Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Review Article

A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques

Author(s): Tariq Sadad, Amjad Rehman, Ayyaz Hussain, Aaqif Afzaal Abbasi* and Muhammad Qasim Khan

Volume 17, Issue 6, 2021

Published on: 17 December, 2020

Page: [686 - 694] Pages: 9

DOI: 10.2174/1573405616666201217112521

Price: $65

Abstract

Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.

Keywords: Classification, colonoscopy, mammography, healthcare, public health, CT, MRI.

Graphical Abstract

[1]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[2]
Kelly KM, Dean J, Comulada WS, Lee SJ. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 2010; 20(3): 734-42.
[http://dx.doi.org/10.1007/s00330-009-1588-y] [PMID: 19727744]
[3]
Kurihara Y, Matsuoka S, Yamashiro T, et al. MRI of pulmonary nodules. AJR Am J Roentgenol 2014; 202(3): W210-6.
[http://dx.doi.org/10.2214/AJR.13.11618] [PMID: 24555616]
[4]
Sadad T, Munir A, Saba T, Hussain A. Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature. J Comput Sci 2018; 29: 34-45.
[http://dx.doi.org/10.1016/j.jocs.2018.09.015]
[5]
Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS. Automatic 3D pulmonary nodule detection in CT images: A survey. Comput Methods Programs Biomed 2016; 124: 91-107.
[http://dx.doi.org/10.1016/j.cmpb.2015.10.006] [PMID: 26652979]
[6]
Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique. Med Phys 2016; 43(6): 2821-7.
[http://dx.doi.org/10.1118/1.4948498] [PMID: 27277030]
[7]
Cancer I of M (US) and NRC Cancer I of M (US) and NRC (US) C on T for the ED of B, Nass SJ, Henderson IC, Lashof JC. Mammography and Beyond Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer 2001.
[8]
American Cancer Society. Cancer Facts & Figures 2017 2017. Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2017.html
[9]
Kaur P, Singh G, Kaur P. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics Med Unlocked 2019; 16: 100151.
[http://dx.doi.org/10.1016/j.imu.2019.01.001]
[10]
Rouhi R, Jafari M, Kasaei S, Keshavarzian P. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 2015; 42(3): 990-1002.
[http://dx.doi.org/10.1016/j.eswa.2014.09.020]
[11]
Nabors LB, Portnow J, Ammirati M, et al. Central nervous system cancers, version 1. J Natl Compr Canc Netw 2015; 13(10): 1191-202.
[http://dx.doi.org/10.6004/jnccn.2015.0148] [PMID: 26483059]
[12]
Amin J, Sharif M, Yasmin M, Fernandes SL. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Gener Comput Syst 2018; 87: 290-7.
[http://dx.doi.org/10.1016/j.future.2018.04.065]
[13]
Causey JL, Zhang J, Ma S, et al. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 2018; 8(1): 9286.
[http://dx.doi.org/10.1038/s41598-018-27569-w] [PMID: 29915334]
[14]
Zhang G, Yang Z, Gong L, et al. An appraisal of nodule diagnosis for lung cancer in CT images. J Med Syst 2019; 43(7): 181.
[http://dx.doi.org/10.1007/s10916-019-1327-0] [PMID: 31093830]
[15]
Stewart BW, Wild CP. World cancer report 2014. World Heal Organ 2014.
[16]
Belgherbi AH. A semi-automated method for the liver lesion extraction from a CT images based on mathematical morphology. J Biomed Sci 2013; 2(2): 1-9.
[17]
Li Q, Chang L, Liu H, Zhou M, Wang Y, Guo F. Skin cells segmentation algorithm based on spectral angle and distance score. Opt Laser Technol 2015; 74: 79-86.
[http://dx.doi.org/10.1016/j.optlastec.2015.05.017]
[18]
Brooke RC. Basal cell carcinoma. Clin Med (Lond) 2005; 5(6): 551-4.
[http://dx.doi.org/10.7861/clinmedicine.5-6-551] [PMID: 16411349]
[19]
Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art. Artif Intell Rev 2021; 54(2): 811-41.
[http://dx.doi.org/10.1007/s10462-020-09865-y]
[20]
Khan MA, Lali IU, Rehman A, et al. Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 2019; 82(6): 909-22.
[http://dx.doi.org/10.1002/jemt.23238] [PMID: 30801840]
[21]
Khan SA, Nazir M, Khan MA, et al. Lungs nodule detection framework from computed tomography images using support vector machine. Microsc Res Tech 2019; 82(8): 1256-66.
[http://dx.doi.org/10.1002/jemt.23275] [PMID: 30974031]
[22]
Toğaçar M, Ergen B, Cömert Z. BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 2020; 134: 109531.
[http://dx.doi.org/10.1016/j.mehy.2019.109531] [PMID: 31877442]
[23]
Smith RA, Duffy SW, Gabe R, Tabar L, Yen AMF, Chen THH. The randomized trials of breast cancer screening: what have we learned? Radiol Clin North Am 2004; 42(5): 793-806, v.
[http://dx.doi.org/10.1016/j.rcl.2004.06.014] [PMID: 15337416]
[24]
Dora L, Agrawal S, Panda R, Abraham A. Optimal breast cancer classification using Gauss–Newton representation based algorithm. Expert Syst Appl 2017; 85: 134-45.
[http://dx.doi.org/10.1016/j.eswa.2017.05.035]
[25]
Ali Khan S, Shariq Hussain SY. Contrast enhancement of low-contrast medical images using modified contrast limited adaptive histogram equalization. J Med Imaging Health Inform 2020; 10(8): 1795-803.
[http://dx.doi.org/10.1166/jmihi.2020.3196]
[26]
Hafiz SMM, Khan SA, Hussain S. Arif Jamal HSAQ. A knowledge-based image enhancement and denoising approach. Comput Math Organ Theory 2020; 25(2): 108-21.
[27]
Mahmood A, Khan SA, Hussain S, Almaghayreh EM. An adaptive image contrast enhancement technique for low-contrast images. IEEE Access 2019; 7: 161584-93.
[http://dx.doi.org/10.1109/ACCESS.2019.2951468]
[28]
Mughal B, Muhammad N, Sharif M, Rehman A, Saba T. Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer 2018; 18(1): 778.
[http://dx.doi.org/10.1186/s12885-018-4638-5] [PMID: 30068304]
[30]
Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision Cengage Learn 2014. 4th edition. 2008.
[31]
Oliver A, Freixenet J, Martí J, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010; 14(2): 87-110.
[http://dx.doi.org/10.1016/j.media.2009.12.005] [PMID: 20071209]
[32]
Khan SU, Islam N, Jan Z, Ud Din I, Rodrigues JJPC. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 2019; 125: 1-6.
[http://dx.doi.org/10.1016/j.patrec.2019.03.022]
[33]
Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3(6): 610-21.http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309314
[http://dx.doi.org/10.1109/TSMC.1973.4309314]
[34]
Mohanaiah P, Sathyanarayana P, Gurukumar L. Image texture feature extraction using GLCM approach. Int J Sci Res Publ 2013; 3(5): 1-5.
[35]
Kamalakannan J, Rajasekhara Babu M. Early detection of breast cancer using GLCM feature extraction in mammograms. IIOAB J 2016; 7(5): 170-9.
[36]
Berbar MA. Hybrid methods for feature extraction for breast masses classification. Egypt Inform J 2018; 19(1): 63-73.
[http://dx.doi.org/10.1016/j.eij.2017.08.001]
[37]
Tambasco Bruno DO, Do Nascimento MZ, Ramos RP, Batista VR, Neves LA, Martins AS. LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. Expert Syst Appl 2016; 55: 329-40.
[http://dx.doi.org/10.1016/j.eswa.2016.02.019]
[38]
Rabidas R, Midya A, Chakraborty J, Arif W. A study of different texture features based on local operator for benign-malignant mass classification. Procedia Comput Sci 2016; 93: 389-95.
[http://dx.doi.org/10.1016/j.procs.2016.07.225]
