Review Article

Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework


Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. 

Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. 

Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. 

Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique  

1. Zhang Q, Yang LT, Chen Z, Li P. A survey on deep learning for big data. Information Fusion. 2018;42:146-57.
2. Sagiroglu S, Sinanc D, editors. Big data: A review. 2013 international conference on collaboration technologies and systems (CTS); 2013: IEEE.
3. Chen M, Mao S, Liu Y. Big data: A survey. Mobile networks and applications. 2014;19(2):171-209.
4. Nazari E, Afkanpour M, Tabesh H. Big Data from A to Z. Frontiers in Health Informatics. 2019;8(1):20.
5. Nazari E, Tabesh H. Big Data In healthcare: A to Z. Journal of Biostatistics and Epidemiology. 2019;5(3):194-203.
6. Nazari E, Chang H-CH, Deldar K, Pour R, Avan A, Tara M, et al. A Comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review. Iranian Red Crescent Medical Journal. 2020;22(10).
7. Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013;309(13):1351-2.
8. Jin X, Wah BW, Cheng X, Wang Y. Significance and challenges of big data research. Big Data Research. 2015;2(2):59-64.
9. Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, et al. Big data and its technical challenges. Communications of the ACM. 2014;57(7):86-94.
10. Nazari E, Aghemiri M, Zeinali N, Delaram Z, Mehrabian A, Tabesh H. Application of Big Data analysis in healthcare based on ‘The 6 building blocks of health systems’ Framework: A survey. Dokkyo Journal of Medical Sciences. 2021;48:01.
11. Bossé É, Solaiman B. Information fusion and analytics for big data and IoT: Artech House; 2016.
12. Zhong H, Xiao J. Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Scientific Programming. 2017;2017.
13. Torra V. Information fusion-methods and aggregation operators. Data Mining and Knowledge Discovery Handbook: Springer; 2009. p. 999-1008.
14. Solaiman B, Debon R, Pipelier F, Cauvin J-M, Roux C. Information fusion, application to data and model fusion for ultrasound image segmentation. IEEE Transactions on Biomedical Engineering. 1999;46(10):1171-5.
15. Nazari E, Farzin AH, Aghemiri M, Avan A, Tara M, Tabesh H. Deep Learning for Acute Myeloid Leukemia Diagnosis. Journal of Medicine and Life. 2020;13(3):382.
16. Bossé É, Roy J, Wark S, Rousseau R, Breton R, Lambert D, et al. Concepts, Models, and Tools for Information Fusion, Artech House Intelligence and Information Operations Library, Artech House. Inc; 2007.
17. Wang Y-q, Yan H-x, Guo R, Li F-f, Xia C-m, Yan J-j, et al. Study on intelligent syndrome differentiation in Traditional Chinese Medicine based on multiple information fusion methods. International Journal of Data Mining and Bioinformatics. 2011;5(4):369-82.
18. Bello-Orgaz G, Jung JJ, Camacho D. Social big data: Recent achievements and new challenges. Information Fusion. 2016;28:45-59.
19. Moher D, Liberati A. A., Tetzlaff, J., Altman, DG (2009). Preferred reporting items for systematic reviews and metaanalyses: the PRISMA statement. BMJ.339:b2535.
20. Nazari E, Shahriari MH, Tabesh H. Applications of Framework In Health Care: A Survey. Frontiers in Health Informatics. 2019;8(1):16.
21. Nazari E, Khodabandeh ME. Create Frameworks From Software Engineering To Health Care: A survey. Journal of Biostatistics and Epidemiology. 2019;5(3):216-25.
22. Paul PP, Gavrilova ML, Alhajj R. Decision fusion for multimodal biometrics using social network analysis. IEEE transactions on systems, man, and cybernetics: systems. 2014;44(11):1522-33.
23. Liu Y-T, Pal NR, Marathe AR, Wang Y-K, Lin C-T. Fuzzy decision-making fuser fdmf) for integrating human-machine autonomous (hma) systems with adaptive evidence sources. Frontiers in neuroscience. 2017;11:332.
24. Tiwari P, Viswanath S, Kurhanewicz J, Sridhar A, Madabhushi A. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR in Biomedicine. 2012;25(4):607-19.
25. Niemeijer M, Abramoff MD, Van Ginneken B. Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE transactions on medical imaging. 2009;28(5):775-85.
26. Fan X-N, Zhang S-W. lncRNAMFDL: identification of human long noncoding RNAs by fusing multiple features and using deep learning. Molecular BioSystems. 2015;11(3):892-7.
27. Liang F, Xie W, Yu Y. Beating heart motion accurate prediction method based on interactive multiple model: An information fusion approach. BioMed research international. 2017;2017.
28. Terrades OR, Valveny E, Tabbone S. Optimal classifier fusion in a non-bayesian probabilistic framework. IEEE transactions on pattern analysis and machine intelligence. 2008;31(9):1630-44.
29. Velikova M, Lucas PJ, Samulski M, Karssemeijer N. A probabilistic framework for image information fusion with an application to mammographic analysis. Medical Image Analysis. 2012;16(4):865-75.
