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IEEE International Conference on Systems, Man and Cybernetics

作者： Gang Li Tianjiao Chen Min Li Delong Han MingLe Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P 3 M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods.

关键词： Training Location awareness Visualization Noise reduction Production Feature extraction Product design Manufacturing Quality assessment Anomaly detection

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FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

FGSNet: A Finer-Grained Siamese Network for Industrial Few-S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： DeLong Han Luo Xu MingLe Zhou Gang Li Min Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image anomaly detection usually relies on a large set of training samples and abnormal samples are relatively rare and challenging to obtain in daily industrial scenarios. In the scenario of Few-Shot Anomaly Detection (FSAD), how to better utilize the fine-grained features of the few images is a key issue. To solve these problems, a Finer-Grained Siamese Network (FGSNet) is proposed in this paper. FGSNet considers the setting of Few-Shot Anomaly Detection (FSAD) and consists of two stages. The first stage is responsible for extracting finer-grained features and roughly aligning the image features, while incorporating an efficient Fine-grained Feature Fusion Module $(\mathbf{F}^{3}\mathbf{M})$ to enhance feature representation. Meanwhile, we design a Deep-supervised Loss to improve the level of fine-grained information extraction. The second is primarily used to refine and denoise the fused features, followed by detailed alignment operations for subsequent feature distribution modeling. Compared to most existing FASD models that use single-class training and single-model evaluation, FGSNet is more suitable for existing industrial scenarios. Extensive experiments are conducted on the publicly available MVTec AD and MPDD datasets in this paper. The results show that, for 2, 4 and 8-shot cases, FGSNet can increase the average Image-AUROC on MVTec AD by 0.87%, 1.27% and 1.33% respectively, compared to RegAD.

关键词： Training Feature extraction Information retrieval Anomaly detection Cybernetics

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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Applications of Artificial Intelligence in Biomedical Image Processing 2

Applications of Artificial Intelligence in Biomedical Image ...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Rajeyyagari, Sivaram Hung, Bui Thanh Chakrabarti, Prasun Shaqra University College of Computing and Information Technology Department of Computer Science Shaqra Saudi Arabia Thu Dau Mot University Data Analytics and Artificial Intelligence Laboratory Engineering Technology Faculty Thu Dau Mot Viet Nam Provost and Institute Endowed Distinguished Senior Chair Professor Techno India Njr Institute of Technology Rajasthan India

ISBN: (纸本)9781665400527

The biomedical applications of nanoparticles are growing in recent years. The uses range from diagnostic to therapeutic purposes, and researchers are constantly introducing new ways. Biosensors, miniaturization techniques, surface enhancement in implantable biomaterials, and better nanofibers in medical textiles are some of the contributions of biomedical nanotechnology. Therapeutic (medical) imaging successfully maps the anatomy of the human body. Image enhancement, analysis, categorization, and image based representation are all possible areas of computer processing and analysis of medical images. Therapeutic image understanding uses computer vision, applied mathematics, signal analysis, and artificial intelligence to recognise objects in medical images. Image processing is the use of computer algorithms to manipulate digital images. Hand-held transducers emit and receive pulsed acoustic waves for medical ultrasound imaging. In this study, a computer-aided diagnosis method is developed to help cytotechnicians locate and classify aberrant cells in cervical cytology images. With the outcomes of morphological and statistical edge based segmentation are compared to traditional segmentation techniques. The proposed segmentation methodology outperforms the other methods. This proposed technique's segmentation accuracy is 98%. © 2022 IEEE.

关键词： Artificial intelligence

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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological data Analysis

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Journal of Chemical Information and Modeling 2025年第11期65卷 5567-5575页

作者： Koseki, Jun Motono, Chie Yanagisawa, Keisuke Kudo, Genki Yoshino, Ryunosuke Hirokawa, Takatsugu Imai, Kenichiro Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Integrated Research Center for Self-Care Technology (irc-sct) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) Waseda University Shinjuku-ku Tokyo 169-8555 Japan Department of Computer Science School of Computing Institute of Science Tokyo Tokyo 152-8550 Japan Middle Molecule IT-based Drug Discovery Laboratory (MIDL) Institute of Science Tokyo Tokyo 152-8550 Japan Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba Ibaraki 305-8571 Japan Division of Biomedical Science Faculty of Medicine University of Tsukuba Ibaraki 305-8575 Japan Transborder Medical Research Center University of Tsukuba Ibaraki 305-8577 Japan Global Research and Development Center for Business By Quantum-AI Technology (G-QuAT) National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8560 Japan

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein’s conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy. © 2025 The Authors. Published by American Chemical Society.

关键词： Binding sites

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological data Analysis and ResNet

Contrastive Learning for Chest X-ray Classification: A Fusio...