[39]
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.
[http://dx.doi.org/10.1016/j.measurement.2019.05.083]
[40]
Toor AA, Usman M, Younas F, M Fong AC, Khan SA, Fong S. Mining massive e-health data streams for IoMT enabled healthcare systems. Sensors (Basel) 2020; 20(7): 2131.
[http://dx.doi.org/10.3390/s20072131] [PMID: 32283841]
[41]
Mughal B, Muhammad N, Sharif M. Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. Int J Med Inform 2019; 126: 26-34.
[http://dx.doi.org/10.1016/j.ijmedinf.2019.02.001] [PMID: 31029261]
[42]
Mughal B, Muhammad N, Sharif M. Deviation analysis for texture segmentation of breast lesions in mammographic images. Eur Phys J Plus 2018; 133: 455.
[http://dx.doi.org/10.1140/epjp/i2018-12294-4]
[43]
Mughal B, Sharif M, Muhammad N, Saba T. A novel classification scheme to decline the mortality rate among women due to breast tumor. Microsc Res Tech 2018; 81(2): 171-80.
[http://dx.doi.org/10.1002/jemt.22961] [PMID: 29143395]
[44]
Duarte MA, Pereira WCA, Alvarenga AV. Calculating texture features from mammograms and evaluating their performance in classifying clusters of microcalcifications. MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019; pp. 322-32.
[http://dx.doi.org/10.1007/978-3-030-31635-8_39]
[45]
Bhartia R, Kumarb V, Rawatc M. Classification of breast cancer mammography image by convolution neural network. World J Technol Eng Res 2018.
[46]
Sadad T, Hussain A, Munir A, et al. Identification of breast malignancy by marker-controlled watershed transformation and hybrid feature set for healthcare. Appl Sci (Basel) 2020; 3: 1-16.
[http://dx.doi.org/10.3390/app10061900]
[47]
Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed 2018; 157: 19-30.
[http://dx.doi.org/10.1016/j.cmpb.2018.01.011] [PMID: 29477427]
[48]
Al-Antari MA, Al-Masni MA, Choi MT, Han SM, Kim TS. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 2018; 117: 44-54.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.06.003] [PMID: 30032964]
[49]
Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep learning to improve breast cancer detection on screening mammography. Sci Rep 2019; 9(1): 12495.
[http://dx.doi.org/10.1038/s41598-019-48995-4] [PMID: 31467326]
[50]
Sadad T, Khan AR, Hussain A, et al. Internet of medical things embedding deep learning with data augmentation for mammogram density classification. Microsc Res Tech 2021; 1-9.
[http://dx.doi.org/10.1002/jemt.23773] [PMID: 33908111]
[51]
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012.
[52]
Roth HR, Lu L, Liu J, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 2016; 35(5): 1170-81.
[http://dx.doi.org/10.1109/TMI.2015.2482920] [PMID: 26441412]
[53]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016; Las Vegas, NV, USA.
[54]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1-12.
[55]
Virmani J, Agarwal R. Deep feature extraction and classification of breast ultrasound images. Multimedia Tools Appl 2020; 79(37): 27257-92.
[http://dx.doi.org/10.1007/s11042-020-09337-z]
[56]
Amin J, Sharif M, Yasmin M, Fernandes SL. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognit Lett 2020; 139: 118-27.
[http://dx.doi.org/10.1016/j.patrec.2017.10.036]
[57]
Sharif M, Amin J, Nisar MW, Anjum MA, Muhammad N, Ali Shad S. A unified patch based method for brain tumor detection using features fusion. Cogn Syst Res 2020; 59: 273-86.
[http://dx.doi.org/10.1016/j.cogsys.2019.10.001]
[58]
Amin J, Sharif M, Yasmin M, Saba T, Raza M. Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimedia Tools Appl 2019; 79: 10955-73.
[http://dx.doi.org/10.1007/s11042-019-7324-y]
[59]
Amin J, Sharif M, Raza M, Saba T, Anjum MA. Brain tumor detection using statistical and machine learning method. Comput Methods Programs Biomed 2019; 177: 69-79.
[http://dx.doi.org/10.1016/j.cmpb.2019.05.015] [PMID: 31319962]
[60]
Amin J, Sharif M, Raza M, Yasmin M. Detection of brain tumor based on features fusion and machine learning. J Ambient Intell Humaniz Comput 2018.
[http://dx.doi.org/10.1007/s12652-018-1092-9]
[61]
Abdel-Maksoud E, Elmogy M, Al-Awadi R. Brain tumor segmentation based on a hybrid clustering technique. Egypt Informatics J 2015; 16(1): 71-81.
[http://dx.doi.org/10.1016/j.eij.2015.01.003]
[62]
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D. DRINet for medical image segmentation. IEEE Trans Med Imaging 2018; 37(11): 2453-62.
[http://dx.doi.org/10.1109/TMI.2018.2835303] [PMID: 29993738]
[63]
Khan SA, Ishtiaq M, Nazir M, Shaheen M. Face recognition under varying expressions and illumination using particle swarm optimization. J Comput Sci 2018; 28: 94-100.
[http://dx.doi.org/10.1016/j.jocs.2018.08.005]
[64]
Fernandes S, Bala J. A novel decision support for composite sketch matching using fusion of probabilistic neural network and dictionary matching. Curr Med Imaging Rev 2017; 13(2): 176-84.
[http://dx.doi.org/10.2174/1573405612666160606143938]
[65]
Iqbal S, Ghani MU, Saba T, Rehman A. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 2018; 81(4): 419-27.
[http://dx.doi.org/10.1002/jemt.22994] [PMID: 29356229]
[66]
Iqbal S, Khan MUG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 2017; 8(1): 5-28.
[http://dx.doi.org/10.1007/s13534-017-0050-3] [PMID: 30603187]
[67]
Esther Rani P, Harsha MVS, Singh A, Singh S. Brain tumor segmentation in MRI images using convolution neural networks. Int J Recent Technol Eng 2019; 35(5): 1240-51.
[68]
Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M. Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Humaniz Comput 2018.
[http://dx.doi.org/10.1007/s12652-018-1075-x]
[69]
Nazir M, Khan MA, Saba T, Rehman A. Brain tumor detection from MRI images using multi-level wavelets. International Conference on Computer and Information Sciences, ICCIS 2019.
[http://dx.doi.org/10.1109/ICCISci.2019.8716413]
[70]
Suhasini G, Vijaya A. An adaptive preprocessing of lung CT images with various filters for better enhancement. Acad J Cancer Res 2014; 3: 179-84.
[71]
Kim H, Nakashima T, Itai Y, Maeda S, Tan JK, Ishikawa S. Automatic detection of ground glass opacity from the thoracic MDCT images by using density features. International Conference on Control, Automation and Systems. 2007; Seoul, Korea (South).
[72]
Teramoto A, Fujita H. Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J CARS 2013; 8(2): 193-205.
[http://dx.doi.org/10.1007/s11548-012-0767-5] [PMID: 22684487]
[73]
Alilou M, Kovalev V, Snezhko E, Taimouri V. A comprehensive framework for automatic detection of pulmonary nodules in lung CT images. Image Anal Stereol 2014; 33(1): 13-27.
[http://dx.doi.org/10.5566/ias.v33.p13-27]
[74]
Singh AK, Saini V, Saini LM. ROI based detection of abnormalities in lungs using medical image processing. Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS. Corpus ID: 29431563.
[http://dx.doi.org/10.1109/ICCONS.2017.8250589]
[75]
Xiang D, Yang B, Yu F, Chen X. Lung tumor segmentation based on multi-scale template matching and region growing. Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105782Q.