30. Anderson F, Birch DW, Boulanger P, Bischof WF. Sensor fusion for laparoscopic surgery skill acquisition. Computer Aided Surgery. 2012;17(6):269-83.
31. Zhang S, Han J, Liu J, Zheng J, Liu R. An improved poly (A) motifs recognition method based on decision level fusion. Computational biology and chemistry. 2015;54:49-56.
32. Yang P, Xu L, Zhou BB, Zhang Z, Zomaya AY, editors. A particle swarm based hybrid system for imbalanced medical data sampling. BMC genomics; 2009: Springer.
33. Bosch M, Zhu F, Khanna N, Boushey CJ, Delp EJ, editors. Combining global and local features for food identification in dietary assessment. 2011 18th IEEE International Conference on Image Processing; 2011: IEEE.
34. Prasad S, Bruce LM, Ball JE, editors. A multi-classifier and decision fusion framework for robust classification of mammographic masses. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2008: IEEE.
35. Mou Q, Xu Z, Liao H. An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences. 2016;374:224-39.
36. Lelandais B, Ruan S, Denœux T, Vera P, Gardin I. Fusion of multi-tracer PET images for dose painting. Medical image analysis. 2014;18(7):1247-59.
37. Zanaty E. An approach based on fusion concepts for improving brain Magnetic Resonance Images (MRIs) segmentation. Journal of Medical Imaging and Health Informatics. 2013;3(1):30-7.
38. Tahir BA, Swift AJ, Marshall H, Parra-Robles J, Hatton MQ, Hartley R, et al. A method for quantitative analysis of regional lung ventilation using deformable image registration of CT and hybrid hyperpolarized gas/1H MRI. Physics in Medicine & Biology. 2014;59(23):7267.
39. Richard N, Dojat M, Garbay C. Automated segmentation of human brain MR images using a multi-agent approach. Artificial Intelligence in Medicine. 2004;30(2):153-76.
40. Kamali T, Boostani R, Parsaei H. A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders. IEEE transactions on neural systems and rehabilitation engineering. 2013;22(1):191-200.
41. Guo P, Banerjee K, Stanley RJ, Long R, Antani S, Thoma G, et al. Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE journal of biomedical and health informatics. 2015;20(6):1595-607.
42. Jiang H, Liang Z, Gao J, Dang C. Classification of weld defect based on information fusion technology for radiographic testing system. Review of Scientific Instruments. 2016;87(3):035110.
43. Kook H, Gupta L, Kota S, Molfese D, editors. A dynamic multi-channel decisionfusion strategy to classify differential brain activity. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007: IEEE.
44. Gupta L, Chung B, Srinath MD, Molfese DL, Kook H. Multichannel fusion models for the parametric classification of differential brain activity. IEEE Transactions on Biomedical Engineering. 2005;52(11):1869-81.
45. Sung W-T, Chang K-Y. Evidencebased multi-sensor information fusion for remote health care systems. Sensors and Actuators A: Physical. 2013;204:1-19.
46. Zhang Z, Luo X. Heartbeat classification using decision level fusion. Biomedical Engineering Letters. 2014;4(4):388-95.
47. Li G-Z, Yan S-X, You M, Sun S, Ou A. Intelligent ZHENG classification of hypertension depending on ML-kNN and information fusion. Evidence-Based Complementary and Alternative Medicine. 2012;2012.
48. Stroud J, Enverga I, Silverstein T, Song B, Rogers T. Ensemble learning and the heritage health prize. California: University of California. 2012.
49. Chen J, Xu H, He P-a, Dai Q, Yao Y. A multiple information fusion method for predicting subcellular locations of two different types of bacterial protein simultaneously. Biosystems. 2016;139:37- 45.
50. Malarvili M, Mesbah M, editors. Combining newborn EEG and HRV information for automatic seizure detection. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2008: IEEE.
51. Barua S. Multi-sensor information fusion for classification of driver's physiological sensor data. 2013.
52. O’Regan S, Marnane W. Multimodal detection of head-movement artefacts in EEG. Journal of Neuroscience Methods. 2013;218(1):110-20.
53. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: Using the rule-based and multi-channel information fusion method. Physiological measurement. 2016;37(8):1298.
54. Santana R, Bielza C, Larrañaga P. Regularized logistic regression and multiobjective variable selection for classifying MEG data. Biological cybernetics. 2012;106(6):389-405.
55. Moslem B, Diab MO, Marque C, Khalil M, editors. Classification of multichannel uterine EMG signals. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE.
56. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage. 2006;33(1):115-26.
57. Zhang Y-C, Zhang S-W, Liu L, Liu H, Zhang L, Cui X, et al. Spatially enhanced differential RNA methylation analysis from affinity-based sequencing data with hidden Markov model. BioMed research international. 2015;2015.
58. Kasturi J, Acharya R. Clustering of diverse genomic data using information fusion. Bioinformatics. 2005;21(4):423-9.
59. Re M, Valentini G. Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines. Neurocomputing. 2010;73(7-9):1533-7.
60. Zhang S-W, Hao L-Y, Zhang T-H. Prediction of protein–protein interaction with pairwise kernel Support Vector Machine. International journal of molecular sciences. 2014;15(2):3220-33.
