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

共 20256 条记录，以下是731-740 订阅

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Measuring Dimensions of Objects Using Camera with Deep learning 3rd

Measuring Dimensions of Objects Using Camera with Deep Learn...

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3rd international conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Subha, T. Ranjana, R. Saikiran Department of Educational Media and Technology NITTTR Chennai India Department of Information Technology Sri Sairam Engineering College Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789819784639

To develop an image processing technique using Python-OpenCV. This paper aims at measuring the dimensions of moving objects in live video through a camera setup. It has two major steps in the process such as calculating ppm value using reference objects and estimating the dimensions of objects. The object detection is involved in each of the steps and it is done at every stage of the process. The proposed model aims to bring the estimation near to the accuracy of 85%. This model also detects the dimensions of objects in live videos. This is implemented using image processing techniques and Python-Open CV. This entire process of dimension measuring can be fully automated in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Python

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Chronic Kidney Disease Prediction Using Robust Approach in Machine learning 3

Chronic Kidney Disease Prediction Using Robust Approach in M...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Anurag Vyas, Narayan Sharma, Vishal Balla, Deepak Chandigarh University Department of CSE Punjab Mohali India

ISBN: (纸本)9798350303360

The degenerative condition known as Chronic Kidney Disease (CKD) impairs kidney function and causes waste products to build up in the body. Early CKD prediction and identification can stop or slow the disease's progression. This study uses demographic, clinical, and lab data to provide a machine learning-based method for CKD prediction. The suggested approach uses a mix of feature selection and classification algorithms to pinpoint important risk factors and provide very accurate predictions for CKD. Effective therapies to stop or delay the course of the illness depend on the early detection and prediction of CKD. This work utilizes demographic, clinical, and laboratory data to present a unique machine learning-based method for CKD prediction and achieving an accuracy of 89%. There is a lot of promise in developing adaptive, automated, and intelligent diagnostic methods for CKD prediction. These algorithms offer individualized forecasts, adapt to unique patient profiles, and continually learn from fresh data. These systems can improve accuracy and help healthcare professionals make knowledgeable decisions about the diagnosis and treatment of CKD by integrating feature selection, dimensionality reduction, and classification algorithms. © 2023 IEEE.

关键词： Forecasting

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Proceedings - 2024 3rd international conference on Big data, Information and Computer Network, BDICN 2024

Proceedings - 2024 3rd International Conference on Big Data,...

引用

3rd international conference on Big data, Information and Computer Network, BDICN 2024

ISBN: (纸本)9798350363821

The proceedings contain 42 papers. The topics discussed include: simulation of complex network based on on-chain data security access control;research on weighted adaptive particle swarm optimization algorithm based on Flink stream processing framework;pedestrian detection method based on improved YOLOv5s for densely occluded scenarios;cross-domain defect GAN: defect generation for defect detection in industrial area;security and privacy protection technology of AIoT intelligent transportation teaching system with 5G technology;optimizing client selection for federated learning: a PPO-based method;vehicle target detection based on improved YOLOv3 algorithm in urban transportation;VAS: an unsupervised visible area sensing module for lane classification;and research on privacy information preserving in social network based on big data.

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Large language model illusion elimination based on fusion domain knowledge mapping and feedback learning 3

Large language model illusion elimination based on fusion do...

引用

3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Cui, Jiajia Huang, Ming Xu, Yongyong Guo, Naiwen The 15th Research Institute of China Electronics Technology Group Corporation Beijing China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The large model of artificial intelligence combines abundant data resources. It provides technical possibilities for more accurate and efficient intelligent question-and-answer and intelligent search functions in the service process. However, because large models use massive training data, these massive data are often collected on the Internet. It may contain a lot of falsified, biased and outdated content, which leads to the illusion of model results. Because of the powerful ability of large models, sometimes even human beings are difficult to find, especially in the field of government and military affairs. In this study, knowledge mapping was used to intervene in the activation of models in the process of reasoning and reduce the output illusion of large models. At the same time, using the feedback content of the user group, the reasoning output of the large model is revised by the way of feedback learning. These two technical means interfere with the direction of multi-head attention in the process of large model reasoning, and do not affect the reasoning time. It can greatly improve the output illusion of large models and has excellent controllability. © 2025 SPIE.

关键词： Military mapping

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A Personalized Content Filter for Mental Health Apps Using BiLSTM 3

A Personalized Content Filter for Mental Health Apps Using B...

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

作者： Joshi, Aditya Bhavsar, Kaustubh Deolikar, Piyush Rajput, Nitesh Chikmurge, Diptee Kharat, Sharmila MIT Academy of Engineering School of Computer Engineering Pune India University Ambassador Multigonal LLC United States

ISBN: (纸本)9798331523893

Mental health applications have gained significant popularity, but many still fall short in delivering personalized emotional support. This study explores a Personalized Content Filter powered by BiLSTM to analyze user-generated text, such as journal entries or chat messages, and identify emotions like sadness, anxiety, joy, and calmness. By determining the prevailing emotion, the system provides customized recommendations, such as mindfulness practices, stress-relief techniques, or uplifting *** BiLSTM model uses contextual insights from text to ensure precise emotion recognition, employing pre-trained word embeddings for robust feature extraction. Over time, the system learns from individual users' emotional trends, refining its recommendations to suit their specific needs. Experimental results confirm the model's accuracy and reliability, emphasizing its capability in emotion *** this system into mental health applications has the potential to boost user engagement while offering timely support for emotional well-being. This research underscores the potential of deep learning in advancing mental health tools and suggests future directions, such as integrating multimodal data and exploring broader real-world implementations. © 2025 IEEE.

