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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511-520 订阅

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Diagnose Kidney Cancers Using Machine learning Classification Algorithm From Transcriptome Profiles of Patients 3

Diagnose Kidney Cancers Using Machine Learning Classificatio...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Keerthika, V. Rakshitha, B. Poojashree, H.G. Alliance University Bangalore India

ISBN: (纸本)9798331515911

Kidney cancer is the ninth most diagnosed kidney cancer in men and the fourth most common in women according to the world health organization Report. Worldwide, kidney cancer causes 144,000 accidental deaths and 338,000 fresh cases of the disease each year. It is very important to diagnose it in the early stage to save one life. So that they can get treatment at the early stage. It is very hard to diagnose manually from clinical data and images by the doctors in the medical field. The recent technologies like artificial intelligence using image processing will help them to diagnose it *** have used TCGA Kidney Cancers dataset, is a bulk RNA-seq dataset that contains transcriptome profiles of patients to identify kidney cancer. In this research we used MRMR and regression. In terms of accuracy, our results indicate that the regression model achieves 97% accuracy, this Machine learning algorithms to create a reliable and accurate diagnostic tool for diagnose the kidney cancer. The potential for improved clinical decision-making will be demonstrated by the combination of transcription profiles and machine learning approaches © 2025 IEEE.

关键词： Diseases

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Design and Investigate the Deep learning Models for White Blood Cell Classification 3

Design and Investigate the Deep Learning Models for White Bl...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Gaikwad, Prajwal. S. Deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant D. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

For the purpose of illness prediction, the objective of this study is to develop automated models for the categorization of white blood cells (WBCs). In the human immune system, white blood cells (WBCs) function to combat infections and shield the body from external substances. Eosinophils, neutrophils, monocytes, basophils, and lymphocytes are the components that make up these cells;each of these cell types accounts for a different proportion and is responsible for a distinct set of functions overall. The clinical laboratory technique for counting the specific types of white blood cells (WBCs) has long been an essential component of a testing procedure known as a complete blood count (CBC), which is used to aid in the monitoring of individuals' health. However, such manual processes are time-consuming and error-prone for disease detection. The recently proposed machine and deep learning-based solutions for the blood cell classification delivered better results, however, suffered from challenges as well. These challenges motivate us to propose a novel AI-based approach for automatic WBCs classification in this research work. © 2024 IEEE.

关键词： Cells

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Women Safety Platform with Safe Route Prediction using Crime data 3

Women Safety Platform with Safe Route Prediction using Crime...

引用

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

作者： Sandyal, Krupashankari S. Anisha Sejal, B.S. Gupta, Laxmi Raj Bansal, Akshiv Department of ISE Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

Women's safety in public spaces is a pressing concern, particularly in urban environments where traditional navigation systems like Google Maps fail to prioritize safety over efficiency. This paper proposes a Women Safety Platform that leverages geospatial data, machine learning models, and user feedback to address these limitations. The platform predicts and highlights the safest route between source and destination by analyzing historical crime data and assigning safety scores to streets using machine learning models, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, Decision Tree, and Bernoulli Naive Bayes. KNN effectively identifies patterns in crime data to provide accurate safety scores, while Random Forest and Decision Tree classifiers achieved perfect training accuracy, showcasing their ability to capture complex patterns, albeit with potential risks of overfitting. Logistic Regression demonstrated a balance of high accuracy and generalization, and Bernoulli Naive Bayes provided reliable performance with the least overfitting tendency. Routes are evaluated based on aggregated street safety scores, visually marked to aid user decision-making and complemented with sentiment-based insights from user feedback. The Women Safety Platform uses geolocation data to monitor user safety and triggers an emergency SOS if safety levels drop below a threshold. By integrating crime data, real-time geospatial APIs, and proactive emergency measures the Women Safety Platform empowers women with a navigation tool that prioritizes safety alongside convenience. This initiative not only fosters confidence and freedom in navigating public spaces but also serves as a scalable model for enhancing mobility and safety for other vulnerable populations. © 2025 IEEE.

