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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Incremental Learning Algorithms for Broad Learning System with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad Learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks

arXiv

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arXiv 2024年

作者： Rebmann, Adrian Schmidt, Fabian David Glavaš, Goran van der Aa, Han Data and Web Science Group University of Mannheim Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Faculty of Computer Science University of Vienna Austria

The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability of LLMs to support process analysis and even, to some extent, that they are able to reason about how processes work. This latter property suggests that LLMs could also be used to tackle process mining tasks that benefit from an understanding of process behavior. Examples of such tasks include (semantic) anomaly detection and next activity prediction, which both involve considerations of the meaning of activities and their interrelations. In this paper, we investigate the capabilities of LLMs to tackle such semantics-aware process mining tasks. Furthermore, whereas most works on the intersection of LLMs and process mining only focus on testing these models out of the box, we provide a more principled investigation of the utility of LLMs for process mining, including their ability to obtain process mining knowledge post-hoc by means of in-context learning and supervised fine-tuning. Concretely, we define three process mining tasks that benefit from an understanding of process semantics and provide extensive benchmarking datasets for each of them. Our evaluation experiments reveal that (1) LLMs fail to solve challenging process mining tasks out of the box and when provided only a handful of in-context examples, (2) but they yield strong performance when fine-tuned for these tasks, consistently surpassing smaller, encoder-based language models. © 2024, CC BY.

关键词： Anomaly detection

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Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks

Evaluating the Ability of LLMs to Solve Semantics-Aware Proc...

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International Conference on Process Mining (ICPM)

作者： Adrian Rebmann Fabian David Schmidt Goran Glavaš Han van Der Aa Data and Web Science Group University of Mannheim Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Faculty of Computer Science University of Vienna Austria

ISBN: (数字)9798350365030

ISBN: (纸本)9798350365047

关键词： Process mining Accuracy Large language models Semantics Benchmark testing Predictive models Anomaly detection

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Dynamic Exit Selection and Counting Area Adjustment for Efficient Crowd Evacuation in Asymmetrical Layouts 25

Dynamic Exit Selection and Counting Area Adjustment for Effi...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alidmat, Omar Khair Alla Alazaidah, Raed Arabiat, Mohammad Khafajeh, Hayel Aljaidi, Mohammad Yusof, Umi Kalsom Elrashidi, Ali Zarqa University Computer Science Department Zarqa Jordan Zarqa University Data Science and Artifical Inteligence Department Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Malaysia University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influenced by enhanced dynamic parameters - congestion, distance, and empty. It also accounts for heterogeneous agent behaviors to more accurately represent evacuee dynamics in complex, asymmetrical environments with dense crowds. Key findings indicate that the model effectively alleviates bottlenecks at congested exits, optimizes exit usage, and enhances overall evacuation safety. Comparative analyses demonstrate that the proposed model significantly improves evacuation efficiency and reduces travel distances compared to previous models. Simulation results indicate that agent density directly affects evacuation time, with lower congestion levels facilitating faster egress. These advancements lead to more precise evacuation time predictions and crowd flow assessments, offering valuable insights for refining emergency egress strategies in complex settings. Future research will focus on refining the model with real-world data to expand its applicability in diverse settings, such as stadiums and public transportation hubs, thereby enhancing emergency preparedness and response. © 2024 IEEE.

关键词： Lead refining

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Automated Detection of Alzheimer's Disease Using EfficientNet-B3 Architecture with Transfer Learning 25

Automated Detection of Alzheimer's Disease Using EfficientNe...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Aburomman, Ahmad Abuowaida, Suhaila Al-Momani, Adai Al Henawi, Esraa Elsoud, Esraa Abu Salah, Zaher Alzoubi, Haneen Chan, Huah Yong Khouj, Mohammed Zarqa University Department of Data Science and Ai Zarqa Jordan Al-Bayt University Department of Computer Science Ai Mafraq Jordan Zarqa University Department of Computer Science Zarqa Jordan The Hashemite University Department of Information Technology Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Gelugor11800 Malaysia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer's disease, an automated approach for Alzheimer's disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer's Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer's disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across. © 2024 IEEE.

关键词： Image annotation

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Crossfire: An Elastic Defense Framework for Graph Neural Networks under Bit Flip Attacks

arXiv

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arXiv 2025年

作者： Kummer, Lorenz Moustafa, Samir Gansterer, Wilfried N. Kriege, Nils Faculty of Computer Science University of Vienna Vienna Austria Doctoral School Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

Bit Flip Attacks (BFAs) are a well-established class of adversarial attacks, originally developed for Convolutional Neural Networks within the computer vision domain. Most recently, these attacks have been extended to target Graph Neural Networks (GNNs), revealing significant vulnerabilities. This new development naturally raises questions about the best strategies to defend GNNs against BFAs, a challenge for which no solutions currently exist. Given the applications of GNNs in critical fields, any defense mechanism must not only maintain network performance, but also verifiably restore the network to its pre-attack state. Verifiably restoring the network to its pre-attack state also eliminates the need for costly evaluations on test data to ensure network quality. We offer first insights into the effectiveness of existing honeypot- and hashing-based defenses against BFAs adapted from the computer vision domain to GNNs, and characterize the shortcomings of these approaches. To overcome their limitations, we propose Crossfire, a hybrid approach that exploits weight sparsity and combines hashing and honeypots with bit-level correction of out-of-distribution weight elements to restore network integrity. Crossfire is retraining-free and does not require labeled data. Averaged over 2,160 experiments on six benchmark datasets, Crossfire offers a 21.8% higher probability than its competitors of reconstructing a GNN attacked by a BFA to its pre-attack state. These experiments cover up to 55 bit flips from various attacks. Moreover, it improves post-repair prediction quality by 10.85%. Computational and storage overheads are negligible compared to the inherent complexity of even the simplest GNNs. © 2025, CC BY.

关键词： Convolutional neural networks

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