[http://dx.doi.org/10.1117/12.2293065]
[76]
Nithila EE, Kumar SS. Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering. Alexandria Eng J 2016; 55(3): 2583-8.
[http://dx.doi.org/10.1016/j.aej.2016.06.002]
[77]
Liu X, Han G, Zhao X, Zhao Y, Zhou C, Huang S. 3D GGO candidate extraction in lung CT images using multilevel thresholding on supervoxels. Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057533.
[78]
Meraj T, Rauf HT, Zahoor S, et al. Lung nodules detection using semantic segmentation and classification with optimal features. Neural Comput Appl 2020.
[http://dx.doi.org/10.1007/s00521-020-04870-2]
[79]
Liu K, Kang G. Multiview convolutional neural networks for lung nodule classification. Int J Imaging Syst Technol 2017.
[http://dx.doi.org/10.1002/ima.22206]
[80]
Khan SA, Hussain S, Xiaoming S, Yang S. An Effective framework for driver fatigue recognition based on intelligent facial expressions analysis. IEEE Access 2018; 6: 67459-68.
[http://dx.doi.org/10.1109/ACCESS.2018.2878601]
[81]
Xie H, Yang D, Sun N, Chen Z, Zhang Y. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 2019; 85: 109-19.
[http://dx.doi.org/10.1016/j.patcog.2018.07.031]
[82]
Moitra D, Mandal RK. Classification of non-small cell lung cancer using one-dimensional convolutional neural network. Expert Syst Appl 2020; 159: 113564.
[http://dx.doi.org/10.1016/j.eswa.2020.113564]
[83]
Chang CC, Chen HH, Chang YC, et al. Computer-aided diagnosis of liver tumors on computed tomography images. Comput Methods Programs Biomed 2017; 145: 45-51.
[http://dx.doi.org/10.1016/j.cmpb.2017.04.008] [PMID: 28552125]
[84]
Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 2019; 29(7): 3338-47.
[http://dx.doi.org/10.1007/s00330-019-06205-9] [PMID: 31016442]
[85]
Abbasi NR, Shaw HM, Rigel DS, et al. Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. JAMA 2004; 292(22): 2771-6.
[http://dx.doi.org/10.1001/jama.292.22.2771] [PMID: 15585738]
[86]
Soyer HP, Argenziano G, Zalaudek I, et al. Three-point checklist of dermoscopy. A new screening method for early detection of melanoma. Dermatology 2004; 208(1): 27-31.
[http://dx.doi.org/10.1159/000075042] [PMID: 14730233]
[87]
Keefe M, Dick DC, Wakeel RA. A study of the value of the seven-point checklist in distinguishing benign pigmented lesions from melanoma. Clin Exp Dermatol 1990; 15(3): 167-71.
[http://dx.doi.org/10.1111/j.1365-2230.1990.tb02064.x] [PMID: 2142028]
[88]
Naqi SM, Sharif M, Lali IU. A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection. Multimedia Tools Appl 2019; 78: 26287-311.
[http://dx.doi.org/10.1007/s11042-019-07819-3]
[89]
Kasinathan G, Jayakumar S, Gandomi AH, Ramachandran M, Fong SJ, Patan R. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Expert Syst Appl 2019; 134: 112-9.
[http://dx.doi.org/10.1016/j.eswa.2019.05.041]
[90]
Jiang H, Ma H, Qian W, et al. An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE J Biomed Health Inform 2018; 22(4): 1227-37.
[http://dx.doi.org/10.1109/JBHI.2017.2725903] [PMID: 28715341]
[91]
Naqi SM, Sharif M, Jaffar A. Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput Appl 2020; 32: 4629-47.
[http://dx.doi.org/10.1007/s00521-018-3773-x]
[92]
Jansen MJA, Kuijf HJ, Veldhuis WB, Wessels FJ, Viergever MA, Pluim JPW. Automatic classification of focal liver lesions based on MRI and risk factors. PLoS One 2019; 14(5): e0217053.
[http://dx.doi.org/10.1371/journal.pone.0217053] [PMID: 31095624]
[93]
Romero FP, Diler A, Bisson-Gregoire G, et al. End-to-end discriminative deep network for liver lesion classification. Proceedings – International Symposium on Biomedical Imaging. 2019.
[http://dx.doi.org/10.1109/ISBI.2019.8759257]
[94]
Parsai A, Miquel ME, Jan H, Kastler A, Szyszko T, Zerizer I. Improving liver lesion characterisation using retrospective fusion of FDG PET/CT and MRI. Clin Imaging 2019; 55: 23-8.
[http://dx.doi.org/10.1016/j.clinimag.2019.01.018] [PMID: 30710749]
[95]
Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging 2019; 100(4): 227-33.
[http://dx.doi.org/10.1016/j.diii.2019.02.009] [PMID: 30926443]
[96]
Khan MQ, Hussain A, Rehman SU, et al. Classification of melanoma and nevus in digital images for diagnosis of skin cancer. IEEE Access 2019; 7: 90132-44.
[http://dx.doi.org/10.1109/ACCESS.2019.2926837]
[97]
Naylor P, Lae M, Reyal F, Walter T. Nuclei segmentation in histopathology images using deep neural networks. Proceedings – International Symposium on Biomedical Imaging. 2017.
[http://dx.doi.org/10.1109/ISBI.2017.7950669]
[98]
Fourcade A, Khonsari RH. Deep learning in medical image analysis: A third eye for doctors. J Stomatol Oral Maxillofac Surg 2019; 120(4): 279-88.
[http://dx.doi.org/10.1016/j.jormas.2019.06.002] [PMID: 31254638]
[99]
Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q. Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognit 2018; 83: 134-49.
[http://dx.doi.org/10.1016/j.patcog.2018.05.014]
[100]
Li Y, Shen L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors (Basel) 2018; 18(2): E556.
[http://dx.doi.org/10.3390/s18020556] [PMID: 29439500]
[101]
Harangi B. Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 2018; 86: 25-32.
[http://dx.doi.org/10.1016/j.jbi.2018.08.006] [PMID: 30103029]
[102]
Dash M, Londhe ND, Ghosh S, Semwal A, Sonawane RS. PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network. Biomed Signal Process Control 2019; 52: 226-37.
[http://dx.doi.org/10.1016/j.bspc.2019.04.002]
[103]
Navarro-Avila FJ, Saint-Hill-Febles Y, Renner J, et al. Computer assisted optical biopsy for colorectal polyps. Medical Imaging Computer-Aided Diagnosis. Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340J (2017).
[104]
Zhang R, Zheng Y, Mak TWC, et al. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J Biomed Health Inform 2017; 21(1): 41-7.
[http://dx.doi.org/10.1109/JBHI.2016.2635662] [PMID: 28114040]
[105]
Chowdhury A, Sevinsky CJ, Santamaria-Pang A, Yener B. A computational study on convolutional feature combination strategies for grade classification in colon cancer using fluorescence microscopy data. Medical Imaging Digital Pathology 2017.
[106]
Shoieb DA, Youssef SM, Aly WM. Computer-aided model for skin diagnosis using deep learning. J Image Graph 2016; 4(2): 116-21.
[http://dx.doi.org/10.18178/joig.4.2.122-129]
[107]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[108]
Nida N, Irtaza A, Javed A, Yousaf MH, Mahmood MT. Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Inform 2019; 124: 37-48.
[http://dx.doi.org/10.1016/j.ijmedinf.2019.01.005] [PMID: 30784425]
[109]
Haj-Hassan H, Chaddad A, Harkouss Y, Desrosiers C, Toews M, Tanougast C. Classifications of multispectral colorectal cancer tissues using convolution neural network. J Pathol Inform 2017; 8: 1.
[http://dx.doi.org/10.4103/jpi.jpi_47_16] [PMID: 28400990]
[110]
Rehman A. Ulcer Recognition based on 6-Layers Deep Convolutional Neural Network. Proceedings of the 2020 9th International Conference on Software and Information Engineering (ICSIE). Cairo Egypt 2020; pp. 97-101.
[http://dx.doi.org/10.1145/3436829.3436837]

