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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7159 条记录，以下是411-420 订阅

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A PFM System design for intelligent manufacturing in Industry 4.0 using FG Technology 4

A PFM System design for intelligent manufacturing in Industr...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Yadav, Kamlesh Kumar Dharmesh Kirit, Dhablia Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798350364729

In recent times, the use of predictive maintenance (PdMt) methods has been widespread in organizations to manage the well-being of Manufacturing Machinery (MM), thanks to the rise of Industry 4.0 (I4.0) and the integration of intelligent systems and Deep learning (DL) in Artificial Intelligence (AI). the data created by the Industrial Internet of things (IIoT) facilitates the accessibility of data and automated execution of processes in Industry 4.0. Digital resources consist of the information generated by the IIoT. the use of industrial big data is to forecast the malfunction in MM. PdMt empowers the organizational leader to make informed decisions on the maintenance or replacement of an accessory when an actual breakdown occurs, preventing any adverse impact on the entire manufacturing stream. Hence, Industry 4.0 necessitates a proficient equipment management system to enhance the allocation of jobs and implement a PdMt model. this work introduces a resource management system for fog computing that combines the Genetic Algorithm (GA) with DL techniques to enable PdMt. the PdMt framework uses Long Short-Term Memory Networks (LSTM) utilizing real-time observations. GA-LSTM for PdMt exhibits the most superior performance, boasting the shortest processing time of 291.32 seconds, the lowest operating expenditures amounting to $385.23, and the lowest energy usage of 10.36 joules [16]. © 2024 IEEE.

关键词： Predictive maintenance

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Research on Persistent Advantage learning Based Deep Q Network for Computational Offloading in Edge Computing 11

Research on Persistent Advantage Learning Based Deep Q Netwo...

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Wang, Cong Zuo, Mingmin Liu, Shuai Li, Di Northeastern University at Qinhuangdao School of Computer and Communication Engineering Qinhuangdao China Northeastern University School of Computer and Communication Engineering Shenyang China

ISBN: (纸本)9798350345346

Due to the increased data transmission and response latency resulting from offloading computational tasks to the cloud in multi-user device scenarios, we construct a typical three-layer system model. To minimize the offloading delay inherent in this system model, we designed a computation offloading strategy leveraging Deep Reinforcement learning. We propose an enhanced algorithm named PDPAL-DQN. this approach integrates Persistent Advantage learning, Double Q- learning, and Prioritized Experience Replay with the Deep Q Network. Simulation results indicate that across various network configurations and with large-scale user devices, PDPAL-DQN enhances system reward by approximately 13%, 6%, and 5% compared to other established strategies. © 2023 IEEE.

关键词： Computation offloading

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Root Cause Analysis of 5G Base Station Faults Based on Catboost Algorithm 11th

Root Cause Analysis of 5G Base Station Faults Based on Catbo...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Zhang, Lufei Guo, Jinge Guo, Bao Shen, Jinhu South China University of Technology ShenZhen518101 China University of Warwick Coventrycv4 7al United Kingdom China Mobile Communication Group Design Institute Co. Ltd. Beijing China

ISBN: (纸本)9789819721238

intelligent fault demarcation and locating technology for 5G base stations is a key technology for intelligent wireless networks. Currently, base station fault analysis relies on expert experience, board status, base station power environment data, and base station fault types, which is inefficient. the problem is not handled in a timely manner, which greatly affects customer experience. this paper uses the historical alarm data of 5G base stations and the marked data of historical processing alarm work orders, uses TF-IDF and Word2vec technology to create feature information data for alarm information, and uses Catboost algorithm to build AI model to automatically learn the hidden feature information in the data. intelligent demarcation and locating of 5G base station fault alarms and prediction of handling solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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LoockMe: An Ever Evolving Artificial Intelligence Platform for Location Scouting in Greece 1

引用

24th international conference on engineering Applications of Neural Networks (EANN)

作者： Trivizakis, Eleftherios Aidonis, Vassilios Pezoulas, Vassilios C. Goletsis, Yorgos Oikonomou, Nikolaos Stefanis, Ioannis Chondromatidou, Leoni Fotiadis, Dimitrios I. Tsiknakis, Manolis Marias, Kostas Fdn Res & Technol Hellas FORTH Computat BioMed Lab CBML Iraklion 70013 Greece Fdn Res & Technol Hellas FORTH Biomed Res Inst BRI Ioannina 45110 Greece Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst Ioannina 45110 Greece Univ Ioannina Dept Econ Lab Business Econ & Decis Ioannina 45110 Greece Hellen Mediterranean Univ Dept Elect & Comp Engn Iraklion 71410 Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

