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Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy. [ABSTRACT FROM AUTHOR]

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS. [ABSTRACT FROM AUTHOR]

Speech Emotion Recognition (SER) is an active area of signal processing research that aims at identifying emotional states from audio speech signals. Applications of SER range from psychological diagnosis to human-computer interaction and as such, a robust framework is needed for accurate classification. To this end, we propose a two-stage hybrid deep feature selection (HDFS) framework that combines deep learning with automated feature engineering for emotion recognition from human speeches, which shines both in terms of accuracy and computational efficiency. Our pipeline extracts self-learned features using a customized Wide-ResNet-50-2 deep learning model from mel-spectrograms of raw audio signals, whose dimensionality is reduced using a hybrid deep feature selection algorithm that comprises a fuzzy entropy and similarity-based feature ranking method, followed by Whale optimization algorithm, which is a popular meta-heuristic optimization algorithm in literature. A k-nearest neighbor classifier is used to classify the optimized feature subset into the respective emotion classes. The proposed pipeline is evaluated on three publicly available SER datasets using a 5-fold cross-validation scheme, where it is found to outperform several state-of-the-art existing works in literature by significant margins thus, justifying the superiority and reliability of the proposed research. The source codes of the proposed method can be found at: https://github.com/soumitri2001/Wrapper-Filter-Speech-Emotion-Recognition. [ABSTRACT FROM AUTHOR]

Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X‐ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X‐ray images, a more robust method is therefore required, which can be achieved by using a computer‐aided diagnosis (CAD) system. In this study, we develop a two‐stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and time‐efficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet‐201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing‐based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X‐ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the highest accuracy of 98.36% and sensitivity of 98.79% with a feature reduction of 85.55% (74 features selected out of 512), using a five‐fold cross‐validation scheme. Extensive additional experiments on continuous benchmark functions as well as the CEC‐2017 test suite further showcase the superiority and suitability of our proposed approach in application to real‐valued optimization problems. The relevant codes for the proposed method can be found in https://github.com/soumitri2001/Pneumonia-Detection-Local-Search-aided-SCA. [ABSTRACT FROM AUTHOR]