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Motivation: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins., Results: Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction., Availability and Implementation: SPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

An automatic fall detection system (FDS) provides timely medical assistance to the elderly by predicting falls and non-falls, thereby preventing serious injuries and death. This article proposes an ensemble-based FDS focuses on improving the fall detection accuracy by selecting optimal classifiers with a greedy algorithm-based majority voting approach. The proposed ensemble learning is better than any of the individual models because it involves multiple machine learning models along with deep learning model, namely Support Vector Machine, K-Nearest Neighbour, Decision Tree, and Deep LSTM that are combined in majority voting fashion. The greedy algorithm-based majority voting uses three searching criteria, namely forward search, backward search, and recovery search to select the optimal classifiers. The forward search fuses the classifiers based on majority voting error (MVE) and goodness of classifiers, while the backward search removes the classifier based on the majority voting improvement selection criteria. Thus, the classifiers with the lowest MVE and processing time are fused, and the others are removed. Also, the recovery search is introduced to prevent any loss of optimal classifiers by backward search. Finally, classifiers with the best performance are combined to produce accurate fall detection. Then simulation is carried out for the individual ML model, and the proposed ensemble model. According to the evaluation outcome, the proposed ensemble-based FDS achieves high accuracy (99.98%), sensitivity (99.8%), specificity (99.9%), and precision (96.23%) than the conventional approaches. Hence, the proposed ensemble-based FDS proved to enhance fall detection accuracy. [ABSTRACT FROM AUTHOR]

Agricultural problems need to be dealt with advanced computing methods to increase food productivity. Automatic classification of plant disease using deep learning methods helps to analyze the food quality and productivity. The existing methods applied Convolutional Neural Network (CNN) based models such as VGG-19, VGG-16, AlexNet, and Resnet-50 for plant disease classification. The existing methods have limitations of vanishing gradient problem and overfitting problem in plant disease classification. The ResNext50-Long Short Term Memory (LSTM) is proposed to improve plant disease classification performance. The hybrid of ResNext50-LSTM model is proposed for effective feature extraction from input images and classification of plant diseases. The ResNext50 architectures helps to limit increases of features in input layer to eliminate bottleneck problem and store relevant features for long term in LSTM for classification. The ResNext 50 model increases the features from 4 to 128 for optimal path construction and existing techniques increases the features more than 128 that causes the overfitting problem. This process of feature limit helps to reduce the overfitting problem and vanishing gradient in LSTM model. The ResNext50 is applied to extract and select the relevant features from input images and LSTM model has advantage of store the relevant information on long term for classification. The ResNext50 model selects the features to differentiate the correlation between the relevant and irrelevant features in feature extraction. The ResNext50 extracted features were applied to the LSTM model to improve classification performance. The proposed Resnext50-LSTM model and existing CNN model performances are tested on plant village dataset to analysis the efficiency. The proposed ResNext50-LSTM model has validation accuracy of 95.44%, and existing ResNext50 model has 93.56% accuracy, and ResNet50 model 93.45% accuracy in plant disease classification. [ABSTRACT FROM AUTHOR]

Motivation Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here, we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39 120 proteins deposited prior to 2016 and an ensemble of hybrid long-short-term-memory bidirectional neural network and convolutional neural network. Results We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12 and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12% to 74.28% by SPOT-1D-Single, compared to 69.1–72.6% by SPIDER3-Single and 70.6–73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff = 1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning. Availability and implementation Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. Results The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, non-canonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. Availability and implementation Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Morphological processing of Indian languages is one of the most escalating fields in the era of Natural Language Processing (NLP) since the last decade. The evaluation of Asian languages is a highly relevant field in the times of text mining and information retrieval. The morphological evaluation of a text can be employed for extraction and classification of knowledge. This paper amalgamates morphological evaluation and sentiment prediction of Punjabi language text. The textual data for Punjabi language is concerned with farmer suicide cases reported for Punjab state of India. The pre-processing phase of this study involves morphological evaluation and normalization of Punjabi words to their respective canonical forms. The next phase carries out training and testing of deep neural network model on refined Punjabi tokens obtained from the earlier phase. The proposed model classifies Punjabi tokens into four negatively oriented classes tailored for farmer suicide cases. The average accuracies of sentiment prediction obtained after 10-fold cross validation are 93.85%, 88.53%, 83.3%, and 95.45% for the four respective classes. The proposed framework yields satisfactory results on 275 Punjabi text documents with the overall accuracy of 90.29% for sentiment classification. [ABSTRACT FROM AUTHOR]