Search
137 articles for “Text Classification”
-
Local Binary Pattern-based Noise Robust Feature for Texture Classification
Abstract: Abstract: The presence of noise degrades the local binary pattern-based classification efficiency. In the present work, a modified local binary pattern ( —modified noise robust local binary pattern) based classification is proposed. In this, a local binary pattern-based feature is modified, which also captures macrostructure information, whereas the existing features capture microstructure texture information only. The new feature is tested on Outex_TC_00010, Outex_TC_00012 and Brodatz datasets for rotation invariant and …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 6, Issue 3, 2019 · pp. 31–47 Read article
-
Efficient Classification of Noisy Text
Abstract: Textual content comprises a significant volume of data generated online on a daily basis. The web-generated data often consists of high levels of noise due to a variety of factors. Development of efficient systems for automatic classification of noisy data is a crucial task in text mining. This paper examines a technique for classification of noisy text which is based on multiple feature selection and supervised learning. The main aim …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 56–61 Read article
-
A Statistical Approach Noise Tolerant Texture Classification
Abstract: A simple, efficient, yet robust multi resolution approach to texture classification binary rotation invariant and noise tolerant. The proposed approach is very fast to build and very compact while remaining robust to illumination variations, rotation changes, and noise. We have developed a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 3, Issue 2, 2016 · pp. 14–19 Read article
-
An Unsupervised Common Sense-based Learning Framework for Emotion Detection and Classification in Textual Social Data
Abstract: AbstractEmotions are inherent to human behavior and generally expressed in response to the stimuli from the external environment or some internal processes. With the widespread use of online social platforms, people often express themselves using tweets, blogs and posts which generally include affective text. The topic of emotion detection and recognition has been widely studied in computational science and in fact a separate area “Affective Computing” is dedicated to its …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 4, Issue 3, 2017 · pp. 44–51 Read article
-
Evaluating the Efficiency of LLMs-SA (Sentiment Analysis) via Social Media Texts
Abstract: Sentiment analysis (SA) is becoming popular in business and scientific communities as the processing of natural language (NLP), computational linguistics, text analytics, image-based processing or video- based processing is used in extracting and mining subjective information in the web, social network, etc. It is able to detect positive, negative or neutral information and can be selected to absorb polarity, sentiments, urgency and goals of mount importance. The majority of the …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
-
TextSum: An Automatic Text Summarization Tool using Bayesian Classification
Abstract: Automatic text summarization is the process of summarizing text from one or more documents in order to help the reader to determine if [s]he has to read the document(s) in full. Although automatic summarization is around for many years, there is a renewed interest in this area from the government, industry, and academia. The reason is the availability of large quantities of information due to world wide web and the …
Published in Recent Trends in Programming languages · Vol. 1, Issue 1, 2014 · pp. 1–3 Read article
-
Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing
Abstract: The rapid expansion of digital communication has resulted in an unprecedented volume of consumer-generated textual data across online reviews, social media platforms, forums, and e-commerce websites. Extracting meaningful insights from this data is increasingly important for organizations seeking to understand customer opinions, preferences, and behavioral trends. Despite significant advances in sentiment analysis, many existing approaches primarily focus on surface-level features and often overlook deeper syntactic and semantic relationships within text. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 01–09 Read article
-
ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
-
Emote_X: An AI-Driven Framework for Real-Time Twitter Sentiment Classification Using Machine Learning
Abstract: Social media has become one of the ways people share their thoughts, feelings and responses to what is happening in the world. Twitter, which is now called X is an example. It creates a constant flow of short messages. These messages contain clues about how people feel in areas like business, health care, politics, entertainment and when there is a disaster. Tweets are very messy. They have abbreviations, hashtags, emojis, …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 3, 2026 Read article
-
K-NN Classification and Thresholding by Fuzzy If-Then Rules using Texture and Shape Feature
Abstract: In present scenario image retrieval is the basic requirement, because of different types of images features are added in database from different sources are available for image retrieval. In Content Based Image Retrieval ‘content based’ means that the search will analyze the actual contents of the image. In this paper texture and shape features are extracted and classifications of images are done with K-Nearest Neighbour. The Fuzzy concept If-Then rules …
Published in Journal of Advances in Shell Programming · Vol. 2, Issue 2, 2015 · pp. 1–11 Read article
-
Automated Fake News Detection System
Abstract: The classification of fake news on social media platforms has drawn a lot of attention recently because it is so simple to put false stuff there. In addition, they prefer social media to traditional television as their news source of choice. These patterns have increased academics' understanding of fake news and inspired an increase in interest in its identification. The primary goal of this work was to classify incorrect information …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 1, 2023 · pp. 70–78 Read article