61. Chen Y, Xu J, Yang B, Zhao Y, He W. A novel method for prediction of protein interaction sites based on integrated RBF neural networks. Computers in biology and medicine. 2012;42(4):402-7.
62. Mnatsakanyan ZR, Burkom HS, Hashemian MR, Coletta MA. Distributed information fusion models for regional public health surveillance. Information Fusion. 2012;13(2):129-36.
63. Liu H, Shi X, Guo D, Zhao Z. Feature selection combined with neural network structure optimization for HIV-1 protease cleavage site prediction. BioMed research international. 2015;2015.
64. Moghadam H, Rahgozar M, Gharaghani S. Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR andQSAR in Environmental Research. 2016;27(8):609-28.
65. Xiong F, Hipszer BR, Joseph J, Kam M, editors. Improved blood glucose estimation through multi-sensor fusion. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE.
66. Depeursinge A, Racoceanu D, Iavindrasana J, Cohen G, Platon A, Poletti PA, et al. Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography. Artificial intelligence in medicine. 2010;50(1):13-21.
67. Ballanger B, Tremblay L, SgambatoFaure V, Beaudoin-Gobert M, Lavenne F, Le Bars D, et al. A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction. Neuroimage. 2013;77:26-43.
68. Rahman MM, Bhattacharya P. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Computerized Medical Imaging and Graphics. 2010;34(6):479-86.
69. Qian M, Aguilar M, Zachery KN, Privitera C, Klein S, Carney T, et al. Decision-level fusion of EEG and pupil features for single-trial visual detection analysis. IEEE Transactions on Biomedical Engineering. 2009;56(7):1929-37.
70. Daunizeau J, Grova C, Marrelec G, Mattout J, Jbabdi S, Pélégrini-Issac M, et al. Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. Neuroimage. 2007;36(1):69-87.
71. Quellec G, Lamard M, Cazuguel G, Roux C, Cochener B. Case retrieval in medical databases by fusing heterogeneous information. IEEE Transactions on Medical Imaging. 2010;30(1):108-18
72. Ooi KEB, Lech M, Allen NB. Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Transactions on Biomedical Engineering. 2012;60(2):497- 506.
73. Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Medical physics. 2006;33(8):2945-54.
74. Leinwoll S. The future of high frequency broadcasting. IEEE transactions on broadcasting. 1988;34(2):94-101.
75. Zhang SW, Zhang TH, Zhang JN, Huang Y. Prediction of signal peptide cleavage sites with subsite‐coupled and template matching fusion algorithm. Molecular informatics. 2014;33(3):230-9.
76. Nath A, Subbiah K. Maximizing lipocalin prediction through balanced and diversified training set and decision fusion. Computational biology and chemistry. 2015;59:101-10.
77. Acharya S, Rajasekar A, Shender BS, Hrebien L, Kam M. Real-time hypoxia prediction using decision fusion. IEEE journal of biomedical and health informatics. 2016;21(3):696-707.
78. Kochi N, Helikar T, Allen L, Rogers JA, Wang Z, Matache MT. Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions. BMC systems biology. 2014;8(1):1-14.
79. Neumuth T, Meißner C. Online recognition of surgical instruments by information fusion. International journal of computer assisted radiology and surgery. 2012;7(2):297-304.
80. Mirian MS, Ahmadabadi MN, Araabi BN, Siegwart RR. Learning active fusion of multiple experts' decisions: an attention-based approach. Neural Computation. 2011;23(2):558-91.
81. Jiang W, Cao Y, Yang L, He Z. A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster–Shafer Evidence Theory. Sensors. 2017;17(9):1972.
82. Wang Y, Zhang C, Luo J, editors. Study on information fusion algorithm and application based on improved SVM. 13th International IEEE Conference on Intelligent Transportation Systems; 2010: IEEE.
83. Abirami T, Taghavi E, Tharmarasa R, Kirubarajan T, Boury-Brisset A-C, editors. Fusing social network data with hard data. 2015 18th International Conference on Information Fusion (Fusion); 2015: IEEE.
84. Xuming Y, Yijun Y, Yong X, Xuanzhong W, Zheyu W, Hongmei N, et al. A precise and accurate acupoint location obtained on the face using consistency matrix pointwise fusion method. Journal of Traditional Chinese Medicine. 2015;35(1):110-6.
85. Comaniciu D, Zhou XS, Krishnan S. Robust real-time myocardial border tracking for echocardiography: an information fusion approach. IEEE transactions on medical imaging. 2004;23(7):849-60.
86. Köhler T, Haase S, Bauer S, Wasza J, Kilgus T, Maier-Hein L, et al. Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery. Medical image analysis. 2015;24(1):220-34.
87. Kubinyi M, Kreibich O, Neuzil J, Smid R. EMAT noise suppression using information fusion in stationary wavelet packets. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2011;58(5):1027-36.
88. Mahdavi SS, Moradi M, Morris WJ, Goldenberg SL, Salcudean SE. Fusion of ultrasound B-mode and vibro-elastography images for automatic 3-D segmentation of the prostate. IEEE transactions on medical imaging. 2012;31(11):2073-82.
