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8th International conference on Data Science and Machine Learning applications, CDMA 2025

作者： Asim, Tayyaba Akbar, Shahzad Shahzore, Usama Rehman, Amjad Saba, Tanzila Ayesha, Noor Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan450001 China

ISBN: (纸本)9798331539696

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. Therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. The proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets;the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional neural networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional neural networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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WiGNet: Windowed Vision Graph neural Network

WiGNet: Windowed Vision Graph Neural Network

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2025 IEEE/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Spadaro, Gabriele Grangetto, Marco Fiandrotti, Attilio Tartaglione, Enzo Giraldo, Jhony H. University of Turin Italy Institut Polytechnique de Paris Ltci Télécom Paris France

ISBN: (纸本)9798331510831

In recent years, Graph neural networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the imageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code and pre-trained models at https://***/EIDOSLAB/WiGNet. © 2025 IEEE.

关键词： NP-hard

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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ActivMedica—MRI Brain image Detection & Captioning with ActivGPT Integration

ActivMedica—MRI Brain Image Detection & Captioning with Act...

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International conference on Advances and applications of artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Malayil, Shanid Krishna, P. Sudeep Pattupara, Rabeen Mohamed, P. M. Rishil Faris, Salman Suhair, K. Muhammed Ahmed, P. Jemsheer MEA Engineering College Kerala Perinthelmanna India

ISBN: (纸本)9789819795062

ActivMedica is an innovator in brain tumor diagnosis in the rapidly changing healthcare industry, employing cutting edge AI tools including deep learning and natural language processing (NLP). The shortcomings of existing manual approaches for medical imaging data analysis include difficulty in identifying diverse brain tumors and increasing medical expenditures. Our approach, which at first is based on transfer learning and GIT architecture, uses the GIT convolutional neural network, or CNN, model. A carefully chosen and preprocessed diverse dataset, enhanced for variety and improved by transfer learning from the imageNet dataset for feature extraction, is essential for successful model training. Using the GIT-based methodology, ActivGPT, an AI chatbot built on OpenAI, enables real-time image analysis through natural language conversations. In addition to brain tumor detection, our research generates brief medical reports based on detected brain tumors, providing medical experts with comprehensive information. Brain tumor diagnosis is ethically improved by ActivMedica, a comprehensive healthcare innovation that combines AI, deep learning, and natural language processing. By doing this, diagnosis accuracy is greatly increased while moral and ethical requirements for AI use in healthcare are maintained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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GM-GAN: Geometric Generative Models Based on Morphological Equivariant PDEs and GANs 27th

GM-GAN: Geometric Generative Models Based on Morphological E...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Diop, El Hadji S. Fall, Thierno Mbengue, Alioune Daoudi, Mohamed Department of Mathematics NAGIP-Nonlinear Analysis and Geometric Information Processing Group University Iba Der Thiam BP 967 Thies Senegal Department of Mathematics and Computer Science University Cheikh Anta Diop Dakar Senegal IMT Nord Europe Univ. Lille Centre for Digital Systems Lille59000 France Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL Lille59000 France

ISBN: (纸本)9783031783883

This work deals with image generation, two main problems are addressed: (i) improvements of specific feature extraction while accounting at multiscale levels intrinsic geometric features, and (ii) equivariance of the network for reducing the complexity and providing a geometric interpretability. We propose a geometric generative model based on an equivariant partial differential equation (PDE) for group convolution neural networks (G-CNNs), so called PDE-G-CNNs, built on morphology operators and generative adversarial networks (GANs). The proposed geometric morphological GAN model, termed as GM-GAN, is obtained thanks to morphological equivariant convolutions in PDE-G-CNNs. GM-GAN is evaluated qualitatively and quantitatively using FID on MNIST and RotoMNIST, preliminary results show noticeable improvements compared classical GAN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

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ADVANCED SCIENCE 2025年第1期12卷 e2409568页

作者： Boahen, Elvis K. Kweon, Hyukmin Oh, Hayoung Kim, Ji Hong Lim, Hayoung Kim, Do Hwan Hanyang Univ Dept Chem Engn Seoul 04763 South Korea Hanyang Univ Inst Nano Sci & Technol Seoul 04763 South Korea Hanyang Univ Clean Energy Res Inst Seoul 04763 South Korea Stanford Univ Dept Chem Engn Stanford CA 94305 USA

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

关键词： artificial synapse bio-inspired machine learning neuromorphic sensory system

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional neural networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

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InceptionNeXt Network with Relative Position Information for Microexpression Recognition 18th

InceptionNeXt Network with Relative Position Information fo...

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18th International conference on Wireless artificial Intelligent Computing Systems and applications, WASA 2024

作者： Cao, Zhilong Dong, Anming Yu, Jiguo Li, Sufang Tian, Xiang Zhang, Li Jinan250014 China Jinan250353 China Jinan250014 China

ISBN: (纸本)9783031714696

Micro-expression recognition is one of the most challenging tasks in affective computing, aiming to identify subtle facial movements that are difficult for humans to perceive within a short period. In recent years, convolutional neural networks (CNNs) have been widely employed for micro-expression recognition. Despite achieving high recognition accuracy, these approaches still exhibit some limitations. Traditional CNNs learn solely from complete images, failing to accurately capture the fine details of facial micro-expressions. Traditional convolution uses small convolution kernels, which ignore global image features during feature extraction, affecting the accuracy of micro expression recognition. In response to the shortcomings of convolutional kernels, this paper proposes a large kernel convolutional neural network for micro expression recognition. Specifically: 1) Introducing a large-kernel convolutional neural network (LKCNN) that extends the model’s receptive field, enabling it to capture data relationships over a larger range. This enhances the model’s performance. To balance computational costs, the network utilizes Inception depthwise convolution, which decomposes expensive depthwise convolutions into three convolutional branches with small kernel sizes, along with an identity mapping branch. 2) The input is transformed from entire images to image blocks, followed by the incorporation of relative position attention (RPI). This not only magnifies detailed features but also filters important feature information, ensuring the full utilization of subtle information. Experiments were conducted on three publicly available databases: CASME ii, SMIC, and SAMM. The results demonstrate superior performance of the proposed method in three-class and five-class recognition compared to state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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