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international conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings

International Conference on Automation, Computing and Renewa...

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1st IEEE international conference on Automation, Computing and Renewable Systems, ICACRS 2022

ISBN: (纸本)9781665460842

The proceedings contain 251 papers. The topics discussed include: optically controlled microwave sensor for biomedical applications;deep learning network for object detection under the poor lighting condition;a brief analysis on machine learning classifiers for intrusion detection to enhance network security;a brief review on melanoma diagnosis models using machine learning techniques;equilibrium optimizer with deep learning model for autism spectral disorder classification;automated intracranial hemorrhage detection and classification using rider optimization with deep learning model;heart disease prediction and classification using machine learning and transfer learning model;review of machine learning algorithms for autism spectrum disorder prediction;novel approach to non-invasive blood glucose monitoring based on visible laser light;a machine learning based approach for breast cancer prediction;covid-19 infection segmentation using deep learning techniques;retinal fundus image retrieval and classification using optimal deep learning model;breast cancer segmentation by k-means and classification by machine learning;an extensive review of machine learning techniques for EEG signal processing;hybrid machine learning based false data injection attack detection and mitigation model for waste water treatment plant;twitter sentiment analysis with machine learning;hybrid particle swarm optimization with deep learning driven sarcasm detection on social media;design of kernel extreme learning machine based intelligent crop yield prediction model;and reliable densely connected network with machine learning based diabetic retinopathy grading approach.

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stock Investor Sentiment Analysis Based on NLP 1st

Stock Investor Sentiment Analysis Based on NLP

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Liang, Jingxin Deng, Chao Yang, Jie Shanghai Normal University Tianhua College AI School Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural language processing (NLP), we gather and categorize investor comments from stock forums pertinent to the Shanghai and Shenzhen Indices. Utilizing Support Vector machines and Logistic Regression within an NLP framework, we develop an automated system that transforms comments into sentiment indicators. These indicators are further quantified into daily sentiment indices following denoising procedures to emphasize persistent market trends. Comparative assessments reveal strong correlations between refined sentiment indices and actual stock index movements, confirming their predictive power for market fluctuations. The study offers investors a sentiment-analytics tool, augmenting the multi-faceted approach to investment strategy formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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Multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action recognition 21st

Multiple Input Branches Shift Graph Convolutional Network wi...

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21st international conference on image Analysis and processing (ICIAP)

作者： Liu, Yan Deng, Yuelin Su, Jinping Wang, Ruonan Li, Chi Taikang Insurance Grp Co Ltd Taikang Innovat Ctr 21-1 Sci Pk Rd Beijing Peoples R China

ISBN: (纸本)9783031064272;9783031064265

Graph Convolutional Networks (GCNs) achieve remarkable success in the skeleton-based action recognition tasks. However, the recent state-of-the-art (SOTA) methods for this task usually have a large model size and too heavy computational complexity. In this work, we propose an early fused model, Multiple Input Branches Shift Graph Convolutional Network with DropEdge (MIBSD-GCN). First, to reduce the complexity of the multi-stream model, we introduce a lightweight Shift Graph Convolutional Network (Shift-GCN) block. It is embedded into an early fused architecture, Multiple Input Branches (MIB), which can enrich input features and suppresses the model redundancy. Then, a novel spherical coordinate representation is added as one of the input branches to enhance the recognition effect. Finally, we design the Shift Graph Convolutional Network with DropEdge (SD-GCN) to prevent over-fitting and over-smoothing, while maintain the model accuracy. Extensive experiments on two large-scale datasets, NTU RGB+D 60 and NTU RGB+D 120, show that the proposed model outperforms previous SOTA methods. We achieve 96.6% accuracy on the Cross-view benchmark of the NTU RGB+D 60, while being 3.4-16.5 times fewer FLOPs than other SOTA models.

