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For: Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 2019;146:800-5. [DOI: 10.1016/j.measurement.2019.05.083] [Cited by in Crossref: 92] [Cited by in F6Publishing: 94] [Article Influence: 30.7] [Reference Citation Analysis]
Number Citing Articles
1 Vidivelli S, Sathiya Devi S. Breast cancer detection model using fuzzy entropy segmentation and ensemble classification. Biomedical Signal Processing and Control 2023;80:104236. [DOI: 10.1016/j.bspc.2022.104236] [Reference Citation Analysis]
2 Hussain L, Qureshi SA, Aldweesh A, Pirzada JUR, Butt FM, eldin ET, Ali M, Algarni A, Nadim MA. Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms. Connection Science 2022;34:2785-2807. [DOI: 10.1080/09540091.2022.2151566] [Reference Citation Analysis]
3 Sowrirajan SR, Balasubramanian S. Brain Tumor Classification Using Machine Learning and Deep Learning Algorithms. IJEER 2022;10:999-1004. [DOI: 10.37391/ijeer.100441] [Reference Citation Analysis]
4 Shaban WM. Insight into breast cancer detection: new hybrid feature selection method. Neural Comput & Applic 2022. [DOI: 10.1007/s00521-022-08062-y] [Reference Citation Analysis]
5 Gautam A. Recent advancements of deep learning in detecting breast cancer: a survey. Multimedia Systems 2022. [DOI: 10.1007/s00530-022-01028-z] [Reference Citation Analysis]
6 Dong Y, Li Z, Chen Z, Xu Y, Zhang Y. Breast cancer classification application based on QGA-SVM. IFS 2022. [DOI: 10.3233/jifs-212957] [Reference Citation Analysis]
7 Wu S, Fan J, Yang Y, Zhang L, Ma F. A detection and diagnosis method for tubing leakage below liquid level in gas wellbore. Measurement 2022;202:111891. [DOI: 10.1016/j.measurement.2022.111891] [Reference Citation Analysis]
8 Khan AR, Saba T, Sadad T, Nobanee H, Bahaj SA, Javed AR. Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/1100775] [Reference Citation Analysis]
9 Thawkar S. Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization. Biocybernetics and Biomedical Engineering 2022. [DOI: 10.1016/j.bbe.2022.09.001] [Reference Citation Analysis]
10 Alyami J, Sadad T, Rehman A, Almutairi F, Saba T, Bahaj SA, Alkhurim A, Roy S. Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM). Computational Intelligence and Neuroscience 2022;2022:1-9. [DOI: 10.1155/2022/7403302] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Rojas-muñoz LF, Rostro-gonzález H, García-capulín CH, Sánchez-solano S. Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing. Electronics 2022;11:2686. [DOI: 10.3390/electronics11172686] [Reference Citation Analysis]
12 Deng J, Sun J, Peng W, Zhang D, Vyatkin V. Imbalanced multiclass classification with active learning in strip rolling process. Knowledge-Based Systems 2022. [DOI: 10.1016/j.knosys.2022.109754] [Reference Citation Analysis]
13 Soulami KB, Kaabouch N, Saidi MN. Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection. Concurrency and Computation. [DOI: 10.1002/cpe.7233] [Reference Citation Analysis]
14 Belhaj Soulami K, Kaabouch N, Nabil Saidi M. Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomedical Signal Processing and Control 2022;76:103696. [DOI: 10.1016/j.bspc.2022.103696] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Samee NA, Alhussan AA, Ghoneim VF, Atteia G, Alkanhel R, Al-antari MA, Kadah YM. A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms. Sensors 2022;22:4938. [DOI: 10.3390/s22134938] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 10.0] [Reference Citation Analysis]
16 Sykiotis S, Tzortzis I, Angeli A, Doulamis N, Kalogeras D. A deep-learning based diagnostic framework for Breast Cancer. The15th International Conference on PErvasive Technologies Related to Assistive Environments 2022. [DOI: 10.1145/3529190.3534769] [Reference Citation Analysis]
17 H. K. MM, Research Scholar, Sri Siddhartha Institute of Technology, Tumkur (Karnataka), India., Ramesh DD, Professor and HOD, Sri Siddhartha Academy of Higher Education, Tumkur (Karnataka), India.. Health Care Data Analytics – Comparative Study of Supervised Model. IJITEE 2022;11:22-28. [DOI: 10.35940/ijitee.f9906.0511622] [Reference Citation Analysis]