89. Fan Y, Yin Y. Active and progressive exoskeleton rehabilitation using multisource information fusion from EMG and forceposition EPP. IEEE Transactions on biomedical engineering. 2013;60(12):3314-21.
90. Lecornu L, Le Guillou C, Le Saux F, Hubert M, Puentes J, Montagner J, et al., editors. Information fusion for diagnosis coding support. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE.
91. Suárez-Araujo CP, Báez PG, Rodríguez ÁS, Santana-Rodrríguez JJ. Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies. Environmental Science and Pollution Research. 2016;23(24):24547-59.
92. Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina J-M, et al. MEG–EEG information fusion and electromagnetic source imaging: from theory to clinical application in epilepsy. Brain topography. 2015;28(6):785-812.
93. Antink CH, Brüser C, Leonhardt S. Detection of heart beats in multimodal data: a robust beat-to-beat interval estimation approach. Physiological measurement. 2015;36(8):1679.
94. Lelandais B, Gardin I, Mouchard L, Vera P, Ruan S, editors. Segmentation of biological target volumes on multi-tracer
PET images based on information fusion for achieving dose painting in radiotherapy. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2012: Springer.
95. Hassan SG, Hasan M. Information fusion in aquaculture: A state-of the art review. Frontiers of Agricultural Science and Engineering. 2016;3(3):206-21.
96. Mei J, Liu H, Li X, Xie GT, Yu Y, editors. A Decision Fusion Framework for Treatment Recommendation Systems. MedInfo; 2015.
97. Akhondi-Asl A, Hoyte L, Lockhart ME, Warfield SK. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights. IEEE transactions on medical imaging. 2014;33(10):1997-2009.
98. Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, Van Ginneken B. Multiatlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. IEEE transactions on medical imaging. 2009;28(7):1000-10.
99. Zhu C, Jiang T. Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images. NeuroImage. 2003;18(3):685-96.
100. Barra V, Boire J-Y. Automatic segmentation of subcortical brain structures in MR images using information fusion. IEEE transactions on medical imaging. 2001;20(7):549-58.
101. Yang K, Koo H-W, Park W, Kim JS, Choi CG, Park JC, et al. Fusion 3-dimensional angiography of both internal carotid arteries in the evaluation of anterior communicating artery aneurysms. World neurosurgery. 2017;98:484-91.
102. Chen J, Yu H. Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients. Journal of biomedical informatics. 2017;68:121-31.
103. Qi J, Yang P, Hanneghan M, Tang S. Multiple density maps information fusion for effectively assessing intensity pattern of lifelogging physical activity. Neurocomputing. 2017;220:199-209.
104. Ren H, Kazanzides P, editors. Hybrid attitude estimation for laparoscopic surgical tools: A preliminary study. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009: IEEE.
105. Ren H, Rank D, Merdes M, Stallkamp J, Kazanzides P, editors. Development of a wireless hybrid navigation system for laparoscopic surgery. MMVR; 2011.
106. Yang P, Dumont GA, Ansermino JM. Sensor fusion using a hybrid median filter for artifact removal in intraoperative heart rate monitoring. Journal of clinical monitoring and computing. 2009;23(2):75-83.
107. Tannous H, Istrate D, Benlarbi-Delai A, Sarrazin J, Gamet D, Ho Ba Tho MC, et al. A new multi-sensor fusion scheme to improve the accuracy of knee flexion kinematics for functional rehabilitation movements. Sensors. 2016;16(11):1914.
108. Antink CH, Gao H, Brüser C, Leonhardt S. Beat-to-beat heart rate estimation fusing multimodal video and sensor data. Biomedical optics express. 2015;6(8):2895-907.
109. Synnergren J, Olsson B, Gamalielsson J. Classification of information fusion methods in systems biology. In silico biology. 2009;9(3):65-76.
110. Monwar MM, Gavrilova ML. Multimodal biometric system using ranklevel fusion approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B Cybernetics). 2009;39(4):867-78.
111. Luo X, Wan Y, He X. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion. Medical physics. 2015;42(4):1808- 17.
112. Chowdhury AK, Tjondronegoro D, Chandran V, Trost SG. Physical activity recognition using posterior-adapted classbased fusion of multiaccelerometer data. IEEE journal of biomedical and health informatics. 2017;22(3):678-85.
113. Haase S, Forman C, Kilgus T, Bammer R, Maier-Hein L, Hornegger J. ToF/RGB sensor fusion for 3-D endoscopy. Current Medical Imaging. 2013;9(2):113-9.
114. Fontana JM, Farooq M, Sazonov E. Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. IEEE Transactions on Biomedical Engineering. 2014;61(6):1772-9.
115. Zheng M, Krishnan S, Tjoa MP. A fusion-based clinical decision support for disease diagnosis from endoscopic images. Computers in Biology and Medicine. 2005;35(3):259-74.
116. Li Y, Porter E, Santorelli A, Popović M, Coates M. Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation. Biomedical Signal Processing and Control. 2017;31:366-76.
117. Kirshin E, Oreshkin B, Zhu GK, Popovic M, Coates M. Microwave radar and microwave-induced thermoacoustics: Dualmodality approach for breast cancer detection. IEEE Transactions on Biomedical Engineering. 2012;60(2):354-60.
