11 research outputs found
A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS
oai:ojs2.ctrj.in:article/1Aim: A similarity evaluation measure for Gene Ontology GO terms is developed.
Results: The proposed method takes into account the semantics hidden in ontologies or the term level information content, membership of term, and topology-based similarity measures. The proposed method is evaluated on positive and negative dataset of UniProt, Protein family clans and the Pearson’s correlation with other existing methods.
Conclusion: The experimental results exhibited a major supremacy of the proposed method over other semantic similarity measures.
HIGHLIGHTS:1. An improved approach for semantic similarity evaluation for GO terms based on the information content and the topological factors is developed.2. The proposed method shows highest correlation for MF (Molecular Function) ontology
Identification of monolingual and code-switch information from English-Kannada code-switch data
Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as code-switching or code-mixing. Some of the important applications of code-switch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram
Data driven algorithm selection to predict agriculture commodities price
Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models
LSTM-Based Air Quality Prediction
In this project we are using Neural networks and Long Short-Term Memory (LSTM) to address the air pollution detection. As each living organism needs fresh and good quality air for every moment, very few of the living things can survive without such air. Increasing industry and populace have end up fundamental contributor for the air pollutants. Over the time, many countries are finding numerous approaches of fighting towards air pollution. The air we breathe every moment causes several health hazards. So we'd like an honest system that predicts such pollutions and is useful in better environment. It leads us to address the advance techniques for predicting the pollution using Air Quality Index. So, here we are predicting air pollution using LSTM and Neural Network techniques for the coming hour mainly on pollutants like ammonia (NH3), lead (Pb), ozone(O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ecosystem aspects inclusive of temperature, strain, rainfall, wind pace according to minute and wind direction
Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction
Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device
Spamming the mainstream: A survey on trending Twitter spam detection techniques
In recent years, social networking sites are being referred frequently by the people, due to this the social networking sites are growing very fast. Twitter is one such micro-blogging site where the users are able to connect with new people and know what is happening in the world through the topics discussed on twitter. For this reason, twitter is targeted by malicious users who post harmful links, unwanted messages which are not of users interest which is called, spam. In this paper, a comprehensive survey on existing methods on twitter spam detection is presented. From this survey, it is clear that detecting URL content in the tweet is very important to know whether the tweet is spam or non-spam. The advantages and faws are discussed. The comparative analysis of the existing detection methods are also presented by reviewing research papers published from 2010-2017. There is a lot of scope for the researchers to
NBLex: emotion prediction in Kannada-English code-switch text using naïve bayes lexicon approach
Emotion analysis is a process of identifying the human emotions derived from the various data sources. Emotions can be expressed either in monolingual text or code-switch text. Emotion prediction can be performed through machine learning (ML), or deep learning (DL), or lexicon-based approach. ML and DL approaches are computationally expensive and require training data. Whereas, the lexicon-based approach does not require any training data and it takes very less time to predict the emotions in comparison with ML and DL. In this paper, we proposed a lexicon-based method called NBLex to predict the emotions associated with Kannada-English code-switch text that no one has addressed till now. We applied the One-vs-Rest approach to generate the scores for lexicon and also to predict the emotions from the code-switch text. The accuracy of the proposed model non-binding lower extremity exoskeleton (NBLex) (87.9%) is better than naïve bayes (NB) (85.8%) and bidirectional long short term memory neural network (BiLSTM) (84.7%) and for true positive rate (TPR), the NBLex (50.6%) is better than NB (37.0%) and BiLSTM (42.2%). From our approach, it is observed that a simple additive model (lexicon approach) can also be an alternative model to predict the emotions in code-switch text
EMPP: Ensemble based Model for Predicting Stock Time Series using Pattern Sequence Similarity
PMEM: Predicting multiple time series using ensemble model
Forecasting Multiple Time Series (MTS) consists of multiple time series with no relation between them and independent of each other. Predicting each time series independently may lead to increase in time and cost. In this paper, we formalize the problem of predicting the multiple time series together over a MTS database. The proposed framework addresses the following issues. First, it build the initial ensemble model for each time series by using a novel Ensemble approach thereby effectively reduce the data storage and time complexity, secondly, by using single ensemble engine for MTS we perform the three major task, i.e., the task of prediction, building new model, ensemble update and lastly predicting the samples by pattern sequence matching using well known Sliding window method. The computational cost for PMEM is O(C * N * (K+ nmatches)) where, nmatches is the Average number of patterns matched