Rights & Permissions Print Cite
© 2025 Bentham Science Publishers | Privacy Policy
Request history8.2.27PHP Version379msRequest Duration28MBMemory UsageGET article/{article_id}/{param?}Route
  • warninglog[13:26:26] LOG.warning: Optional parameter $article_id declared before required parameter $request i...
  • warninglog[13:26:26] LOG.warning: Function strftime() is deprecated in /home/alphaeurekaselec/public_html/stor...
  • warninglog[13:26:26] LOG.warning: trim(): Passing null to parameter #1 ($string) of type string is deprecated ...
  • warninglog[13:26:26] LOG.warning: Function strftime() is deprecated in /home/alphaeurekaselec/public_html/stor...
  • Booting (104ms)time
  • Application (275ms)time
  • 1 x Application (72.59%)
    275ms
    1 x Booting (27.41%)
    104ms
    14 templates were rendered
    • articlearticle.blade.php#?blade
    • includes.article_top_bannerarticle_top_banner.blade.php#?blade
    • includes.article_referencearticle_reference.blade.php#?blade
    • export_citation_formexport_citation_form.blade.php#?blade
    • includes.article_bottom_bannerarticle_bottom_banner.blade.php#?blade
    • includes.inc_related_journalsinc_related_journals.blade.php#?blade
    • includes.inc_related_ebooksinc_related_ebooks.blade.php#?blade
    • includes.journal_rightmenujournal_rightmenu.blade.php#?blade
    • includes.journal_right_bannerjournal_right_banner.blade.php#?blade
    • layouts.appapp.blade.php#?blade
    • layouts._login_access_login_access.blade.php#?blade
    • layouts._header_header.blade.php#?blade
    • layouts._main_menu_main_menu.blade.php#?blade
    • layouts._footer_menu_footer_menu.blade.php#?blade
    uri
    GET article/{article_id}/{param?}
    middleware
    web, check_user_access
    controller
    App\Http\Controllers\ArticleController@GetArticleDetails
    namespace
    App\Http\Controllers
    where
    as
    article
    file
    app/Http/Controllers/ArticleController.php:1974-2510
    37 statements were executed, 4 of which were duplicates, 33 unique. Show only duplicated180ms
    • Connection Establishedalphaeurekaselec_live_10_06_2022UserAccessCheck.php#24
      Backtrace
      • 9. middleware::check_user_access:24
      • 10. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 11. vendor/laravel/framework/src/Illuminate/Routing/Middleware/SubstituteBindings.php:50
      • 12. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 13. vendor/laravel/framework/src/Illuminate/Foundation/Http/Middleware/VerifyCsrfToken.php:88
    • INSERT INTO visitor_ip_address (ip_address,visitor_agent,visitor_count, created_on,updated_on) VALUES ('51833843','',1,1739971586,1739971586) ON DUPLICATE KEY UPDATE visitor_count=visitor_count + 1, updated_on=1739971586
      1.03msalphaeurekaselec_live_10_06_2022UserAccessCheck.php#24
      Backtrace
      • 11. middleware::check_user_access:24
      • 12. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Middleware/SubstituteBindings.php:50
      • 14. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 15. vendor/laravel/framework/src/Illuminate/Foundation/Http/Middleware/VerifyCsrfToken.php:88
    • INSERT INTO visitor_ip_address_detail (ip_address,visitor_url,visitor_count, created_on,updated_on) VALUES ('51833843','/article/112494',1,1739971586,1739971586)
      1.08msalphaeurekaselec_live_10_06_2022UserAccessCheck.php#27
      Backtrace
      • 11. middleware::check_user_access:27
      • 12. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Middleware/SubstituteBindings.php:50
      • 14. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 15. vendor/laravel/framework/src/Illuminate/Foundation/Http/Middleware/VerifyCsrfToken.php:88
    • (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_journal_volume_trail WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_journal_volume_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_journal_volume_token WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_ebook_volume_trail WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_ebook_volume_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_ebook_volume_token WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_issue_trail WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_issue_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_issue_token WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_article_trail WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_article_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_article_token WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_chapter_trail WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_chapter_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_chapter_token WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) union (select `user_id`, `to_ip`, `from_ip` from (select user_id, to_ip, from_ip FROM user_access_disease_corporate WHERE to_ip >= 51833843 ORDER BY to_ip ASC LIMIT 1) as `tjv` where `from_ip` <= 51833843) limit 1
      23.21msalphaeurekaselec_live_10_06_2022UserAccess.php#784
      Bindings
      • 0: 51833843
      • 1: 51833843
      • 2: 51833843
      • 3: 51833843
      • 4: 51833843
      • 5: 51833843
      • 6: 51833843
      • 7: 51833843
      • 8: 51833843
      • 9: 51833843
      • 10: 51833843
      • 11: 51833843
      • 12: 51833843
      • 13: 51833843
      • 14: 51833843
      • 15: 51833843
      Backtrace
      • 14. app/Models/UserAccess/UserAccess.php:784
      • 15. middleware::check_user_access:114
      • 16. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Middleware/SubstituteBindings.php:50
      • 18. vendor/laravel/framework/src/Illuminate/Pipeline/Pipeline.php:183
    • SELECT at.type_name, p.copyright, p.publisher_location,m.month,y.year, a.publish_on, a.nid article_nid,a.article_id, a.pmid, a.issue_id, a.title, a.abstract,a.podcast,a.altmetric_score,a.altmetric_url,a.kudos_url, a.page_count, a.first_page, a.elocator, a.last_page, i.publication_date, a.file_name, a.file_path, a.doi, a.is_epub,a.epub_type, a.volume_id, a.is_uploaded, a.editor_choice, a.is_epub, j.image_file_name, a.article_no, a.art_type AS article_type, a.supplementary_filename,a.html_filename,a.prc_filename,a.epub_filename, a.graph_abs_lg_filename, a.graph_abs_sm_filename, a.oa_image_lg_filename, a.oa_image_sm_filename, j.journal_banner,a.animated_abstract,a.erratum_in,a.erratum_for, (CASE WHEN a.issue_id =0 THEN j.epub_price ELSE j.price END) AS sell_price, j.epub_price, j.nid AS jour_nid, j.title AS journal_title, j.issn, j.eissn,j.journal_id, j.subtitle AS jour_subtitle, v.volume_id,v.volume_name AS volume, v.year_id, i.title AS issue, i.month_id ,i.type issue_type, j.subtitle,j.formerly_title, ac.subtitle AS epub_title,a.crossmark_enabled,a.text_mining_urls,a.license_urls,a.created, a.receivedate, a.revisedate, a.acceptdate,js.js_title FROM article a LEFT JOIN article_section ac ON a.is_epub = ac.art_sec_id LEFT JOIN journal_section js ON a.js_id = js.js_id LEFT JOIN article_type at ON a.art_type = at.art_type_id LEFT JOIN issue i ON a.issue_id = i.issue_id LEFT JOIN month m ON m.id = i.month_id LEFT JOIN volume v ON v.volume_id = a.volume_id INNER JOIN year y ON y.id = v.year_id INNER JOIN journal j ON j.journal_id = a.journal_id INNER JOIN publisher p ON p.publisher_id = j.publisher_id WHERE a.article_id=112494
      1.8msalphaeurekaselec_live_10_06_2022Article.php#408
      Backtrace
      • 11. app/Models/Article.php:408
      • 12. app/Http/Controllers/ArticleController.php:2515
      • 13. app/Http/Controllers/ArticleController.php:2035
      • 14. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 15. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select * from `journal` where `journal_id` = 33 limit 1
      1.24msalphaeurekaselec_live_10_06_2022Journal.php#64
      Bindings
      • 0: 33
      Backtrace
