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Journal articles on the topic 'Greedy algorithms; Kernel discrimination'
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1
Zang, Miao, Huimin Xu, and Yongmei Zhang. "Kernel-Based Multiview Joint Sparse Coding for Image Annotation." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6727105.
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It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal sparse representations and discriminative dictionaries adaptively, which can effectively employ the complementary information of different views. An optimization algorithm is presented by extending K-singular value decomposition (KSVD) and accelerated proximal gradient (APG) algorithms to the kernel multiview framework. In addition, a label propagation scheme using the sparse reconstruction and weighted greedy label transfer algorithm is also proposed. Comparative experiments on three datasets have demonstrated the competitiveness of proposed approach compared with other related methods.
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Diethe, Tom. "An Empirical Study of Greedy Kernel Fisher Discriminants." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/793986.
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A sparse version of Kernel Fisher Discriminant Analysis using an approach based on Matching Pursuit (MPKFDA) has been shown to be competitive with Kernel Fisher Discriminant Analysis and the Support Vector Machines on publicly available datasets, with additional experiments showing that MPKFDA on average outperforms these algorithms in extremely high dimensional settings. In (nearly) all cases, the resulting classifier was sparser than the Support Vector Machine. Natural questions that arise are what is the relative importance of the use of the Fisher criterion for selecting bases and the deflation step? Can we speed the algorithm up without degrading performance? Here we analyse the algorithm in more detail, providing alternatives to the optimisation criterion and the deflation procedure of the algorithm, and also propose a stagewise version. We demonstrate empirically that these alternatives can provide considerable improvements in the computational complexity, whilst maintaining the performance of the original algorithm (and in some cases improving it).
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SRIVASTAVA, ANKUR, and ANDREW J. MEADE. "A SPARSE GREEDY SELF-ADAPTIVE ALGORITHM FOR CLASSIFICATION OF DATA." Advances in Adaptive Data Analysis 02, no. 01 (January 2010): 97–114. http://dx.doi.org/10.1142/s1793536910000355.
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Kernels have become an integral part of most data classification algorithms. However, the kernel parameters are generally not optimized during learning. In this work a novel adaptive technique called Sequential Function Approximation (SFA) has been developed for classification that determines the values of the control and kernel hyper-parameters during learning. This tool constructs sparse radial basis function networks in a greedy fashion. Experiments were carried out on synthetic and real-world data sets where SFA had comparable performance to other popular classification schemes with parameters optimized by an exhaustive grid search.
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Wenzel, Tizian, Gabriele Santin, and Bernard Haasdonk. "A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution." Journal of Approximation Theory 262 (February 2021): 105508. http://dx.doi.org/10.1016/j.jat.2020.105508.
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Bian, Lu Sha, Yong Fang Yao, Xiao Yuan Jing, Sheng Li, Jiang Yue Man, and Jie Sun. "Face Recognition Based on a Fast Kernel Discriminant Analysis Approach." Advanced Materials Research 433-440 (January 2012): 6205–11. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6205.
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The computational cost of kernel discrimination is usually higher than linear discrimination, making many kernel methods impractically slow. To overcome this disadvantage, several accelerated algorithms have been presented, which express kernel discriminant vectors using a part of mapped training samples that are selected by some criterions. However, they still need to calculate a large kernel matrix using all training samples, so they only save rather limited computing time. In this paper, we propose the fast and effective kernel discriminations based on the mapped mean samples (MMS). It calculates a small kernel matrix by constructing a few mean samples in input space, then expresses the kernel discriminant vectors using MMS. The proposed kernel approach is tested on the public AR and FERET face databases. Experimental results show that this approach is effective in both saving computing time and acquiring favorable recognition results.
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SCHLEIF, F. M., THOMAS VILLMANN, BARBARA HAMMER, and PETRA SCHNEIDER. "EFFICIENT KERNELIZED PROTOTYPE BASED CLASSIFICATION." International Journal of Neural Systems 21, no. 06 (December 2011): 443–57. http://dx.doi.org/10.1142/s012906571100295x.
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Prototype based classifiers are effective algorithms in modeling classification problems and have been applied in multiple domains. While many supervised learning algorithms have been successfully extended to kernels to improve the discrimination power by means of the kernel concept, prototype based classifiers are typically still used with Euclidean distance measures. Kernelized variants of prototype based classifiers are currently too complex to be applied for larger data sets. Here we propose an extension of Kernelized Generalized Learning Vector Quantization (KGLVQ) employing a sparsity and approximation technique to reduce the learning complexity. We provide generalization error bounds and experimental results on real world data, showing that the extended approach is comparable to SVM on different public data.
