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GRU combined model based on multi-objective optimization for short-term residential load forecasting

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10423-10440页

作者： Yi, Lingzhi Peng, Xinlong Fan, Chaodong Wang, Yahui Li, Yunfan Liu, Jiangyong College of Automation and Electronic Engineering Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan China School of Computer Science and Technology Hainan University Hainan China College of Electrical and Information Engineering Hunan University Changsha China

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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A Comprehensive Analysis of Autonomous Medical Assistants for Improving Healthcare Outcomes 15

A Comprehensive Analysis of Autonomous Medical Assistants fo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Devrani, Rekha Dasarathy Pandey, Arvind Kumar Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

The cause of this complete evaluation is to examine the modern-day and projected use of self-sustaining medical assistants (AMAs) for improving healthcare effects. This evaluation evaluates current research on the implementation of AMAs in healthcare settings, the technical challenges related to AMAs, and the advantages and downsides related to the use of AMAs. Moreover, the evaluation consists of Discussions on potential criminal and ethical concerns. Assessment of user stories. Techniques and recommendations for introducing and coping with AMAs in healthcare shipping models. The effects of this evaluation provide insights into the advantages and demanding situations of imposing AMAs in healthcare and imply the capacity for leveraging such technology as a feasible method of enhancing healthcare imposing. © 2024 IEEE.

关键词： Ethical Evaluation Leveraging Potential Recommendations Settings Sustaining

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Poly(alkyl-biphenyl pyridinium)anion exchange membranes with a hydrophobic side chain for mono-/divalent anion separation

Industrial Chemistry & Materials

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Industrial Chemistry & Materials 2023年第1期1卷 129-139页

作者： Hongxin Yang Noor Ul Afsar Qian Chen Xiaolin Ge Xingya Li Liang Ge Tongwen Xu Anhui Provincial Engineering Laboratory of Functional Membrane Materials and Technology Department of Applied ChemistrySchool of Chemistry and Materials ScienceUniversity of Science and Technology of ChinaHefei 230026PR China Applied Engineering Technology Research Center for Functional Membranes Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei230088PR China

A series of poly(alkyl-biphenyl pyridinium)anion exchange membranes(AEMs)with a hydrophobic side chain were prepared for mono-/divalent anion separation using electrodialysis(ED).A poly(alkyl-biphenyl pyridinium)polymer was synthesized via superacid-catalyzed polymerization,and then quaternization was conducted using Menshutkin reactions with *** obtained quaternized product had excellent solubility in common organic solvents,making it flexible to form homogeneous membranes by a solution casting *** introduction of a hydrophobic side chain resulted in a microphase separation structure in the membrane,which is favorable to the active transport of Cl−(higher Cl−flux of up to 3.37 mol m−2 h−1 at a 10 mA cm−2 current density)compared with that of SO42−ions giving a high permselectivity of 11.9 in a mixed salt(NaCl/Na2SO4)*** addition,the prepared membrane exhibited excellent alkaline stability in successive ED *** showed an OH−flux of up to 3.6 mol m−2 h−1 with a permselectivity of 361.2 between OH−and WO42−,which is much higher than that of Neosepta ACS *** ED results manifest that the poly(alkyl-biphenyl pyridinium)AEMs can be promising candidates for practical mono-/divalent anion separation in industry.

关键词： Superacid-catalyzed polymerization Anion exchange membrane Mono-/divalent anion separation Electrodialysis Permselectivity

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Dynamic Trust Management and AI-Based Multi-Factor Authentication for 6G Machine Type Communications 1

Dynamic Trust Management and AI-Based Multi-Factor Authentic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sinha, Garima Agarwal, Ankita Faculty of Engineering and Technology Department of Computer Science Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

