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•Proposed a Multi Criteria Decision Making based Reverse Vaccinology model for predicting Bacterial Protective Antigens.•The proposed model tested on extracted physicochemical features from bacterial protein sequence.•Applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour to handle the data imbalance problem.•Consider MCDM-based TOPSIS and CRITIC methods for order preference with soft and hard ranking model. Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

The prevalence of multi-label (ML) data has witnessed a significant increase in numerous fields as big data technology continues to expand. However, these datasets often contain a high degree of redundancy and irrelevant attributes, which can negatively impact the efficiency and predictive performance of machine learning models. To address these challenges, this paper introduces PSI-MFS, a novel lightweight multi-objective feature selection (MLFS) approach that efficiently optimizes feature selection criteria while maintaining computational efficiency. Despite the availability of several MLFS approaches, many existing methods struggle to achieve optimal balance between computational efficiency and selection quality. PSI-MFS overcomes this by simultaneously optimizing three conflicting feature selection (FS) objectives: minimizing feature–feature redundancy (↓), maximizing feature–label relevancy (↑), and maximizing feature–label interaction (↑), using a preference selection index (PSI)-based optimization strategy. PSI-MFS provides a trade-off between feature correlation and classification performance while significantly reducing memory consumption and execution time. The time complexity of PSI-MFS is low, and experimental assessments on ten benchmark datasets demonstrate its superior or competitive performance compared to 11 state-of-the-art (SOTA) FS methods. The effectiveness of PSI-MFS is further validated through statistical analysis using Friedman’s test, which confirms its significant performance improvements. Notably, PSI-MFS achieves faster execution times and outperforms existing methods in 80% of cases, making it a robust and scalable solution for ML feature selection.

Miscanthus x giganteus (Mxg) is a promising perennial crop for producing natural colorants, renewable fuels, and bioproducts. However, natural recalcitrance and high pretreatment cost are major barriers to their complete conversion. In this study, a green processing method has been investigated for efficient recovery of natural pigments (anthocyanins), fermentable sugars, and pure lignin from Mxg genotypes using choline chloride‐based natural deep eutectic solvents (NADES) systems. Interestingly, choline chloride: lactic acid (ChCl: LA) NADES‐processed biomass resulted in 67.8 ± 2.1 μg g−1 of anthocyanins from dry biomass. A maximum of 87.4%–94.1% glucose yield was achieved after enzymatic saccharification. The effective extraction of lignin with high purity with higher β‐aryl ether (βO4) bonds from advanced crops is crucial for lignin valorization. Notably, highly pure lignin (≈93.4% ± 1.4%) is achieved after low‐temperature NADES pretreatment while retaining lignin's native structure. 31P nuclear magnetic resonance demonstrated that total phenolics for ChCl: LA‐lignin resulted in 1.20 mmol g−1 hydroxyls. The relative monolignol composition of syringyl (S), guaiacyl (G), and p‐hydroxyphenyl (H) is 19.0, 65.7, and 14.3%, respectively, as evidenced by heteronuclear single quantum coherence analysis. This study provides a novel approach for obtaining high‐purity lignin for catalytic depolymerization for oligomers and bifunctional monoaromatics production and leverages current cellulosic biorefinery technologies. Integrated production of pigments, fermentation sugars, and pure lignin has been investigated for the creation of profitable biorefinery developments. Choline chloride‐based natural deep eutectic solvents possess unique solvent capabilities to interact with plant cell wall components to disintegrate lignin‐carbohydrate complex. This method produces high‐digestible cellulose‐rich pulp and results in high glucose yield and high‐purity lignin (>90%) with reduced S/G.

Feature selection (FS) has become an indispensable data preprocessing task because of the huge amount of high dimensional data being generated by current technologies. These high dimensional data contains irrelevant, redundant, and noisy features that deteriorate classification accuracy. FS reduces dimensionality by removing the unwanted features thus improves classification accuracy. FS can be considered as a binary optimization problem. In order to solve this problem, this work proposes a new wrapper feature selection technique based on the Jaya algorithm. Three binary variants of the Jaya algorithm are proposed, the first and second ones are based on transfer functions namely BJaya-S and BJaya-V. The third variant (BJaya-JS) explores the search space on the basis of the Jaccard Similarity index. In addition, a probability-based local search technique, namely Neighbourhood Search is proposed to balance the exploration and exploitation. The variants of Jaya algorithm are evaluated and the best variant is selected. The best variant is further compared with six state-of-the-art feature selection techniques. All the performances are tested on 18 high dimensional standard UCI datasets. Experimental result comparison shows that the proposed feature selection technique performs better than other competitors.