      • 14. app/Models/Journal.php:64
      • 15. app/Http/Controllers/ArticleController.php:2516
      • 16. app/Http/Controllers/ArticleController.php:2035
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 18. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select count(*) as aggregate from (select j.nid as journal_nid,j.flyer_link,j.title ,j.issn,j.eissn,j.keyword, j.journal_id as journal_id, j.subtitle, j.image_file_name,j.description,j.journal_insight_url,j.doi from `subject_journal` as `sj` inner join `journal` as `j` on `j`.`journal_id` = `sj`.`journal_id` where j.journal_status=1 and subject_id IN (SELECT subject_id FROM subject_journal WHERE journal_id=33) and sj.journal_id!=33 group by `j`.`nid`) as `aggregate_table`
      580μsalphaeurekaselec_live_10_06_2022Subject.php#200
      Backtrace
      • 16. app/Models/Subject.php:200
      • 17. app/Http/Controllers/ArticleController.php:2517
      • 18. app/Http/Controllers/ArticleController.php:2035
      • 19. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 20. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select j.nid as journal_nid,j.flyer_link,j.title ,j.issn,j.eissn,j.keyword, j.journal_id as journal_id, j.subtitle, j.image_file_name,j.description,j.journal_insight_url,j.doi from `subject_journal` as `sj` inner join `journal` as `j` on `j`.`journal_id` = `sj`.`journal_id` where j.journal_status=1 and subject_id IN (SELECT subject_id FROM subject_journal WHERE journal_id=33) and sj.journal_id!=33 group by `j`.`nid` order by `j`.`journal_id` asc limit 10 offset 0
      470μsalphaeurekaselec_live_10_06_2022Subject.php#200
      Backtrace
      • 14. app/Models/Subject.php:200
      • 15. app/Http/Controllers/ArticleController.php:2517
      • 16. app/Http/Controllers/ArticleController.php:2035
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 18. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select count(*) as aggregate from (select v.nid as volume_id,e.nid,e.title,v.issn,v.eissn,v.isbn,v.eisbn,e.ebook_id, v.image_file_name,v.file_path,v.flyer_link,v.ebook_volume_id, v.volume_name,v.doi,v.year_id,v.introduction from `subject_ebook` as `se` inner join `ebook` as `e` on `e`.`ebook_id` = `se`.`ebook_id` inner join `ebook_volume` as `v` on `v`.`ebook_id` = `se`.`ebook_id` inner join `year` as `y` on `y`.`id` = `v`.`year_id` where subject_id IN (SELECT subject_id FROM subject_journal WHERE journal_id=33) and v.ebook_status='1' and y.year > 2020 group by `v`.`nid`) as `aggregate_table`
      14.17msalphaeurekaselec_live_10_06_2022Subject.php#220
      Backtrace
      • 16. app/Models/Subject.php:220
      • 17. app/Http/Controllers/ArticleController.php:2518
      • 18. app/Http/Controllers/ArticleController.php:2035
      • 19. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 20. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select v.nid as volume_id,e.nid,e.title,v.issn,v.eissn,v.isbn,v.eisbn,e.ebook_id, v.image_file_name,v.file_path,v.flyer_link,v.ebook_volume_id, v.volume_name,v.doi,v.year_id,v.introduction from `subject_ebook` as `se` inner join `ebook` as `e` on `e`.`ebook_id` = `se`.`ebook_id` inner join `ebook_volume` as `v` on `v`.`ebook_id` = `se`.`ebook_id` inner join `year` as `y` on `y`.`id` = `v`.`year_id` where subject_id IN (SELECT subject_id FROM subject_journal WHERE journal_id=33) and v.ebook_status='1' and y.year > 2020 group by `v`.`nid` order by `v`.`ebook_volume_id` desc limit 10 offset 0
      13.19msalphaeurekaselec_live_10_06_2022Subject.php#220
      Backtrace
      • 14. app/Models/Subject.php:220
      • 15. app/Http/Controllers/ArticleController.php:2518
      • 16. app/Http/Controllers/ArticleController.php:2035
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 18. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select * from `keywords` where `article_id` = '112494'
      1.21msalphaeurekaselec_live_10_06_2022Keywords.php#43
      Bindings
      • 0: 112494
      Backtrace
      • 13. app/Models/Keywords.php:43
      • 14. app/Http/Controllers/ArticleController.php:2519
      • 15. app/Http/Controllers/ArticleController.php:2035
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select * from `article_citation` where `article_id` = '112494'
      5.93msalphaeurekaselec_live_10_06_2022Article.php#1570
      Bindings
      • 0: 112494
      Backtrace
      • 13. app/Models/Article.php:1570
      • 14. app/Http/Controllers/ArticleController.php:2520
      • 15. app/Http/Controllers/ArticleController.php:2035
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select CONCAT_WS(' ',au.first_name, ' ', ifnull(au.initials,''), ' ', au.last_name) as authors, CONCAT(au.last_name, IFNULL(CONCAT(' ', au.initials), ''), ' ', au.first_name) AS authorsRIS, CONCAT_WS(' ',au.last_name, ' ', ifnull(au.initials,''), ' ', au.first_name) as authorsCiteAs, CONCAT_WS(' ',au.first_name, IFNULL(CONCAT(' ', au.initials), ''), IFNULL(CONCAT(' ', au.last_name), '')) as authorsmodal , `au`.*, `af`.`ror_id`, `af`.`institution`, `af`.`department`, `af`.`country`, `af`.`city`, `af`.`address`, (GROUP_CONCAT(TRIM(BOTH ', ' FROM CONCAT(ifnull(concat(af.institution,','),''), ifnull(concat(af.department,','),''), ifnull(concat(af.address,','),''), ifnull(concat(af.city,','),''), ifnull(concat(af.country,','),''), ifnull(concat(af.postal_code,','),''), ifnull(concat(af.phone,','),''), ifnull(concat(af.fax,','),''))) SEPARATOR '|')) as `author_affiliation`, (GROUP_CONCAT(TRIM(BOTH ', ' FROM CONCAT(ifnull(concat(af.web_view,','),''))) SEPARATOR '|')) as `web_view` from `author` as `au` left join `author_affiliation` as `af` on `au`.`author_id` = `af`.`author_id` where `au`.`article_id` = '112494' group by `au`.`author_id` order by `au`.`article_id` asc, `au`.`sequence` asc
      2.05msalphaeurekaselec_live_10_06_2022Author.php#86
      Bindings
      • 0: 112494
      Backtrace
      • 13. app/Models/Author.php:86
      • 14. app/Http/Controllers/ArticleController.php:2533
      • 15. app/Http/Controllers/ArticleController.php:2035
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select `bo`.* from `bundle_offer` as `bo` where `bo`.`bundle_status` = 'A' order by `bo`.`bundle_id` desc
      250μsalphaeurekaselec_live_10_06_2022BundleOffer.php#57
      Bindings
      • 0: A
      Backtrace
      • 13. app/Models/BundleOffer.php:57
      • 14. app/Http/Controllers/ArticleController.php:2535
      • 15. app/Http/Controllers/ArticleController.php:2035
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
    • select `subtitle` from `journal` where `continues_publication_journal` = 1
      490μsalphaeurekaselec_live_10_06_2022Article.php#1960
      Bindings
      • 0: 1
      Backtrace
      • 13. app/Models/Article.php:1960
      • 14. app/Http/Controllers/ArticleController.php:2062
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • Select j.subtitle, v.volume_id as volume_id,v.year_id,y.year as year,v.volume_name, v.total_no_of_issues, i.issue_id as issue_id,i.title as issue, i.publication_date as issue_pub_date from journal j join volume v on j.journal_id = v.journal_id join issue i on v.volume_id = i.volume_id join article a on a.issue_id = i.issue_id join year y on v.year_id = y.id where j.journal_id = 33 and i.is_uploaded = 1 order by v.volume_name+0 desc, i.title+0 desc limit 1
      31.84msalphaeurekaselec_live_10_06_2022Issue.php#298
      Backtrace
      • 11. app/Models/Issue.php:298
      • 12. app/Http/Controllers/ArticleController.php:2068
