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Deep learning Approach Combining GAN and BiGRU for Diabetes Prediction 3

Deep Learning Approach Combining GAN and BiGRU for Diabetes ...

引用

3rd international conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Fatima, Tehreem Yang, Wenbiao Xia, Kewen Zia, Syed Muhammad Khalid Bin Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Nantong Vocat Univ Internet Things Nantong 226007 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

The increasing prevalence of diabetes in contemporary society underscores the pressing need for reliable and early diagnosis methods. However, accurately diagnosing diabetes poses significant challenges due to limitations in available datasets, often characterized by restrictions and imbalances. To address these constraints, an upgraded diabetes prediction model is proposed in this study, which combines a Bidirectional Gated Recurrent Unit (BiGRU) architecture with Generative Adversarial Network (GAN)-based data augmentation. By leveraging GAN, synthetic yet realistic samples are generated, thereby enriching the dataset and facilitating a more robust training procedure. The enhanced BiGRU model is subsequently employed for binary diabetes classification, exhibiting a noteworthy improvement in predictive accuracy. Remarkably, an astounding validation accuracy of 96.74% and test accuracy of 97.62% are achieved by our model, underscoring the efficacy of integrating GAN-based data augmentation with BiGRU for diabetes prediction. This study not only introduces a novel approach to medical data augmentation but also establishes a new standard for diabetes classification accuracy, thereby advancing the field of diabetes diagnosis.

关键词： Diabetes Prediction Deep learning GAN BiGRU

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Study on the label generation model of power suppliers' bad behavior based on supervised machine learning 3

Study on the label generation model of power suppliers' bad ...

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2023 3rd international conference on intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Shen, Mengru Zhang, Hongchun Pi, Te Wang, Xinkai Xu, Nan Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China Beijing Jiaotong University Beijing China The University Of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781510672963

The occurrence of bad behavior of power enterprise suppliers is serious, and it is difficult to obtain and reuse a large amount of effective information. This has caused a certain impact on the subsequent supplier evaluation work, especially for the work of the bidding and procurement department. In order to extract the core information in the bad behavior data, the business department can take targeted management countermeasures in the follow-up bidding, procurement and cooperation process. Based on the characteristics of the bad behavior data of power enterprise suppliers, the framework design, model construction and experimental evaluation of the bad behavior label model based on supervised machine learning are conducted. © 2024 SPIE. All rights reserved.

关键词： Supervised learning

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Current Signal Enhancement of High Voltage Circuit Breaker Based on W-CGAN-GP Algorithm 3

Current Signal Enhancement of High Voltage Circuit Breaker B...

引用

3rd international conference on Computer Science, Electronic Information engineering and intelligent Control Technology, CEI 2023

作者： Tang, Li Sheng, Yifa School of Electrical Engineering University of South China Hengyang421001 China

ISBN: (纸本)9798350372052

The availability of extensive sample data ensures the training effectiveness, although deep learning could significantly enhance the accuracy of the fault diagnosis, potentially. For fault diagnosis, the application of deep learning is hampered in the case of High-Voltage Circuit Breakers (HCVBs) as a result of the insufficient fault signals. To address this limitation, this paper introduces a variant of the generative adversarial network (W-CGAN-GP) tailored for enhancing fault electrical signals. The proposed method generates electrical signals corresponding to various fault conditions, thereby expanding the training dataset. To validate the efficacy of the approach in enhancing the fault diagnosis accuracy of HVCB, a comparative analysis of fault diagnosis accuracy was conducted before and after data generation. The experimental findings demonstrate that the fault signals generated by the method exhibit a high degree of similarity, leading to an improvement in fault diagnosis accuracy. © 2023 IEEE.

关键词： Failure analysis

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Effective Machine learning-Based Predictive Analytics for Loan Default Prediction in Banking Sector 3

Effective Machine Learning-Based Predictive Analytics for Lo...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Kumar Jain, Yogesh Mannepalli, Praveen Kumar Kaur, Kirat Maheshwari, Abhishek Singh, Jagriti Ranka, Monica Finance Dr.D.Y.Patil Vidyapeeth Centre for Online Learning Maharashtra Pune India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India University School of Commerce Finance and Accountancy Ghaziabad India Manipal University Jaipur India Dayananda Sagar College of Arts Science and Commerce Bengaluru India

ISBN: (纸本)9798331528201

These days, a crucial topic is the early prediction of loan failure in banks. Customer loan default may be predicted using a variety of statistical and AI classification methods. This paper introduces a machine learning-based predictive analytics model aimed at improving loan default prediction in the banking sector by leveraging open P2P loan data from Lending Club. To improve the precision and consistency of predictions, the model uses state-of-the-art ML methods, namely RF. By analyzing critical borrower information such as credit history, loan purpose, and debt status, the model identifies high-risk borrowers with greater precision. The study evaluates a performance of different ML models, including LR, DT, and SVM, but identifies RF as the most effective, demonstrating superior metrics with 89% accuracy, 99.5% sensitivity, and 80.3% specificity. These results highlight the model's ability to not only predict defaults accurately but also reduce false positives and negatives. © 2024 IEEE.

关键词： Prediction models

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Detection of Maize Plant Insects using Lightweight Deep-learning Scheme 3

Detection of Maize Plant Insects using Lightweight Deep-Lear...