118. Freedman DD, editor Overview of decision level fusion techniques for identification and their application. Proceedings of 1994 American Control Conference-ACC'94; 1994: IEEE.
119. Chowdhury AK, Tjondronegoro D, Chandran V, Trost S. Ensemble methods for classification of physical activities from wrist accelerometry. Medicine and science in sports and exercise. 2017;49(9):1965-73.
120. Singh R, Murad W, editors. Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using DempsterShafer theory. BMC bioinformatics; 2013: Springer.
121. Liu F, Zhang S-W, Guo W-F, Wei ZG, Chen L. Inference of gene regulatory network based on local Bayesian networks. PLoS computational biology. 2016;12(8):e1005024.
123. Lee MW. Fusion imaging of real-time ultrasonography with CT or MRI for hepatic intervention. Ultrasonography. 2014;33(4):227.
124. Wei J, Chan HP, Zhou C, Wu YT, Sahiner B, Hadjiiski LM, et al. Computer‐aided detection of breast masses: four‐view strategy for screening mammography. Medical physics. 2011;38(4):1867-76.
125. Castanedo F. A review of data fusion techniques. The scientific world journal. 2013;2013.
126. Wang J, Hu Y, Xiao F, Deng X, Deng Y. A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster–Shafer theory of evidence: an application in medical diagnosis. Artificial intelligence in medicine. 2016;69:1-11.
127. Hu W, Hu R, Xie N, Ling H, Maybank S. Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering. IEEE transactions on image processing. 2014;23(4):1513-26.
128. Nakamura EF, Loureiro AA, editors. Information fusion in wireless sensor networks. Proceedings of the 2008 ACM SIGMOD international conference on Management of data; 2008.
129. Balazs JA, Velásquez JD. Opinion mining and information fusion: a survey. Information Fusion. 2016;27:95-110.
130. Liu S, Gao RX, John D, Staudenmayer J, Freedson PS, editors. SVMbased multi-sensor fusion for free-living physical activity assessment. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE.
131. Ehrenfeld S, Butz MV. The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion. Biological cybernetics. 2013;107(1):61-82.
132. Sokolova MV, Fernández-Caballero A. Modeling and implementing an agentbased environmental health impact decision support system. Expert Systems with Applications. 2009;36(2):2603-14.
133. Kolesar I, Parulek J, Viola I, Bruckner S, Stavrum A-K, Hauser H. Interactively illustrating polymerization using three-level model fusion. BMC bioinformatics. 2014;15(1):1-16.
134. Li X, Dick A, Shen C, Zhang Z, van den Hengel A, Wang H. Visual tracking with spatio-temporal Dempster–Shafer
information fusion. IEEE Transactions on Image Processing. 2013;22(8):3028-40.
135. Meng J, Li R, Luan Y. Classification by integrating plant stress response gene expression data with biological knowledge. Mathematical biosciences. 2015;266:65-72.
136. Waldron SM, Patrick J, Duggan GB, Banbury S, Howes A. Designing information fusion for the encoding of visual–spatial information. Ergonomics. 2008;51(6):775-97.
137. Kushki A, Androutsos P, Plataniotis KN, Venetsanopoulos AN. Retrieval ofimages from artistic repositories using a decision fusion framework. IEEE Transactions on Image Processing. 2004;13(3):277-92.
138. Promyarut I, Choksuriwong A, editors. A Review Perceptual Information Fusion. 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP); 2014: IEEE.
139. Su K-L, Jau Y-M, Jeng J-T. Modeling of nonlinear aggregation for information fusion systems with outliers based on the Choquet integral. Sensors. 2011;11(3):2426-46.
140. Yao J, Raghavan VV, Wu Z. Web information fusion: a review of the state of the art. Information Fusion. 2008;9(4):446-9.
141. Fouad MM, Oweis NE, Gaber T, Ahmed M, Snasel V. Data mining and fusion techniques for WSNs as a source of the big data. Procedia Computer Science. 2015;65:778-86.
142. Sakkalis V, Zervakis M, Micheloyannis S, editors. Biopattern initiative: towards the development and integration of next-generation information fusion approaches. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2004: IEEE.
143. Li H, Jeremic A, editors. Neonatal seizure detection using blind distributed detection with correlated decisions. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011: IEEE.
144. Li Y. Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decisionmaking. Environmental Science and Pollution Research. 2019;26(18):17866-74.
145. Zhang Y, Ji Q. Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2006;36(2):467-72.
146. Antink CH, Leonhardt S, Walter M. A synthesizer framework for multimodal cardiorespiratory signals. Biomedical Physics & Engineering Express. 2017;3(3):035028.
147. Rao NS, Reister DB, Barhen J. Information fusion methods based on physical laws. IEEE transactions on pattern analysis and machine intelligence. 2005;27(1):66-77.
148. Lee D, Kim S, Kim Y, editors. BioCAD: an information fusion platform for bio-network inference and analysis. Proceedings of the 1st international workshop on Text mining in bioinformatics; 2006.
149. Woźniak M, Grana M, Corchado E. A survey of multiple classifier systems as hybrid systems. Information Fusion. 2014;16:3-17.