LoockMe is an artificial intelligence-powered location scouting platform that combines deep learning image analysis, cutting-edge machine learning natural language processing (NLP) for automated content annotation, and intelligent search. the platform's objective is to label input images of local landscapes, and/or any other assets that regional film offices want to expose to those interested in identifying potential locations for the film production industry. the deep learning-based image analysis achieved high classification performance with an AUCscore of 99.4%. Moreover, the state-of-the-art machine learning NLP module enhances the platform's capabilities by analyzing text descriptions of the locations and thus allowing for automated annotation, while the intelligent search engine combines image analysis with NLP to extract relevant context from available data. the proposed artificial intelligence platform has the potential to substantially assist asset publishers and revolutionize the location scouting process for the film production industry in Greece.

关键词： artificial intelligence deep learning transfer learning natural language processing location scouting film production search engine

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Traditional and Deep learning Techniques in Image Steganography: Recent Advances 11

Traditional and Deep Learning Techniques in Image Steganogra...

引用

11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Abdelrazik, Hesham F. Elsayed, Ahmed S. Ismail, Sally S. Mahmoud, Abeer M. Ain Shams University Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

For many decades however recently super increased;the importance of securing the private information of individuals or organizations. therefore, numerous researchers are motivated to be involved in the challenge of building intelligent, flexible, secured, imperceptive, bumped bit embedding rates and robust models of Stenography. Image steganography is a way of concealing data, such as text, pictures, or videos, under an image that acts as a cover. Recent years have seen an increase in interest in deep learning technology, which has been proven to be a powerful mechanism in a number of uses, such as image steganography. this paper's primary goal is to investigate and clarify three main types of image steganography techniques: traditional, convolutional neural network-based, and generative adversarial network-based. An explanation of the datasets and evaluation measures is provided along with the approach. A comprehensive summary of various image steganography techniques is introduced. Finally, a discussion on the potential directions for this field's future is introduced. © 2023 IEEE.

关键词： Generative adversarial networks

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Advances in Facial Emotion Recognition: Leveraging Machine learning for Human Emotion Analysis 11

Advances in Facial Emotion Recognition: Leveraging Machine L...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： thakur, Mohit Singh, Sarbjot Singh, Sargun Pal Kaur, Harmanpreet Chandigarh University Computer Science & Engineering Mohali India

ISBN: (纸本)9798350318159

A rapidly developing field called facial expression detection holds enormous promise in a variety of industries, in-cluding marketing, healthcare, and human-computer interaction. this research provides a thorough analysis of machine learning methods for this job based on neural networks. We explore the application of deep neural networks, such as CNNs and RNNs, emphasising the value of reliable training data. We compare model performance to benchmarks while investigating elements like precision and computational complexity. Also included are ethical issues, privacy worries, and appropriate deployment tactics. Our test results demonstrate how well these models perform at identifying facial expressions of emotion, as well as providing information about their current limitations and potential future study areas. © 2023 IEEE.

关键词： Benchmarking

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Synthesis of Wilms tumor images using Generative Adversarial Networks 11

Synthesis of Wilms tumor images using Generative Adversarial...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Jha, Manika Gupta, Richa Saxena, Rajiv Jaypee Institute of Information Technology Department of Electronics and Communication Engineering Noida India

ISBN: (纸本)9798350308433

Wilms tumor or Nephroblastoma is paediatric cancer, that can be treated if diagnosed at an early stage. However, no appropriate image dataset is publicly available except the Wilms MRI dataset of Mathematical Image Analysis Group, Saarland University, Germany, which only contain 28 multisequence MR scans. As, huge amount of data is required to train a machine or deep learning model, image synthesis could be beneficial. the absence of professionals who can label data was another problem. In order to meet the data requirements two different Generative Adversarial Networks (GANs)-Wasserstein GAN (WGAN) and Pix2Pix GAN have been utilized. these GAN architectures generate high-quality and realistic MRI images in terms critical evaluation metrics. these architectures may potentially benefit the detection of rare diseases which lack sufficient size of medical datasets. With the help of image synthesis and precise amount of data, effective computer-Aided diagnostic models could be developed. © 2024 IEEE.