18 Alshutbi M, Li Z, Alrifaey M, Ahmadipour M, Othman MM. A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07290-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Ramesh S, Sasikala S, Gomathi S, Geetha V, Anbumani V. Segmentation and classification of breast cancer using novel deep learning architecture. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07230-4] [Cited by in Crossref: 7] [Article Influence: 7.0] [Reference Citation Analysis]
20 Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022;146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
21 Ameh Joseph A, Abdullahi M, Junaidu SB, Hassan Ibrahim H, Chiroma H. Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer). Intelligent Systems with Applications 2022;14:200066. [DOI: 10.1016/j.iswa.2022.200066] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Nemade V, Pathak S, Dubey AK. A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques. Arch Computat Methods Eng. [DOI: 10.1007/s11831-022-09738-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Amin SA, Al Shanabari H, Iqbal R, Karyotis C. An Intelligent Framework for Automatic Breast Cancer Classification Using Novel Feature Extraction and Machine Learning Techniques. J Sign Process Syst 2022. [DOI: 10.1007/s11265-022-01753-8] [Reference Citation Analysis]
24 Rehman KU, Li J, Pei Y, Yasin A. A review on machine learning techniques for the assessment of image grading in breast mammogram. Int J Mach Learn & Cyber . [DOI: 10.1007/s13042-022-01546-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 O K G, Elayidom M. S. Mammogram pectoral muscle removal and classification using histo-sigmoid based ROI clustering and SDNN. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12599-4] [Reference Citation Analysis]
26 Chandraraju TS, Jeyaprakash A. Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22718] [Reference Citation Analysis]
27 Babu M, Jesudas T. An artificial intelligence‐based smart health system for biological cognitive detection based on wireless telecommunication. Computational Intelligence. [DOI: 10.1111/coin.12513] [Reference Citation Analysis]
28 Patil RS, Biradar N, Pawar R. A new automated segmentation and classification of mammogram images. Multimed Tools Appl. [DOI: 10.1007/s11042-022-11932-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
29 Geetha P, Umamaheswari S. Choose most efficient features of breast cancer using an SVM classifier for breast cancer diagnosis. 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) 2022. [DOI: 10.1109/accai53970.2022.9752597] [Reference Citation Analysis]
30 Ittannavar SS, Havaldar RH, Parameshachari BD. Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm. BioMed Research International 2022;2022:1-14. [DOI: 10.1155/2022/8576768] [Reference Citation Analysis]
31 Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med 2022;142:105221. [PMID: 35016100 DOI: 10.1016/j.compbiomed.2022.105221] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 10.0] [Reference Citation Analysis]
32 Dina AS, Siddique AB, Manivannan D. Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks. IEEE Access 2022;10:96731-47. [DOI: 10.1109/access.2022.3205337] [Reference Citation Analysis]
33 Kaur A, Rashid M, Bashir AK, Parah SA. Detection of Breast Cancer Masses in Mammogram Images with Watershed Segmentation and Machine Learning Approach. Artificial Intelligence for Innovative Healthcare Informatics 2022. [DOI: 10.1007/978-3-030-96569-3_2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Devi TC, Thaoroijam K. Autoencoder-Based Speech Features for Manipuri Dialect Identification. Lecture Notes in Electrical Engineering 2022. [DOI: 10.1007/978-981-19-0840-8_54] [Reference Citation Analysis]
35 Sheikh MH, Mittal S, Bashir R. An Analysis of Various Machine Learning Techniques Used for Diseases Prediction: A Review. Lecture Notes in Electrical Engineering 2022. [DOI: 10.1007/978-981-16-8892-8_35] [Reference Citation Analysis]
36 Bacha S, Taouali O. A novel machine learning approach for breast cancer diagnosis. Measurement 2022;187:110233. [DOI: 10.1016/j.measurement.2021.110233] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
37 Zebari DA, Ibrahim DA, Zeebaree DQ, Mohammed MA, Haron H, Zebari NA, Damaševičius R, Maskeliūnas R. Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion. Applied Sciences 2021;11:12122. [DOI: 10.3390/app112412122] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 11.0] [Reference Citation Analysis]
38 Ahmed MM, Palaniswamy T. A novel TMGWO–SLBNC‐based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities. Int J of Intelligent Sys 2022;37:2131-62. [DOI: 10.1002/int.22768] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 R. P. S. M, A. M. K. Big data feature selection using fish and frog optimization. Computational Intelligence. [DOI: 10.1111/coin.12483] [Reference Citation Analysis]
40 Ghazouani H, Barhoumi W. Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images. Comput Biol Med 2021;139:105011. [PMID: 34753080 DOI: 10.1016/j.compbiomed.2021.105011] [Reference Citation Analysis]
41 Rezaei Z. A review on image-based approaches for breast cancer detection, segmentation, and classification. Expert Systems with Applications 2021;182:115204. [DOI: 10.1016/j.eswa.2021.115204] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 12.0] [Reference Citation Analysis]
42 Wahyuni ES, Rasmi RP, Murnani S. Performance Improvement of Breast Cancer Diagnosis based on Mammogram Images using Feature Extraction and Classification Methods. 2021 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) 2021. [DOI: 10.1109/ibitec53045.2021.9649237] [Reference Citation Analysis]
43 Sahu A, Gm H, Gourisaria MK, Rautaray SS, Pandey M. Cardiovascular risk assessment using data mining inferencing and feature engineering techniques. Int j inf tecnol 2021;13:2011-23. [DOI: 10.1007/s41870-021-00650-w] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 10.0] [Reference Citation Analysis]
44 Abinash MJ, Vasudevan V. Boundaries tuned support vector machine (BT-SVM) classifier for cancer prediction from gene selection. Comput Methods Biomech Biomed Engin 2021;:1-14. [PMID: 34585639 DOI: 10.1080/10255842.2021.1981300] [Reference Citation Analysis]
45 Yang X, Stamp M. Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS). Comput Biol Med 2021;138:104874. [PMID: 34571437 DOI: 10.1016/j.compbiomed.2021.104874] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
46 Shastry KA, Sanjay HA. Cancer diagnosis using artificial intelligence: a review. Artif Intell Rev. [DOI: 10.1007/s10462-021-10074-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
47 Bensaoucha S. Breast Cancer Diagnosis Using Optimized Machine Learning Algorithms. 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI) 2021. [DOI: 10.1109/icrami52622.2021.9585977] [Reference Citation Analysis]
48 Karabulut B, Arslan G, Ünver HM. Classification Based on Structural Information in Data. Arab J Sci Eng 2022;47:2239-53. [DOI: 10.1007/s13369-021-06177-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Supriya M, Deepa AJ, Mythili C. Mamographic image for breast cancer detection and identification of stages of cancer using MFFC and optimized ANFIS. J Ambient Intell Human Comput 2021;12:8731-8745. [DOI: 10.1007/s12652-020-02639-y] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
50 Hussein IJ, Burhanuddin MA, Mohammed MA, Benameur N, Maashi MS, Maashi MS. Fully‐automatic identification of gynaecological abnormality using a new adaptive frequency filter and histogram of oriented gradients ( HOG ). Expert Systems. [DOI: 10.1111/exsy.12789] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