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SONG, HAN, FENG LI, PEIWEN GUANG, XINHAO YANG, HUANYU PAN, and FURONG HUANG. "Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models." Journal of Food Protection 84, no. 8 (March 12, 2021): 1315–20. http://dx.doi.org/10.4315/jfp-20-447.
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ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
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Xiao, Wendong, and Yingjie Lu. "Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/790412.
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Wearable sensor based human physical activity recognition has extensive applications in many fields such as physical training and health care. This paper will be focused on the development of highly efficient approach for daily human activity recognition by a triaxial accelerometer. In the proposed approach, a number of features, including the tilt angle, the signal magnitude area (SMA), and the wavelet energy, are extracted from the raw measurement signal via the time domain, the frequency domain, and the time-frequency domain analysis. A nonlinear kernel discriminant analysis (KDA) scheme is introduced to enhance the discrimination between different activities. Extreme learning machine (ELM) is proposed as a novel activity recognition algorithm. Experimental results show that the proposed KDA based ELM classifier can achieve superior recognition performance with higher accuracy and faster learning speed than the back-propagation (BP) and the support vector machine (SVM) algorithms.
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Kim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (March 11, 2021): 3099. http://dx.doi.org/10.3390/su13063099.
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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.
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Villa, Amalia, Abhijith Mundanad Narayanan, Sabine Van Huffel, Alexander Bertrand, and Carolina Varon. "Utility metric for unsupervised feature selection." PeerJ Computer Science 7 (April 21, 2021): e477. http://dx.doi.org/10.7717/peerj-cs.477.
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Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for this aim exist in the literature, but despite their large applicability, they can be very inaccessible or cumbersome to use, mainly due to the need for tuning non-intuitive parameters and the high computational demands. In this work, a publicly available ready-to-use unsupervised feature selector is proposed, with comparable results to the state-of-the-art at a much lower computational cost. The suggested approach belongs to the methods known as spectral feature selectors. These methods generally consist of two stages: manifold learning and subset selection. In the first stage, the underlying structures in the high-dimensional data are extracted, while in the second stage a subset of the features is selected to replicate these structures. This paper suggests two contributions to this field, related to each of the stages involved. In the manifold learning stage, the effect of non-linearities in the data is explored, making use of a radial basis function (RBF) kernel, for which an alternative solution for the estimation of the kernel parameter is presented for cases with high-dimensional data. Additionally, the use of a backwards greedy approach based on the least-squares utility metric for the subset selection stage is proposed. The combination of these new ingredients results in the utility metric for unsupervised feature selection U2FS algorithm. The proposed U2FS algorithm succeeds in selecting the correct features in a simulation environment. In addition, the performance of the method on benchmark datasets is comparable to the state-of-the-art, while requiring less computational time. Moreover, unlike the state-of-the-art, U2FS does not require any tuning of parameters.
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Loukides, Grigorios, and George Theodorakopoulos. "Location histogram privacy by Sensitive Location Hiding and Target Histogram Avoidance/Resemblance." Knowledge and Information Systems 62, no. 7 (December 31, 2019): 2613–51. http://dx.doi.org/10.1007/s10115-019-01432-4.
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AbstractA location histogram is comprised of the number of times a user has visited locations as they move in an area of interest, and it is often obtained from the user in the context of applications such as recommendation and advertising. However, a location histogram that leaves a user’s computer or device may threaten privacy when it contains visits to locations that the user does not want to disclose (sensitive locations), or when it can be used to profile the user in a way that leads to price discrimination and unsolicited advertising (e.g., as “wealthy” or “minority member”). Our work introduces two privacy notions to protect a location histogram from these threats: Sensitive Location Hiding, which aims at concealing all visits to sensitive locations, and Target Avoidance/Resemblance, which aims at concealing the similarity/dissimilarity of the user’s histogram to a target histogram that corresponds to an undesired/desired profile. We formulate an optimization problem around each notion: Sensitive Location Hiding ($${ SLH}$$SLH), which seeks to construct a histogram that is as similar as possible to the user’s histogram but associates all visits with nonsensitive locations, and Target Avoidance/Resemblance ($${ TA}$$TA/$${ TR}$$TR), which seeks to construct a histogram that is as dissimilar/similar as possible to a given target histogram but remains useful for getting a good response from the application that analyzes the histogram. We develop an optimal algorithm for each notion, which operates on a notion-specific search space graph and finds a shortest or longest path in the graph that corresponds to a solution histogram. In addition, we develop a greedy heuristic for the $${ TA}$$TA/$${ TR}$$TR problem, which operates directly on a user’s histogram. Our experiments demonstrate that all algorithms are effective at preserving the distribution of locations in a histogram and the quality of location recommendation. They also demonstrate that the heuristic produces near-optimal solutions while being orders of magnitude faster than the optimal algorithm for $${ TA}$$TA/$${ TR}$$TR.
