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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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3rd BenchCouncil international Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023

3rd BenchCouncil International Symposium on Intelligent Comp...

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3rd BenchCouncil international Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023

ISBN: (纸本)9789819700646

The proceedings contain 26 papers. The special focus in this conference is on Intelligent Computers, Algorithms, and Applications. The topics include: KGCN-DDA: A Knowledge Graph Based GCN Method for Drug-Disease Association Prediction;machine Learning for Time-to-Event Prediction and Survival Clustering: A Review from Statistics to Deep Neural Networks;label-Independent Information Compression for Skin Diseases recognition;3D Approach Trajectory Optimization Based on Combined intelligence Algorithms;A-SMGCS: Innovation, Applications, and Future Prospects of Modern Aviation Ground Movement Management System;an Intelligent Image Segmentation Annotation Method Based on Segment Anything Model;ParticleNet for Jet Tagging in Particle Physics on FPGA;application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment;second-Order Gradient Loss Guided Single-Image Super-Resolution;Neutrino Reconstruction in TRIDENT Based on Graph Neural Network;charged Particle Reconstruction for Future High Energy Colliders with Quantum Approximate Optimization Algorithm;A Levy Scheme for User-Generated-Content Platforms and Its Implication for Generative AI Providers;Moving Beyond Text: Multi-modal Expansion of the Toulmin Model for Enhanced AI Legal Reasoning;The Worldwide Contradiction of the GAI Regulatory Theory Paradigm and China’s Response: Focus on the Theories of Normative Models and Regulatory Systems;Intelligent Forecasting of Trademark Registration Appeal with TF-IDF and XGBoost;review of Big Data Evidence in Criminal Proceedings: Basis of Academic Theory, Practical pattern and Mode Selection;The Implementation and Optimization of FFT Calculation Based on the MT-3000 Chip;EDFI: Endogenous Database Fault Injection with a Fine-Grained and Controllable Method;diffusion Probabilistic Models for Underwater Image Super-Resolution;classification Method for Ship-Radiated Noise Based on Joint Feature Extraction;Forecasting the Price of Bitcoin

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Enhancing Student Engagement and Performance in Online Classrooms through a Smart Management Application 3

Enhancing Student Engagement and Performance in Online Class...

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

作者： Gowthambalagi, M. Vishnu, S.M. NaveenKumar, M. Raja, S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode India

ISBN: (纸本)9798331509675

The fast pace of technological growth has revolutionized the education industry, and there is a need for embracing intelligent solutions to improve classroom management and student engagement. The Smart Classroom Management System (SCMS) takes advantage of innovative technologies like facial recognition, screen sharing, and real-time analytics to automate administrative processes, track student attentiveness, and promote interactive learning spaces. With process automation for features such as tracking attendance and evaluating student interaction, SCMS eliminates the strain on teachers but presents meaningful analytics for enhancing instructional techniques. Further, the software integrates AI-enabled components for one-to-one testing and adaptive questioning capabilities to promote tailored learning outcomes. SCMS allows hybrid learning and remote schooling by leveraging cloud capabilities, assuring accessibility, flexibility, and safety. This review research investigates current literature regarding smart classroom technologies, identifies major implementation challenges, and discusses future developments in AI-based education. The results focus on the use of SCMS to develop a dynamic and student-focused learning environment, ultimately enhancing educational outcomes and preparing students for the changing digital world. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Review on Artificial intelligence for Health Care 3

A Comprehensive Review on Artificial Intelligence for Health...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sudha, S. Anitha Kumari, D. Nirmal Raaghav, N. VISTAS Department of MBA Chennai India Anna University Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798350391565

The study aims to do research analysis on AI usage in the field of health care. Technology has been in use in daily and practical life. People are able to feel its presence every single moment. Artificial intelligence usage like tracking customers data every day based on their daily routine are normal nowadays. AI cannot be restricted to mobile phone usage alone. There are lot of emerging technologies like machine learning, deep learning, sensors etc. practically used in everyday routine. AI have intruded in people lives and they are aware its tracking. Knowing more about the AI environment, its benefits enjoyed in every sector especially in health care which is emerging sector in India provides lot of insights to the readers through review of literature and bibliometric analysis. The results shows that in the year 2020, the number of publications has increased by 67.8 percent. In the year 2021 tremendous increase of 70 percent of the articles and in the year 2022 it has been 56 percent. Hence this paper has attempted to find answers to the above research questions. © 2024 IEEE.