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

作者： Vickram, A.S. Bhavani Sowndharya, B. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Automatic plant condition monitoring using a chosen computer algorithm is one of the recent advancement in the agriculture domain. To ensure the plant health, it is necessary to monitor the pest which causes the considerable damage to the plant. This work considered the monitoring of maize (corn) from the study and the common pests which causes the severe damage to maize, like the grasshopper and beetle are analyzed automatically using a chosen Lightweight Deep-learning (LD) methodology. This work considered the MobileNet (MN) variants for the study and this proposed technique consist the following phases;digital image collection and converting it to 224x224 pixels, feature extraction using chosen MN-model, feature optimization using Butterfly-Algorithm (BA) and fused-features generation, and binary classification with 5-fold cross validation. This work considered the MN-variant model to examine the pest images and the achieved result confirms that this scheme presents a detection accuracy up to 96% when SoftMax based classification is executed. © 2025 IEEE.

关键词： Smart agriculture Condition monitoring Accuracy databases Plants (biology) Digital images Insects Feature extraction Monitoring Optimization

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intelligent Adaptive Control of Magnetically Growing Rod-Like System 3

Intelligent Adaptive Control of Magnetically Growing Rod-Lik...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Cokatar, Safer Yildirim, Alper Akgun, Gazi Akdogan, Erhan Demir, Ugur Mechatronics Engineering Marmara University Health Institute of Turkey Istanbul Turkey Marmara University Mechatronics Engineering Istanbul Turkey

ISBN: (纸本)9798350349207

This paper represents on intelligent adaptive control of magnetic controlled growing rods (MGCR) which used in the treatment of early-onset scoliosis. With intelligent adaptive control theory system can be controlled precisely when system includes high order non-linearities or uncertainties. Radial basis functions (RBF)-based neuron structure continuously update itself with online learning model for the reason of dynamic responses to the system behaviour. Therefore, robust control of the system against all kinds of variable conditions can be achieved. In the context of this scholarly paper, a configuration akin to that of MCGR has been devised to serve as the experimental setup. Additionally, this setup includes uncertainties and non-linearities, the same as in a real system, which is an opportunity to analyse the robustness and adaptivity of control theory. © 2024 IEEE.

关键词： Robust control

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Enhancing RIS-aided D2D communication for IoT applications 3

Enhancing RIS-aided D2D communication for IoT applications

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Varun, V. Vishwaa, M. Shanjay, R. Noor Mohammed, V. Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

Reconfigurable intelligent Surface (RIS) is a potential 5G/6G technology that provides energy efficient solutions to the increasing demand of communication network resources. The number of Internet of Things (IoT) nodes in a network is also on the rise as automation is being implemented in different fields. In this paper, the data rates for RIS-aided Device to Device (D2D) communication between two IoT nodes is maximized by defining a non-convex optimization problem with Quality of Service (Q 0 ∼S) constraints. Due to the non-convex nature of the problem, an alternate Reinforcement learning based approach is implemented instead of a traditional mathematical approach as it is computationally exhaustive. The results obtained on the implementation of the proposed approach gives enhanced performance in the communication link. © 2024 IEEE.

关键词： Internet of things

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A lightweight intrusion detection method for intelligent substations 3

A lightweight intrusion detection method for intelligent sub...

引用

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

作者： Zhang, Zidong Liu, Dongqi Liang, Haolan School of Electrical and Information Engineering Changsha University of Science and Technology Hunan410114 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan410012 China

ISBN: (纸本)9781510680531

Aiming at the current problem of lack of effective data and high service latency for intrusion detection in smart substations, this paper proposes a lightweight intrusion detection method for smart substations. Through the federated learning method and knowledge distillation method this paper trains a lightweight intrusion detection model based on LSTM. Experiments show that this paper's method has a large improvement in the accuracy and service delay on the NSL-KDD dataset. © 2024 SPIE.

关键词： Distillation

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Optimization Strategy of University Library Knowledge Service Based on data Management 3

Optimization Strategy of University Library Knowledge Servic...

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3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Zhou, Yanjun Jianghan Univ Wuhan Peoples R China

ISBN: (纸本)9798350388732;9798350388725

With the rapid development of science and technology, big data technology is profoundly changing all walks of life, and the library management of public libraries is also deeply affected. In the era of big data, how to use advanced technology to innovate the management of public libraries, improve the quality and efficiency of services, and meet the growing information needs of the public is an urgent problem to be solved. This paper mainly analyses the relevant content of library management innovation in public libraries in the era of big data. The study used a questionnaire to investigate the acceptance of teachers and students of the new improved library service system. The reliability of the questionnaire was above 0.7, and the results showed that students' overall evaluation of the library management system was high, with scores above 5.

关键词： library management university library data management book lending

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Suggesting Playlist and Playing Preferred Music Based on Emotion from Facial Expression 3

Suggesting Playlist and Playing Preferred Music Based on Emo...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Akter Asha, Mst. Lima Ahmed Rafi, Muntasir Ahamed, Md. Sazzadur Imran, Md. Hafizul Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350381931

Facial expression is a powerful indicator of human emotion and plays a crucial role in interpersonal communication. The mood of a person or his intention can be analyzed by detecting his expression. For detecting the expression, artificial intelligence and machine learning are very useful for automated detection. By analyzing facial features and expressions, a music recommendation system can predict the user's mood and recommend songs that align with their emotional state. Many researchers have worked on *** proposed system works on 8 moods of humans which are angry, contempt, disgust, fear, happy, neutral, sad, and surprise. This study utilizes a machine learning concept to achieve this goal. The methodology involves data collection, model training using a combination of Convolutional Neural Network (CNN) and VGG16 CNN, and recommending songs from the Spotify music dataset. The results show that both CNN and VGG16 CNN performed well in detecting facial expressions, with CNN achieving 66% accuracy and VGG16 achieving 92% accuracy. This system effectively recommends songs from the Spotify dataset based on the user's mood. © 2024 IEEE.

关键词： Machine learning

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