New security concerns about the transmission of sensitive data over enormous networks of linked devices have arisen with the advent of the 6G era and the broad adoption of massive machine-type communication (MTC). The authors of this paper provide a fresh strategy for MFA and dynamic trust management in 6G networks that make use of AI to solve these issues and improve MTC security. This study presents a novel approach to behavioural profiling that takes into account the strengths and weaknesses of machine-type devices. It was necessary to create simulated data to represent many situations due to the wide range of possible network circumstances, device characteristics, ambient variables, applications load of work, and security needs. The efficacy of the suggested remedy was evaluated during six simulation cycles. The findings showed that the framework can adapt to changing network circumstances and device behaviours, with stability demonstrated across many situations. The accuracy rates for implementing MFA ranged from 0.49 to 0.57. This MFA and AI-driven dynamic trust management framework has great potential to improve 6G network MTC security;its adaptability and resilience provide a solid basis for meeting the security concerns that will inevitably arise in the future of 6G networks. To maximize performance and assure successful deployment, more modifications and real-life testing are suggested. © 2025 IEEE.

关键词： Authentication Protocol

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An Early Detection of Tomato Plant Disease with Deep Reinforcement Learning and CNN 5th

An Early Detection of Tomato Plant Disease with Deep Reinfor...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Singh, Jagendra Saraswat, Mala Ramesh, Gopisetty Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Murugesan, Ashok School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of EIE CVR College of Engineering Telangana Ibrahimpatnam India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science & Engineering Srinivasa Ramanujan Institute of Technology Autonomous Anantapur India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Kuthambakkam India

ISBN: (纸本)9789819795147

Machine learning models have been tested in the current research for detecting and classifying tomato diseases at their early stages. This is one of the core tasks of modern agriculture, being extremely beneficial in maintaining the health and, accordingly, the production of tomatoes. In order to achieve this, a dataset of 3450 images was totaled, containing both the diseased and healthy leaves of tomato. The dataset was split with a 70% portion for training and the rest 30% for testing. Therefore, the current research seeks to train and evaluate the performance of several machine learning models, including Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and VGG-16 architecture;the results showed that the CNN model had the highest measure over the precision, recall, F1 score, and accuracy that averaged over 96.8%, 96.5%, 96.6%, and 96.77%, respectively. The KNN model showed particularly high performance over the metrics of interest with precision, recall, F1 score, and accuracy of 94.3%, 94.0%, 94.1%, and 94.32%, respectively. The RNN model depicted some excellent indicators that pointed to the ability to catch the sequential dependencies with a precision, recall, F1 score, and accuracy of 88.2%, 83.5%, 85.7%, and 85.60%, respectively, which was still lower than CNN and KNN. Finally, the VGG-16 architecture showed quite competitive results over the precision, recall, F1 score, and accuracy—91.7%, 93.2%, 92.4%, and 92.20%, respectively. Therefore, it is evident that the current research provided clear evidence that the employment of ML was effective as pertaining to the accurate detection and classification of the tomato diseases with the CNN model displaying robustness and being highly potential. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Advancing Liver Cancer Diagnosis: Innovative Deep Learning Algorithms for Segmentation and Classification

Advancing Liver Cancer Diagnosis: Innovative Deep Learning A...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Suganthi, B. Vidhya, U. Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798331541170

Liver cancer is a significant worldwide health issue that requires precise diagnosis techniques and efficient treatment options. This paper presents sophisticated deep learning (DL) algorithms for the segmentation and classification of liver cancers, providing a thorough comparative comparison. The advancements in DL have led to significant progress in other domains. As a result, there is a rising excitement for using DL techniques in liver research to aid clinicians in diagnosing and treating medical conditions. The Improved Fully Convolutional Neural Network (IFCNN) has been introduced in this study as a solution to the problem of liver tumor segmentation in cases of liver cancer. The IFCNN has been mathematically designed to address this issue. Transfer learning improves the ability to apply knowledge gained from one patient group to other patient groups with different characteristics. Comparative studies performed against ResNet and VGG16 demonstrate higher sensitivity, specificity, and overall classification metrics. The algorithms' efficacy and real-time processing capabilities have been extensively validated on multiple datasets, including different kinds and stages of liver cancer. This paves the way for eventual incorporation into clinical processes. These advanced algorithms exhibit superior precision and efficiency compared to current models, offering significant potential for improving clinical practice. They provide more dependable tools for precise diagnosis and informed treatment planning in the fight against liver cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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VIEPred: Predicting Viral Immune Evasion with Multimodal Representation Learning