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Dong, Yan-mei, Jia-hao Bi, Qi-en He, and Kai Song. "ESDA: An Improved Approach to Accurately Identify Human snoRNAs for Precision Cancer Therapy." Current Bioinformatics 15, no. 1 (February 6, 2020): 34–40. http://dx.doi.org/10.2174/1574893614666190424162230.
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Background: SnoRNAs (Small nucleolar RNAs) are small RNA molecules with approximately 60-300 nucleotides in sequence length. They have been proved to play important roles in cancer occurrence and progression. It is of great clinical importance to identify new snoRNAs as fast and accurately as possible. Objective: A novel algorithm, ESDA (Elastically Sparse Partial Least Squares Discriminant Analysis), was proposed to improve the speed and the performance of recognizing snoRNAs from other RNAs in human genomes. Methods: In ESDA algorithm, to optimize the extracted information, kernel features were selected from the variables extracted from both primary sequences and secondary structures. Then they were used by SPLSDA (sparse partial least squares discriminant analysis) algorithm as input variables for the final classification model training to distinguish snoRNA sequences from other Human RNAs. Due to the fact that no prior biological knowledge is request to optimize the classification model, ESDA is a very practical method especially for completely new sequences. Results: 89 H/ACA snoRNAs and 269 C/D snoRNAs of human were used as positive samples and 3403 non-snoRNAs as negative samples to test the identification performance of the proposed ESDA. For the H/ACA snoRNAs identification, the sensitivity and specificity were respectively as high as 99.6% and 98.8%. For C/D snoRNAs, they were respectively 96.1% and 98.3%. Furthermore, we compared ESDA with other widely used algorithms and classifiers: SnoReport, RF (Random Forest), DWD (Distance Weighted Discrimination) and SVM (Support Vector Machine). The highest improvement of accuracy obtained by ESDA was 25.1%. Conclusion: Strongly proved the superiority performance of ESDA and make it promising for identifying SnoRNAs for further development of the precision medicine for cancers.
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Ma, Da, Evangeline Yee, Jane K. Stocks, Lisanne M. Jenkins, Karteek Popuri, Guillaume Chausse, Lei Wang, Stephan Probst, and Mirza Faisal Beg. "Blinded Clinical Evaluation for Dementia of Alzheimer’s Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods." Journal of Alzheimer's Disease 80, no. 2 (March 23, 2021): 715–26. http://dx.doi.org/10.3233/jad-201591.
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Background: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature. Objective: In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer’s type (DAT) and Non-DAT controls. Methods: FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians. Results: Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases. Conclusion: In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.
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Zhang, Hengyi. "Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification." Frontiers in Genetics 12 (March 30, 2021). http://dx.doi.org/10.3389/fgene.2021.631505.
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Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy.
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Huang, Weijie, Xuanyu Li, Xin Li, Guixia Kang, Ying Han, and Ni Shu. "Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients." Frontiers in Aging Neuroscience 13 (July 30, 2021). http://dx.doi.org/10.3389/fnagi.2021.687927.
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ObjectiveIndividuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer’s disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI.MethodsA total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation.ResultsFor the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks.ConclusionWhite matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.
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Rajabizadeh, Mahdi, and Mansoor Rezghi. "A comparative study on image-based snake identification using machine learning." Scientific Reports 11, no. 1 (September 27, 2021). http://dx.doi.org/10.1038/s41598-021-96031-1.
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AbstractAutomated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. In this study, for the first time, it’s been attempted to compare the accuracy of a series of state-of-the-art machine learning methods, ranging from the holistic to neural network algorithms. The study is performed on six snake species in Lar National Park, Tehran Province, Iran. In this research, the holistic methods [k-nearest neighbors (kNN), support vector machine (SVM) and logistic regression (LR)] are used in combination with a dimension reduction approach [principle component analysis (PCA) and linear discriminant analysis (LDA)] as the feature extractor. In holistic methods (kNN, SVM, LR), the classifier in combination with PCA does not yield an accuracy of more than 50%, But the use of LDA to extract the important features significantly improves the performance of the classifier. A combination of LDA and SVM (kernel = 'rbf') is achieved to a test accuracy of 84%. Compared to holistic methods, convolutional neural networks show similar to better performance, and accuracy reaches 93.16% using MobileNetV2. Visualizing intermediate activation layers in VGG model reveals that just in deep activation layers, the color pattern and the shape of the snake contribute to the discrimination of snake species. This study presents MobileNetV2 as a powerful deep convolutional neural network algorithm for snake image classification that could be used even on mobile devices. This finding pave the road for generating mobile applications for snake image identification.