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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Dehazing using Generative Adversarial Network - A Review

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SN computer Science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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Tweeting the Market: Leveraging LSTM for Stock Price Prediction 2

Tweeting the Market: Leveraging LSTM for Stock Price Predict...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Modi, Vinni Agrawal, Nandini Sardana, Neetu Jain, Ishika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology U.P Noida India

ISBN: (纸本)9798331541217

Investors and traders constantly seek an edge, a source of insight that can help them predict market movements more accurately. Over the past several years, the emergence of social media and the increase in use of platforms such as Twitter, Facebook, etc. has given birth to a new dimension of market analysis - sentiment analysis and its potential effect on stock prices. The mood dimensions that correlate with market movements can be complex and multifaceted. Only a few researches have studied the effect of social media on stock price predictions. Prior investigations have examined discussions surrounding the COVID-19 virus, its societal repercussions, and its potential sway over financial markets. This research has specifically scrutinized the effect of negative sentiments and their potential repercussions on stock prices of major tech companies like Microsoft, Google, and Tesla. The paper aims to identify the most effective model among ARIMA and LSTM for predicting stock prices by correlating them with sentiments expressed in social media posts. The motive of this paper is to help investors better analyze the market and adjust their strategies accordingly to earn more profits. © 2024 IEEE.

关键词： Tweets

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Enhancing Heart Disease Prediction with GANs in Clinical Decision Support Systems 6

Enhancing Heart Disease Prediction with GANs in Clinical Dec...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Periya Nayaki, A. VishwaShree, S.V. Mithra, N. Priyadharshini, C. Thanabal, M.S. Penyameen, K. Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

Heart disease remains a leading global cause of mortality, emphasizing the need for innovative diagnostic solutions. Traditional Clinical Decision Support Systems (CDSS) often struggle with limited datasets and imbalances, impacting prediction accuracy. This study proposes a novel approach by integrating Conditional Generative Adversarial Networks (CGANs) into a CDSS framework, enhancing its ability to generate high-quality synthetic data. The augmented dataset is utilized to train a Gradient Boosting model capable of identifying subtle patterns in patient data. This methodology significantly improves prediction precision and addresses limitations of traditional techniques. The results demonstrate the model's superiority inaccurately identifying heart disease risks, making it a valuable tool for early diagnosis and clinical decision-making. By combining advanced machine learning techniques with domain- specific innovations, this research sets a new benchmark in the application of AI for healthcare. © 2025 IEEE.

关键词： Generative adversarial networks

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Exploring IoT and Machine Learning Integration for Soil Nutrients Monitoring and Crop Recommendation: A Survey 3

Exploring IoT and Machine Learning Integration for Soil Nutr...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khemnar, Vijaya R. Bendre, Mininath R. Kolekar, Vikas K. Pravara Rural Engineering College Department of Computer Engineering Loni India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331509675

The integration of machine learning (ML) and Internet of Things (IoT) technologies has a scope of improvement in precision farming techniques and revolutionise the agriculture sector. This research paper examines the integration of IoT and ML in soil nutrient monitoring and crop recommendation systems. The aim is to provide a thorough overview of the most recent developments, applications, challenges, and potential future directions. IoT sensors collect and give immediate soil health information, while ML algorithms analyses these data to suggest nutrient deficits and recommend the most suitable crops. This research work evaluates advancements in IoT and Machine Learning applications, emphasising their benefits for precision agriculture. This integration aims to improve agricultural production and sustainability via data driven decision making and resource optimisation. At the end research gaps are discussed, along with future research directions. © 2025 IEEE.

关键词： Smart agriculture

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Early Diagnosis of Alzheimer’s Disease with Transfer Learning Techniques Via ResNet50 and FSBi-LSTM

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 149-164页

作者： Khaleel, Z.S. Lakizadeh, Amir Computer Engineering and Information Technology Department University of Qom Qom Iran Department of mathematic Open Educational College Basra Study Center Iraq

Alzheimer's Disease (AD) is a neurological disorder marked by cognitive deterioration and neurological impairment that affects cognition, memory, and behavioral patterns. Alzheimer's is an incurable disease that predominantly impacts individuals over the age of 40. A patient's MRI (Magnetic Resonance Imaging) scan and cognitive assessment are manually analyzed to diagnose Alzheimer's disease. Recently, Artificial Intelligence (AI), particularly through Deep Learning, has pioneered innovative techniques for automated medical image identification. We devised a deep learning methodology for Alzheimer's disease identification utilizing Magnetic Resonance Imaging (MRI) data. The suggested method, termed Res+FSBILSTM, employs ResNet50 as a pre-trained model for feature extraction from MRIs, thereafter identifying Alzheimer's disease through a Fully-Stack Bidirectional Long-Short Term Memory deep learning model. The experimental results demonstrate that the suggested method surpasses state-of-the-art techniques across all evaluation metrics, rendering it a viable tool for medical professionals in identifying Alzheimer's disease using brain radiological images. Ultimately, we achieved results with an accuracy of 99.6%, an F1-score of 97.7%, an area under the curve of 99%, a recall of 97.3%, and a precision of 99.6%. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Magnetic resonance imaging

