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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

3rd World Conference on Intelligent and 3D Technologies, WCI...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

ISBN: (纸本)9789819791231

The proceedings contain 45 papers. The special focus in this conference is on Intelligent and 3D Technologies. The topics include: Multi-scale Point Cloud Shape Completion Network Based on Deep Learning;research on Application and Effect Evaluation of Product Innovation Management Based on Deep Learning;study on Deep Learning-Based Personalised Product Recommendation Model for Autonomous Question-and-Answer Robot;application of Image Watermarking Technology Based on Deep Learning in Copyright Protection;research on the Combination of Building Structural Health Monitoring and Deep Learning Image Processing;electrical Equipment Prediction in a Variable Electromagnetic Field Using Deep Learning;state Monitoring and Fault Prediction of Wind Farm Transmission and Transformation Equipment Based on Deep Learning;application of Deep Learning Algorithms in the Innovation Ecosystem of Electric Power;optimization Strategy for Inventory Management Based on machine Learning;intelligent Design and Evaluation of Aging Adaptable Public Spaces Based on Deep Learning;performance Optimization and Acceleration of machine Learning Algorithms in Task Allocation of Mine Maintenance Robots;Application of Deep Learning to Improve the Performance of Automotive Electronic Control Unit (ECU);deep Learning-Based Scene Classification for Remote Sensing Images;Improved Swarm intelligence Optimization Algorithm Based on SL-Relu Activation Function Improvement Strategy and Its Application in Price Forecasting;Automatic Segmentation of Traumatic Penumbra in Rat Brain Based on Improved UNet++;A Brain Tumor Classification Method Based on ResNeXt-SESA Network;A LSTM Algorithm for Coastal City Cultural Scene Value Sustainable Development Forecast Improvement;pattern recognition in Archive Analysis Using Data Mining;building Crack Detection Method Based on Convolutional Neural Network.

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3rd international Workshop on Applications of Medical Artificial intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of Medical Artificial intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial intelligence. The topics include: SP-NAS: Surgical Phase recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in Medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot Medical

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The Scientific Analysis on Effective Yoga Posture recognition Techniques 3rd

The Scientific Analysis on Effective Yoga Posture Recognitio...

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

作者： Rajgure, E.G. Patidar, Raghavendra School of Engineering ECE University of Technology Jaipur India

ISBN: (纸本)9789819743582

Today’s globalized scenario of competition has increased beyond expectations and to fulfill it, everyone has to face many competitive challenges and stressful work culture. In addition to prescribing the proper medication, many medical professionals advise optimal food, pranayama, and yoga to maintain this environment. Due to this physical health awareness number of professional yoga practitioners has been increased. Yoga exercises with proper postures are very important to get the benefits of increasing flexibility, muscle strength and also improve the functionality of the respiratory system. Along with spirituality, yoga has a number of impacts on the human body which ultimately integrates the coordination between mind and body. Due to which regular practitioners can maintain self-discipline and self-awareness. Due to improper methods of practice, maximum beginners do not get benefited from the routine yoga practice. Our main goal is to review the literature on yoga posture recognition systems based on scientific analysis. It also presents our methodical approach to posture recognition and classification based on AI and machine learning techniques, which we hope will help validate the accuracy of the postures performed during yoga practice. In order to collect time series data from sensors for various postures, our ongoing work will create an embedded wearable sensor-based system. Along with this, we have also enlisted measurable benefits of practicing yoga in the improvements of human health parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence machine learning Posture recognition Yoga

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Artificial intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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The application of artificial intelligence seismic inversion techniques in sandstone and shale reservoir studies 3

The application of artificial intelligence seismic inversion...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Lin, Yaoting Liang, Lin Zhou, Wei School of Computer Science and Engineering Guangdong Ocean University Yangjiang529500 China Kunlun Digital Technology Co. Ltd. Beijing Beijing100043 China

ISBN: (纸本)9781510689077

Artificial intelligence Probabilistic Neural Networks are a type of neural network based on the theory of probability density functions, widely applicable in areas such as pattern recognition. Addressing the current issue of low prediction accuracy in seismic lithology inversion, the authors propose a specific probabilistic neural network method, including the construction of network models and steps for prediction and identification. The primary target layer in the study area is the lower part of the Sha-1 Member of the Shaximiao Formation, characterized by lakeshore and river channel sands. The reservoirs are thin-layered with significant lateral lithological variations, making conventional reservoir prediction using seismic data challenging. Therefore, leveraging the correlation between well log responses, seismic attributes, and geological lithological characteristics of the reservoirs in the area, the probabilistic neural network method is employed to transform seismic attribute data for predicting and identifying stratigraphic features. This approach has achieved good results in practical data processing. © 2025 SPIE.

