Awais Ahmad¹, Sohail Jabbar^1,*, Sheeraz Akram¹, Anand Paul², Umar Raza³, Nuha Mohammed Alshuqayran¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385

Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to overcoming these obstacles, making ChatGPT… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

Asif Khan¹, Huaping Zhang^1,*, Nada Boudjellal², Arshad Ahmad³, Maqbool Khan³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520

Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment… More >

Fida Mohammad^1,*, Mukhtaj Khan¹, Safdar Nawaz Khan Marwat², Naveed Jan³, Neelam Gohar⁴, Muhammad Bilal³, Amal Al-Rasheed⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202

Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep neural network (DNN) model, especially… More >

Batyrkhan Omarov^1,*, Sergazi Narynov², Zhandos Zhumanov²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5105-5122, 2023, DOI:10.32604/cmc.2023.034655

Abstract Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners… More >

Inshirah Ali AlMutairi^*, Ali Mustafa Qamar

Journal on Artificial Intelligence, Vol.4, No.3, pp. 155-165, 2022, DOI:10.32604/jai.2022.032390

Abstract Neural talk models play a leading role in the growing popular building of conversational managers. A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently. The development of profound concentration on innovations has increased the use of neural models for a discussion display. In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques. The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment. In this paper, we mainly… More >

Sami Ullah¹, Muhammad Ramzan Talib^1,*, Toqir A. Rana^2,3, Muhammad Kashif Hanif¹, Muhammad Awais⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2323-2339, 2022, DOI:10.32604/cmc.2022.025543

Abstract In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu… More >

Sung Park^1,*, Seongeon Park², Mincheol Whang²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3761-3784, 2022, DOI:10.32604/cmc.2022.023738

Abstract Artificial entities, such as virtual agents, have become more pervasive. Their long-term presence among humans requires the virtual agent's ability to express appropriate emotions to elicit the necessary empathy from the users. Affective empathy involves behavioral mimicry, a synchronized co-movement between dyadic pairs. However, the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions. Our study evaluates the participant's behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions, behavioral gestures, and voice. Participants viewed an emotion-eliciting video stimulus (negative or positive) with a virtual agent. The… More >

Abdulaziz Al-Besher¹, Kailash Kumar¹, M. Sangeetha^2,*, Tinashe Butsa³

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4763-4780, 2022, DOI:10.32604/cmc.2022.021033

Abstract Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers to more past CS. The… More >

Zhongqin Bi¹, Shiyang Wang¹, Yan Chen^2,*, Yongbin Li¹, Jung Yoon Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 403-426, 2021, DOI:10.32604/cmes.2021.016729

Abstract With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper… More >