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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20255 条记录，以下是371-380 订阅

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Classification of Structures and Monuments based on Indian Architectural Styles 3

Classification of Structures and Monuments based on Indian A...

引用

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

作者： Ganapathi, Ananya Saravanan, Deivanai Raghavan, Sneha T Jyotsna, C. Aiswariya Milan, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation. © 2025 IEEE.

关键词： Photointerpretation

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A Review on framework to Detect and Diagnose Body System based Cancer using deep learning Techniques 3

A Review on framework to Detect and Diagnose Body System bas...

引用

3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Gandikota, Hari Prasad Abirami, S. Annamalai University Department of Computer Science & Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350399264

Cancer imaging examination is based mainly on doctors' manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors' diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation. © 2023 IEEE.

关键词： Digestive system

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Footprint-Based Animal Identification: A Hybrid Model Combining CNN, SVM, and Transfer learning for Wildlife Encroachment 3

Footprint-Based Animal Identification: A Hybrid Model Combin...

引用

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

作者： Jerlin Joy, C.G. Subhashree, S. Jasmine Mystica, K. St. Joseph's College of Engineering Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Animals can be identified by using various machine learning techniques among which a hybrid model combines multiple techniques together. In this approach a hybrid model with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Transfer learning is used to recognize and classify animal footprints. The goal is to prevent animal intrusion into human dwellings and protect endangered species by enabling timely detection. Using a pre-trained CNN (e.g., VGG16) for feature extraction, the system identifies distinct footprint patterns from images. These features are then passed to an SVM for accurate classification. The model benefits from Transfer learning, leveraging knowledge from large-scale datasets to enhance detection accuracy, even with limited footprint data. This approach enables efficient monitoring of wildlife near human settlements, allowing for proactive measures to prevent conflicts and supports biodiversity protection. © 2025 IEEE.

关键词： Convolutional neural networks

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IMLRLS: A method for ship collision avoidance by integrating meta-learning with reinforcement learning 3

IMLRLS: A method for ship collision avoidance by integrating...

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Jia, Xinyu Gao, Shu He, Wei School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Province Wuhan China School of Physics and Electronic Information Engineering Minjiang University Fujian Province Fuzhou China

ISBN: (纸本)9781510680531

Autonomous collision avoidance is vital for intelligent ship navigation. To improve the adaptability and effectiveness of collision avoidance policies, we propose a method that integrates meta-learning with reinforcement learning. Drawing inspiration from meta-learning, we developed a two-layered recurrent model to boost the adaptability and effectiveness of these policies. We then introduced a ship motion model considering wind, waves, and currents to better adapt to environmental changes. Subsequently, we designed a noise-enhanced autonomous ship collision avoidance reinforcement learning model to augment the exploration capabilities of the vessel agents. Lastly, simulation experiments were conducted to validate the feasibility of our approach. The results demonstrate that our collision avoidance policies surpass various comparative methods, showing superior effectiveness and adaptability. Overall, our innovative method offers a safer solution for enhancing intelligent ship navigation. © 2024 SPIE.

关键词： Reinforcement learning

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R2I: A Deep Fault Diagnosis Semantic Segmentation System Based on Knowledge Distillation in Photovoltaic Panels 3

R2I: A Deep Fault Diagnosis Semantic Segmentation System Bas...

引用

3rd international conference on Energy, Power and Electrical engineering, EPEE 2023

作者： Lei, Xiaoyanghao Ma, Kai Zhao, Junyi University of Liverpool Department of Electrical and Electronic Engineering Liverpool United Kingdom Xi'an Jiaotong-liverpool University Department of Computer Science Suzhou China

ISBN: (纸本)9798350318180

With the growing adoption of photovoltaic power systems, ensuring the reliable and efficient operation of photovoltaic panels is critical. However, defects such as cracks, dirt, and failures are common during long-term use. Manual inspection is subjective and resource intensive. Recent computer vision and machine learning techniques present new opportunities for automated diagnosis. This paper reviews photovoltaic panel defect detection using deep learning. Two main approaches exist: visible light and infrared imaging. Earlier works employed object detection with lower precision. Newer methods use semantic segmentation for pixel-level accuracy. However, most focus on single modalities and datasets. Transfer learning across diverse data could improve generalization. Few works estimate defect areas, needed for automated analysis. This paper proposes a dual-modality semantic segmentation approach with knowledge transfer learning. It shares representations across visible and infrared datasets via distillation. A lightweight network reduces parameters and computations for deployment. Soft targets provide latent features transferable to new data. An algorithm estimates defect area proportions to assess hazard levels. The multi-modal transferable learning system aims to advance automated, accurate photovoltaic panel defect diagnosis. Key innovations include tackling insufficient data via shared knowledge, reducing model complexity, improving generalization, and pioneering defect severity assessment. This can facilitate intelligent photovoltaic inspection to ensure system health, safety and longevity. © 2023 IEEE.

