--3385, 2015.
[189] Achanta, Radhakrishna and Shaji, Appu and Smith, Kevin and Lucchi, Aurelien and Fua, Pascal and S{\"u}sstrunk, Sabine. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence. 34(11): 2274--2282, IEEE. 2012.
[190] Chen, Shengcong and Ding, Changxing and Tao, Dacheng. Boundary-assisted region proposal networks for nucleus segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. 279--288, 2020.
[191] Zhangli, Qilong and Yi, Jingru and Liu, Di and He, Xiaoxiao and Xia, Zhaoyang and Tang, Haiming and Wang, He and Zhou, Mu and Metaxas, Dimitris. Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images. arXiv preprint arXiv:2203.02846. 2022.
[192] Liu, Jianxin and Geng, Yushui and Zhao, Jing and Zhang, Kang and Li, Wenxiao. Image semantic segmentation use multiple-threshold probabilistic R-CNN with feature fusion. Symmetry. 13(2): 207, Multidisciplinary Digital Publishing Institute. 2021.
[193] Cheng, Tianheng and Wang, Xinggang and Huang, Lichao and Liu, Wenyu. Boundary-preserving mask r-cnn. European conference on computer vision. 660--676, 2020.
[194] Zhang, Yunfeng and Chi, Mingmin. Mask-R-FCN: A deep fusion network for semantic segmentation. IEEE Access. 8: 155753--155765, IEEE. 2020.
[195] Fan, Heng and Ling, Haibin. Siamese cascaded region proposal networks for real-time visual tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7952--7961, 2019.
[196] Liu, Jun and Li, PengFei. A mask R-CNN model with improved region proposal network for medical ultrasound image. International Conference on Intelligent Computing. 26--33, 2018.
[197] Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedingshttp://arxiv.org/abs/1409.1556. 2015.
[198] Hosang, Jan and Benenson, Rodrigo and Doll{\'a}r, Piotr and Schiele, Bernt. What makes for effective detection proposals?. IEEE transactions on pattern analysis and machine intelligence. 38(4): 814--830, IEEE. 2015.
[199] Humeau-Heurtier, Anne. Texture feature extraction methods: A survey. IEEE Access. 7: 8975--9000, IEEE. 2019.
[200] Tabatabaei, Sayed Mohamad and Chalechale, Abdolah. Noise-tolerant texture feature extraction through directional thresholded local binary pattern. The Visual Computer. 36(5): 967--987, Springer. 2020.
[201] Hafiane, Adel and Seetharaman, Guna and Zavidovique, Bertrand. Median binary pattern for textures classification. International Conference Image Analysis and Recognition. 387--398, 2007.
[202] Rivera, Adin Ramirez and Castillo, Jorge Rojas and Chae, Oksam. Local directional texture pattern image descriptor. Pattern Recognition Letters. 51: 94--100, Elsevier. 2015.
[203] Ahmed, F. Gradient directional pattern: a robust feature descriptor for facial expression recognition. Electronics letters. 48(19): 1203--1204, IET. 2012.
[204] Ahmed, Faisal and Hossain, Emam. Automated facial expression recognition using gradient-based ternary texture patterns. Chin. J. Eng. 2013: 831747, 2013.
[205] Rivera, Adin Ramirez and Castillo, Jorge Rojas and Chae, Oksam Oksam. Local directional number pattern for face analysis: Face and expression recognition. IEEE transactions on image processing. 22(5): 1740--1752, IEEE. 2012.
[206] Cheng, Gong and Yang, Junyu and Gao, Decheng and Guo, Lei and Han, Junwei. High-quality proposals for weakly supervised object detection. IEEE Transactions on Image Processing. 29: 5794--5804, IEEE. 2020.
[207] Mirjalili, Seyedali and Mirjalili, Seyed Mohammad and Lewis, Andrew. Grey wolf optimizer. Advances in engineering software. 69: 46--61, Elsevier. 2014. [DOI ]
[208] Mao, Wencan and Akgul, Ozgur Umut and Cho, Byungjin and Xiao, Yu and Yl{\"a}-J{\"a}{\"a}ski, Antti. On-demand vehicular fog computing for beyond 5G networks. IEEE Transactions on Vehicular Technology. IEEE. 2023. [DOI ]
[209] Liu, Jianwei and Wei, Xianglin and Wang, Tongxiang and Wang, Junwei. An Ant Colony Optimization Fuzzy Clustering Task Scheduling Algorithm in Mobile Edge Computing. Security and Privacy in New Computing Environments: Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13--14, 2019, Proceedings 2. 615--624, 2019. [DOI ]
[210] Yin, Luxiu and Luo, Juan and Luo, Haibo. Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Transactions on Industrial Informatics. 14(10): 4712--4721, IEEE. 2018. [DOI ]
[211] Freund, Richard F and Gherrity, Michael and Ambrosius, Stephen and Campbell, Mark and Halderman, Mike and Hensgen, Debra and Keith, Elaine and Kidd, Taylor and Kussow, Matt and Lima, John D and others. Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet. Proceedings Seventh Heterogeneous Computing Workshop (HCW'98). 184--199, 1998. [DOI ]
[212] Taghizadeh, Maryam and Ahmadi, Mahmood. A Heuristic Task Scheduling Algorithm for Vehicular Fog Computing. 2024 8th International Conference on Smart Cities, Internet of Things and Applications (SCIoT). 168--173, 2024. [DOI ]