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DIEONet:Domain-Invariant information Extraction and Optimization Network for Visual Place Recognition

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computers, Materials & Continua 2025年第3期82卷 5019-5033页

作者： Shaoqi Hou Zebang Qin Chenyu Wu Guangqiang Yin Xinzhong Wang Zhiguo Wang School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and *** is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR *** this end,this paper proposes a Domain-invariant information Extraction and Optimization Network(DIEONet)for *** core of the algorithm is a newly designed Domain-invariant information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object *** Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during *** demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative *** particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

关键词： Visual place recognition domain-invariant information mining module multi-sample joint triplet loss

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Chinese Relation Extraction Based on Cross-Attention and Multi-feature Perception

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1837-1853页

作者： Xu, Shiao Sun, Shuihua Zhang, Zhiyuan Zhou, Huan School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

Relation extraction aims at extracting semantic relations between given pairs of entities from unstructured textual data and is a critical task in information extraction. A relation extraction model based on cross-attention and multi-feature perception is proposed to address the limitations of Chinese relation extraction models that rely solely on characters, which struggle to capture rich semantic information, and the inability to ful ly utilize the textual structural information during neural network feature extraction. First, the model uses BERT to encode sentences and entities to obtain character vector sequences and word vector sequences of sentences, and uses word cross-attention mechanism to capture the key word and word information in sentences to form character and word cross-fused sentence sequences. Second, uses graph convolutional network and deep separable convolutional network to capture syntactic information between nodes and multi-granularity local semantic information in sentence sequences, respectively, and forms sentence features for classification by attention mechanism. Final ly, combines with Softmax classifier for relation classification. Experimental results on the Chinese SanWen dataset demonstrate the superiority of the proposed method over mainstream neural network-based relation extraction methods, with an F1-score of 71.57%. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Dynamic Neurological Insights: Real-Time Stress Analysis Utilizing Portable EEG Kits 21

Dynamic Neurological Insights: Real-Time Stress Analysis Uti...

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21st International engineering Research Conference 2024, EURECA 2024

作者： Saranya, K. Gokila, N.M. Computer Science Engineering Kumaraguru College of Technology India Information Science and Engineering Kumaraguru College of Technology India

Clinical Electroencephalogram(EEG) devices are effective in hospitals, but for everyday monitoring, portable options are more accessible and convenient, albeit less accurate. In this research, the working of three portable devices -Neurosky MindWave Mobile 2, Muse 2 headband and Emotiv Insight that have different channels 1,4,5 respectively are explored. They are used to understand students' state of stress in two different scenarios-listening to online lecture videos (Scenario 1) and live lecture classes in their classrooms (Scenario 2). Twelve engineering college students participated in the experiment and were asked to watch 10 video lectures from Coursera each video is 4 to 8-minute duration and their EEG data was collected with all the three devices. The same participants were engaged in EEG data collection during their live lecture classes for one hour. A questionnaire consisting of a Likert scale like Self-Assessment Manikin (SAM) was prepared to assess the levels of arousal and valence experienced by the participants. The collected EEG signals from both scenarios are tested with the pre-trained model (Artificial neural network (ANN )model trained using DEAP (A dataset for emotion analysis using EEG, physiological and video signals dataset) and the results of real-time data are analysed. In Scenario 1 which is short duration, EEG recordings showed an accuracy of 55% in Neurosky and 63% in Muse and 76% in Emotiv Insight and in Scenario 2 which is long duration EEG recordings showed an accuracy of 47% in Neurosky and 60% in the muse and 67% in Emotiv Insight. Emotiv shows high accuracy, but Muse proves more comfortable for extended use due to its consistent accuracy in both scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Enhancing Ddos Attack Detection and Prevention in Mobile IP Networks Using HCIF 5

Enhancing Ddos Attack Detection and Prevention in Mobile IP ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Shirke, Sonali Bharat Jaleel, Uruj Kadam, Sandeep Junnarkar, Aparna Atul Kalinga University Department of Computer Sci. and Engg India Keystone School of Engineering Department of Computer Engineering India Vishwakarma Institute of Information Technology Department of Information Technology India

ISBN: (纸本)9798350376685

This research suggests a Hybrid Cache-based Intelligent Framework (HCIF) to help mobile IP networks better identify and mitigate DDoS attacks. To improve network security and QoS, the HCIF technique integrates source monitoring, attack detection, and verification based on Turing tests. We evaluate HCIF and the state-of-the-art RBPBoost approach in NS-3 simulations, taking into account different mobility situations and attacker numbers. Throughput, packet delivery ratio, and packet loss ratio are all areas where HCIF excels compared to RBPBoost, while delay performance is still an issue. © 2024 IEEE.

关键词： Internet protocols

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