A Quantum-Based Chameleon Swarm for Feature Selection

Elaziz, Mohamed Abd; Ahmadein, Mahmoud; Ataya, Sabbah; Alsaleh, Naser A.; Forestiero, Agostino; Elsheikh, Ammar H.

doi:10.3390/math10193606

Cited by 9 publications

(3 citation statements)

References 67 publications

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“…It has been analyzed that certain limitations of classical computations can be addressed by applying quantum technologies (QT). Therefore, various studies incorporate quantum technologies such as quantum transfer learning and quantumbased chameleon swarm quantum annealing for FE and FS methodologies [27][28][29]. In 2023, research has been published that shows how a quantum computation (QC) based feature selection method can reduce the model complexity and improve the ML interpretability.…”

Section: Related Workmentioning

confidence: 99%

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Shivwanshi,

Nirala

2024

Biomed. Phys. Eng. Express

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The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. When applied to a prominent feature space, traditional techniques may also struggle with combinatorial optimization problems. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quadratic unconstrained binary optimization (FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

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Section: Related Workmentioning

confidence: 99%

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Shivwanshi,

Nirala

2024

Biomed. Phys. Eng. Express

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“…PCA reduces the dimensionality of the predictor space, creating classification models that prevent overfitting. PCA linearly transforms predictors to remove redundant dimensions and generates a new set of variables called principal components [22], [23].…”

Section: Apreprocessingmentioning

confidence: 99%

MRI Brain Tumor Detection Methods Using Contourlet Transform Based on Time Adaptive Self-Organizing Map

Farzamnia,

Hazaveh,

Siadat

et al. 2023

IEEE Access

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The brain is one of the most complex organs in the body, composed of billions of cells that work together to ensure proper functioning. However, when cells divide in a disorderly manner, abnormal growths can occur, forming colonies that can disrupt the normal functioning of the brain and damage healthy cells. Brain tumors can be classified as either benign or low-grade (grade 1 and 2), or malignant or high-grade (grade 3 and 4). In this article, we propose a novel method that uses contourlet transform and time adaptive self-organizing map, optimized by the whale optimization algorithm, in order to distinguish between benign and malignant brain tumors in MRI images. Accurate classification of these images is critical for medical diagnosis and treatment. Our method is compared to other methods used in past research and shows promising results for the precise classification of MRI brain images. Through conducting experiments on different test samples, our system has successfully attained a classification accuracy exceeding 98.5%. Furthermore, it has managed to maintain a satisfactory level of efficiency in terms of run-time.INDEX TERMS brain; classification; contourlet transform; detection; neural networks; time adaptive selforganizing map (TASOM); tumor; whale optimization algorithm (WOA)

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“…Many efforts have been made to improve the performance of emotional state recognition in speech through feature selection. The main aim of feature selection is to choose the most important acoustic features, which can reduce the computational cost of SERs and improve their recognition accuracy [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition

Yue,

Hu,

Chu

et al. 2023

Applied Sciences

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The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch.

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A Quantum-Based Chameleon Swarm for Feature Selection

Cited by 9 publications

References 67 publications

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

MRI Brain Tumor Detection Methods Using Contourlet Transform Based on Time Adaptive Self-Organizing Map

A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition

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