关键词： component Digital Power System Power System Fault Diagnosis Power System Security Smart Grid

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Deep learning for Predicting the Strength of 3D-Printable Engineered Cementitious Composites 3

Deep Learning for Predicting the Strength of 3D-Printable En...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lai, Xin Gong, Chen He, Enpei Li, Yinmian Zhou, Yixin Zhang, Fang Institute of Smart City and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu611756 China School of Civil Engineering Southwest Jiaotong University Sichuan Chengdu611756 China Southwest Jiaotong University National Key Laboratory of Bridge Intelligent and Green Construction Sichuan Chengdu611756 China

ISBN: (纸本)9798350380989

The construction industry, grappling with challenges like high costs, labor shortages, and environmental issues, is undergoing a transformative phase with the advent of 3D concrete printing (3DCP). This innovative approach, characterized by automation and sustainability, has introduced 3D-printable engineered cementitious composites (3DP-ECC), enabling the creation of complex structures without the need for traditional formwork or reinforcement. However, predicting the mechanical properties of 3DP-ECC, especially compressive strength, is challenging due to the influence of numerous variables such as printing parameters, material composition, and curing conditions. This paper presents a novel approach using a deep learning methodology, specifically a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture, to predict the compressive strength of 3DP-ECC. The study involves meticulous feature engineering, including the selection and preprocessing of relevant input features like printing speed, nozzle diameter, fiber content, and water-cement ratio. The performance of the LSTM model is evaluated in terms of accuracy, precision, and robustness against traditional empirical models. Our results demonstrate that deep learning, particularly RNN with LSTM, offers a reliable and efficient tool for predicting the compressive strength of 3DP-ECC, outperforming conventional models. This research not only highlights the potential of deep learning in advancing construction methodologies but also provides a foundation for future innovations in the field. © 2024 IEEE.

关键词： 3DP-ECC Compressive Strength Prediction Feature engineering Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN)

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Cervical Cancer Classification Using Transfer learning with Hybrid SVM Kernels and ResNet-50 3

Cervical Cancer Classification Using Transfer Learning with ...

引用

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

作者： Das, Anauksa Nath, Basab Choudhury, Joshika Chhteri, Neelvie Shah, Hilal Ahmad Ahmad Wani, Niyaz Sikkim Manipal Institute of Technology SMU Computer Science and Engineering Gangtok India IILM University Computer Science and Engineering Greater Noida India Sikkim Manipal Institute of Technology SMU Electronics and Communications Engineering Gangtok India

ISBN: (纸本)9798331523893

Cervical cancer is a serious health issue and a leading reason of cancer-related deaths among women, particularly in less economically developed countries (LEDCs) where there is limited availability of screening. Early detection is crucial to improving survival rates and quality of life. In this study, we propose an enhanced Support Vector Machine (SVM) model for automated cervical cancer detection, utilizing a novel hybrid kernel that combines the radial basis function (RBF) kernel with a secondary kernel (polynomial). The hybrid kernel approach enables the model to capture both local and global patterns in cell morphology, improving its sensitivity to subtle cell abnormalities and improving diagnostic accuracy. Additionally, we incorporate a pre-trained transfer learning model, Resnet-50 for feature extraction reducing dependency on large labeled datasets while improving robustness. Experimental results demonstrate that the hybrid kernel SVM with Resnet-50 achieves an accuracy of 92%, outperforming traditional SVMs (67%)and hybrid kernel SVMs without Resnet-50 models (69%). © 2025 IEEE.

关键词： Support vector machines

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Optimizing Human-AI Interaction: Innovations in Prompt engineering 3

Optimizing Human-AI Interaction: Innovations in Prompt Engin...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deshmukh, Rushali Raut, Rutuj Bhavsar, Mayur Gurav, Sanika Patil, Yash Computer Department JSPM’s Rajarshi Shahu College of Engineering Pune India

ISBN: (纸本)9798331527549

In the realm of artificial intelligence, effective communication between humans and large language models (LLMs) such as ChatGPT is vital. This research paper proposes a structured methodology for prompt generation, developed through an in-depth analysis of existing research studies, to optimize interactions with LLMs. The exploration in the crucial role of prompt engineering in enhancing the quality and relevance of AI-generated outputs is also made. By conducting a comprehensive review of the literature, the aim is to identify best practices for prompt design, emphasizing the importance of clarity, specificity, and contextualization. Our findings indicate that tailored prompts can significantly improve accuracy across various applications, including healthcare, education, and customer service. Furthermore, the challenges faced are address the challenges of maintaining consistency and reliability in AI responses, highlighting the necessity for standardized guidelines in prompt engineering. This work underscores the transformative potential of prompt engineering, advocating for its integration in high-stakes environments to promote effective and ethical communication with AI systems. © 2025 IEEE.

关键词： Human engineering

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Research on Educational Intelligence Platform Integrating Big data Technology 3

Research on Educational Intelligence Platform Integrating Bi...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Yilin, Gong Jingchu University of Technology Hubei Province Jingmen China

ISBN: (纸本)9798350380989

This paper proposes a big data intelligent teaching system for teachers and students of the whole school. The core function of the education cloud system is to collect various service data using the network, sensors and other devices, and store service data in large storage devices. The students were tested using a fusion collection algorithm of multiple features. 4.49% of the students gave a high evaluation after the trial of this platform. It has been proved that this system can greatly improve communication in spare time, and its application effect is also relatively good. Through the implementation of this project, the problem of storing massive teaching information resources can be solved well. Teaching resources are available to anyone, anywhere, anytime. It can solve the problem that the data storage in the current network is too large, which leads to the user's access delay, and the legitimacy, integrity and availability of the data are not guaranteed. This method improves the performance of permanent storage of educational data. © 2024 IEEE.

关键词： Big data cloud storage correlation analysis technique education platform independent learning multi-feature fusion

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A Hybrid Deep learning Model for Violence Detection 3

A Hybrid Deep Learning Model for Violence Detection

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Chandira Prabha, S. Kaviyadharshini, S. Annie Micheal, A. Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios. © 2025 IEEE.

关键词： Deep learning

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