An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

[EN] The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056.García, J.; Astorga, G.; Yepes, V. (2021). An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics. Mathematics. 9(3):1-20. https://doi.org/10.3390/math9030225S12093Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. 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Performance modeling of the sparse matrix-vector product via convolutional neural networks

[EN] Modeling the execution time of the sparse matrix-vector multiplication (SpMV) on a current CPU architecture is especially complex due to (i) irregular memory accesses; (ii) indirect memory referencing; and (iii) low arithmetic intensity. While analytical models may yield accurate estimates for the total number of cache hits/misses, they often fail to predict accurately the total execution time. In this paper, we depart from the analytic approach to instead leverage convolutional neural networks (CNNs) in order to provide an effective estimation of the performance of the SpMV operation. For this purpose, we present a high-level abstraction of the sparsity pattern of the problem matrix and propose a blockwise strategy to feed the CNN models by blocks of nonzero elements. The experimental evaluation on a representative subset of the matrices from the SuiteSparse Matrix collection demonstrates the robustness of the CNN models for predicting the SpMV performance on an Intel Haswell core. Furthermore, we show how to generalize the network models to other target architectures to estimate the performance of SpMV on an ARM A57 coreThis work was supported by project TIN2017-82972-R from the MINECO, Spain. Manuel F. Dolz was also supported by the Plan GenT project CDEIGENT/2018/014 from the Generalitat Valenciana, Spain. Maria Barreda was also supported by the POSDOC-A/2017/11 project from the Universitat Jaume IBarreda, M.; Dolz, MF.; Castaño Alvarez, MA.; Alonso-Jordá, P.; Quintana-Orti, ES. (2020). Performance modeling of the sparse matrix-vector product via convolutional neural networks. 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Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies

[EN] Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used here.This work has been funded by GALAHAD project [H2020-ICT-2016-2017, 732613], SICAP project (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109. The work of Gabriel García has been supported by the State Research Spanish Agency PTA2017-14610-I.García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V. (2021). Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies. Computer Methods and Programs in Biomedicine. 200:1-16. https://doi.org/10.1016/j.cmpb.2020.105855S116200Weinreb, R. N., & Khaw, P. T. (2004). Primary open-angle glaucoma. The Lancet, 363(9422), 1711-1720. doi:10.1016/s0140-6736(04)16257-0Jonas, J. B., Aung, T., Bourne, R. R., Bron, A. M., Ritch, R., & Panda-Jonas, S. (2018). Glaucoma – Authors’ reply. The Lancet, 391(10122), 740. doi:10.1016/s0140-6736(18)30305-2Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., … Fujimoto, J. G. 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Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 38(9), 2211-2218. doi:10.1109/tmi.2019.2903434Bussel, I. I., Wollstein, G., & Schuman, J. S. (2013). OCT for glaucoma diagnosis, screening and detection of glaucoma progression. British Journal of Ophthalmology, 98(Suppl 2), ii15-ii19. doi:10.1136/bjophthalmol-2013-304326Varma, R., Steinmann, W. C., & Scott, I. U. (1992). Expert Agreement in Evaluating the Optic Disc for Glaucoma. Ophthalmology, 99(2), 215-221. doi:10.1016/s0161-6420(92)31990-6Jaffe, G. J., & Caprioli, J. (2004). Optical coherence tomography to detect and manage retinal disease and glaucoma. American Journal of Ophthalmology, 137(1), 156-169. doi:10.1016/s0002-9394(03)00792-xHood, D. C., & Raza, A. S. (2014). On improving the use of OCT imaging for detecting glaucomatous damage. British Journal of Ophthalmology, 98(Suppl 2), ii1-ii9. doi:10.1136/bjophthalmol-2014-305156Bizios, D., Heijl, A., Hougaard, J. L., & Bengtsson, B. (2010). Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT. Acta Ophthalmologica, 88(1), 44-52. doi:10.1111/j.1755-3768.2009.01784.xKim, S. J., Cho, K. J., & Oh, S. (2017). Development of machine learning models for diagnosis of glaucoma. PLOS ONE, 12(5), e0177726. doi:10.1371/journal.pone.0177726Medeiros, F. A., Jammal, A. A., & Thompson, A. C. (2019). From Machine to Machine. Ophthalmology, 126(4), 513-521. doi:10.1016/j.ophtha.2018.12.033An, G., Omodaka, K., Hashimoto, K., Tsuda, S., Shiga, Y., Takada, N., … Nakazawa, T. (2019). Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images. Journal of Healthcare Engineering, 2019, 1-9. doi:10.1155/2019/4061313Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S. (2017). 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Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE