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Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye 27th

Adaptive Class Learning to Screen Diabetic Disorders in Fu...

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27th international conference on pattern recognition, ICPR 2024

作者： Dey, Shramana Dutta, Pallabi Bhattacharyya, Riddhasree Pal, Surochita Mitra, Sushmita Raman, Rajiv Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road West Bengal Kolkata700108 India Shri Bhagwan Mahavir Vitreoretinal Services Sankara Nethralaya Chennai India

ISBN: (纸本)9783031781032

The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. The classifier is initially trained to identify relevant features concerning Healthy and Diabetic Retinopathy (DR) classes and later fine-tuned to adapt to the task of classifying the input images into three classes, viz. Healthy, DR and Glaucoma. This strategy allows the model to gradually enhance its classification capabilities, which is beneficial in situations where there are only a limited number of labeled datasets available. Perturbation methods are also used to identify the input image characteristics responsible for influencing the model’s decision-making process. We achieve an overall accuracy of 91% on publicly available datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ophthalmology

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Advanced non-linear 3D FEM modeling of masonry structures for the preservation of cultural heritage 3

Advanced non-linear 3D FEM modeling of masonry structures fo...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Rainone, Luigi Salvatore Tateo, Vito Casolo, Siro Uva, Giuseppina DICATECh Department Politecnico di Bari Via Edoardo Orabona 4 Bari70125 Italy ABC Department Politecnico di Milano Piazza Leonardo da Vinci 32 Milano20133 Italy

The Italian building stock is largely made up of masonry structures of different ages, having high social and cultural value. Such constructions constitute a heritage to be preserved. Therefore, it is necessary to evaluate their safety level, making use of proper numerical models that should be sufficiently advanced, accurate and detailed, with a specific attention to the 3D modelling, but still computationally feasible. The development of such models presents several difficulties because of the peculiar behavior of these systems, which is very variable according to the observation scale. Therefore, the modelling and analysis of masonry structures is still an open question in the scientific world. In the literature, Concrete Damage Plasticity (CDP) is widely applied to the numerical modeling of this type of structures by means of FEM approaches. This is a material model originally developed for the analysis of concrete elements (reinforced and not). In this work, we report some applications of Concrete Damage Plasticity to problems on masonry structures at different scales. © 2024 Copyright for this paper by its authors.

关键词： Digital elevation model

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Advancing 3D Mesh Analysis: A Graph Learning Approach for Intersecting 3D Geometry Classification 27th

Advancing 3D Mesh Analysis: A Graph Learning Approach for I...

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27th international conference on pattern recognition, ICPR 2024

作者： Böhm, Stefan Andreas Neumayer, Martin Zagar, Bare Luka Riß, Fabian Kortüm, Christian Knoll, Alois Technical University of Munich Boltzmannstraße 3 Garching85748 Germany Rosenheim Technical University of Applied Sciences Hochschulstraße 1 Rosenheim83024 Germany

ISBN: (纸本)9783031781650

Driven by increasing customer demands, manufacturing processes now encompass increasingly intricate workflows. The industry uses computer-aided process planning to manage these complex manufacturing processes effectively. A crucial task here is to analyze product data and determine the required machining features, represented as 3D mesh geometries. However, a notable challenge arises, particularly with custom products, where the interpretation of the 3D mesh geometry varies significantly depending on the available machinery and expert preferences. This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can use a configurable synthetic data generator to encode their requirements within this framework via the training data. A machine-learning graph classification approach is used to recognize the 3D geometries of machining features in the generated data, based on to the user requirements. The system accomplishes this without requiring for data conversion into alternative formats, such as voxel or pixel representations, like other approaches are forced to. © The Author(s) 2025.

关键词： Mesh generation

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An Investigation of machine Learning Algorithms for High-bandwidth SQL Injection Detection Utilising BlueField-3 DPU Technology 36

An Investigation of Machine Learning Algorithms for High-ban...

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36th IEEE international System-on-Chip conference (SOCC)

作者： Tasdemir, Kasim Khan, Rafiullah Siddiqui, Fahad Sezer, Sakir Kurugollu, Fatih Bolat, Alperen Queens Univ Belfast Ctr Secure Informat Technol Belfast Antrim North Ireland QUB GDR LAB Grp Belfast Antrim North Ireland NVIDIA Corp Belfast Antrim North Ireland Univ Sharjah Dept Comp Sci Coll Comp & Informat Sharjah U Arab Emirates

ISBN: (纸本)9798350300116

SQL injection attacks present a significant risk to data center security. Traditional rule-based pattern matching techniques exhibit limitations, such as inability to adapt to new attack types, to give decision confidence and lower detection accuracy. machine learning (ML) based approaches offer promising alternatives;however, their computational requirements and the increasing volume of network traffic pose challenges for their application in conventional hardware. Data Processing Units (DPUs) have emerged as the tailored computing platform for infrastructure related workloads within data centers including security. This paper evaluates the performance and efficiency of classical ML methods for SQL injection detection utilising computing resources on DPUs. In this study, 20 prominent ML models are tested against a dataset comprising 30,000 SQL payloads, and their performance is compared in a series of experiments. The results indicate that the Passive Aggressive Classifier is the most suitable model for near-real-time detection, achieving a detection latency of approximately 0.3 mu s/sample with an accuracy of 99.78%. This paper demonstrates that ML methods can be efficiently and effectively deployed on DPUs for SQL injection detection, providing valuable insights into threat intelligence for enhancing data center security. The codes of this study can be found at: https://***/gdrlab/dpu- sqli- detection.

关键词： SQL injection Attack Detection machine Learning Passive Aggressive Analysis Data Processing Unit Network

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Binary Optimization Using Hybrid Owl Optimization for Biomarker Selection from Cancer Datasets 3

Binary Optimization Using Hybrid Owl Optimization for Biomar...

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3rd IEEE international conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Sahu, Bibhuprasad Madhavi, Chakradharamahanthi Pani, Sasmita Ravindra, Jvr Department of Artificial Intelligence and Data Science Hyderabad India Balasore College of Engineering and Technology Department of Computer Science and Engineering Odisha Balasore India Department of Electronics & Communication Engineering Hyderabad India

ISBN: (纸本)9798350397819

The enhancement of medical technology generates a massive amount of disease data. For accurate disease detection, feature subset selection plays an important role in solving different classification problems. The selection of a good feature subset enhances the accuracy of the machine learning model and reduces the training time. However, it becomes tricky and challenging to identify the optimal feature subset from the high-dimensional datasets. Different hybrid models are proposed by various researchers to deal with these types of issues. The Owl optimization algorithm proves its efficiency in selecting the optimal features. Keeping an eye on the performance of this stochastic optimization, a binary version of the hybrid Owl optimization algorithm with simulating annealing is proposed to detect the biomarker features from the cancer datasets. K nearest neighbors (KNN) are used as a wrapper to find the best biomarkers. Eight benchmark datasets were employed for the proposed model's performance evaluation. The results show that the proposed model is significantly better than binary Owl optimization, and other counterparts using various performance matrices such as accuracy, selection of best biomarkers, and computational time. The proposed model Owl-SA performs with an accuracy of 100% in the case of CNS and lymphoma datasets. And it retains the accuracy of 99.14%, 97.16%, 98.41%, 97.94%, 98.93%,98.32% for breast, colon, ALL-AML, ovarian, SRBCT, and MLL respectively. © 2023 IEEE.

关键词： Biomarkers

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Practical and Ethical Considerations for Generative AI in Medical Imaging 2nd

Practical and Ethical Considerations for Generative AI in...

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2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image Computing and Computer Assisted Interventions, MICCAI 2024

作者： Jha, Debesh Rauniyar, Ashish Hagos, Desta Haileselassie Sharma, Vanshali Tomar, Nikhil Kumar Zhang, Zheyuan Isler, Ilkin Durak, Gorkem Wallace, Michael Yazici, Cemal Berzin, Tyler Biswas, Koushik Bagci, Ulas Machine and Hybrid Intelligence Laboratory Northwestern University Evanston United States SINTEF Digital Trondheim Norway Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Department of Computer Science University of Central Florida Orlando United States Mayo Clinic Florida Jacksonville United States Department of Medicine University of Chicago Chicago United States Beth Israel Deaconess Medical Center Harvard Medical School Boston United States

ISBN: (纸本)9783031727863

Generative Artificial intelligence (AI) has the potential to transform medicine. It is helpful to clinicians and radiologists for diagnosis, screening, treatment planning, interventions, and drug development. It benefits the clinical flow with real-time decision-support systems. While generative AI can potentially improve healthcare, it also introduces new ethical issues that require careful analysis and mitigation strategies. This work emphasizes the ethical aspects of generative AI in medical imaging, aiming to ensure that advancements in this field align with established ethical principles and societal values. We delve into the ethical implications surrounding bias, fairness, patient privacy, consent, transparency, explainability, intellectual property, and data ownership. Furthermore, we discuss regulations governing the use of synthetic medical data. To promote equitable application of these powerful tools, we also propose clear guidelines for promoting fairness, mitigating bias, and ensuring diversity within generative AI models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm 3

Recognition of Traffic Signs with Artificial Neural Networks...

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3rd international conference on Artificial intelligence in Information and Communication (IEEE ICAIIC)

作者： Kerim, Abdulrahman Efe, Mehmet Onder Hacettepe Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781728176383

Traffic sign classification is a prime Issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary pattern) features.

