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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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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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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 Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

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, domainspecific 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. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

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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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Enhancing intra-aural disease classification with attention-based deep learning models

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Neural Computing and Applications 2025年第9期37卷 6601-6616页

作者： Demircan, Furkancan Ekinci, Murat Cömert, Zafer Software Engineering Faculty of Engineering and Natural Sciences Samsun University 19 Mayıs Samsun Turkey Computer Engineering Faculty of Engineering Karadeniz Technical University Trabzon Ortahisar Turkey Department of Technical Sciences of the Western Caspian University Baku Azerbaijan

Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance functions. These diseases usually manifest as conditions that affect the internal components of the ear structure and can manifest themselves with symptoms such as hearing loss, ear pain, balance problems, and fluid accumulation in the ear. The accuracy of the diagnosis depends on expert knowledge and subjective opinion. This method is prone to human error. This study presents a novel computer-aided diagnosis system for otoscope images of ear diseases, utilizing a vision transformer-based feature extractor combined with machine learning classifiers to provide accurate second opinions for ENT specialists. For this purpose, a new model based on state-of-the-art vision transformer feature extractor and machine learning models is proposed. In the experimental study, the dataset, comprising 880 eardrum images categorized into four classes (CSOM, earwax, myringosclerosis, and normal), was split into training (70%), validation (10%), and testing (20%) subsets. Each image was preprocessed to 420 × 380 pixels to fit the input dimensions of the models. The vision transformer architecture was utilized for feature extraction, followed by classification using various machine learning algorithms including kNN, SVM, and random forest. As a result, the model using vision transformer feature extractor and k-nearest neighbors (kNN) algorithm achieved 99.00% accuracy. In this study, a deep learning-based and computer-aided diagnosis system, in other words, a computational model, was developed instead of the current human error-prone disease diagnosis method used by ear nose throat (ENT) specialists. The main purpose of the deep learning-based decision support system is to support the diagnosis process where expert knowledge is difficult to access and to provide an alternative opi

关键词： Deep learning

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Priority Service in Data Transmission Networks with Non-Stationary Flows

Priority Service in Data Transmission Networks with Non-Stat...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

ISBN: (纸本)9798350395044

The subject of research. Analysis of the impact of priority data transmission in high-load multi-channel systems represented as a mass service system with request prioritization under different variations in the distribution of random variables of packet arrival intensity in the network and service time in the channels. Method. The study of the influence of priorities on the efficiency of data transmission in multi-channel systems was carried out using simulation and mathematical statistics methods. Main results. The method of numerical calculation of time parameters based on the simulation model of multi-channel data transmission system with heterogeneous load is proposed. Simulation experiments evaluating the impact of priority data transmission at different coefficients of variation of packet intensity and service time in the channels on the time characteristics of the multi-channel system are conducted. Practical significance. The obtained results can be applied to the design of multi-channel computer systems of high load data transmission, providing the optimal use of priorities to improve the efficiency of data transmission. © 2024 IEEE.

关键词： Random variables

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Efficiency in Multi-Channel Data Transmission Systems with Priority Service Under High Load

Efficiency in Multi-Channel Data Transmission Systems with P...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Lygdenov, Sodnom Itmo University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

The subject of research. Analysis of the influence of priority service in multi-channel data transmission systems characterized by limited storage capacity and high load, taking into account the non-stationary characteristics of the intensity of packet arrivals and service time. Method. The functional characteristics of the multi-channel system were calculated and analyzed using simulation methods and mathematical statistics. Main results. A simulation model of a multi-channel system with priority service has been developed, designed to evaluate the functional characteristics of the system under high load. Experiments have been conducted to study the impact of priority service on delays and packet losses of heterogeneous traffic. Practical significance. The results obtained can be used in the design of multi-threaded data transmission systems operating under conditions of high heterogeneous load. © 2024 IEEE.

关键词： data transmission networks limited-capacity storage devices multi-channel systems network efficiency priority servicing of packets queuing systems simulation experiments

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A Trustworthy Analysis Approach for Chatbots on Health Data: ChatGPT-4 Example 29

A Trustworthy Analysis Approach for Chatbots on Health Data:...

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29th International Conference on Information Technology, IT 2025

作者： Arslanoǧlu, Kübra Karaköse, Mehmet The University of Firat The Faculty of Engineering The Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering The Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798331517649

Considering the increasing and widespread use of chatbots, it is of great importance to provide methods and tools to address ethical concerns and to make users aware of various aspects of a chatbot, including non-functional features. The Generative Pre-Trained Transformer (GPT) is a state-of-the-art Natural Language Generation (NLG) model, enhanced by the latest version of GPT-4. This study evaluates the trustworthiness of the responses of three different chatbot models, BlenderBot with Hugging Face, ChatGPT-3.5 and ChatGPT-4, to 100 randomly selected medical questions. The accuracy and semantic similarity of the answers were measured by BLEU, ROUGE-1 and Cosine Similarity metrics, and response times were also recorded as an important performance factor. The results showed that GPT-4 exhibited superior performance, thus being able to produce more accurate and contextually reliable responses. However, the significantly longer response time of GPT-4 emerged as a disadvantage that may affect real-time utilisation. These findings provide an important reference for the effective use of GPT-4 in the context of health chatbots. The study contributes to the literature on improving the effectiveness of chatbots in healthcare by drawing attention to the importance of balancing speed, accuracy and trustworthiness, as well as the fact that the answers obtained are drawn with Application Programming Interface (API). © 2025 IEEE.

关键词： Electronic health record

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