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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Mustafa, Mumtaz Begum Asemi, Adeleh University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Branch Faculty of Engineering Department of Computer Engineering Isfahan81551-39998 Iran

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domain-specific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting,"arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains - Laptops, Restaurants, Tweets, and Finance - this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2013 IEEE.

关键词： Contrastive Learning

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FDM-RTDETR: A Multi-Scale Small Target Detection Algorithm

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IEEE Access 2025年 13卷 88747-88761页

作者： Wang, Hongya Yu, Yongtao Tang, Zhaoxia Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huaian223003 China

To address challenges in uncrewed aerial vehicles (UAV) object detection including complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions, we propose FDM-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. This method effectively captures global image information by fusing multi-scale spatial features and performing feature extraction in the frequency domain within the backbone network. We design a Dynamic Feature Interaction (DIFI) module with position-based biases in the encoder, enhancing the model’s perception of local features for small objects. In the neck network, we introduce a Multi-Scale Feature Enhancement Pyramid (MSFEP) module to improve feature extraction capabilities for small object detection. Compared to RT-DETR, our improved model achieves performance gains of 2.5% and 2.7% in AP on the Vis-Drone2019 validation and test sets, respectively. While maintaining low computational complexity and parameter count, the method demonstrates significant improvements in detection performance. FDM-DETR exhibits robust practicality and reliability in UAV-based small object detection tasks. © 2013 IEEE.

关键词： Target drones

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software Test Data Management Based on Knowledge Graph

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Informatica (Slovenia) 2024年第16期48卷 27-36页

作者： Gao, Li Qiu, Junlin Chen, Guanhua Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an223003 China

As software development models and methods mature, large-scale software systems emerge. However, a critical challenge remains: the lack of a comprehensive software test data management model that integrates basic data management with advanced knowledge reasoning. To address this issue, we developed a software test data management model based on knowledge graphs, enabling intelligent management and reasoning of software test data. The model incorporates an entity extraction model based on a feed-forward neural network, a knowledge graph integration method based on graph databases, and a knowledge reasoning submodule based on deep learning. To validate the effectiveness of our model, we evaluated the performance of each component individually. Our deep learning-based entity extraction model achieved an accuracy of 0.92, a recall of 0.88, and an F1 score of 0.90, significantly outperforming traditional methods such as regular expressions and dictionary-based approaches. Utilizing Cypher for graph database querying, our system provides accurate answers with a response time of 0.12 seconds, outperforming SQL and SPARQL-based querying methods. Furthermore, our approach excels in knowledge-based reasoning with an accuracy of 0.89 and site coverage of 0.81, surpassing both ontology-based and graph-based reasoning methods. These results highlight the enhanced construction, querying, and reasoning capabilities of our knowledge graph-based approach for managing software testing data. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Knowledge graph

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Makespan and energy based virtual machine scheduling in cloud systems

Journal of the Faculty of Engineering and Architecture of Ga...

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Journal of the faculty of engineering and Architecture of Gazi University 2024年第3期39卷 1661-1672页

作者： Kizil, Alper Karabulut, Korhan Department of Computer Engineering Faculty of Engineering Yasar University Izmir Bornova35100 Turkey Department of Software Engineering Faculty of Engineering Yasar University Izmir Bornova35100 Turkey

Cloud computing is one of the newest computing paradigms that emerged after worldwide development of Internet infrastructure. It is a technology that saves both large companies and small and medium scale companies as well as independent developers from the cost of keeping infrastructure hardware up to date, and operational while also providing flexibility on resource use as well providing additional opportunity to minimize data losses. While in the future, it is evident that demand for cloud computing will be on the rise. These kinds of datacenters, due to their nature, consume large amounts of energy and even the savings on smallest scales will enable these gigantic centers to save a significant amount of energy in total. If we have a look at the literature, we can see green computing is gaining immense popularity over the years. The Cloud Scheduling problem is a proven problem to be NP-Hard, aiming to find the best solution for a limited number of cloud resources, which could theoretically be serving an unlimited number of user demands. In this study, firstly, an experimental workload / power consumption model is proposed for a server computer, and then two genetic algorithms optimizing makespan and energy consumption are compared on these metrics at different server loads. As a result, it has been seen that these two criteria are closely related to each other, and it has been determined that optimizing the energy criterion has a more positive effect between 10% and 13% compared to the time criterion optimization at full or near full server loads. In this way, it has been shown that significant energy savings can be achieved by using energy optimization as an objective function at high server loads. © 2024 Gazi Universitesi. All rights reserved.

