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In recent years, feature selection has emerged as a major challenge in machine learning. In this paper, considering the promising performance of metaheuristics on different types of applications, six physics-inspired metaphor algorithms are employed for this problem. To evaluate the capability of dimensionality reduction in these algorithms, six diverse-natured datasets are used. The performance is compared in terms of the average number of features selected (AFS), accuracy, fitness, convergence capabilities, and computational cost. It is found through experiments that the accuracy and fitness of the Equilibrium Optimizer (EO) are comparatively better than the others. Finally, the average rank from the perspective of average fitness, average accuracy, and AFS shows that EO outperforms all other algorithms.

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Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.

Introduction: Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. Materials and Methods: The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. Results: The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. Conclusion: The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..

The selection of elective courses based on an individual’s domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual’s domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student’s domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.

Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In classification problems generalization control is obtained by maximizing the margin, which corresponds to minimization of the weight vector. The minimization of the weight vector can be used in regression problems, with a loss function. The problem of classification for linearly separable data and introduces the concept of margin and the essence of SVM -margin maximization. In this paper gives the soft margin SVM introduces the idea of slack variables and the trade-off between maximizing the margin and minimizing the number of misclassified variables. A presentation of linear SVM followed by its extension to nonlinear SVM and SVM regression is then provided to give the basic mathematical details. SRM minimizes an upper bound on the expected risk, where as ERM minimizes the error on the training data. It also develops the concept of SVM technique can be used for regression. SVR attempts to minimize the generalization error bound so as to achieve generalized performance instead of minimizing the observed training error.