150. White JR, Levy T, Bishop W, Beaty JD. Real-time decision fusion for multimodal neural prosthetic devices. PloS one. 2010;5(3):e9493.
151. Zhou Y, Chang L, Qian B. A beliefrule-based model for information fusion with insufficient multi-sensor data and domain knowledge using evolutionary algorithms with operator recommendations. Soft Computing. 2019;23(13):5129-42.
152. Zhou D, Wei T, Zhang H, Ma S, Wei F. An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 2018;4(2).
153. Zheng L, Ji R, Liao W, Li M. A Positioning Method for Apple Fruits Based on Image Processing and Information Fusion. IFAC-PapersOnLine. 2018;51(17):764-9.
154. Xiong S, Fu Y, Ray A. Bayesian nonparametric modeling of categorical data for information fusion and causal inference. Entropy. 2018;20(6):396.
155. Xinhua J, Heru X, Lina Z, Xiaojing G, Guodong W, Jie B. Nondestructive detection of chilled mutton freshness based on multi-label information fusion and adaptive bp neural network. Computers and Electronics in Agriculture. 2018;155:371-7.
156. Wang Y, Duan H. Spectral–spatial classification of hyperspectral images by algebraic multigrid based multiscale information fusion. International Journal of Remote Sensing. 2019;40(4):1301-30.
157. Wang J, Feng Z, Lu N, Sun L, Luo J. An information fusion scheme based common spatial pattern method for classification of motor imagery tasks. Biomedical Signal Processing and Control. 2018;46:10-7.
158. Surathong S AS, Theera-Umpon N. Incorporating fuzzy sets into dempsterShafer theory for decision fusion. 2018.
159. Shang S, Li H, Lu M, editors. Research of GNSS spoofer localization using information fusion based on particle filter. China Satellite Navigation Conference; 2018: Springer.
160. Guan L, Gao L, Elmadany NED, Liang C, editors. Statistical machine learning vs deep learning in information fusion: Competition or collaboration? 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR); 2018: IEEE.
161. Dong L, Li Q, Xu T, Sun X, Wang D, Yin Q, editors. Multi-information Fusion Based Mobile Attendance Scheme with FaceRecognition. International Conference on Intelligent Computing; 2018: Springer.
163. Mosalanejad M, Arefi MM. UKFbased soft sensor design for joint estimation of chemical processes with multi-sensor information fusion and infrequent measurements. IET Science, Measurement & Technology. 2018;12(6):755-63.
164. Luo L-x. Information fusion for wireless sensor network based on mass deep auto-encoder learning and adaptive weighted D–S evidence synthesis. Journal of Ambient Intelligence and Humanized Computing. 2020;11(2):519-26.
165. Luo J, He X. A soft–hard combination decision fusion scheme for a clustered distributed detection system with multiple sensors. Sensors. 2018;18(12):4370.
166. Li S, Yao Y, Hu J, Liu G, Yao X, Hu J. An ensemble stacked convolutional neural network model for environmental event sound recognition. Applied Sciences. 2018;8(7):1152.
167. Lahmiri S. A technical analysis information fusion approach for stock price analysis and modeling. Fluctuation and Noise Letters. 2018;17(01):1850007.
168. Kanjo E, Younis EM, Sherkat N. Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach. Information Fusion. 2018;40:18-31.
169. Jin Z, Hu Y, Sun C. Event-triggered information fusion for networked systems with missing measurements and correlated noises. Neurocomputing. 2019;332:15-28.
170. Guo Y, Yin C, Li M, Ren X, Liu P. Mobile e-commerce recommendation system based on multi-source information fusion for sustainable e-business. Sustainability. 2018;10(1):147.
171. Berger D, Zaiß M, Lanza G, Summa J, Schwarz M, Herrmann H-G, et al. Predictive quality control of hybrid components using information fusion. Production Engineering. 2018;12(2):161-72.
172. Wang J-Q, Zhang Z-G, Wang C-H, Wang L. A Maximum Entropy Multisource Information Fusion Method to Evaluate the MTBF of Low-Voltage Switchgear. Discrete Dynamics in Nature and Society. 2018;2018.
173. Seo D, Yoo B, Ko H. Information fusion of heterogeneous sensors for enriched personal healthcare activity logging. International Journal of Ad Hoc and Ubiquitous Computing. 2018;27(4):256-69.
174. Mohammad FR, Ciuonzo D, Mohammed ZAK. Mean-based blind hard decision fusion rules. IEEE Signal Processing Letters. 2018;25(5):630-4.
175. He Z, Lu D, Yang Y, Gao M. An elderly care system based on multiple information fusion. Journal of healthcare engineering. 2018;2018.
176. Liu X, Xu Y, Cheng Y, Li Y, Zhao L, Zhang X. A heterogeneous information fusion deep reinforcement learning for intelligent frequency selection of HF communication. China Communications. 2018;15(9):73-84.
177. Aiswarya K, Thomas AB, Motti AS, Kuriakose A, Jacob J. Decision fusion in cognitive radio using improved fuzzy approach. Procedia computer science. 2018;143:219-25.
178. Xu J, Luo N, Fu B, Li S, An T. Checking unscented information fusion algorithm for autonomous navigation vehicles. Optik. 2019;179:1140-51.