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select a.nid,a.publish_on, a.pmid,a.article_id,a.doi,j.journal_id,j.subtitle,v.volume_name,a.volume_id as volume_id, a.issue_id,a.title,first_page, last_page, page_count,abstract,ifnull(is_abstract,"y"),asec.title as article_section,at.type_name as article_type_name, a.article_id as ArticleID ,is_epub,epub_price,epub_type,a.created, y.year from `article` as `a` left join `article_section` as `asec` on `asec`.`art_sec_id` = `a`.`is_epub` left join `article_type` as `at` on `at`.`art_type_id` = `a`.`art_type` left join `journal` as `j` on `j`.`journal_id` = `a`.`journal_id` inner join `volume` as `v` on `a`.`volume_id` = `v`.`volume_id` inner join `year` as `y` on `v`.`year_id` = `y`.`id` where a.article_status != "W" and a.is_epub = 1 and a.journal_id = 33 and a.is_uploaded = 1 group by `a`.`article_id` order by `a`.`publish_on` desc
      13.74msalphaeurekaselec_live_10_06_2022Article.php#528
      Backtrace
      • 13. app/Models/Article.php:528
      • 14. app/Http/Controllers/ArticleController.php:2070
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select a.nid,a.publish_on, a.pmid,a.article_id,a.doi,j.journal_id,j.subtitle,v.volume_name,a.volume_id as volume_id, a.issue_id,a.title,first_page, last_page, page_count,abstract,ifnull(is_abstract,"y"),asec.title as article_section,at.type_name as article_type_name, a.article_id as ArticleID ,is_epub,epub_price,epub_type,a.created, y.year from `article` as `a` left join `article_section` as `asec` on `asec`.`art_sec_id` = `a`.`is_epub` left join `article_type` as `at` on `at`.`art_type_id` = `a`.`art_type` left join `journal` as `j` on `j`.`journal_id` = `a`.`journal_id` inner join `volume` as `v` on `a`.`volume_id` = `v`.`volume_id` inner join `year` as `y` on `v`.`year_id` = `y`.`id` where a.article_status != "W" and a.is_epub = 4 and a.journal_id = 33 and a.is_uploaded = 1 group by `a`.`article_id` order by `a`.`publish_on` desc
      10.01msalphaeurekaselec_live_10_06_2022Article.php#528
      Backtrace
      • 13. app/Models/Article.php:528
      • 14. app/Http/Controllers/ArticleController.php:2071
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select pdf, html, epub, prc from `article_metrics` where `article_id` = 112494 limit 1
      280μsalphaeurekaselec_live_10_06_2022Article.php#744
      Bindings
      • 0: 112494
      Backtrace
      • 14. app/Models/Article.php:744
      • 15. app/Http/Controllers/ArticleController.php:2091
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 18. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select * from content_by_diseases where content_id = 112494 and content_type = 'article' order by status desc limit 1
      2.11msalphaeurekaselec_live_10_06_2022Article.php#751
      Backtrace
      • 11. app/Models/Article.php:751
      • 12. app/Http/Controllers/ArticleController.php:2094
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select a.nid article_nid,a.title article_title,j.title journal_title from content_by_diseases cd inner join article a on a.article_id=cd.content_id inner join journal j on j.journal_id=a.journal_id where cd.pid = 16 and cd.sub_disease_id = 432 and content_id!=112494 and cd.content_type = 'article' order by rand() limit 20
      36.62msalphaeurekaselec_live_10_06_2022Article.php#769
      Backtrace
      • 11. app/Models/Article.php:769
      • 12. app/Http/Controllers/ArticleController.php:2098
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select * from `node_meta` where `nid` = 189208 limit 1
      270μsalphaeurekaselec_live_10_06_2022Meta.php#30
      Bindings
      • 0: 189208
      Backtrace
      • 14. app/Models/Meta.php:30
      • 15. app/Http/Controllers/ArticleController.php:2101
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 18. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select `bo`.* from `bundle_offer` as `bo` where `bo`.`bundle_status` = 'A' order by `bo`.`bundle_id` desc
      310μsalphaeurekaselec_live_10_06_2022BundleOffer.php#57
      Bindings
      • 0: A
      Backtrace
      • 13. app/Models/BundleOffer.php:57
      • 14. app/Http/Controllers/ArticleController.php:2108
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select * from `tbl_ht_submission` as `h` where `h`.`subtitle` = 'CMIM' and (h.proposal_closing_date = 0 OR TO_DAYS(FROM_UNIXTIME(h.proposal_closing_date)) >= TO_DAYS(NOW())) order by `h`.`manuscript` asc
      440μsalphaeurekaselec_live_10_06_2022Article.php#2025
      Bindings
      • 0: CMIM
      Backtrace
      • 13. app/Models/Article.php:2025
      • 14. app/Http/Controllers/ArticleController.php:2112
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select count(*) as total_count, content_type_access_id as access_type from journal_access where content_type_id = '112494' and content_type = 'a' and ( (from_date <= CURDATE() and to_date >= CURDATE() and perpetual= 0) or (from_date is null and to_date is null and perpetual= 1) )
      500μsalphaeurekaselec_live_10_06_2022ContentAccess.php#647
      Backtrace
      • 11. app/Models/ContentAccess.php:647
      • 12. app/Http/Controllers/ArticleController.php:2186
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select count(*) as total_count, content_type_access_id as access_type from journal_access where content_type_id = '10683' and content_type = 'i' and ( (from_date <= CURDATE() and to_date >= CURDATE() and perpetual= 0) or (from_date is null and to_date is null and perpetual= 1) )
      410μsalphaeurekaselec_live_10_06_2022ContentAccess.php#647
      Backtrace
      • 11. app/Models/ContentAccess.php:647
      • 12. app/Http/Controllers/ArticleController.php:2207
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select count(*) as total_count, content_type_access_id as access_type from journal_access where content_type_id = '2983' and content_type = 'v' and ( (from_date <= CURDATE() and to_date >= CURDATE() and perpetual= 0) or (from_date is null and to_date is null and perpetual= 1) )
      370μsalphaeurekaselec_live_10_06_2022ContentAccess.php#647
      Backtrace
      • 11. app/Models/ContentAccess.php:647
      • 12. app/Http/Controllers/ArticleController.php:2231
      • 13. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 14. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Trial Access" as access_base_text, CASE j.type WHEN 111 THEN true ELSE false END as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"volume" as access_level,"" as issue_id,"" as article_id from `user_access_journal_volume_trail` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.volume_id = 2983) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Subscribed" as access_base_text, false as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"volume" as access_level,"" as issue_id,"" as article_id from `user_access_journal_volume_corporate` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.volume_id = 2983) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Token Based Access" as access_base_text, false as terms_popup, "R" as content_download_type, j.uc_id as restricted_user_access_key, "v" as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"volume" as access_level,"" as issue_id,"" as article_id from `user_access_journal_volume_token` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.volume_id = 2983)
      7.26msalphaeurekaselec_live_10_06_2022UserAccess.php#1266
      Backtrace
      • 13. app/Models/UserAccess/UserAccess.php:1266
      • 14. app/Http/Controllers/ArticleController.php:2272
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Trial Access" as access_base_text, CASE j.type WHEN 111 THEN true ELSE false END as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"issue" as access_level,j.issue_id,"" as article_id from `user_access_issue_trail` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.issue_id = 10683) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Subscribed" as access_base_text, false as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"issue" as access_level,j.issue_id,"" as article_id from `user_access_issue_corporate` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.issue_id = 10683) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Token Based Access" as access_base_text, false as terms_popup, "R" as content_download_type, j.uc_id as restricted_user_access_key, "v" as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"issue" as access_level,j.issue_id,"" as article_id from `user_access_issue_token` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.issue_id = 10683)