关键词： Skeleton-based action recognition Multiple Input Branches Shift Graph Convolutional Network DropEdge Spherical coordinate

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Medical image denoising and classification based on machine learning- A review 1

Medical image denoising and classification based on machine ...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Chaturvedi, Saumya Srinivas, T. Aditya Sai Karthikeyan, R. Vijayaraj, M. Kumar, A. Nirmal Sangeetha, M. Department of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida201310 India Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Near Venkayapalle Andhra Pradesh Kurnool518002 India Department of Computer Science and Engineering Mohamed Sathak Engineering College Kilakarai 623806 India Department of Information Technology Mohamed Sathak Engineering College Kilakarai 623 806 India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad501401 India School of Computing and Information Technology Reva University Karnataka Bengaluru560064 India

ISBN: (纸本)9781607685395

Advances in medical imaging technology continue to create new possibilities for the collection of medical data that are important in timely and accurate diagnosis, in monitoring progress, and in the treatment of various diseases and in medical research. The capabilities of the new skills arise mainly from the technologies depicted in the vivo interior of the human body. Thus the study of the morphology and function of the various organs and the detection of any pathogens is achieved in a very direct way. The "source imaging data" provided by them is important information, but their large number is constantly growing, but their nature also creates the need for further processing with the help of computers. The primary purpose of processing images is to use denoising that includes the elimination of noise due to technical errors and feature preservation. Following noise reduction, the image segment, i.e. the location or areas of interest in an image, is the central objective of the process. In addition, usually, the complexity of the data in large volumes and charts requires a lot of time to study and a lot of experience to do their interpretation correctly. Therefore, in many cases, its automation using machine learning seeks out the partitioning process, but also categorizes images, i.e. classifying an image or parts of an image into specific categories. In most applications, machine learning performance is better than conventional techniques. © The Electrochemical Society

关键词： Deep learning Denoising image processing machine learning Neural Network

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Comparative Analysis of Edge Detectors Applying on the Noisy image Using Edge-Preserving Filter 1st

Comparative Analysis of Edge Detectors Applying on the Noisy...

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1st international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Kumar, Tapendra Bardhan, Shawli Indian Inst Informat Technol Sch Comp Una 177220 Himachal Prades India

ISBN: (纸本)9783030960407;9783030960391

In computer vision, edge detection is a fundamental technique. It is used as a pre-processing technique to make image segmentation, pattern recognition, and feature extraction more comfortable. Digital images are often corrupted by the noise that causes the detection of spurious edges during edge detection. Thus, we'd like to suppress the maximum amount of noise as potential while retaining important image features such as edges, corners, and other sharp structures. This research compares multiple edge detection methods applied to a filtered image by adding speckle noise. In this paper, four edge detection operators have been applied to an image denoised by various edge-preserving filters, and their performance is evaluated based on the performance metrics peak signal-to-noise ratio (PSNR) and mean squared error (MSE). images from the Barcelona images for Perceptual Edge Detection Dataset (BIPED) are used for performance evaluation of filters and edge detection techniques. The experimental results show that a bilateral filter with a Canny edge detection operator is the most optimized method for edge detection of speckle-noise-affected images.

关键词： image processing Edge detection Noise Edge-preserving filters PSNR MSE BIPED

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Implementation of Plant Disease recognition Using Deep learning in Telegram Messenger

Implementation of Plant Disease Recognition Using Deep Learn...

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Advanced Engineering and Technologies (ICONNIC), international conference on

作者： Harry Yuliansyah Rudy Hartanto Indah Soesanti Department of Electrical Engineering and Information Technology Gadjah Mada University Yogyakarta Indonesia

Agriculture has an essential role in the national economic development structure. It is necessary to detect plant diseases as early as possible to prevent losses to increase production yields. Recent advances in image processing and artificial intelligence propose a novel method of plant disease recognition. The primary challenge in integrating the CNN model into the Telegram messenger bot lies in synchronizing the CNN services to efficiently receive inputs sent via the Telegram API. Resolving this challenge is the focal point of this research. This study has developed a new plant diseases method using telegram messenger based on deep learning and a convolutional neural network. The user can easily upload their plant image through telegram messenger. The response was immediately sent back after the server processed the input image. Experimental results show the performance system is fast response and highly accurate.

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Baby learning with Vision Transformer for Face recognition

Baby Learning with Vision Transformer for Face Recognition

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2022 international conference on Multimedia Analysis and pattern recognition, MAPR 2022

作者： Tran, Chi-Phuc Vu, Anh-Khoa Nguyen Nguyen, Vinh-Tiep University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (数字)9781665474108

ISBN: (纸本)9781665474108

Computer vision and natural language processing (NLP) are the two active machine learning research areas. In the recent decades, the integration of these two areas gives rise to a new interdisciplinary field, which is currently attracting more attention of researchers. Transformer-based model, an approach which had great success in NLP tasks, is now being widely utilized in computer vision projects relating to image classification and object detection. However, there are not many studies exploiting this technique in face recognition tasks. Besides, when applying a deep learning model in real-world applications such as face recognition task, it is hard to have a sufficient number of samples like standard datasets, especially for very important persons. To simulate the practical condition, we build a new face dataset named VFD, which contains 3000 images of 1000 famous Vietnamese people. Moreover, to tackle face recognition in the context of limited training samples, we adopt the baby learning approach to train the Vision Transformer (ViT). The method trains the ViT models via iterations that gradually increase the number of parameters to enhance the generalization. Finally, we validate the method with our dataset with various configurations of face tasks to demonstrate the robustness of the model. The experiments show that our method has comparative results in general and outperforms in the context of scarce data compared to CNN-based methods such as ResNet. © 2022 IEEE.