-
Sentiment Analysis on Financial News Using Deep Learning Algorithm
Abstract: Sentiment analysis is the technique of computationally figuring out and categorizing reviews or comments expressed in a bit of textual content, especially a good way to decide whether or not the writer's mind-set in the direction and also very helpful to identify the customer’s opinion about the particular product or content. It is one of the active and wanted research areas in natural language processing. In existing work, machine learning …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 1, 2021 · pp. 24–27 Read article
-
A Machine Learning-Based Approach for Assessing and Monitoring Women’s Safety Across Social Media Platforms
Abstract: This research introduces a machine learning-based approach for examining and assessing women’s safety concerns across social media platforms. The proposed framework aims to identify potentially harmful online interactions, recognize patterns associated with harassment, and support systematic safety assessment. Rather than replacing human judgment with fully automated moderation, the framework serves as an analytical support tool to assist in identifying risks and improving awareness of online threats. The approach focuses on …
Published in Current Trends in Signal Processing · Vol. 16, Issue 3, 2026 Read article
-
Defect Detection in Patterned Fabrics Using Local Binary Pattern and Its VLSI Implementation
Abstract: For texture classification in computer vision Local Binary Patterns (LBP) is used as one of the efficient method. In this paper, LBP texture operator is used to label the pixels of an image of any fabric or texture using the property of thresholding the neighborhood of each and every pixel and considers the compared result as a binary number or a digit which will be used for detecting the defects …
Published in Journal of Microcontroller Engineering and Applications · Vol. 1, Issue 1, 2015 · pp. 45–51 Read article
-
Machine Learning Based Sentiment Analysis of Student Feedback in Higher Education
Abstract: Educational institutions routinely collect feedback from students to understand their perceptions of academic programs, infrastructure, and campus facilities, to improve the overall quality of the college environment. In current practice, feedback is often gathered using numerical or grade-based rating systems, which tend to oversimplify student opinions and may overlook important details related to their level of satisfaction. In contrast, open-ended textual feedback allows students to clearly express their views, concerns, …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 01–10 Read article
-
Object Recognition by Multi-Scale Color Local Binary Pattern
Abstract: This paper presents a multi-scale color local binary pattern based method for object recognition. Several advanced methods have been proposed for object recognition using deep neural networks. Although these methods offer high accuracy with many number of object classes, the high complexity of these methods requires large number of training examples and computational resources. This paper attempts to solve the object recognition problem for limited number of well known object …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 57s–61s Read article
-
Text Analysis in Health Care Study Using IoT
Abstract: The Internet of Things (IoT) should be a recent buzzword that has steadily acquired acceptance (IoT). Organizations, governments, and the scientific community have already acknowledged the significance of the Internet of Things as a result of a surge in publications over the past few years. Despite scant research on goals and potential breakthroughs, IoT in health had a significant role in this development. In this study, the only method used …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 11–18 Read article
-
Scalability Analysis of Semantic Relational Key Terms based Document Clustering Using Parsing Text Calcification
Abstract: Abstract: Document agglomeration, one among the standard info mining ways, is an unsupervised learning worldview wherever agglomeration techniques endeavor to tell apart-inborn groupings of the text documents, with the goal that a meeting of clusters is made during which clusters show high intra-cluster closeness and low bury cluster likeness. The importance of document agglomeration rises out of the big volumes of matter documents created. Albeit varied document agglomeration techniques are …
Published in Recent Trends in Programming languages · Vol. 6, Issue 3, 2019 · pp. 33–40 Read article
-
Retrospective Study on the Concept of Pratimarsha Nasya
Abstract: In this modern era, maintaining a healthy life for a person is challenging. In Ayurveda four main types of Shodhana (purification) procedures are mentioned i.e., Vamana, Virechana, Asthapana Basti, and Nasya. One of these, known as Nasya, involves the administration of medicine or medicated oil through the nostrils in order to prevent the sickness of the organs above the Urdhva Jatrugata (supraclavicular). Different Ayurvedic texts classify Nasya differently; all forms …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 11, Issue 2, 2022 · pp. 23–27 Read article
-
Sentiment Analysis and Stock Market Prediction-Using news to predict stock markets
Abstract: AbstractThe aim of this paper is to propose a relatively new approach regarding the influence of financial news on stock market prices. Previously, a lot of effort and research has been done to study the effect of historical stock market prices. This approach mainly involved time series numerical data to predict the future trend of stock markets. But, with the advent of superior computing power and computing techniques such as …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 2, 2019 · pp. 17–24 Read article