VIEPred: Predicting Viral Immune Evasion with Multimodal Rep...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Liang, Banqing Wang, Na Huang, Hao Hou, Yijie Qiang, Xiaoli Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

Predicting coronaviral immune evasion is crucial for identifying and responding to new COVID-19 variants in advance, thereby optimizing vaccine development and public health strategies to prevent further outbreaks. Hence, we present VIEPred, a model designed to predict mutations in every amino acid of the RBD. However, protein representation learning is a challenge task due to the hierarchical structure of proteins. Our method uses a sequence encoder based on dilated convolutions and attention mechanisms to generate sequence embeddings and a structure encoder employing geometric vector perceptrons (GVP) and graph neural networks (GNN) to produce protein structure representations. These features are combined using a cross-attention fusion module and subsequently processed through a fully connected layer to output the probability of viral immune evasion. Extensive ablation studies led to an optimal model with ACC of 88.4% and AUC of 86.8%. Compared to existing prediction methods, VIEPred demonstrates superior predictive performance across most evaluation metrics for both SARS-CoV-2 and influenza viruses. © Published under licence by IOP Publishing Ltd.

关键词： Graph neural networks

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A Review on Advancing Cybersecurity Frameworks by Integrating Machine Intelligence with Federated Learning 5

A Review on Advancing Cybersecurity Frameworks by Integratin...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Patil, Vijayta Moolchandani, Jhankar Dubey, Ashutosh Kumar Amity School of Engineering and Technology Department of Computer Science & Engineering Amity University Madhaya Pradesh Gwalior India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India

ISBN: (纸本)9798331518097

In today's world, the growth of connected networks has also created a significant risk due to increasing risks of cyber-attacks. In this paper, various hybrid cybersecurity frameworks have been reviewed and analyzed that utilize machine learning approaches in combination with federated learning. These frameworks are assessed in terms of their security against complex cyber threats. In particular, machine learning focuses on improving threats detection by searching for patterns and other oddities in complex datasets, while the federated learning decentralizes the topology of the model training. This decentralization helps to enhance strict data compliance requirements and reduce data exposure risks. When synergized in the dual approach, the security system is strengthened as well as the enforcement of data privacy and other compliance where available. Empirical results have proved that this methodological approach provides better outcomes especially aimed at detection among other benchmarks. The improvement due to the hybridization indicates a scalable and robust solution that can adapt the evolving cyber threats landscape. The hybrid combination may establish new cybersecurity standards and open the door to more secure digital interactions across various sectors. © 2024 IEEE.

关键词： Federated learning

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A survey on event connections and their discovery for cloud service

A survey on event connections and their discovery for cloud ...

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2023 International Conference on Electronic Information engineering and Data Processing, EIEDP 2023

作者： Zhang, Zhen Xia, Chunhe Li, Shuyu Liu, Zhiying Huang, Mingzhi Huang, Chengling Li, Xiaojian School of Computer Science & Engineering Guangxi Normal University Guilin China Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781510666160

Connection is the effect between events. It is an important basis for judging of violations and source-trace of failure in cloud service accountability. This paper presents a survey on the event connections including the features, method of discovery and correction. Most of recent research on event connections in cloud services relying on manually labeling and correcting to event connections, and a few of them have achieved automatically labeling of event connections. Finally, we give out a taxonomy of the recent research, some problems in the method of discovery and correction, and an approach in the paper. © 2023 SPIE.

关键词： Distributed database systems

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Joint self-supervised and reference-guided learning for depth inpainting

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Computational Visual Media 2022年第4期8卷 597-612页

作者： Heng Wu Kui Fu Yifan Zhao Haokun Song Jia Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Depth information can benefit various computer vision tasks on both images and ***,depth maps may suffer from invalid values in many pixels,and also large *** improve such data,we propose a joint self-supervised and reference-guided learning approach for depth *** the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge *** module alternately learns a convolutional dictionary and sparse coding from a corrupted depth ***,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual *** reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth *** thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large *** summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent *** results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

关键词： depth inpainting self-supervised learning reference-guided learning

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