179. Benzerrouk H, Nebylov A, Li M. Multi-UAV Doppler information fusion for target tracking based on distributed high degrees information filters. Aerospace. 2018;5(1):28.
180. Cai W, Guo S, Zhang S, Lin J. Unified Framework of Modeling and Simulations for Multi-platforms Multisensors Multi-objects Source Information Fusion (M3SIF) System.
181. Liu G-X, Shi L-F, Xun J-H, Chen S, Zhao L, Shi Y-F. An orientation estimation algorithm based on multi-source information fusion. Measurement Science and Technology. 2018;29(11):115101.
182. Li W, Fu Z. Unmanned aerial vehicle positioning based on multi-sensor information fusion. Geo-Spatial Information Science. 2018;21(4):302-10.
183. Li R, Lu C, Liu J, Lei T. Air data estimation algorithm under unknown wind based on information fusion. Journal of Aerospace Engineering. 2018;31(5):04018072.
184. Farid MS, Lucenteforte M, Grangetto M. Evaluating virtual image quality using the side-views information fusion and depth maps. Information Fusion. 2018;43:47-56.
185. Fang H, Zhou A, Zhang H. Information fusion in offspring generation: A case study in DE and EDA. Swarm and Evolutionary computation. 2018;42:99-108.
186. Benzerouk H, Nebylov A, Nebylov V. Distributed consensus cubature information fusion in saturated inertial sensors network. IFAC-PapersOnLine. 2018;51(12):26-31.
187. Zhou N, Xu G, Wei J, Tang L. Relative height measurement based on collaborative information fusion of acceleration and barometric pressure. Ferroelectrics. 2018;530(1):73-81.
188. Li B, Pang F. Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques. International Journal of Distributed Sensor Networks. 2018;14(4):1550147718772543.
189. Elmadany NED, He Y, Guan L. Information fusion for human action recognition via biset/multiset globality locality preserving canonical correlation analysis. IEEE Transactions on Image Processing. 2018;27(11):5275-87.
190. An J, Zhang J, Wu M, She J, Terano T. Soft-sensing method for slag-crust state of blast furnace based on two-dimensional decision fusion. Neurocomputing. 2018;315:405-11.
191. Al-rimy BAS, Maarof MA, Prasetyo YA, Shaid SZM, Ariffin AFM. Zero-day aware decision fusion-based model for crypto-ransomware early detection. International Journal of Integrated Engineering. 2018;10(6).
192. Chen Z, Li X, Zheng H, Gao H, Wang H. Domain adaptation and adaptive information fusion for object detection on foggy days. Sensors. 2018;18(10):3286.
193. Gao L, Zhang R, Qi L, Chen E, Guan L. The labeled multiple canonical correlation analysis for information fusion. IEEE Transactions on Multimedia. 2018;21(2):375-87.
194. Al-Jarrah MA, Al-Dweik A, Kalil M, Ikki SS. Decision fusion in distributed cooperative wireless sensor networks. IEEE Transactions on Vehicular Technology. 2018;68(1):797-811.
195. Chandra BS, Sastry CS, Jana S. Robust heartbeat detection from multimodal data via CNN-based generalizable information fusion. IEEE Transactions on Biomedical Engineering. 2018;66(3):710-7.
196. Gupta K, Merchant SN, Desai UB. Inherence of Hard Decision Fusion in Soft Decision Fusion and a Generalized Radix-2 Multistage Decision Fusion Strategy. IEEE Access. 2018;6:55701-11.
197. Hu J, Huang T, Zhou J, Zeng J. Electronic Systems Diagnosis Fault in Gasoline Engines Based on MultiInformation Fusion. Sensors. 2018;18(9):2917.
198. Huang P, Qiu W. A robust decision fusion strategy for SAR target recognition. Remote Sensing Letters. (6):507-14.
199. Li J, Si Y, Xu T, Jiang S. Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques. Mathematical Problems in Engineering. 2018;2018.
200. Lang X, Li P, Cao J, Li Y, Ren H. A small leak localization method for oil pipelines based on information fusion. IEEE Sensors Journal. 2018;18(15):6115-22.
201. Zhou K-p, Bai X-f, Bi W-h. Determination of ethanol content in ethanolgasoline based on derivative absorption spectrometry and information fusion. Optoelectronics Letters. 2018;14(6):442-6.
202. Liu Y, Wang H, Zhao W, Zhang M, Qin H, Xie Y. Flexible, stretchable sensors for wearable health monitoring: sensing mechanisms, materials, fabrication strategies and features. Sensors. 2018;18(2):645.
203. Tidriri K, Tiplica T, Chatti N, Verron S. A generic framework for decision fusion in fault detection and diagnosis. Engineering Applications of Artificial Intelligence. 2018;71:73-86.
204. Liu A LJ, Li L. Study on indoor positioning method based on subchannel information fusion. Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument. 2018;39(3):242-9.
205. Liu S, Yang J. Target recognition in synthetic aperture radar images via joint multifeature decision fusion. Journal of Applied Remote Sensing. 2018;12(1):016012.