51 Ittannavar SS, Havaldar RH. Detection of breast cancer using the infinite feature selection with genetic algorithm and deep neural network. Distrib Parallel Databases. [DOI: 10.1007/s10619-021-07355-w] [Reference Citation Analysis]
52 Mu'jizah H, Novitasari DCR. Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM. J Teknol dan Sist Komput 2021;9:150-6. [DOI: 10.14710/jtsiskom.2021.14104] [Reference Citation Analysis]
53 Vani PS, Rathi S. Improved data clustering methods and integrated A-FP algorithm for crop yield prediction. Distrib Parallel Databases. [DOI: 10.1007/s10619-021-07350-1] [Reference Citation Analysis]
54 Mirobi GJ, Arockiam L. DAVmS: Distance Aware Virtual Machine Scheduling approach for reducing the response time in cloud computing. J Supercomput 2021;77:6664-75. [DOI: 10.1007/s11227-020-03563-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
55 Jiang M, Han L, Sun H, Li J, Bao N, Li H, Zhou S, Yu T. Cross-modality image feature fusion diagnosis in breast cancer. Phys Med Biol 2021;66. [PMID: 33784653 DOI: 10.1088/1361-6560/abf38b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
56 Jagadeesan S, Amutha B. An efficient botnet detection with the enhanced support vector neural network. Measurement 2021;176:109140. [DOI: 10.1016/j.measurement.2021.109140] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
57 Tariq M, Iqbal S, Ayesha H, Abbas I, Ahmad KT, Niazi MFK. Medical image based breast cancer diagnosis: State of the art and future directions. Expert Systems with Applications 2021;167:114095. [DOI: 10.1016/j.eswa.2020.114095] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 13.0] [Reference Citation Analysis]
58 Houssein EH, Emam MM, Ali AA, Suganthan PN. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications 2021;167:114161. [DOI: 10.1016/j.eswa.2020.114161] [Cited by in Crossref: 62] [Cited by in F6Publishing: 68] [Article Influence: 62.0] [Reference Citation Analysis]
59 Deshmukh YS, Kumar P, Karan R, Singh SK. Breast Cancer Detection-Based Feature Optimization Using Firefly Algorithm and Ensemble Classifier. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) 2021. [DOI: 10.1109/icais50930.2021.9395788] [Reference Citation Analysis]
60 Chen Z, Zhao F, Zhou J, Huang P, Song W. A novel approach applied to fault diagnosis for micro-defects on piston throat. Measurement 2021;173:108508. [DOI: 10.1016/j.measurement.2020.108508] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
61 Pezeshki H, Rastgarpour M, Sharifi A, Yazdani S. Mass classification of mammograms using fractal dimensions and statistical features. Multidim Syst Sign Process 2021;32:573-605. [DOI: 10.1007/s11045-020-00749-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
62 Jebarani PE, Umadevi N, Dang H, Pomplun M. A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection. IEEE Access 2021;9:146153-62. [DOI: 10.1109/access.2021.3123425] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
63 Dabass J, Hanmandlu M, Vig R. Multi-class classification of mammograms with hesitancy based Hanman transform classifier on pervasive information set texture features. Informatics in Medicine Unlocked 2021;26:100756. [DOI: 10.1016/j.imu.2021.100756] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Jiang L, Wang Y, Tang Z, Miao Y, Chen S. Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation. Measurement 2021;170:108736. [DOI: 10.1016/j.measurement.2020.108736] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
65 Batchuluun S, Matsune H, Shiomori K, Bayanjargal O, Baasankhuu T. Analysis of the microcapsule structure based on machine learning algorithm. J Phys : Conf Ser 2021;1763:012030. [DOI: 10.1088/1742-6596/1763/1/012030] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
66 Duarte LM, de Alencar Santos JD, Freitas FNC, Filho PPR, de Abreu HFG. A novel approach based on pattern recognition techniques to evaluate magnetic properties of a non-grain oriented electrical steel in the secondary recrystallization process. Measurement 2021;167:108135. [DOI: 10.1016/j.measurement.2020.108135] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
