Designing Punjabi Poetry Classifiers Using Machine Learning and Different Textual Features

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

show abstract

“…Processing (NLP) (Kaur and Jatinderkumar, 2019). TF-IDF is consisted of Term Frequency (TF) and Inverse Document Frequency (IDF).…”

Section: Polypeptide Frequency Of Word Frequencymentioning

confidence: 99%

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Wang

Yifeng

Lü

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…A lightweight stemmer is proposed for Hindi, which conflates terms by providing suffix list. The stemmer has been evaluated by computing under stemming and over stemming figures for a corpus of documents [12][13][14].…”

Section: Bakgroundmentioning

confidence: 99%

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Bafna¹,

Jatinderkumar²

2020

IJACSA

View full text Add to dashboard Cite

Implementing supervised machine learning on the Hindi corpus for classification and prediction of verses is an untouched and useful area. Classifying and predictions benefits many applications like organizing a large corpus, information retrieval and so on. The metalinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. Text classification algorithms along with Natural Language Processing (NLP) facilitates fast, cost-effective, and scalable solution. Performance evaluation of these predictors is a challenging task. To reduce manual efforts and time spent for reading the document, classification of text data is important. In this paper, 697 Hindi poems are classified based on four topics using four eager machine-learning algorithms. In the absence of any other technique, which achieves prediction on Hindi corpus, misclassification error is used and compared to prove the betterment of the technique. Support vector machine performs best amongst all.

show abstract

“…Different Indian languages [20] such as Hindi [27][28] [29] are explored by different researchers and NLP elements explored for each language are stated. A new framework that is bag of synset is proposed for multilingual document classification using synset document matrix and BabelNet knowledge base [15] Poetry corpus creation along with preprocessing of the corpus is achieved by Punjabi corpus and classifiers are executed [25]. Diacritic extraction methods are used for the Gujarati language along with information retrieval, stop word identification and classification and machine translation.…”

Section: Different Types Of Stemming Techniques For Indian Andmentioning

confidence: 99%

Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique

Bafna¹,

Jatinderkumar²

2020

IJACSA

View full text Add to dashboard Cite

Textual data is increasing exponentially and to extract the required information from the text, different techniques are being researched. Some of these techniques require the data to be presented in the tabular or matrix format. The proposed approach designs the Document Term Matrix for Marathi (DTMM) corpus and converts unstructured data into a tabular format. This approach has been called DTMM in this paper and it fails to consider the semantics of the terms. We propose another approach that forms synsets and in turn reduces dimensions to formulate a Document Synset Matrix for Marathi (DSMM) corpus. This also helps in better capturing the semantics and hence is context-based. We abbreviate and call this approach as DSMM and carry out experiments for documentsimilarity measurement on a corpus consisting of more than 1200 documents, consisting of both verses as well as proses, of Marathi language of India. Marathi text processing has been largely an untouched area. The precision, recall, accuracy, F1-score and error rate are used to prove the betterment of the proposed technique.

show abstract

Designing Punjabi Poetry Classifiers Using Machine Learning and Different Textual Features

Cited by 19 publications

References 12 publications

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique

Contact Info

Product

Resources

About