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8th IFIP TC 12 international conference.on Computer, Communication and Signal processing, ICCCSP 2024

8th IFIP TC 12 International Conference on Computer, Communi...

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8th IFIP TC 12 international conference.on Computer, Communication and Signal processing, ICCCSP 2024

ISBN: (纸本)9783031736162

The proceedings contain 36 papers. The special focus in this conference.is on Computer, Communication and Signal processing. The topics include: Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model;Automated Depression Detection Using CNN and Transformer-Based Pre-trained Language Models;integrating Deep Learning Frameworks for Automated Medical image Diagnosis;explaining Parkinson's Disease Detection by Ensemble Classifiers with Local Interpretable Features;Mental Stress Assessment in Working Environment for an Individual Using Wearable Sensor of EEG and Pulse Signal Measured with Help of Deep Learning Algorithm;speech Recording Analysis for Parkinson’s Detection Using Machine Learning Approach;Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan images;diabetic Foot Ulcer Classification Using Deep Learning Approach;medisync: Fingerprint-Enabled Authorization of Medical Device Calibration;renal Irregularities Detection Using Convolutional Neural Network;Surveillance and Mitigation of External Stimuli-Induced Sensory Overload in Autism with IoMT: Communicating Insights to Caregivers;a New Innovative Initialization Strategy in Population-Based Evolutionary Optimization Algorithms;AI-Enhanced Sign Language Interpreter;An NLP Based Approach to Automate and Enhance the Systematic Review Within PRISMA Format;closing the Communication Divide: Enhancing Sign Language recognition with Gesture-to-Text Conversion Through Computer Vision;Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data;assessing Degradation Levels of Palm Leaf Manuscripts with Random Forest Using Gabor Features;sign Language recognition System – A Review;Implementing a NFT Based Album Purchasing Web Application Using ReactJs by Integrating with Ethereum Blockchain;a Deep Learning-Based Algorithm for Predicting the Turning Point of Cloud Workload.

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Foreground Prediction for image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th international conference.on Graphics and image processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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2nd international conference.on Next-Generation Computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd international conference.on Next-Generation Computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference.is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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FNOReg: Resolution-Robust Medical image Registration Method Based on Fourier Neural Operator 27th

FNOReg: Resolution-Robust Medical Image Registration Method ...

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27th international conference.on pattern recognition, ICPR 2024

作者： Drozdov, Nikita A. Sorokin, Dmitry V. Laboratory of Mathematical Methods of Image Processing Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9783031782008

Medical image registration plays a crucial role in diagnosis, treatment planning, and anatomical studies. Classical methods, relying on iterative optimization algorithms, are complex and computationally intensive. Recent advances in deep learning, particularly with convolutional neural networks (CNNs) like VoxelMorph, have shown promise. However, they often yield non-smooth deformation fields and require inference at the same image resolution as the training data. To overcome these challenges, we introduce FNOReg, a novel model based on Fourier Neural Operators (FNOs), which can be trained on reduced-resolution images without quality loss and produces smoother deformation fields. We evaluated FNOReg on 2D and 3D datasets, demonstrating comparable quality to popular models like VoxelMorph, Fourier-Net, and TransMorph when trained at the same resolution. However, these models exhibit significant quality decreases of up to 24.9% for 2D and 24.6% for 3D data, when trained at halved resolutions. In contrast, FNOReg demonstrates only marginal quality decreases of up to 0.8% for 2D and 2.7% for 3D data. This flexibility is essential for efficiently handling large image resolutions, particularly in 3D imaging. Moreover, FNOReg produces smoother deformation fields. The code is available at https://***/anac0der/fnoreg. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Streamlined Lesion Segmentation Method Using Deep Learning and image processing for a Further Melanoma Diagnosis 11

A Streamlined Lesion Segmentation Method Using Deep Learning...

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11th international conference.on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Daghrir, Jinen Mbarki, Wafa Tlig, Lotfi Bouchouicha, Moez Litaiem, Noureddine Zeglaoui, Faten Sayadi, Mounir Université de Tunis ENSIT Laboratory SIME Tunisia Université de Toulon Aix Marseille Univ CNRS LIS Marseille France University of Tunis El-Manar Faculty of Medicine of Tunis Department of Dermatology Charles Nicolle Hospital Tunis Tunisia