关键词： Generative adversarial networks

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Speech Emotion Recognition using Deep learning 11

Speech Emotion Recognition using Deep Learning

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Garg, Divya Singh, Amrita Gupta, Anshika Sharma, Sanjiv Kiet Group of Institutions Computer Science and Engineering Department Ghaziabad India

ISBN: (纸本)9798350350357

Speech Emotion Recognition (SER) is an emerging field that involves recognizing emotions conveyed in speech. Emotions expressed through speech can greatly impact decision-making. this paper delves into the topic of speech emotion recognition (SER) and its focus on interpreting emotions conveyed through spoken language. the importance of SER lies in its potential to improve human-computer interaction, cognitive analysis, and psychiatric assessment. the study combines and preprocesses audio data from various datasets, such as RAVDESS, CREMA-D, TESS, and SAVEE, and uses log mel spectrograms to effectively extract features. Various methods including CNN models, and standard and optimized feature extraction techniques are used. the results suggest that SER has significant real-world applications and the approaches provided effectively identify emotional and voice signals. © 2024 IEEE.

关键词： Decision making

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Enhancing Disease Prediction through Ensemble learning Techniques 18

Enhancing Disease Prediction through Ensemble Learning Techn...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Wani, Suhail Rashid Attri, Shree Harsh Setia, Sonia Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

the study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as cardiovascular disease (CVD), Chronic renal illness, and Chronic Mellitus Diabetes offer significant global health issues, using existing diagnostic procedures that are time-consuming and error-prone. the report emphasizes the rising burden of chronic illnesses, estimating that 73% of deaths will be related to these ailments by 2025. Recognizing the links between illnesses such as CKD and CVD is critical for optimal care, with a particular emphasis on the increased prevalence and health hazards associated with Chronic Diabetes Mellitus (CDM). the fundamental goal of the research is to create an effective prediction model for early illness diagnosis by employing an adaptable machine learning model that can adapt to a variety of datasets. the review of the literature looks at several machine learning algorithms that have been utilized in prior research, emphasizing their potential for automating illness identification. the methods section describes the procedures required in collecting data, preparing it, and modeling it using ensemble learning. Metrics of performance evaluation confirm the Extra tree classifier's advantage and it performs the best among all the four ensemble techniques and improves the accuracy of predicting diseases. Finally, the above investigation highlights the promise of computational learning in solving the complex issues faced by chronic illnesses, emphasizing the crucial need for accurate early disease prediction in the context of a growing global health crisis. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cat Boost Chronic Diabetes Mellitus Chronic Disease Computational learning Ensemble Model Feature Selection Heart Disease Random Forest XG Boost

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Utilizing Advanced Deep learning Techniques for Brain Tumor Classification 18

Utilizing Advanced Deep Learning Techniques for Brain Tumor ...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sreedevi, Pogula Rani, K. Pushpa Anand, M. Madhavi, A. Balaram, Allam Kiran, Ajmeera Rgm College of Engineering and Technology Department of Cse Andhra Pradesh Nandyal India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India G. Pulla Reddy Engineering College Department of Cse Andhra Pradesh Kurnool India Gokaraju Rangaraju Institute of Engineering and Technology Department of Aimle Telangana Hyderabad India

ISBN: (纸本)9789380544519

Brain tumors, both benign and malignant, pose a substantial hazard to human health, and needing accurate and prompt diagnosis for successful treatment. the goal of this work is to improve brain tumor detection accuracy and effectiveness of categorization through the implementation of an advanced technique. the proposed method leverages the ResNet50 deep neural network, renowned for its proficiency with extensive datasets. By utilizing the pre-trained ResNet50 model from the ImageNet dataset and employing the Kaggle dataset for testing, we conducted comprehensive data preparation, including partitioning into distinct training and validation sets. Over 20,000 categories and over 14 million records make up the ImageNet collection or classes. this vast dataset has been instrumental in training various deep learning models, including ResNet50, to recognize and classify objects accurately. In our suggested method, comprehensive data preparation, including partitioning into distinct training and validation sets, was conducted. data augmentation techniques were employed to enhance model robustness, resulting in impressive accuracy levels of up to 98% in brain tumor categorization. Our research sets the stage for more precise clinical diagnoses and improved treatment strategies in the realm of brain tumor classification, thereby potentially saving lives. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor data augmentation healthcare ImageNet ResNet50 model

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