      800μsalphaeurekaselec_live_10_06_2022UserAccess.php#1266
      Backtrace
      • 13. app/Models/UserAccess/UserAccess.php:1266
      • 14. app/Http/Controllers/ArticleController.php:2297
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Trial Access" as access_base_text, CASE j.type WHEN 111 THEN true ELSE false END as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"article" as access_level, j.issue_id, j.article_id from `user_access_article_trail` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.article_id = 112494) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Subscribed" as access_base_text, false as terms_popup, "S" as content_download_type, null as restricted_user_access_key, null as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"article" as access_level, j.issue_id, j.article_id from `user_access_article_corporate` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.article_id = 112494) union (select j.user_access_id,j.volume_id,j.user_id,j.from_ip,j.to_ip,j.type,j.track,"Token Based Access" as access_base_text, false as terms_popup, "R" as content_download_type, j.uc_id as restricted_user_access_key, "v" as restricted_user_access_type, CASE j.track WHEN 0 THEN false ELSE true END as tracksAccessByVolume,"article" as access_level, j.issue_id, j.article_id from `user_access_article_token` as `j` inner join `user_access_info` as `info` on `j`.`user_access_id` = `info`.`user_access_id` where ((j.from_ip <= 51833843 and j.to_ip >=51833843 )) and ((j.from_date <= 1739971586 and j.to_date >= 1739971586 and j.perpetual= 0) or (j.from_date is null and j.to_date is null and j.perpetual= 1)) and j.article_id = 112494)
      840μsalphaeurekaselec_live_10_06_2022UserAccess.php#1266
      Backtrace
      • 13. app/Models/UserAccess/UserAccess.php:1266
      • 14. app/Http/Controllers/ArticleController.php:2329
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select * from `article` where `first_page` < '686' and is_uploaded = 1 and issue_id = 10683 order by cast(first_page as SIGNED) desc limit 1
      670μsalphaeurekaselec_live_10_06_2022Article.php#1819
      Bindings
      • 0: 686
      Backtrace
      • 14. app/Models/Article.php:1819
      • 15. app/Http/Controllers/ArticleController.php:2470
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 18. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select * from `article` where `last_page` > '694' and is_uploaded = 1 and issue_id = 10683 order by cast(first_page as SIGNED) asc limit 1
      630μsalphaeurekaselec_live_10_06_2022Article.php#1831
      Bindings
      • 0: 694
      Backtrace
      • 14. app/Models/Article.php:1831
      • 15. app/Http/Controllers/ArticleController.php:2472
      • 16. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 17. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 18. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select `language_id` from `multilanguage` where `short_code` = 'en'
      190μsalphaeurekaselec_live_10_06_2022ArticleController.php#2497
      Bindings
      • 0: en
      Backtrace
      • 14. app/Http/Controllers/ArticleController.php:2497
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
      • 18. vendor/laravel/framework/src/Illuminate/Routing/Route.php:206
    • select `source_lang_label`, `target_lang_label` from `multilanguage_labels` where `target_lang_id` = 1
      230μsalphaeurekaselec_live_10_06_2022MultiLanguage.php#23
      Bindings
      • 0: 1
      Backtrace
      • 13. app/Models/MultiLanguage.php:23
      • 14. app/Http/Controllers/ArticleController.php:2498
      • 15. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 16. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
    • select `id` from `multilanguage_article` where `article_id` = 112494 limit 1
      2.49msalphaeurekaselec_live_10_06_2022ArticleController.php#2506
      Bindings
      • 0: 112494
      Backtrace
      • 16. app/Http/Controllers/ArticleController.php:2506
      • 17. vendor/laravel/framework/src/Illuminate/Routing/Controller.php:54
      • 18. vendor/laravel/framework/src/Illuminate/Routing/ControllerDispatcher.php:43
      • 19. vendor/laravel/framework/src/Illuminate/Routing/Route.php:260
      • 20. vendor/laravel/framework/src/Illuminate/Routing/Route.php:206
    • select b.banner_link, b.banner_name, b.banner_script, b.banner_remarks, b.banner_file_name, b.banner_placement, bd.content_type, bd.content_id from `tbl_banner` as `b` inner join `tbl_banner_detail` as `bd` on `bd`.`banner_id` = `b`.`banner_id` inner join `users` as `u` on `u`.`id` = `b`.`updated_by` inner join `journal` as `j` on `j`.`journal_id` = `bd`.`content_id` where `b`.`banner_journal` = 1 and `bd`.`content_type` = 'J' and `bd`.`content_id` = 33 and `b`.`banner_from_date` <= 1739971586 and `b`.`banner_to_date` >= 1739971586 and `b`.`banner_status` = 'A' order by `b`.`banner_id` desc
      1.77msalphaeurekaselec_live_10_06_2022Banner.php#95
      Bindings
      • 0: 1
      • 1: J
      • 2: 33
      • 3: 1739971586
      • 4: 1739971586
      • 5: A
      Backtrace
      • 13. app/Models/Banner.php:95
      • 14. app/Http/helpers.php:404
      • 17. vendor/laravel/framework/src/Illuminate/Filesystem/Filesystem.php:124
      • 18. vendor/laravel/framework/src/Illuminate/View/Engines/PhpEngine.php:58
      • 19. vendor/laravel/framework/src/Illuminate/View/Engines/CompilerEngine.php:73
    • select b.banner_link, b.banner_name, b.banner_script, b.banner_remarks, b.banner_file_name, b.banner_placement, bd.content_type, bd.content_id from `tbl_banner` as `b` inner join `tbl_banner_detail` as `bd` on `bd`.`banner_id` = `b`.`banner_id` inner join `users` as `u` on `u`.`id` = `b`.`updated_by` inner join `journal` as `j` on `j`.`journal_id` = `bd`.`content_id` where `b`.`banner_journal` = 1 and `bd`.`content_type` = 'J' and `bd`.`content_id` = 33 and `b`.`banner_from_date` <= 1739971586 and `b`.`banner_to_date` >= 1739971586 and `b`.`banner_status` = 'A' order by `b`.`banner_id` desc
      1.61msalphaeurekaselec_live_10_06_2022Banner.php#95
      Bindings
      • 0: 1
      • 1: J
      • 2: 33
      • 3: 1739971586
      • 4: 1739971586
      • 5: A
      Backtrace
      • 13. app/Models/Banner.php:95
      • 14. app/Http/helpers.php:404
      • 17. vendor/laravel/framework/src/Illuminate/Filesystem/Filesystem.php:124
      • 18. vendor/laravel/framework/src/Illuminate/View/Engines/PhpEngine.php:58
      • 19. vendor/laravel/framework/src/Illuminate/View/Engines/CompilerEngine.php:73
    • select count(*) as aggregate from `uc_cart_products` where cart_id='77f2ed2d23443e570d92e28dc2461304'
      350μsalphaeurekaselec_live_10_06_2022Cart.php#271
      Backtrace
      • 15. app/Models/Cart.php:271
      • 16. view::layouts._header:260
      • 18. vendor/laravel/framework/src/Illuminate/Filesystem/Filesystem.php:124
      • 19. vendor/laravel/framework/src/Illuminate/View/Engines/PhpEngine.php:58
      • 20. vendor/laravel/framework/src/Illuminate/View/Engines/CompilerEngine.php:73
    App\Models\MultiLanguage
    1MultiLanguage.php#?
        _token
        DX73AzrI7kU2DD7WR18xQulo8RV6YAsFmb0dBlN4
        uc_cart_id
        77f2ed2d23443e570d92e28dc2461304
        _previous
        array:1 [ "url" => "http://alpha.eurekaselect.com/article/112494" ]
        _flash
        array:2 [ "old" => [] "new" => [] ]
        PHPDEBUGBAR_STACK_DATA
        []
        path_info
        /article/112494
        status_code
        200
        