Stock market forecasting is done by analyzing multivariate financial time series generated through technical analysis. However, high-dimensional data deteriorates the prediction performance due to irrelevant features that lead to higher computational costs. Feature selection is used to reduce data dimensionality and select the most informative features. A two-stage hybrid feature selection method is proposed to improve the performance of the forecasting model. In the first stage, a way to aggregate multiple filter methods is introduced as Multi-Filter Feature Selection (MFFS). Three filter methods are used for MFFS to scan the dataset from different aspects. In the second stage, Levy’s Flight-based Chicken Swarm Optimization (LFCSO) is proposed. Levy’s flight is introduced to update the position of Chickens to handle local optima and early convergence. The proposed MFFS reduces the computational cost by filtering the ambiguous features with reduced computational load for the second stage. Deep learning models are used for forecasting using a reduced feature set. Extensive experiments have been performed with three stock indices. The proposed model is assessed against the feature subsets obtained under different scenarios. Performance validation is done by comparing the proposed model and the existing work based on various performance metrics. The experimental result shows that the proposed model outperforms the existing models.

The role of simulation in medical education is crucial to the development of surgeons' skills. Surgical simulation can be used to improve surgical skills in a secure and risk-free environment. Animal models, simulated patients, virtual reality, and mannequins are some types of surgical simulation. As a result, feedback encourages students to reflect on their strengths and weaknesses, enabling them to focus on improvement. Healthcare simulation is a strong educational instrument, and the main goal of this is to give the students an opportunity to do a practical application of what they have learned through theory. Before taking it to the patients, they will already have certain tools they have previously acquired during the practice. This makes it easier for students to identify the knowledge gaps that they must fill to improve patient outcomes. Moreover, simulation brings a wonderful opportunity for students to acquire skills, gain confidence, and experience success before working with real patients, especially when their clinical exposure is limited.The use of simulation to teach technical skills to surgical trainees has become more prevalent. The cost of setting up a simulation lab ranges from$100,000 to $ 300,000. There are several ways to evaluate the effectiveness of simulation-based surgical training. Repetitive surgical simulation training can improve speed and fluidity in general surgical skills in comparison to conventional training. Few previous studies compared learners who received structured simulation training to a group of trainees who did not receive any simulation training in single-center randomized control research. Significantly faster and less time-consuming skill proficiency was noticeable in simulated trainees. Despite being anxious in the operating room for the first time, simulated trainees completed the surgery on time, demonstrating the effectiveness of surgical simulation training.Traditional surgical training involves senior-surgeon supervision in the operating room. In simulation-based training, the trainees have full control over clinical scenarios and settings; however, guidance and assessment are also crucial. Simulators allow users to practice tasks under conditions resembling real-life scenarios. Simulators can be compared with traditional surgical training methods for different reasons. For example, intraoperative bleeding may occasionally show up not only visibly on the screen but also by shaking the trocars erratically. Without haptics, training on virtual simulators can cause one's pulling and pushing forces, which are frequently greater than what the tissue needs, to be distorted. A good method of simulation training is using virtual reality simulators with haptics and simulated patients. The availability of these facilities is limited, though, and a typical session might include an exercise involving stacking sugar cubes and box trainers. The degree of expertise or competency is one area that needs clarification as medical education transitions to a competency-based paradigm.The article aims to provide an overview of simulation, methods of simulation training, and the key role and importance of surgical simulation in improving skills in surgical residents.

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

Multi-label classification is used to solve the problem where multiple labels are associated with single sample. Naive Bayes (NB) classifier is widely used for single label classification due to its high performance and simplicity. Therefore it is vital to extend NB for multi-label classification. In single label classification feature weighted NB gives high accuracy by solving the conditional independence assumption of NB. However, NB is not much explored for multi-label classification. This paper proposes correlation dependent feature weighted NB (MLCFWNB) for multi-label classification. The proposed MLCFWNB is tested over eight benchmark datasets. The experimental result suggest that MLCFWNB wins 60% times in case of different multi-label learning evaluation parameters.

The prevalence of multi-label (ML) data has witnessed a significant increase in numerous fields as big data technology continues to expand. However, these datasets often contain a high degree of redundancy and irrelevant attributes, which can negatively impact the efficiency and predictive performance of machine learning models. To address these challenges, this paper introduces PSI-MFS, a novel lightweight multi-objective feature selection (MLFS) approach that efficiently optimizes feature selection criteria while maintaining computational efficiency. Despite the availability of several MLFS approaches, many existing methods struggle to achieve optimal balance between computational efficiency and selection quality. PSI-MFS overcomes this by simultaneously optimizing three conflicting feature selection (FS) objectives: minimizing feature–feature redundancy ( ↓ ), maximizing feature–label relevancy ( ↑ ), and maximizing feature–label interaction ( ↑ ), using a preference selection index (PSI)-based optimization strategy. PSI-MFS provides a trade-off between feature correlation and classification performance while significantly reducing memory consumption and execution time. The time complexity of PSI-MFS is low, and experimental assessments on ten benchmark datasets demonstrate its superior or competitive performance compared to 11 state-of-the-art (SOTA) FS methods. The effectiveness of PSI-MFS is further validated through statistical analysis using Friedman’s test, which confirms its significant performance improvements. Notably, PSI-MFS achieves faster execution times and outperforms existing methods in 80% of cases, making it a robust and scalable solution for ML feature selection.