关键词： Logistic regression

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Application of Machine learning Algorithms in Digital Twin Monitoring Systems: An Overview of Approaches, Methods, and Prospects 3

Application of Machine Learning Algorithms in Digital Twin M...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Amirkhanova, Gulshat Amirkhanov, Bayurzham Tyulepberdinova, Gulnur Ishmurzin, Timur Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798331529222

Digital Twins represent virtual replicas of real-world objects, processes, or systems, enabling continuous real-time monitoring, dynamic adaptation, and predictive analytics. Their integration has become central in industrial, urban, and transportation domains under the paradigms of Industry 4.0, the Industrial Internet of Things (IIoT), and smart cities. However, effectively harnessing the torrents of time-series data and complex system states integral to Digital Twins (DTs) requires advanced analytical tools. Machine learning (ML) methods—ranging from classical ensemble models to deep learning architectures and reinforcement learning—provide powerful mechanisms for anomaly detection, failure prediction, operational optimization, and scenario modeling. This review explores the essential ML algorithms, their application contexts, and the supportive infrastructures required for implementing ML-driven DT solutions. We also discuss interpretability techniques, address security and concept drift challenges, examine the role of MLOps in ensuring reliable deployment, and highlight future research directions, including physics-informed ML, multimodal data integration, and advanced transformers for time-series forecasting. ©2024 IEEE.

关键词： Predictive maintenance

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E-RL:Vehicle Driving Strategy learning with Environmental Reinforcement learning Framework 3

E-RL:Vehicle Driving Strategy Learning with Environmental Re...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Shuai, Yanshu Zheng, Jiaoling Wang, Haoquan Zhang, Sihui Liu, Yiming Guo, Peng Zeng, Yu Chengdu University of Information Technology School of Software Engineering Sichuan Chengdu China Sichuan Yifang Intelligent Technology Co. Sichuan Chengdu China

ISBN: (纸本)9798350369151

To address urban traffic congestion simulation after Traffic Management Initiatives (TMI) implementation, a method combining deep reinforcement learning with environmental optimization is proposed, optimizing the environmental strategy through a fixed agent policy. To tackle the challenges posed by road network topology changes post-TMI, the method designs an environmental Markov reinforcement learning framework and introduces graph neural networks to dynamically calculate road resistance in urban areas. Combined with the Dijkstra algorithm, the vehicle routing strategy is determined. This strategy is then applied to the road network after TMI to predict road resistance post-TMI. Experiments validate that this method achieves good congestion replication in the original road network environment and effectively predicts congestion after TMI implementation. © 2024 IEEE.

关键词： Traffic congestion

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Research on the Application of Computer Artificial Intelligence Animation Technology in Short Video Production 3

Research on the Application of Computer Artificial Intellige...

引用

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

作者： Lin, Huiyu Lin, Tongxi School of Information Engineering Guangzhou Huashang Vocational College Guangdong Guangzhou510000 China

ISBN: (纸本)9798350380989

This paper introduces a computer-based artificial intelligence short video generation system. A stepwise optimization conversion algorithm is presented. This algorithm can realize the fast calculation of large Angle offset and greatly improve the efficiency of short video generation. The complexity of the existing prediction unit is determined by its correlation with time, space and parent neighbor PUs, and the corresponding prediction model is quickly selected according to its complexity, so as to improve the compression efficiency. The system has the functions of content collection to release process, multi-format material synthesis, transcoding processing and so on. The software mainly realizes face recognition, speech recognition, speech synthesis, text recognition, sensitive information recognition, natural language processing and so on. It can better achieve content aggregation, classification and retrieval, thus improving the production effect. The results show that this method has strong functions of learning and intelligent processing. The system can quickly retrieve and organize the text, so as to improve the efficiency of writing, editing and publishing. © 2024 IEEE.

关键词： AI animation design artificial intelligence Computer short video production

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Development and Application of Online learning System Based on Artificial Intelligence 3

Development and Application of Online Learning System Based ...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Gao, Luyun Jinan Preschool Education College Jinan China National University Manila ManilaPH 1008 Philippines

ISBN: (纸本)9798350318609

With the continuous progress of subject knowledge such as Internet and computer technology, as well as the update and improvement of educational models and methods, people's cognition of the concept of artificial intelligence is getting deeper and deeper. Based on the current intelligent background, aiming at various types of problems involved in virtual reality, this paper uses machine automation algorithm to apply online learning system to real life, and studies the optimization of online learning system by artificial intelligence algorithm. After that, this paper also conducts related tests on the performance of the learning system, and the test results show that the resource utilization rate of the system is above 0.7, the data throughput is large, the stability is relatively good, and it is safe enough to run and meet the needs of users. © 2024 IEEE.