关键词： Energy utilization

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HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

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computers, Materials & Continua 2024年第8期80卷 3351-3377页

作者： TugbaÇelikten Aytug Onan Department of Software Engineering Faculty of TechnologyManisa Celal Bayar UniversityManisa45140Turkey Department of Computer Engineering Faculty of Engineering and ArchitectureIzmir KatipÇelebi UniversityIzmir35620Turkey

Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

关键词： Generative artificial intelligence AI-generated text detection attention mechanism hybrid model for text classification

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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks

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IEEE Access 2025年 13卷 97765-97778页

作者： Ahmad, Omair Khan, Muhammad Sohail Jan, Salman Khan, Inayat University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Arab Open University Faculty of Computer Studies A’Ali732 Bahrain University of Engineering and Technology Department of Computer Science Mardan Pakistan

Audio Deepfakes, which are highly realistic fake audio recordings driven by AI tools that clone human voices, With Advancements in Text-Based Speech Generation (TTS) and Vocal Conversion (VC) technologies have enabled it easier to create realistic synthetic and imitative speech, making audio Deepfakes a common and potentially dangerous form of deception. Well-known people, like politicians and celebrities, are often targeted. They get tricked into saying controversial things in fake recordings, causing trouble on social media. Even kids’ voices are cloned to scam parents into ransom payments, etc. Therefore, developing effective algorithms to distinguish Deepfake audio from real audio is critical to preventing such frauds. Various Machine learning (ML) and Deep learning (DL) techniques have been created to identify audio Deepfakes. However, most of these solutions are trained on datasets in English, Portuguese, French, and Spanish, expressing concerns regarding their correctness for other languages. The main goal of the research presented in this paper is to evaluate the effectiveness of deep learning neural networks in detecting audio Deepfakes in the Urdu language. Since there’s no suitable dataset of Urdu audio available for this purpose, we created our own dataset (URFV) utilizing both genuine and fake audio recordings. The Urdu Original/real audio recordings were gathered from random youtube podcasts and generated as Deepfake audios using the RVC model. Our dataset has three versions with clips of 5, 10, and 15 seconds. We have built various deep learning neural networks like (RNN+LSTM, CNN+attention, TCN, CNN+RNN) to detect Deepfake audio made through imitation or synthetic techniques. The proposed approach extracts Mel-Frequency-Cepstral-Coefficients (MFCC) features from the audios in the dataset. When tested and evaluated, Our models’ accuracy across datasets was noteworthy. 97.78% (5s), 98.89% (10s), and 98.33% (15s) were remarkable results for the RNN+LSTM

关键词： Deep neural networks

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran Department of Computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran Department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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An innovative approach to classify meniscus tears by reducing vision transformers features with elasticnet approach

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Journal of Supercomputing 2025年第4期81卷 1-29页

作者： Genç, Hasan Koç, Canan Yüzgeç Özdemir, Esra Özyurt, Fatih Elazig Fethi Sekin City Hospital Elazig Turkey Faculty of Engineer Software Engineering Firat University Elazig Turkey Faculty of Engineering and Architecture Computer Engineering Bingöl University Elazig Turkey

Meniscal tears, a prevalent orthopedic condition caused by abrupt knee movements, excessive load, or injury, require an accurate diagnosis for effective treatment. This study investigates the vision transformer (ViT) models' efficacy in automated classification of meniscus pathologies. It also explores how feature reduction using the ElasticNet method can improve classification accuracy and computational efficiency. The study utilized MRI scans from a dataset comprising 5000 images collected from clinical cases. Initially, classification was performed using EfficientNet and SqueezeNet architectures. Subsequently, feature extraction was conducted using ViT models, generating a feature set of 1000 dimensions. ElasticNet was employed to reduce features before reclassification using support vector machines (SVM). Model performance was evaluated based on accuracy, precision, sensitivity, and specificity. The ViT_base_32 model achieved a classification accuracy of 99.9% with a processing time of 1.2 s. Feature reduction via ElasticNet significantly enhanced classification performance while maintaining high precision, sensitivity, and specificity. These improvements demonstrate the effectiveness of combining ViT models with ElasticNet to diagnose meniscal tears. The findings highlight the potential of vision transformer models, in conjunction with ElasticNet, to provide rapid and highly accurate diagnostic assistance for meniscal injuries. This methodology shows promise for application to other medical diagnostic domains, offering valuable advancements in healthcare technology. © The Author(s) 2025.

关键词： Medical imaging

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