Daily Archives: May 28, 2026

Authors: K. Vigneshwar, P. Shruthi, J. Rahul Naik, P. Khaleel Basha

Abstract: Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms like Multinomial NB Classifier & Random Forest, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining using Multinomial NB Classifier & Random Forest. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

DOI: https://doi.org/10.5281/zenodo.20425270

Published by: vikaspatanker

Authors: P. Anusha, Syed Mannan Uddin, T.Sree Chandana, V.Vaishnavi

Abstract: Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.

DOI: http://doi.org/10.5281/zenodo.20423275

Published by: vikaspatanker

Authors: P. Anusha, E. Naveen Kumar, G. Sravanthi, E. Rohitha

Abstract: Sentiment analysis is a crucial task in natural language processing (NLP) that aims to determine the overall sentiment or opinion expressed by a reviewer towards a movie. This study focuses on the sentiment analysis of IMDB movie reviews using various machine learning and NLP techniques. The findings indicate that feature selection can enhance the accuracy of sentiment-based classification, but the effectiveness depends on the specific method and number of features selected. The paper also presents a comprehensive comparison of traditional machine learning techniques and advanced transformer-based models for sentiment analysis of IMDB movie reviews. The results provide insights into choosing appropriate methods for accurate and timely sentiment analysis on IMDB data. The study employs feature extraction techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and word2vec. Feature selection using methods like chi-square is shown to improve classification performance.

DOI: http://doi.org/10.5281/zenodo.20423234

Published by: vikaspatanker