        status_text
        OK
        format
        html
        content_type
        text/html; charset=UTF-8
        request_query
        []
        
        request_request
        []
        
        request_headers
        0 of 0
        array:8 [ "accept-encoding" => array:1 [ 0 => "gzip, deflate" ] "accept" => array:1 [ 0 => "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7" ] "user-agent" => array:1 [ 0 => "Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)" ] "upgrade-insecure-requests" => array:1 [ 0 => "1" ] "cache-control" => array:1 [ 0 => "no-cache" ] "pragma" => array:1 [ 0 => "no-cache" ] "connection" => array:1 [ 0 => "keep-alive" ] "host" => array:1 [ 0 => "alpha.eurekaselect.com" ] ]
        request_cookies
        []
        
        response_headers
        0 of 0
        array:5 [ "content-type" => array:1 [ 0 => "text/html; charset=UTF-8" ] "cache-control" => array:1 [ 0 => "no-cache, private" ] "date" => array:1 [ 0 => "Wed, 19 Feb 2025 13:26:26 GMT" ] "set-cookie" => array:2 [ 0 => "XSRF-TOKEN=eyJpdiI6IlEyUlV4RmRmNFRSUStPdXV6VU1ZblE9PSIsInZhbHVlIjoiRTZpREhwbTVXczh4MUNDUWFuUU1LYytLeGJFSkRFZjRtdS8wOUg4bVVRMG84UFdFL1FvN04rcmluWVc3ZEhxV3kzRjV4eWlPZDhpd3MwTnk3Tjd1aUxSSGZzRE1jWWlHR2RCdGtxZ2M3L0NjS3Ayb2N6YWhNcHRQUUtKRzZmUmwiLCJtYWMiOiJkNDhiYzY2OWRlZTk0OWZkNmQyODAzZjBhNWY5YTE0M2I0ODdiZDRkMGUwYWYxYmMyYzVjNzIyY2NhNGQ0OWRjIiwidGFnIjoiIn0%3D; expires=Sat, 22 Feb 2025 01:26:26 GMT; Max-Age=216000; path=/; secure; samesite=laxXSRF-TOKEN=eyJpdiI6IlEyUlV4RmRmNFRSUStPdXV6VU1ZblE9PSIsInZhbHVlIjoiRTZpREhwbTVXczh4MUNDUWFuUU1LYytLeGJFSkRFZjRtdS8wOUg4bVVRMG84UFdFL1FvN04rcmluWVc3ZEhxV3kzRjV4e" 1 => "alphaeurekaselectnet_session=eyJpdiI6ImxaSXNqS1A0NnFReURKUW5ORmo3T3c9PSIsInZhbHVlIjoiWThSY2VxTGRJTkd1TDVCM2hhcTI1Ykc1WWNzd1BaMDFMbTljQmFMNTlBYTZrLy8wdWZMRUNiaU5NU21rZVlhTWhwbmUxVm5CN2g1cDNBeWtiQUVlL1ZBbnVjdzRLZkpTS2NIOElDYmVPbktNUmlxNmZ3R1Q1UjNISFFvQ2FDaEUiLCJtYWMiOiJiZTU4MTZkYmRiNjJlZWM2ODdjYzBhOWI2MjBlYWQ5NzI1MTQ3NzRiOTA3MGRmMjUyZWU5MzQ4NTQ2ZmI4MGMwIiwidGFnIjoiIn0%3D; path=/; secure; httponly; samesite=laxalphaeurekaselectnet_session=eyJpdiI6ImxaSXNqS1A0NnFReURKUW5ORmo3T3c9PSIsInZhbHVlIjoiWThSY2VxTGRJTkd1TDVCM2hhcTI1Ykc1WWNzd1BaMDFMbTljQmFMNTlBYTZrLy8wdWZMRUNiaU5" ] "Set-Cookie" => array:2 [ 0 => "XSRF-TOKEN=eyJpdiI6IlEyUlV4RmRmNFRSUStPdXV6VU1ZblE9PSIsInZhbHVlIjoiRTZpREhwbTVXczh4MUNDUWFuUU1LYytLeGJFSkRFZjRtdS8wOUg4bVVRMG84UFdFL1FvN04rcmluWVc3ZEhxV3kzRjV4eWlPZDhpd3MwTnk3Tjd1aUxSSGZzRE1jWWlHR2RCdGtxZ2M3L0NjS3Ayb2N6YWhNcHRQUUtKRzZmUmwiLCJtYWMiOiJkNDhiYzY2OWRlZTk0OWZkNmQyODAzZjBhNWY5YTE0M2I0ODdiZDRkMGUwYWYxYmMyYzVjNzIyY2NhNGQ0OWRjIiwidGFnIjoiIn0%3D; expires=Sat, 22-Feb-2025 01:26:26 GMT; path=/; secureXSRF-TOKEN=eyJpdiI6IlEyUlV4RmRmNFRSUStPdXV6VU1ZblE9PSIsInZhbHVlIjoiRTZpREhwbTVXczh4MUNDUWFuUU1LYytLeGJFSkRFZjRtdS8wOUg4bVVRMG84UFdFL1FvN04rcmluWVc3ZEhxV3kzRjV4e" 1 => "alphaeurekaselectnet_session=eyJpdiI6ImxaSXNqS1A0NnFReURKUW5ORmo3T3c9PSIsInZhbHVlIjoiWThSY2VxTGRJTkd1TDVCM2hhcTI1Ykc1WWNzd1BaMDFMbTljQmFMNTlBYTZrLy8wdWZMRUNiaU5NU21rZVlhTWhwbmUxVm5CN2g1cDNBeWtiQUVlL1ZBbnVjdzRLZkpTS2NIOElDYmVPbktNUmlxNmZ3R1Q1UjNISFFvQ2FDaEUiLCJtYWMiOiJiZTU4MTZkYmRiNjJlZWM2ODdjYzBhOWI2MjBlYWQ5NzI1MTQ3NzRiOTA3MGRmMjUyZWU5MzQ4NTQ2ZmI4MGMwIiwidGFnIjoiIn0%3D; path=/; secure; httponlyalphaeurekaselectnet_session=eyJpdiI6ImxaSXNqS1A0NnFReURKUW5ORmo3T3c9PSIsInZhbHVlIjoiWThSY2VxTGRJTkd1TDVCM2hhcTI1Ykc1WWNzd1BaMDFMbTljQmFMNTlBYTZrLy8wdWZMRUNiaU5" ] ]
        session_attributes
        0 of 0
        array:5 [ "_token" => "DX73AzrI7kU2DD7WR18xQulo8RV6YAsFmb0dBlN4" "uc_cart_id" => "77f2ed2d23443e570d92e28dc2461304" "_previous" => array:1 [ "url" => "http://alpha.eurekaselect.com/article/112494" ] "_flash" => array:2 [ "old" => [] "new" => [] ] "PHPDEBUGBAR_STACK_DATA" => [] ]
        ClearShow all
        Date ↕MethodURLData
        #12025-02-19 13:26:26GET/article/112494414371