关键词： Electronic health record

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Artificial intelligence in Product Management: Enhancing User Experience Personalization through GenAI 3

Artificial Intelligence in Product Management: Enhancing Use...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Pilaniwala, Pankaj University of Arizona TucsonAZ United States

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

In today's digital age, personalized customer experiences are crucial for businesses to thrive. Traditional methods of customer experience personalization often rely on static models that struggle to adapt to dynamic consumer behaviors and preferences. This research study proposes a novel dynamic integrated model that leverages Generative AI to continuously update and refine customer experiences. By analyzing multi-dimensional data sources such as transaction history, online interactions, and social media activity, the model can capture the evolving nature of customer preferences. This research study explores the potential of integrating advanced algorithms like Generative Adversarial Networks (GANs) and Large Language Models (LLMs) to generate personalized content, recommendations, and interactions. By combining these techniques with traditional machine learning approaches, the model can deliver highly tailored and engaging experiences. This research contributes to the advancement of personalized customer experiences, empowering businesses to build stronger customer relationships and drive business growth. © 2024 IEEE.

关键词： Generative adversarial networks

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Green Runner: A tool for efficient deep learning component selection 24

Green Runner: A tool for efficient deep learning component s...

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IEEE/ACM 3rd international conference on AI Engineering - Software Engineering for AI (CAIN)

作者： Kannan, Jai Barnett, Scott Cruz, Luis Simmons, Anj Selvi, Taylan Appl Artificial Intelligence Inst Geelong Vic Australia Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400705915

For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a model requires developers to i) select from many models (e.g. the Hugging face model repository), ii) select evaluation metrics and training strategy, and iii) tailor trade-offs based on the problem domain. However, current evaluation approaches are either ad-hoc resulting in sub-optimal model selection or brute force leading to wasted compute. In this work, we present GreenRunner, a novel tool to automatically select and evaluate models based on the application scenario provided in natural language. We leverage the reasoning capabilities of large language models to propose a training strategy and extract desired trade-offs from a problem description. GreenRunner features a resource-efficient experimentation engine that integrates constraints and trade-offs based on the problem into the model selection process. Our preliminary evaluation demonstrates that GreenRunner is both efficient and accurate compared to ad-hoc evaluations and brute force. This work presents an important step toward energy-efficient tools to help reduce the environmental impact caused by the growing demand for software with machine-learned functionality. Our tool is available at Figshare GreenRunner.

关键词： Green-AI Large Language Model Component Selection

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Research and Application of YOLOv3-Based Intelligent Module for Remote Sensing Image recognition on Satellites 3

Research and Application of YOLOv3-Based Intelligent Module ...

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3rd international conference on Artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Tang, Hua Ye, Bingxin Shi, Huajun Wang, He Yu, Tao China Electronics Technology Group Corporation China The 32nd Research Institute of China Electronics Technology Group Corporation China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the continuous development of satellite technology, the acquisition and processing of satellite remote sensing data have become increasingly common. However, at present, satellites still use a mode of transmitting data back to the ground and utilizing ground CPU/GPU platforms for intelligent processing, which is inefficient due to the process of data transmission between the satellite and the ground. This article studies the application method and implementation of real-time remote sensing image recognition by integrating the Yulong810 on-board intelligent module on a satellite based on the YOLOv3 object detection *** response to the actual needs of satellite remote sensing image recognition, the algorithm is optimized based on the Yulong810 on-board intelligent module, which improves the recognition efficiency and reduces power consumption while ensuring the accuracy of remote sensing image recognition. By comparing with ground CPU/GPU platforms, the results show that the Yulong810 on-board intelligent module has lower power consumption and higher efficiency, and the recognition accuracy is comparable to that of ground platforms, which can completely replace ground remote sensing image detection equipment when integrated into *** integrating the module into the satellite, real-time intelligent processing can be achieved in orbit, improving the efficiency of satellite remote sensing image recognition. © 2023 SPIE.