关键词： Face recognition

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Application of Advanced Deep learning Techniques for Face Detection and Age Estimation

Application of Advanced Deep Learning Techniques for Face De...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Rawan A. AlQadi Mohamed Batouche College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

In recent years, AI-based algorithms have been used to solve a wide range of previously intractable issues. Face detection and recognition from large groups, as well as feature identification from a description, are two of the most important security issues that can be addressed with image processing and modern AI. In this research, we use deep learning to analyze facial images to detect faces and estimate ages. The proposed method was used on photos of King Abdullah bin Abdulaziz AL Saud to determine his face and approximate his age. For this purpose, we relied on powerful deep learning models like DELWO and MTCNN to perform tasks like identifying faces and determining how old they are. The findings are very encouraging and promising. The experimental results have shown that the proposed method has good accuracy for face recognition (98.50%) and age estimation (82%).

关键词： Deep learning Technological innovation image recognition Smart cities Face recognition Estimation Feature extraction

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Evaluating Performance of Adam Optimization by Proposing Energy Index 4th

Evaluating Performance of Adam Optimization by Proposing E...

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4th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2021

作者： Bhandari, Mohan Parajuli, Pramod Chapagain, Pralhad Gaur, Loveleen Nepal College of Information Technology Lalitpur Nepal CG Institutue of Management Kathmandu Nepal Amity International Business School Amity University Noida India

ISBN: (纸本)9783031070044

The adjustment of learning rate (η ), bias and additional parameters throughout back propagation are crucial for the performance of machine learning algorithms. Regarding optimization for algorithms, adam optimization technique tune the learning parameters by utilising the exponential decay of past gradients and their squares. However, the optimizer requires the engagement of frequently occurring features from the datasets that play a significant role for performance improvement in machine learning algorithms. In this paper, the energy model of a neuron is designed to calculate the energy index from frequently occurring features and introduced in adam optimizer. The classification performance of the proposed energy modeled adam optimizer is experimented on Logistic Regression (single layered) and Support Vector machine (hyperplane based) machine learning algorithms utlising CIFAR10, MNIst and Fashion MNIst datasets. Optimized with proposed optimizer, Logistic Regression achieved training accuracy of 90.79%, 99.02% and 95.87% whereas Support Vector machine achieved training accuracy of 39.04%, 80.80% and 82.29% for CIFAR10, MNIst and Fashion MNIst datasets respectively. © 2022, Springer Nature Switzerland AG.

关键词： Support vector machines

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USING machine learning ON DEPTH MAPS AND imageS FOR TUNNEL EQUIPMENT SURVEYING

USING MACHINE LEARNING ON DEPTH MAPS AND IMAGES FOR TUNNEL E...

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conference on Optical 3D Metrology (O3DM)

作者： Barcet, Florian Tual, Maxime Foucher, Philippe Charbonnier, Pierre Cerema Project Team ENDSUM Strasbourg France

In this contribution, we illustrate how to use high-resolution 3D data and images to model the global shape of a tunnel and to survey its equipment, in a semi-automatic way using pattern recognition and machine learning techniques. We first implement a robust Bspline fitting algorithm, based on a parametric family of M-estimators that allows an efficient deterministic optimization strategy, to accurately model the tunnel lining using range data, despite the presence of acquisition artifacts and significant perturbations related to the equipment and some surface defects. The residual maps from the robust fit can be exploited to segment the equipment in an unsupervised manner using clustering algorithms, but at the cost of post-processing which makes the method rather ineffective for routine use. However, we deploy it to annotate data for the supervised learning of a deep learning model, namely Mask R-CNN. We comment on the first results, obtained on a still limited number of examples, and from image data only, and discuss the possibilities of improving the method, in the immediate or longer term.

关键词： Tunnel surveying Laser Scanning Mobile acquisition Documentation Building Information Modeling

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