Ebtesam Ahmad Alomari^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256 - 20 August 2024

Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >

Kai Jiang, Bin Cao^*, Jing Fan

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2965-2984, 2024, DOI:10.32604/cmes.2023.046348 - 11 March 2024

Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate More >

Moona Kanwal^1,2,*, Najeed A. Khan¹, Aftab A. Khan³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2419-2440, 2024, DOI:10.32604/cmc.2024.047223 - 27 February 2024

Abstract The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach… More >

Shabir Hussain¹, Muhammad Ayoub², Yang Yu¹, Junaid Abdul Wahid¹, Akmal Khan³, Dietmar P. F. Moller⁴, Hou Weiyan^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779 - 29 April 2023

Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the… More >

Muhammad Islam Satti¹, Jawad Ahmed², Hafiz Syed Muhammad Muslim¹, Akber Abid Gardezi³, Shafiq Ahmad⁴, Abdelaty Edrees Sayed⁴, Salman Naseer⁵, Muhammad Shafiq^6,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3913-3929, 2023, DOI:10.32604/cmc.2023.033001 - 31 October 2022

Abstract Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and… More >

Zhichen Hu¹, Huali Ren², Jielin Jiang¹, Yan Cui⁴, Xiumian Hu³, Xiaolong Xu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 91-108, 2023, DOI:10.32604/cmes.2022.022268 - 29 September 2022

Abstract An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by… More >

R. Jayaraj^1,*, S. Lokesh²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 481-497, 2023, DOI:10.32604/iasc.2023.030495 - 29 September 2022

Abstract Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.… More >

C. Narmadha¹, T. Kavitha², R. Poonguzhali², V. Hamsadhwani³, Ranjan walia⁴, Monia⁵, B. Jegajothi^6,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3613-3626, 2023, DOI:10.32604/iasc.2023.029323 - 17 August 2022

Abstract At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use… More >

K. Vijaya Kumar¹, E. Laxmi Lydia², Ashit Kumar Dutta³, Velmurugan Subbiah Parvathy⁴, Gobi Ramasamy⁵, Irina V. Pustokhina^6,*, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2541-2554, 2022, DOI:10.32604/cmc.2022.028570 - 16 June 2022

Abstract Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN More >

Badriya Murdhi Alenzi, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar^*, Mohammed AlKhathami, Abdullah AlTameem

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1311-1331, 2022, DOI:10.32604/iasc.2022.025861 - 03 May 2022

Abstract With the recent boom in the corpus size of sentiment analysis tasks, automatic annotation is poised to be a necessary alternative to manual annotation for generating ground truth dataset labels. This article aims to investigate and validate the performance of two widely used lexicon-based automatic annotation approaches, TextBlob and Valence Aware Dictionary and Sentiment Reasoner (VADER), by comparing them with manual annotation. The dataset of 5402 Arabic tweets was annotated manually, containing 3124 positive tweets, 1463 negative tweets, and 815 neutral tweets. The tweets were translated into English so that TextBlob and VADER could be More >