67 Meenalochini G, Ramkumar S. Survey of machine learning algorithms for breast cancer detection using mammogram images. Materials Today: Proceedings 2021;37:2738-43. [DOI: 10.1016/j.matpr.2020.08.543] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
68 Bajcsi A, Andreica A, Chira C. Towards feature selection for digital mammogram classification. Procedia Computer Science 2021;192:632-641. [DOI: 10.1016/j.procs.2021.08.065] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
69 Liu M, Wang M, He Q, Yin M. Design and application of time series algorithm model in information assisted sensing system of nursing measurement in neurology. Measurement 2020;162:107894. [DOI: 10.1016/j.measurement.2020.107894] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
70 Saba T. Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges. Journal of Infection and Public Health 2020;13:1274-89. [DOI: 10.1016/j.jiph.2020.06.033] [Cited by in Crossref: 65] [Cited by in F6Publishing: 73] [Article Influence: 32.5] [Reference Citation Analysis]
71 Das P, Das A. Shift invariant extrema based feature analysis scheme to discriminate the spiculation nature of mammograms. ISA Trans 2020;103:156-65. [PMID: 32216985 DOI: 10.1016/j.isatra.2020.03.018] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
72 Sheik Mohideen Shah S, Meganathan S. Machine learning approach for power consumption model based on monsoon data for smart cities applications. Computational Intelligence 2021;37:1309-21. [DOI: 10.1111/coin.12368] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
73 Jiarpinijnun A, Osako K, Siripatrawan U. Visualization of volatomic profiles for early detection of fungal infection on storage Jasmine brown rice using electronic nose coupled with chemometrics. Measurement 2020;157:107561. [DOI: 10.1016/j.measurement.2020.107561] [Cited by in Crossref: 27] [Cited by in F6Publishing: 29] [Article Influence: 13.5] [Reference Citation Analysis]
74 Alavudeen Basha A, Vivekanandan S. A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum. Neural Comput & Applic. [DOI: 10.1007/s00521-020-04975-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
75 Sahaya Sheela MA, Prabakaran R. Improvement of battery lifetime in software‐defined network using particle swarm optimization based cluster‐head gateway switch routing protocol with fuzzy rules. Computational Intelligence 2020;36:813-23. [DOI: 10.1111/coin.12271] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
76 Patil RS, Biradar N. Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier. IJICC 2020;13:181-205. [DOI: 10.1108/ijicc-10-2019-0116] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
77 Sumathi G, Akilandeswari J. Improved fuzzy weighted‐iterative association rule based ontology postprocessing in data mining for query recommendation applications. Computational Intelligence 2020;36:773-82. [DOI: 10.1111/coin.12269] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
78 Sharif MI, Li JP, Naz J, Rashid I. A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters 2020;131:30-7. [DOI: 10.1016/j.patrec.2019.12.006] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 8.5] [Reference Citation Analysis]
79 Wang J, Liao J, Huang W. A density-based maximum margin machine classifier. Cluster Comput 2020;23:3069-78. [DOI: 10.1007/s10586-020-03070-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
80 Li L, Yang R, Guo C, Ge S, Chang B. The data learning and anomaly detection based on the rudder system testing facility. Measurement 2020;152:107324. [DOI: 10.1016/j.measurement.2019.107324] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
81 Priya TS, Ramaprabha T. An Effective Feature Extraction Based Particle Swarm Optimization with Support Vector Machine for Biomedical Mammogram Image Diagnosis. 2020 International Conference on Inventive Computation Technologies (ICICT) 2020. [DOI: 10.1109/icict48043.2020.9112486] [Reference Citation Analysis]