关键词： BiLSTM Deep learning Emotion Detection LSTM

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Application of Artificial Intelligence in Power Grid 3

Application of Artificial Intelligence in Power Grid

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Shi, Luyan School of Information Engineering Shanghai Maritime University Software engineering Nantong China

ISBN: (纸本)9798331506582

At present, artificial intelligence has been widely used in power system fault diagnosis, power grid operation and regulation, fault warning system, power grid equipment load prediction and other fields. Artificial intelligence can improve the efficiency and accuracy of fault diagnosis in power system. It can realize intelligent power grid management and optimal dispatching in power grid operation and regulation. In the fault warning system, the power grid faults can be predicted in advance and corresponding measures can be taken. In the load forecasting of power grid equipment, the load changes can be accurately predicted and reasonably adjusted. However, there are still some challenges in the application of AI in the power grid, such as data security and processing. This article provides a comprehensive overview and analysis of the application of artificial intelligence in the power grid. © 2024 IEEE.

关键词： Power distribution faults

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Enhancing Precision Agriculture with Machine learning & IoT: SVR and DTR Comparative Analysis 3

Enhancing Precision Agriculture with Machine Learning & IoT:...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Ifty, Rashedul Arefin Hossain, Afif Ismail, Md. Saim, Mohammad Abu Huraira International Islamic University Chittagong Kumira Department of Computer Science and Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9798350388282

In many nations, including Bangladesh, the importance of agricultural revenue and employment has dwindled over time. A sizable section of the population works directly in the agricultural industry, typically depending on past crop cultivating expertise. Because of this lack of accurate agricultural information, crop selection may be poor. Furthermore, agricultural institutions rely heavily on manual data, which is insufficient for accurate crop selection and output forecasts. Precision farming remains a distant dream. Using multiple regression methods, this research analyzes the most efficient way for agricultural production prediction. There has been little study into using machine learning methods integrating with IoT to estimate agricultural production. This research also incorporates multiple regression techniques on the obtained dataset using machine learning algorithms. The fundamental goal of this study is to improve the predictability of optimum crop selections, mitigate food issues, and encourage intelligent information prediction analysis in agriculture. This strategy is intended to help farmers make better informed and profitable agricultural choices. data from Bangladesh Bureau of Statistics & Bangladesh Meteorological Department are used, which spans 43 years. This study takes into account one important crop - Aman rice. Furthermore, this research takes into account seven environmental characteristics, including monthly averages of Bangladesh's hottest and lowest temperatures, yearly rainfall, cloud cover, wind speed, relative humidity, and strong sunlight and proposed an IoT model for data collection. The characteristics of optimum control for Bangladesh's primary crop agroecosystems are used. The Decision Tree Regression technique is proposed in this study for agricultural crop selection & yield prediction. © 2024 IEEE.

关键词： Crops

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

Classification of Brain Autoimmune Diseases using Deep Learn...

引用

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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Novel Preprocessing and data Augmentation Method for Bearing Fault Diagnosis 3

Novel Preprocessing and Data Augmentation Method for Bearing...

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3rd international conference on Electrical Machines and Drives, ICEMD 2023

作者： Montasseri, Mohammad Mahdi Faiz, Jawad Parvin, Farbod Kalhor, Ahmad University of Tehran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350393743

To avoid financial and life risks, early diagnose of bearing faults is particularly important, and this has made intelligent fault diagnosis popular. intelligent fault diagnosis faces challenges such as the need for sufficient data and suitable performance for noisy data. This paper presents a preprocessing and data augmentation method to improve the performance of a few-shot learning method based on these two challenges. In this method first vibration signal is converted to two-dimensional images by means of mel spectrogram, then two image processing methods, namely, histogram equalization and gamma correction are applied. Finally, a data augmentation method based on gamma correction method is used. By using the Case Western Reserve University dataset, it is shown that the proposed method can improve the fault diagnosis process. © 2023 IEEE.

关键词： Image enhancement

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Harnessing Satellite Imagery for Effective Flood Detection using Convolution Neural Network 3

Harnessing Satellite Imagery for Effective Flood Detection u...

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

作者： Ambarkar, Bhakti Singh, Aman Gupta, Piyush Dhule, Chetankumar Agrawal, Rahul Morris, Nekita Chavhan G H Raisoni College Of Engineering Department Of DIC Nagpur India Department of Data Science G H Raisoni College of Engineering Nagpur India Department of DIC G H Raisoni college of Engineering Nagpur India

ISBN: (纸本)9798331527549

Earth Observation has become essential for monitoring geosciences and human activity. With the increasing availability of data, Artificial Intelligence (AI) algorithms have delivered significant advancements in remote sensing. However, weather-related disasters like hurricanes and floods, often accompanied by dense cloud cover, limit the effectiveness of optical imaging. Synthetic Aperture Radar (SAR), which penetrates clouds, offers a robust alternative. This study presents a novel dataset of co-registered optical and SAR image time series for flood event detection. By integrating spatial and temporal information using a ResNet-GRU architecture, we overcome the challenges posed by limited labeled SAR data. Our method achieves state-of-the-art accuracy, demonstrating resilience in detecting floods under adverse conditions. Performance metrics indicate substantial improvements in disaster response capabilities, emphasizing the practical utility of SAR in flood monitoring. This work provides critical insights for researchers and practitioners in leveraging SAR and optical data for disaster management. © 2025 IEEE.

关键词： Radar imaging

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