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Now a days, secure data communication over computer network system is a major issue in which impact of feature reduction plays a vital role to secure network by early detection of intrusion. It not only keeps a deep impact on the performance of existing Intrusion Detection System (IDS) algorithms but also affects the computational complexity. Although lots of techniques have been offered for feature reduction by researchers and they have their own perks and quirks, but still they are several flows. To manipulate the same dataset for different classifiers and to select different number of features for the detection of attacks are not only having too much computational cost but also time consuming. The experiments have been carried out using "Python" programming language based library "Scikit‐Learn" software on "Kddcup99" dataset from UCI machine learning repository as a test bed. In this article a classification and regression trees (CART) based feature selection algorithm has been proposed which offers optimum set of features. Further optimum set of features has been offered by our proposed work passed over various classifiers for training and testing to establish network intrusion detection system (NIDS). We have compared the performance accuracy of various existing machine learning (ML) based classification algorithms and obtained higher performance accuracy with lower computational cost. The proposed algorithm having optimum time complexity and accuracy in designing of IDS. [ABSTRACT FROM AUTHOR]

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr 3 AlO 6 , Pr 4 Al 2 O 9 , Pr 3 ScO 6 and Pr 3 Sc 5 O 12 ) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design. [ABSTRACT FROM AUTHOR]

Predicting bandgap is a crucial topic in materials informatics, however, it is still difficult when the available dataset is limited and unbalanced. Here, we applied a machine learning approach to construct a prediction model for transition metal dichalcogenides and oxides. Using an oversampling technique and atomistic feature engineering, we successfully constructed the machine learning model and analyzed the correlation with other physical properties. Furthermore, we also utilized the model to obtain a compressive sensing model based on physical quantities for analytic interpretation and quick prediction. • Machine learning model for bandgap prediction. • Oversampling technique. • Compressive sensing. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server "ASRmiRNA" has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs. [ABSTRACT FROM AUTHOR]

[Display omitted] • A deep learning model is developed for efficient process modeling using a limited amount of experimental data. • The model facilitates an under 0.9% discrepancy rate with the actual process by distilling physics-based knowledge from FEM simulation. • The data augmentation based on the deep learning model generates realistic data from FEM simulation and experimental data. • The developed deep learning strategy alleviates the overfitting of learning weights, thereby achieving the high performance of inverse design. In manufacturing industry, finding optimal design parameters for targeted properties has traditionally been guided by trial and error. However, limited data availability to few hundreds sets of experimental data in typical materials processes, the machine-learning capabilities and other data-driven modeling (DDM) techniques are too far from it to be practical. In this study, we show how a versatile design strategy, tightly coupled with physics-based modeling (PBM) data, can be applied to small set of experimental data to improve the optimization of process parameters. Our strategy uses PBM to achieve augmented data that includes essential physics: in other words, the PBM data allows the inverse design model to 'learn' physics, indirectly. We demonstrated the accuracy of both forward-prediction and inverse-optimization have been dramatically improved with the help of PBM data, which are relatively cheap and abundant. Furthermore, we found that the inverse model with augmented data can accurately optimize process parameters, even for ones those were not considered in the simulation. Such versatile strategy can be helpful for processes/experiments for the cases where the number of collectable data is limited, which is most of the case in industries. [ABSTRACT FROM AUTHOR]