关键词： Image recognition

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A Critical Study of ICT Oriented Innovations based on Artificial intelligence (AI) Applications towards BFSI Sector 3

A Critical Study of ICT Oriented Innovations based on Artifi...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Talreja, Pooja Shrivastava, Amit Mishra, Ajay Gupta, Neha Indore India

ISBN: (纸本)9798350343274

The Financial Service and Insurers (BFSI) sector refers to firms who provide a wide variety of accounting products and assistance across the world. This work adds to the technical innovation of services in processing administration, a topic that has yet to be settled in the available research. AI is an interdisciplinary field of study that has distinguished itself for the technical dynamism it brings to an organization products and processes. Artificial intelligence (AI) is one of the developing technologies driving the continued expansion of ICT-enabled applications. This study investigates the application of the use of AI and machine learning (ML), respectively, in the field of banking, as well as the policy ramifications. It grants a nontechnical foundation to understand the growth and possibilities of AI/ML systems. Artificial intelligence, or the emulation of human brain in machines, is gaining popularity and will play an important role in the new financial century. The current study aims to examine consumers' perspectives on the widespread implementation of intelligent systems with artificial intelligence in Asian countries. The banking industry also prioritizes novel AI technology to boost customer service and overall growth by making greater revenue. © 2023 IEEE.

关键词： machine learning

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Person Re-identification from Video using Hybrid Approach 3

Person Re-identification from Video using Hybrid Approach

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Manjith, Aman Nawaz Karthick, S. Bandaru, Vikranth SRM Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9781665494007

Because of advances in technology, it is now feasible to employ massive picture collections for a variety of purposes. This trend is expected to continue. The process of maintaining and recovering face data from a video may be improved with the use of the person reidentification approach. In order to scan and collect the photos, machine learning methods such as OpenCV are used. The goal of this phase is to split the picture into components or areas of interest for the purpose of conducting a more in-depth examination of one or more of these parts. For the purpose of carrying out face detection, the Haar Cascade algorithm is being evaluated for the project. Image processing is the primary use of OpenCV, which is a library of programming functions. Picture segmentation may also be employed as a preprocessing step in order to produce a superior image before moving on to denoising or deblurring, both of which can be accomplished in a subsequent phase of image processing. When it comes to retrieving information, some of the most crucial features are feature extraction, image representation, and image matching. Picture processing is done with the intention of assisting a computer in comprehending the subject matter of an image. © 2023 IEEE.

关键词： Face recognition

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial intelligence to Quantum machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Biopsy

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Facial Emotional recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model 3

Facial Emotional Recognition Using Faster Regional Convoluti...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yamsani, Nagendar Jabar, Muhammed Basim Adnan, Myasar Mundher Hussein, A.H.A. Chakraborty, Subhra School of Computer Science and Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317022

Facial Emotion recognition (FER) poses a significant challenge when training deep learning (DL) models like Artificial Neural Network (ANN) and machine learning (ML). The primary issue in FER is the detection of the face, the creation of bounding boxes, and the classification of emotional expressions. This paper proposes the use of Faster Regional Convolutional Neural Network (RCNN) for facial recognition and employs a median filter to filter the image dataset. The model leverages the Facial Emotional recognition (FER 2013) dataset for training and predicting accurate classifications. The Median Filter is used to eliminate noise in the image, such as Salt and Pepper noise, and data augmentation is utilized to generate synthetic data for addressing minor imbalances in classes. Subsequently, the VGG 16 architecture is implemented for precise feature extraction to reduce complexity in the classification process. This paper, achieved accuracy, precision, recall, specificity, and f1-score values of approximately 78.22%, 75.40%, 80.20%, 85.90%, and 71.40%, respectively, when compared to other classification methods like Convolutional Neural Network, ResNet 50, Divide-And-Conquer-Based learning, and Resnet 18. © 2023 IEEE.

关键词： Artificial Neural Network Convolutional Neural Network Deep Learning Facial Emotion recognition Median Filter

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