A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm

Rana, Anurag; Vaidya, Pankaj; Gupta, Gaurav

doi:10.1016/j.matpr.2021.11.350

Cited by 11 publications

(12 citation statements)

References 7 publications

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“…The decision to use an SVM for individual Sika deer classification in our study was deliberate, driven by its versatility in handling both linear and non-linear classification tasks, robustness to outliers, and interpretability. While CNN models are adept at classifying images, the SVM's adaptability to both linear and non-linear patterns makes it ideal for capturing the complex features [54] associated with Sika deer identification. Furthermore, the SVM's robustness to outliers [55] ensures accurate classification even in the presence of irregular data points.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Sika Deer Identification: Integrating CNN-Based Siamese Networks with SVM Classification

Sharma,

Timilsina,

Gautam

et al. 2024

Electronics

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Accurately identifying individual wildlife is critical to effective species management and conservation efforts. However, it becomes particularly challenging when distinctive features, such as spot shape and size, serve as primary discriminators, as in the case of Sika deer. To address this challenge, we employed four different Convolutional Neural Network (CNN) base models (EfficientNetB7, VGG19, ResNet152, Inception_v3) within a Siamese Network Architecture that used triplet loss functions for the identification and re-identification of Sika deer. Subsequently, we then determined the best-performing model based on its ability to capture discriminative features. From this model, we extracted embeddings representing the learned features. We then applied a Support Vector Machine (SVM) to these embeddings to classify individual Sika deer. We analyzed 5169 image datasets consisting of images of seven individual Sika deers captured with three camera traps deployed on farmland in Hokkaido, Japan, for over 60 days. During our analysis, ResNet152 performed exceptionally well, achieving a training accuracy of 0.97, and a validation accuracy of 0.96, with mAP scores for the training and validation datasets of 0.97 and 0.96, respectively. We extracted 128 dimensional embeddings of ResNet152 and performed Principal Component Analysis (PCA) for dimensionality reduction. PCA1 and PCA2, which together accounted for over 80% of the variance collectively, were selected for subsequent SVM analysis. Utilizing the Radial Basis Function (RBF) kernel, which yielded a cross-validation score of 0.96, proved to be most suitable for our research. Hyperparameter optimization using the GridSearchCV library resulted in a gamma value of 10 and C value of 0.001. The OneVsRest SVM classifier achieved an impressive overall accuracy of 0.97 and 0.96, respectively, for the training and validation datasets. This study presents a precise model for identifying individual Sika deer using images and video frames, which can be replicated for other species with unique patterns, thereby assisting conservationists and researchers in effectively monitoring and protecting the species.

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Section: Discussionmentioning

confidence: 99%

Enhancing Sika Deer Identification: Integrating CNN-Based Siamese Networks with SVM Classification

Sharma,

Timilsina,

Gautam

et al. 2024

Electronics

View full text Add to dashboard Cite

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“…The main objective of an SVM algorithm is to create an optimal hyperplane that separates the classes as much as possible. In the case of a bi-dimensional space, the hyperplane is a line; in tridimensional space, the hyperplane is 2-dimensional planes; and so on the SVM creates n-dimensional R n−1 planes, where n is the dimension or number of features [39]. A kernel function is needed to map the data.…”

Section: Classification Models 2431 Support Vector Machinementioning

confidence: 99%

“…The decision function for nonlinear data of the algorithm is given by (3) [39]. Where m is the bias parameter and α determines the maximal margin classifier, a parameter related to the input vector.…”

Section: Classification Models 2431 Support Vector Machinementioning

confidence: 99%

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

et al. 2023

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Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

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“…The practice of employing the bootstrap aggregating algorithm is a common and effective approach in the training of RF models. Central to this technique is the process of randomly collecting samples from the dataset, which is formally termed bootstrap resampling [31].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Machine Learning-Based Fatigue Life Prediction of Functionally Graded Materials Using Material Extrusion Technology

Alkunte,

Fidan

2023

J. Compos. Sci.

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In this study, the research investigates the prediction of fatigue life for Functionally Graded Materials (FGM) specimens comprising Polylactic acid (PLA) and Thermoplastic Polyurethane (TPU). For this, Machine learning (ML) techniques, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are utilized. A predictive in-house code is developed for each technique, thereby facilitating the fatigue performance of layered deposited specimens subjected to varying cyclic loadings. In order to verify the effectiveness of the ML technique, a comparative analysis among all is reported based on empirically determined fatigue life obtained values. RF is proven to be the most suitable technique with minimal error percentage in obtained results with optimally synchronized data sets in a minimum time frame. Subsequently, the application of ML in those predictions is reported for future aspects in augmenting the operational efficiency associated with fatigue life prediction.

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A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm

Cited by 11 publications

References 7 publications

Enhancing Sika Deer Identification: Integrating CNN-Based Siamese Networks with SVM Classification

Enhancing Sika Deer Identification: Integrating CNN-Based Siamese Networks with SVM Classification

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

Machine Learning-Based Fatigue Life Prediction of Functionally Graded Materials Using Material Extrusion Technology

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