关键词： Stratigraphy

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Perception and Structure Association Network for Micro-Expression recognition 24

Perception and Structure Association Network for Micro-Expre...

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3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wang, Zhihao Zhang, Yuanyuan Yu, Bofan Yu, Yang School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9798400707308

Micro-expression recognition (MER) poses a formidable challenge due to the fleeting and subtle nature of micro-expressions (MEs). Current approaches predominantly focus on either motion features or facial structure, yet fail to effectively integrate both perspectives. This paper introduces a novel Perception and Structure Association Network (PSA-Net) for MER, aiming to effectively extract key features in MEs by performing multi-scale perception of motion features and constructing adaptive facial structure association. Specifically, to perceive fine-grained ME features and achieve multi-scale key feature extraction, we have designed the Multi-scale Micro-motion Perception module. This module extracts motion features by dividing windows and performing self-attention calculations within each window. The motion features of each window are then fused layer by layer using shifted window attention and spatial attention. Simultaneously, to effectively construct the structure association of facial ME regions, we have designed the Adaptive Facial Structure Association module. This module employs graph attention mechanisms and global adaptive attention maps to achieve the adaptive structure association of facial regions of interest, enabling the constructed facial graph structure to effectively extract global ME information and the relationships between nodes. Additionally, to effectively fuse these two key ME features, we propose the Fusion Loss, which can effectively learn the similarities between them, promoting the model to learn more discriminative feature representations, thus achieving effective feature fusion. Extensive experiments conducted on four public ME datasets (CASME, CASME II, SAMM, CAS(ME)3) have achieved recognition accuracy/F1 scores of 81.87%/79.26%, 87.80%/88.08%, 82.35%/76.76%, and 63.10%/49.47% respectively, surpassing most existing methods, which indicates that our method can effectively improve MER performance. © 2024 Copyright held by the owner/auth

关键词： Contrastive Learning

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Classification of Brain Autoimmune Diseases using Deep Learning: A Predictive Approach 3

Classification of Brain Autoimmune Diseases using Deep Learn...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shivanii, M. Madhumetha, K. Poornima, M. St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Autoimmune brain conditions such as pituitary, meningioma, glioma are some of the present challenges in early diagnosis due to their complexities and image characteristics. The proposed system, Autoimmune Disease Detection in the Brain- Deep Learning (AIDB-DL) is built over four stages: early, intermediate, advanced and late. By using deep learning techniques, AIDB-DL identifies imaging biomarkers and unique patterns to each conditions. AIDB-DL uses data pre-processing techniques to handle issues such as noise and other unwanted characteristics which gave a demonstrated accuracy of 91%, facilitating precise classification and supporting personalized treatment planning thereby having a significant advancement in precise medicine and paving way for global improvements in brain healthcare. © 2025 IEEE.

关键词： Contrastive Learning

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CH and CMiGIN 2024 - Proceedings of the 3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks

CH and CMiGIN 2024 - Proceedings of the 3rd International Co...

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3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks, CH and CMiGIN 2024

The proceedings contain 34 papers. The topics discussed include: a comparative analysis of cyber threat intelligence models;cloud shared hosting DDoS resistance and potential ways of protection;protection of IoT networks: cryptographic solutions for cybersecurity management;development of a modified steganographic model of data transmission using IPv6 protocol;optimization of the integrated video surveillance system with elements of data analysis;ecological disasters as a result of cyber-attacks in the energy sector;estimation of the aircraft's position based on optical channel data;analysis of cross-sectional neural network recognition of satellite and aerial survey data;and predictive control for failure risk assessment during navigation equipment operation.

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Generation of Pseudo X-Ray and Digitally Reconstructed Radiograph from CT images 3

Generation of Pseudo X-Ray and Digitally Reconstructed Radio...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arya, R. Kochuvila, Sreeja Dept. of Artificial Intelligence Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Computed Tomography (CT) scanning offers 3D insights into the human body but involves higher radiation exposure than traditional X-Rays, increasing the relevance of 3D reconstructions from 2D data. In this work, datasets of CT- pseudo-X-Ray and CT- Digitally Reconstructed Radiograph (DRR) pairs are generated using ray-sum tracing and projection techniques. This can be helpful for implementing machine learning and deep learning architecture as 2-D data availability is limited. The described approach generates CT–pseudo-X-Ray and CT–Digitally Reconstructed Radiograph (DRR) pairs using ray-sum tracing and projection methods. The pseudo-X-Ray is created by combining bone and soft tissue projections, while the DRR is derived via ray-sum tracing. By retaining the CT images in Digital Imaging and Communications in Medicine (DICOM) format, the created dataset enhances robustness and efficiency, supporting 3D reconstruction—a rapidly advancing field. © 2025 IEEE.

关键词： X ray radiography

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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