ISBN: (纸本)9789897587436

Over the past two decades, the world has known a significant number of deaths from cancer. More specifically, melanoma which is considered as the deadliest form of skin cancer causes a remarkable percentage of all cancer deaths. Therefore, the health and disease management community has exceedingly invested in creating automated systems to help doctors better analyze such diseases. Correspondingly, we were interested in creating an automatic lesion detection task for further melanoma diagnosis. The lesion segmentation is considered to be a critical step in a pattern recognition system. Our proposed segmentation technique consists of finding lesions’ masks using a baseline, edge-based, and more sophisticated and state-of-the-art method: thresholding using Otsu’s technique, morphological snakes, and a fully CNN (Convolutional Neural Network) model based on the U-net architecture, respectively. These methods are commonly used when dealing with skin lesion segmentation, and each one of them has its advantages and drawbacks. The U-net architecture is improved by the use of the pre-trained encoder ResNet-50 on the imageNet dataset. A majority voting is applied to generate the final segmentation map using these three methods. The experiments were conducted using a benchmark dataset and showed promising results compared to using these methods separately, the majority voting of the three methods can significantly improve the segmentation task by refining the borders of the masks issued by the Deep learning model. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： image segmentation

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28th international conference.on image processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd international conference.on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th international conference.on image processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd international conference.on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference.is on image processing, Computer Vision, and pattern recognition. The topics include: image-Based Seal recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch images;residential Real Estate image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Food Nutritional Detection Personalized Dietary Recommendations for Health Monitoring using image processing with Machine Learning

Food Nutritional Detection Personalized Dietary Recommendati...

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international conference.on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu M. Vinay M. Likhitha G. Shivaramu Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

A good nutrition monitoring that involves promoting healthy lifestyle and dietary challenges. In this paper, we propose an AI driven nutrition monitoring system utilizing deep learning, computer vision, and natural language processing to handle dish detection, freshness analysis and ingredient recognition. The proposed system takes into account the accuracy of food classification using a fine-tuned convolutional neural network, OpenCV based techniques to measure ingredient quality, and NLP to pull out detailed nutritional information. The system applies real time feedback and actionable insights to the limitations of manual logging and traditional dietary tracking. Built with a modular architecture, it is adaptable to different cuisines, and dietary needs, and can be used by individuals as well as healthcare professionals. One of the innovations of such an approach is to make nutrition management easier, to promote healthier eating habits, and to create a launchpad for future personalized health monitoring advances.

关键词： Deep learning Computer vision Technological innovation Shape Medical services Manuals Natural language processing Real-time systems pattern recognition Monitoring

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1st international conference.on advances in Artificial Intelligence and Machine Learning in Big Data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference.on advances in Artificial Intelligence and Machine Learning in Big Data processing, AAIMB 2023

ISBN: (纸本)9783031730672

The proceedings contain 51 papers. The special focus in this conference.is on advances in Artificial Intelligence and Machine Learning in Big Data processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile Robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through Computer Vision and Deep Learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance images Using CNN;diagnosis of Parkinson’s Disease Using Machine Learning and Deep Learning Techniques;a Survey on Deep Learning Based Human Activity recognition System;a Deep Learning Approach for Non - invasive Body Mass Index Calculation;Early-Stage Detection of Alzheimer’s Disease Using MRI Scans with Deep Learning;penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using Machine Learning and Deep Learning Algorithms: A Review;URL Phishing Detection Using Deep Learning and Machine Learning Techniques;enhanced Disease recognition and Classification in Black Gram Plant Leaves Using Deep Learning;Ensemble Deep Learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet Data Using Hybrid Deep Learning Techniques;computer Vision to Animal Footprint Classification Based on Deep Learning Model;Speech Emotion recognition Using CNN Classifier Based on Deep Learning Model;face Detection and recognition for Criminal Identification System Using Deep Learning.

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1st international conference.on advances in Artificial Intelligence and Machine Learning in Big Data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference.on advances in Artificial Intelligence and Machine Learning in Big Data processing, AAIMB 2023

ISBN: (纸本)9783031730641

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Deep Learning Technique for image-Based Bladder Cancer Diagnosis: A Comprehensive Review

Deep Learning Technique for Image-Based Bladder Cancer Diagn...

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Machine Learning and Autonomous Systems (ICMLAS), international conference.on

作者： R Reena S Amala Shanthi Department of Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Bladder cancer represents a significant global health challenge. Recent advances in deep learning (DL) have shown promise in enhancing bladder cancer (BC) detection and diagnosis through improved image analysis and pattern recognition capabilities. This review emphasizes current research on the application of DL methods in BC detection, emphasizing the diverse technique, dataset, and performance metrics employed across studies. DL, particularly convolutional neural network (CNN), has been extensively utilized for processing medical data such as CT, MRI, and cystoscopy images. These models have demonstrated high accuracy in identifying bladder tumors, often surpassing traditional machine learning (ML) approaches. Key methodologies reviewed include data preprocessing techniques, model architectures, and strategies for addressing class imbalances, which are critical in medical datasets. The review further discusses the importance of external validation and the integration of advanced techniques like transfer learning and hybrid models to bolster performance. Future research directions focus on overcoming current limitations by leveraging multi-modal data, improving interpretability, and refining model training processes to ensure reliable clinical application.

关键词： Deep learning Accuracy Reviews Transfer learning Bladder Data models Convolutional neural networks Medical diagnostic imaging Cancer Tumors

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