关键词： learning systems

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intelligent Adaptive Systems and Methods Thereof for Household Energy Control and Management 3

Intelligent Adaptive Systems and Methods Thereof for Househo...

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Krishna, Shiva Ram Kumar, Rakesh Gaurav, Amit Singh, Nagendra Sharma, Mamta Almas, Shekh Kulsum Government Engineering College Department of Electrical Engineering Bihar Samastipur India Govt Engineering College Dept. Of Electrical Engineering Bihar Samastipur India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar505001 India Raj Rishi Government College Department of Zoology Alwar India Sage University Department of ECE Indore India

ISBN: (纸本)9798350390728

intelligent home energy consumption systems have been made possible by the growing need for sustainability and energy efficiency. This study describes a novel intelligent adaptive system and the related technique for effective distribution and management of home energy. The system uses sensor networks and cutting-edge technologies like IOT, AI, and machine learning to manage and distribute energy in a home in an adaptable manner. The suggested approach is to improve waste reduction, increase energy efficiency, and support a more ecologically conscious and sustainable way of living. © 2024 IEEE.

关键词： Smart homes

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Active integration of regional integrated energy side data based on multi-scale channel fusion learning 3

Active integration of regional integrated energy side data b...

引用

3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Bao, Weidong Gao, Qiang Ma, Sanjiang Wu, Bo State Grid Zhejiang Elect Power Co Ltd Yiwu Power Supply Co Jinhua Zhejiang Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Zhong Neng Ju Chuang Hangzhou Energy Technol Co L Hangzhou Peoples R China Yiwu Power Transmiss & Transformat Engn Co Ltd Elect Power Ind Branch Jinhua Zhejiang Peoples R China

ISBN: (纸本)9798350366105;9798350366099

Regional integrated energy systems involve multiple energy subsystems, such as power, heating, cooling, etc., and the data types and formats of each subsystem may be different, resulting in data heterogeneity. It is difficult to integrate heterogeneous data. Because multi-scale channel fusion learning can simultaneously process data of different data types and resolutions, and retain key information at different scales. Therefore, this paper proposes an active integration technology of regional integrated energy data based on multi-scale channel fusion learning. The comprehensive energy label matrix is constructed, and the weight of energy data is calculated to realize the collection of regional comprehensive energy data. A multiscale channel fusion learning classifier is designed to classify energy side data. Based on this, SOA architecture is used to construct the overall architecture of regional integrated energy side data active integration technology, which includes data source service layer and application service layer. The experimental results show that: for different energy data sets, the research technology can complete the active integration of regional integrated energy data with high precision, and the integration rate can reach 95%.

关键词： Multi-scale channel fusion learning Classifier Regional integrated energy side Active data integration

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Blood Group Typing using CNN 3

Blood Group Typing using CNN

引用

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

作者： Hemalatha, S. Nimalanantham, N. Pawin, A. Rajalakshmi Engineering College Department of AIML Chennai India

ISBN: (纸本)9798331523893

Blood group typing is a critical procedure in transfusion medicine, organ transplantation, and pregnancy management. Ensuring patient safety requires accurate and rapid blood typing, as incompatible transfusions can lead to severe complications, including hemolytic reactions. Traditional serological methods are labor-intensive, time-consuming, and prone to human error due to reliance on manual interpretation. This study presents an automated approach to blood group classification using Convolutional Neural Networks (CNNs), a deep learning algorithm renowned for its proficiency in image analysis. Leveraging a labeled dataset of blood sample images, the proposed model achieved an overall classification accuracy of 87.50%. Additionally, precision and recall values for each blood group classification were notably high, underscoring the model's reliability and effectiveness. By identifying specific patterns and features in blood sample images, the CNN-based system enhances diagnostic efficiency, minimizes human intervention, and reduces errors, particularly in resource-limited clinical settings. This work demonstrates the feasibility of integrating artificial intelligence into blood typing workflows, contributing to modernized transfusion practices and improved patient outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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