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Emerging Artificial Intelligence Technologies and Applications

Submission Deadline: 20 December 2024 View: 828 Submit to Special Issue

Guest Editors

Prof. Wenfeng Zheng, King Saud University, Saudi Arabia; the University of Electronic Science and Technology of China, China
Dr. Chao Liu, CNRS (French National Center for Scientific Research), France
Dr. Lirong Yin, Louisiana State University, USA

Summary

Artificial Intelligence (AI) has rapidly advanced in recent years, leading to the emergence of groundbreaking technologies and applications with far-reaching implications. AI has the potential to revolutionize industries, enhance human capabilities, and address complex challenges in innovative ways.

From using convolutional neural networks to extract image features, to advanced natural language processing achieved by Transformer models, and further to employing graph neural networks to analyze complex topological structures, artificial intelligence is driving unprecedented progress. In medical field, AI is facilitating early disease detection and personalized treatment plans, while in finance, it's optimizing risk management and fraud detection. Additionally, autonomous vehicles and smart manufacturing systems are benefiting from AI's ability to process vast amounts of data in real time, enhancing safety and efficiency. With the continued evolution of AI technologies, the potential for their application across diverse domains is immense, promising to reshape the way we live, work, and interact with the world around us.

As AI continues to advance, its impact will be profound, offering transformative solutions to complex problems and paving the way for a more sustainable and inclusive future. This special issue is open to fresh research contributions that introduce novel theories, innovative methodologies, unique application strategies, and investigations of AI across diverse fields. The potential topics encompassed may include, but are not limited to, the following topics:

· Artificial intelligence.

· Multimodal artificial intelligence

· Potential problems, challenges, and applications of large models

· Visual question and answer (VQA), visual reasoning

· Semantic reasoning, semantic representation, knowledge base

· Characterization inference, natural language reasoning

· Machine translation, text sentiment analysis, text classification

· Meta-learning, transfer learning, few-shot learning.

· Contrastive learning, representation learning, reinforcement learning

· Geospatial artificial intelligence, geospatial AI (GeoAI)

· AI in geostatistics, remote sensing, spatio-temporal simulation

· AI for geospatial data acquisition, analysis, planning, and prediction

· Visual augmentation and reconstruction, 3D reconstruction of deformable surfaces

· Visual- and spatial-based perception enhancement and reasoning

Published Papers

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Open Access

ARTICLE

Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features
Amjad Rehman, Muhammad Mujahid, Robertas Damasevicius, Faten S Alamri, Tanzila Saba
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

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ARTICLE

Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model
Mohamed A. Mahdi, Suliman Mohamed Fati, Mohamed A.G. Hazber, Shahanawaj Ahamad, Sawsan A. Saad
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1651-1671, 2024, DOI:10.32604/cmes.2024.052291
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract Cyberbullying, a critical concern for digital safety, necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces. To tackle this challenge, our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers (BERT) base model (cased), originally pretrained in English. This model is uniquely adapted to recognize the intricate nuances of Arabic online communication, a key aspect often overlooked in conventional cyberbullying detection methods. Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media (SM) tweets showing a notable… More >

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ARTICLE

A Pooling Method Developed for Use in Convolutional Neural Networks
İsmail Akgül
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 751-770, 2024, DOI:10.32604/cmes.2024.052549
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural… More >

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ARTICLE

DPAL-BERT: A Faster and Lighter Question Answering Model
Lirong Yin, Lei Wang, Zhuohang Cai, Siyu Lu, Ruiyang Wang, Ahmed AlSanad, Salman A. AlQahtani, Xiaobing Chen, Zhengtong Yin, Xiaolu Li, Wenfeng Zheng
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

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REVIEW

Unlocking the Potential: A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks
Ebtesam Ahmad Alomari
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
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 >

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ARTICLE

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions
Adéla Hamplová, Alexey Lyavdansky, Tomáš Novák, Ondřej Svojše, David Franc, Arnošt Veselý
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

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ARTICLE

Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English
Ronghao Pan, José Antonio García-Díaz, Rafael Valencia-García
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

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ARTICLE

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder
Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1757-1781, 2024, DOI:10.32604/cmes.2024.049208
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and… More >

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Emerging Artificial Intelligence Technologies and Applications

Guest Editors

Summary

Published Papers

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359

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108

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585

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184

View

720

Download

258

View

692

Download

220

View

2005

Download

440

View

879

Download

270

View

916

Download

373

View

994

Download

432

Like

6

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