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? 2023 Elsevier B.V.Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learnin...
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? 2023 Elsevier B.V.Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly.
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Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world...
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Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress in accuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVMPCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.
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Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the...
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Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the computational resources requested, so the number of features has to be carefully selected. In this work, we propose a methodology for feature selection based on the principal component analysis. The case study has been the classification of classroom sounds during face-to-face module delivery and six sound types have been defined. The proposed method is applied upon a set of 143 sound features to produce feature ranking. The ranking results are compared with those provided by the Relief-F. Then the selected features are used by five classification algorithms, Linear Discriminant Analysis (LDA), Quadratic Support Vector Machine (QSVM), k Nearest Neighbors, Boosted Trees, and Random Forest. The algorithms are executed with increasing number of features, from 1 to 143, considering both feature rankings, creating 1430 models. The performance of the classification algorithms increases rapidly with the number of features with LDA, QSVM, and Boosted Trees outperforming other methods and surpassing the accuracy ratio of 90% with 25 features.
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Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. In this work, we examine our quantum machine learning models, which are based on quantum support vector classificat...
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Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. In this work, we examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR). We investigate these models using a quantum circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor. For the QSVC tasks, we use a dataset containing fraudulent credit card transactions and image datasets (the MNIST and the Fashion-MNIST datasets); for the QSVR tasks, we use a financial dataset and a materials dataset. For the classification tasks, the performance of our QSVC models using 4 qubits of the trapped-ion quantum computer was comparable to that obtained from noiseless quantum circuit simulations. The result is consistent with the analysis of our device noise simulations with varying qubit gate error rates. For the regression tasks, applying a low-rank approximation to the noisy quantum kernel, in combination with hyperparameter tuning in ε-SVR, improved the performance of the QSVR models on the near-term quantum device. The alignment, as measured by the Frobenius inner product between the noiseless and noisy quantum kernels, can serve as an indicator of the relative prediction performance on noisy quantum devices in comparison with their ideal counterparts. Our results suggest that the quantum kernel, as described by our shallow quantum circuit, can be effectively used for both QSVC and QSVR tasks, indicating its resistance to noise and its adaptability to various datasets.
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Long-term stress can have a significant impact on a person's mental health, which can result in several diseases. With the use of modern technologies such as artificial intelligence and IOT, mental health monitoring may be done ef...
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Long-term stress can have a significant impact on a person's mental health, which can result in several diseases. With the use of modern technologies such as artificial intelligence and IOT, mental health monitoring may be done effectively. The primary goal of the research is to devise an efficient machine learning model with quantum advantages for continuous mental health monitoring. This goal is achieved in three phases. In phase-I, various classical machine learning models with different quantum advantages have been studied and their predictive performances have been analysed on time-series multimodal mental state monitoring datasets. In phase-II, principal component analysis of the high-dimensional time-series data has been made to accentuate variation and elicit strong patterns in the dataset to improve the performance of quantum machine learning models. Finally, in phase-III, a meta-approach is devised to combine the predictive abilities of different quantum-enhanced models trained with accentuated sample variance to improve predictive performance. Experiments have been conducted on seven publicly available datasets. According to experimental findings, the proposed model on Swell body postures data and psykose dataset produces the maximum F1-score of 0.9. A paired t-test confirms that the proposed approach has superior performance than individual quantum models at a 95% confidence level.
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Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this...
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Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist's perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.
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