206. Shu Y, Zhang H. Multimodal information fusion based human movement recognition. Multimedia Tools and Applications. 2020;79(7):5043-52.
207. Shrivastava S, Kothari D. SU throughput enhancement in a decision fusion based cooperative sensing system. AEUInternational Journal of Electronics and Communications. 2018;87:95-100.
208. Meng Z, Han S, Liu P, Tong Y. Improving speech related facial action unit recognition by audiovisual information fusion. IEEE transactions on cybernetics. 2018;49(9):3293-306.
209. Shaban M, Mahmood A, Al-Maadeed SA, Rajpoot N. An information fusion framework for person localization via body pose in spectator crowds. Information Fusion. 2019;51:178-88.
211. Mu N, Xu X, Zhang X, Lin X. Discrete stationary wavelet transform based saliency information fusion from frequency and spatial domain in low contrast images. Pattern Recognition Letters. 2018;115:84-91.
212. Lu F, Huang Y, Huang J, Qiu X. Gas turbine performance monitoring based on extended information fusion filter. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2019;233(2):483-97.
213. Li C, Wang P, Dong H. Krigingbased multi-fidelity optimization via information fusion with uncertainty. Journal of Mechanical Science and Technology. 2018;32(1):245-59.
214. Karanikola L, Karali I. Towards a Dempster–Shafer Fuzzy Description Logic—Handling Imprecision in the Semantic Web. IEEE Transactions on Fuzzy Systems. 2018;26(5):3016-26.
215. Li S, Ma H, Saha T, Wu G. Bayesian information fusion for probabilistic health index of power transformer. IET Generation, Transmission & Distribution. 2017;12(2):279-87.
216. Zhang X, Mahadevan S, Lau N, Weinger MB. Multi-source information fusion to assess control room operator performance. Reliability Engineering & System Safety. 2018;194:106287.
217. Zhang L, Zhang M, Sun X, Wang L, Cen Y. Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion. International Journal of Remote Sensing. 2018;39(20):6646-56.
218. Wang M, Li Z, Huang D, Guo X. Performance analysis of information fusion method based on bell function. International Journal of Performability Engineering. 2018;14(4):729.
219. Shrinivasan L, Raol JR. Interval type2 fuzzy-logic-based decision fusion system for air-lane monitoring. IET Intelligent Transport Systems. 2018;12(8):860-7.
220. Sarabi-Jamab A, Araabi BN. How to decide when the sources of evidence are unreliable: A multi-criteria discounting approach in the Dempster–Shafer theory. Information Sciences. 2018;448:233-48.
221. Fatemipour F, Akbarzadeh-T M. Dynamic Fuzzy Rule-based Source Selection in Distributed Decision Fusion Systems. Fuzzy Information and Engineering. 2018;10(1):107-27.
222. Saadi I, Farooq B, Mustafa A, Teller J, Cools M. An efficient hierarchical model for multi-source information fusion. Expert Systems with Applications. 2018;110:352-62.
223. Dabrowski JJ, de Villiers JP, Beyers C. Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion. Information Fusion. 2018;42:75-101.
224. Paggi H, Lara JA, Soriano J. Structures generated in a multiagent system performing information fusion in peer-topeer resource-constrained networks. Neural Computing and Applications. 2018:1-19.
225. Li H, Huang H-Z, Li Y-F, Zhou J, Mi J. Physics of failure-based reliability prediction of turbine blades using multisource information fusion. Applied Soft Computing. 2018;72:624-35.
226. Chahine C, Vachier-Lagorre C, Chenoune Y, El Berbari R, El Fawal Z, Petit E. Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix. IET Image Processing. 2018;12(4):525-31.
227. Che X, Mi J, Chen D. Information fusion and numerical characterization of a multi-source information system. Knowledge-Based Systems. 2018;145:121-33.
228. Paggi H, Soriano J, Lara JA. A multiagent system for minimizing information indeterminacy within information fusion scenarios in peer-to-peer networks with limited resources. Information Sciences. 2018;451:271-94.
229. Li S, Zhao S, Cheng B, Chen J. Accelerated particle filter for real-time visual tracking with decision fusion. IEEE Signal Processing Letters. 2018;25(7):1094-8.
230. Wang L, Li Y, Liao Y, Pan K, Zhang W. Course control of unmanned wave glider with heading information fusion. IEEE Transactions on Industrial Electronics. 2018;66(10):7997-8007.
231. Mu Z, Zeng S. Some novel intuitionistic fuzzy information fusionmethods in decision making with interaction among attributes. Soft Computing. 2019;23(20):10439-48.
232. Luo H, Lan W, Chen Q, Wang Z, Liu Z, Yue X, et al. Inferring microRNAenvironmental factor interactions based on multiple biological information fusion. Molecules. 2018;23(10):2439.
233. Sultana M, Paul PP, Gavrilova ML. Social behavioral information fusion in multimodal biometrics. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;48(12):2176-87.
IssueVol 7 No 2 (2021) QRcode
SectionReview Article(s)
Information fusion Symbol fusion Decision fusion Big data Data mining

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Nazari E, Biviji R, Farzin A, Asgari P, Tabesh H. Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework. jbe. 2021;7(2):193-220.