关键词： artificial neural networks image classification pattern recognition data preprocessing

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Multiple Roles of Big Data Platform and Artificial intelligence Architecture 3

Multiple Roles of Big Data Platform and Artificial Intellige...

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Zhang, Jing Film School Modern College of Northwest University Shanxi Xi'an710130 China

ISBN: (纸本)9781643684581

Multi-role and artificial intelligence have gradually pushed the industry to a new stage of 'human-machine integration' and 'role intelligence'. Methods: Based on the big data platform, the feasibility of artificial intelligence and multiple roles of products was studied, and artificial intelligence and multiple roles provided intelligent interaction opportunities for the social production and visual dissemination of product knowledge system. Content: The construction of products by big data platform can realize the collaborative production of intelligent machines and professional designers, and multi-role interaction can realize the new dynamic level of knowledge of copywriting planning, visual design, structure generation and function reconstruction. As the artificial intelligence matching users and designers, the product big data platform has dynamic connection and interaction, which can further promote the hierarchical transformation of the knowledge system of users and designers from independent thinking to diversified vision, and from broad connection to multi-dimensional connection. The multi-role and artificial intelligence architecture can aggregate the multi-dynamic and interactive freedom of the product's main content economy, assets and culture in the upstream and downstream, as well as the traceable flow information of the big data platform, thus bringing the increment of the product's internal and external stock content and the value increment of the asset. Results: The product big data platform further proposed to build a 'human-machine integration' and 'role intelligence' data system with multi-role communication foreground, artificial intelligence knowledge center and blockchain technology background as the core. © 2023 The Authors.

关键词： Big data

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Autism Spectrum Detection Using 3D CNN 3rd

Autism Spectrum Detection Using 3D CNN

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Ganeshan, Harini Suresh, Chalumuru Annireddy, A. Pamidimukkala, Chandralekha Alleni, Supraja Department of CSE VNR Vignana Jyoti Institute of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819743582

Autism Spectrum Disorder (ASD), a form of neurological sickness, appears in youngsters among 6 and 17 years of age and impacts conversation abilities and social conduct. ASD impacts social interactions and communication and reasons repetitive behaviors in patients. Consistent with the WHO, ASD impacts one baby in one hundred sixty. Early diagnosis during adolescence is critical and might improve the social capabilities and communiqué problems of youngsters. So, it is very important to find a proper way to detect Autism. For diagnosing neurological diseases, it is highly required to study the structural vicinity relationships within the brain. Hence, to study the brain and its patterns, fMRI (functional magnetic resonance imaging) is used. It detects correlated fluctuations inside the blood oxygen degree-dependent (bold) alerts from the brain regions. This paper is proposed with the primary motive of building, training and testing a 3D Convolutional Neural Network (CNN) model using a dataset abbreviated as ABIDE—Autism Brain Imaging Data Exchange dataset and predict outcome of the class labels as autism or no-autism for unseen health records. The project also aims to present a comprehensive analysis and provide some recommendations related to ASD results generated by the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Ensemble Nonlinear machine Learning Model for Chronic Kidney Diseases Prediction 3

Ensemble Nonlinear Machine Learning Model for Chronic Kidney...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Sampath, S. Prasad, Mudarakola Lakshmi Hussain, Mohammad Manzoor Parameswari, R. Anil Kumar, D. Reddy, Pundru Chandra Shaker Adichunchanagiri Institute of Technology Department of Information Science and Engineering Karnataka Chikkamagaluru India Institute of Aeronautical Engineering Computer Science and Engineering Hyderabad Telangana Dundigal India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India Sathyabama Institute of Science and Technology Department of Mathematics TamilNadu India PACE Institute of Technology and Sciences Department of Electronics and Communication Engineering AP Ongole India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350340358

Living in a major metropolitan area has been linked to an increased risk of developing multiple forms of chronic-kidney-disease(CKD). In developed nations, predicting CKDs is a top priority. Predictive analytics for the purpose of predicting CKDs are the primary focus of this work. However, it is getting harder and harder to forecast outcomes for massive samples. While doing so, the MapReduce architecture makes it possible to write predictive algorithms by combining map and reduce operations. Problems with the scalability and effectiveness of anticipative learning approaches are alleviated by the comparatively straightforward programming interface. To efficiently handle small subsets of massive datasets, the authors propose using an iterative weighted mapreduce approach. Ensemble-nonlinear support-vector-machines(ENSVM) and random-forests(RF) are used to design a binary classification issue. As a result, the suggested approach generates nonlinear blends of kernel activations in example prototypes, as opposed to the conventional linear combination of activations. In addition, an ensemble of deep-SVM is utilized to integrate the descriptors, with the product rule being employed to merge the classifiers' likelihood estimates. Prediction accuracy and results interpretability are used to gauge performance. © 2023 IEEE.

关键词： MapReduce

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