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Hao Ren Zeyu Luo Fengshi Jing Xinyue Zhang Han He Yonghao Yu Dawei Zhao Faculty of Data Science City University of Macau China Minzu University of China Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model’s discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework’s robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility.

关键词： Deep learning Accuracy Computational modeling Contrastive learning Feature extraction Robustness Real-time systems Computational efficiency X-ray imaging Biomedical imaging

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A Review of the Effectiveness of Management Information System in Decision Making

A Review of the Effectiveness of Management Information Syst...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Hazem Mohammad Zeidan Al-kaseasbeh Firas Rashed Wahsheh Fatma Susilawati Mohamad Julaily Aida Jusoh Mazen Mohammed Farea Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of Management Information Systems Faculty of Business Ajloun National University Ajloun Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Jain Deemed To Be University Bangalore India

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information Systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information Systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information Systems, including Executive Information Systems (EIS), Decision Support Systems (DSS), and Transaction Processing Systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information Systems and decision-making effectiveness

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Fortifying database Security: Integrating Advanced Honeypot Technology for Resilient SQL Injection Defense

Fortifying Database Security: Integrating Advanced Honeypot ...

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Communication, Computer sciences and Engineering (IC3SE), International Conference on

作者： Kumaran U Thammisetty Swetha G. S. N. Santhosh Reddy Harini Ala Senthil Murugan Nagarajan B. Umah Department of Computer Science & Engineering Amrita School of Computing Bengaluru India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Luxembourg Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The dynamic nature of cyber threats necessitates the development of novel approaches to strengthen database system security. The novel method for fortifying defenses against SQL injection attacks—a pervasive and ongoing danger in the field of cybersecurity—is presented in this study. The suggested methodology creates a strong and adaptable defense mechanism by seamlessly integrating cutting-edge honeypot technology with current SQL injection mitigation techniques. Our approach, which we have named HoneySQLGuard, uses carefully con- structed honeypot tables inside the database system. These tables serve as sly bait, drawing potential attackers’ attention away from genuine data and toward themselves. By utilizing the most recent developments in technology, Honey SQL Guard surpasses the limitations of conventional signature-based detection techniques and is able to adjust to changing attack strategies via anomaly and machine learning. The honeypot tables are cleverly incorporated within the database architecture, making them invisible to an outside observer when compared to real tables. The technology immediately recognizes and isolates unusual behavior suggestive of SQL injection attempts by closely monitoring and analyzing query patterns. Furthermore, HoneySQLGuard has real-time reporting and alerting functions that guarantee an early reaction to any possible intrusions. Extensive testing is conducted to validate the effectiveness of the proposed solution, which shows it can dramatically lower the success rate of SQL injection attacks while keeping false-positive rates low. This study advances the subject by providing a thorough and flexible method for fortifying databases against SQL injection attacks, in line with the industry's increasing demand for robust cybersecurity defenses.

关键词： Protocols Prevention and mitigation Machine learning SQL injection Observers database systems Real-time systems

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When Artificial Intelligence meets Navarasas - Facial Expression Classification in Kathakali Using DSFD and CNN

When Artificial Intelligence meets Navarasas - Facial Expres...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Ganesh Neelakanta Iyer Nattapong Saengarunvong Minh Hien Le Department of Computer Science School of Computing National University of Singapore Singapore Department of Computer Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Computer and Data Sciences Case School of Engineering Case Western Reserve University Cleveland Ohio USA

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor’s face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form.

关键词： Training Accuracy Art Computational modeling Signal processing algorithms Detectors data models Faces Tuning Testing

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Detection of ARP Spoofing Attack in Healthcare Networks Using a LSTM Model

Detection of ARP Spoofing Attack in Healthcare Networks Usin...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Ghader Kurdi Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Department of Data Science College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Healthcare networks face significant cybersecurity threats due to their extensive interconnectivity, with Address Resolution Protocol (ARP) spoofing posing a major risk to the confidentiality and integrity of medical data. ARP spoofing is a Man-in-the-Middle (MITM) attack where an attacker sends falsified ARP messages to associate their MAC address with the IP address of a legitimate device, enabling unauthorized interception or manipulation of network traffic. In this paper, we propose an LSTM-based detection model designed to effectively identify ARP spoofing attacks in healthcare environments. Unlike traditional methods that rely on static markers at fixed time intervals, the LSTM model leverages temporal dependencies in network traffic, enhancing detection accuracy and adaptability to evolving attack patterns. Experimental results based on the CICIoMT dataset 2024 demonstrate that the proposed model achieves a detection accuracy of 98.5%, significantly outperforming conventional approaches. These findings highlight the effectiveness of LSTM networks in mitigating ARP spoofing threats, offering a scalable solution for securing healthcare networks.

关键词： Adaptation models Protocols Accuracy Computational modeling Intrusion detection Telecommunication traffic Internet of Medical Things IP networks Long short term memory Faces

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