[213] Khiat, Abdelhamid and Haddadi, Mohamed and Bahnes, Nacera. Genetic-Based Algorithm for Task Scheduling in Fog--Cloud Environment. Journal of Network and Systems Management. 32(1): 3, Springer. 2024. [DOI ]
[214] Sharma, Neetu and Garg, Puneet and others. Ant colony based optimization model for QoS-Based task scheduling in cloud computing environment. Measurement: Sensors. 24: 100531, Elsevier. 2022. [DOI ]
[215] Jena, RK. Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Computer Science. 57: 1219--1227, Elsevier. 2015. [DOI ]
[216] Deng, Zexi and Yan, Zihan and Huang, Huimin and Shen, Hong. Energy-aware task scheduling on heterogeneous computing systems with time constraint. IEEE Access. 8: 23936--23950, IEEE. 2020. [DOI ]
[217] Paknejad, Peyman and Khorsand, Reihaneh and Ramezanpour, Mohammadreza. Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment. Future Generation Computer Systems. 117: 12--28, Elsevier. 2021. [DOI ]
[218] Juve, Gideon and Chervenak, Ann and Deelman, Ewa and Bharathi, Shishir and Mehta, Gaurang and Vahi, Karan. Characterizing and profiling scientific workflows. Future generation computer systems. 29(3): 682--692, Elsevier. 2013. [DOI ]
[219] V Nikolskiy and V Stegailov. Floating-point performance of ARM cores and their efficiency in classical molecular dynamics. Journal of Physics: Conference Series. 681(1): 012049, IOP Publishing. 2016. [DOI ]
[220] PassMark® Software Pty Ltd. CPU Benchmarks: ARM Cortex-A78 4 Core 2000 MHz. Last visit (3/24/2024). 2024.
[221] FRONTIER - HPE CRAY EX235A, AMD OPTIMIZED 3RD GENERATION EPYC 64C 2GHZ, AMD INSTINCT MI250X, SLINGSHOT-11. 2024.
[222] Akgül, Özgür Umut and Mao, Wencan and Cho, Byungjin and Xiao, Yu. VFogSim: A Data-Driven Platform for Simulating Vehicular Fog Computing Environment. IEEE Systems Journal. 17(3): 5002-5013, 2023. [DOI ]
[223] Minh, Tran Ngoc and Nam, Thoai and Epema, Dick HJ. Parallel workload modeling with realistic characteristics. IEEE Transactions on Parallel and Distributed Systems. 25(8): 2138--2148, IEEE. 2013. [DOI ]
[224] Huang, Xumin and Yu, Rong and Liu, Jianqi and Shu, Lei. Parked vehicle edge computing: Exploiting opportunistic resources for distributed mobile applications. IEEE Access. 6: 66649--66663, IEEE. 2018. [DOI ]
[225] Ashok, Ashwin and Steenkiste, Peter and Bai, Fan. Vehicular cloud computing through dynamic computation offloading. Computer Communications. 120: 125--137, Elsevier. 2018. [DOI ]
[226] Hu, Pengfei and Dhelim, Sahraoui and Ning, Huansheng and Qiu, Tie. Survey on fog computing: architecture, key technologies, applications and open issues. Journal of network and computer applications. 98: 27--42, Elsevier. 2017. [DOI ]
[227] Keshari, Niharika and Singh, Dinesh and Maurya, Ashish Kumar. A survey on Vehicular Fog Computing: Current state-of-the-art and future directions. Vehicular Communications. 38: 100512, Elsevier. 2022. [DOI ]
[228] Bos, Joppe W and Carlson, Brian and Renes, Joost and Rotaru, Marius and Sprenkels, Daan and Waters, Geoffrey P. Post-quantum secure boot on vehicle network processors. Cryptology ePrint Archive. 2022. [DOI ]
[229] Scheme, BPT and Fog, Establishing Trusted Vehicular. Computing Service for Rural Area Based On Blockchain Approach. 2015.
[230] Mishra, Kaushik and Rajareddy, Goluguri NV and Ghugar, Umashankar and Chhabra, Gurpreet Singh and Gandomi, Amir H. A collaborative computation and offloading for compute-intensive and latency-sensitive dependency-aware tasks in dew-enabled vehicular fog computing: A federated deep Q-learning approach. IEEE Transactions on Network and Service Management. 20(4): 4600--4614, IEEE. 2023. [DOI ]
[231] Sookhak, Mehdi and Yu, F Richard and He, Ying and Talebian, Hamid and Safa, Nader Sohrabi and Zhao, Nan and Khan, Muhammad Khurram and Kumar, Neeraj. Fog vehicular computing: Augmentation of fog computing using vehicular cloud computing. IEEE Vehicular Technology Magazine. 12(3): 55--64, IEEE. 2017. [DOI ]
[232] Li, Yuwei and Yang, Bo and Chen, Zhijie and Chen, Cailian and Guan, Xinping. A contract-stackelberg offloading incentive mechanism for vehicular parked-edge computing networks. 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). 1--5, 2019. [DOI ]
[233] Liu, Yang and Xu, Changqiao and Zhan, Yufeng and Liu, Zhixin and Guan, Jianfeng and Zhang, Hongke. Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach. Computer Networks. 129: 399--409, Elsevier. 2017. [DOI ]
[234] Sethi, Vivek and Pal, Sujata. FedDove: A Federated Deep Q-learning-based offloading for vehicular fog computing. Future Generation Computer Systems. 141: 96--105, Elsevier. 2023. [DOI ]