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A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network

Tsinghua science and technology 2025年第2期30卷 864-874页

作者： Chun Chen Yong Zhang Boon Han Lim Li Ning Shengzhong Feng Peng Xie School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information TechnologyShenzhen 518172China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul RahmanKajang 43000Malaysia Shenzhen Institute for Advanced Study Shenzhen 518028China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesBeijing 100864China High-performance Intelligent Computing Research Group Guangdong Institute of Intelligent Science and TechnologyZhuhai 519031China

The rapid evolution of distributed energy resources,particularly photovoltaic systems,poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing *** integrated nature of distributed markets,blending centralized and decentralized elements,holds the promise of maximizing social welfare and significantly reducing overall costs,including computational and communication ***,achieving this balance requires careful consideration of various hyperparameter sets,encompassing factors such as the number of communities,community detection methods,and trading mechanisms employed among *** address this challenge,we introduce a groundbreaking neural network-based framework,the Energy Trading-based Artificial Neural Network(ET-ANN),which excels in performance compared to existing *** experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.

关键词： photovoltaic energy trading hyperparameter prediction

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Brain Tumor Detection from MRI Images using Convolutional Neural Networks 2024

Brain Tumor Detection from MRI Images using Convolutional Ne...

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16th International Conference on Contemporary computing, IC3 2024

作者： Patel, N.D. Jain, Vikas Kumar Yadav, Anil Kumar Bano, Sidra Rajpoot, Dharmveer Singh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Madhya Pradesh India Dept. of CS&IT Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Brain tumors present a formidable healthcare challenge worldwide, especially in India, where around 50,000 new cases emerge annually. This study proposes an innovative method utilizing Machine Learning (ML) and Deep Learning (DL), particularly Convolutional Neural Networks (CNN), to swiftly and accurately detect brain tumors from MRI images. By leveraging ML and DL, the research aims to transform patient care by aiding radiologists in prompt decision-making and ensuring timely treatment. Additionally, it offers insights into the performance comparison of ML and DL models, guiding future advancements in automated brain tumor detection systems. The results showcase a remarkable 92.86% accuracy rate, with detailed analysis revealing high precision and recall metrics. This comprehensive evaluation underscores the model's balanced performance, marking significant progress in the development of dependable brain tumor detection systems. Integration of ML and DL not only enhances diagnostic accuracy but also opens avenues for personalized treatment strategies, ultimately improving healthcare outcomes and saving lives. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Intelligent personalised learning system based on emotions in e-learning

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Personal and Ubiquitous computing 2023年第6期27卷 2211-2223页

作者： Karthika, R. Jesi, V. Elizabeth Christo, Mary Subaja Deborah, L. Jegatha Sivaraman, Audithan Kumar, Sanjeev Department of Computer Science and Engineering University College of Engineering Tindivanam Tamil Nadu Melpakkam604001 India Department of Networking and Communications School of Computing College of Engineering SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Information Technology Netaji Subhas University of Technology New Delhi110078 India Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi110078 India

One of the greatest challenges in the success of a personalised e-learning system lies in the behaviour identification of the learners during the learning and evaluation phase. The content delivery in an e-learning system must be modified and updated periodically according to the preference and behaviour of the learners. Usually, the behaviour of the learners drastically changes according to their affective states during the learning phase. The accurate identification of the learner’s negative emotions and addressing such emotions carefully in a positive sense can greatly provide success to the learners. In this paper, the learner’s emotions especially frustration emotion are automatically and accurately estimated using the information received from learning management systems (LMS) using the Takagi sugeno fuzzy inference engine. Based on this estimation, several motivational messages are distributed according to the identified emotions which had greatly helped them to succeed during e-learning. The motivational messages that are used for sending to the learners are based on regulatory fit theory. Several kinds of statistical tests are used in this paper for deep analysis. Two sets of learners, namely, control and experimental sets, are identified from undergraduate students. Experimental results are shown for these two sets to reveal the increase in their emotional strength after receiving the motivational messages. Statistical analysis t-test is also applied to the two sets, and the experimental results have shown that there is a significant difference between the two groups which shows the dominance of the proposed system. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： E-learning

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Domain Adaptation Using Generative Adversarial Networks for Medical Image Synthesis 5th

Domain Adaptation Using Generative Adversarial Networks for ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ganesh, D. Verma, Upendra K. Patil, Smita Alam, Intekhab Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India 3School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Latest advancements in state-of-the-art scientific photograph evaluation depend heavily on the availability of large new datasets of cutting-edge annotated scientific photographs for version training. Unfortunately, obtaining massive samples of cutting-edge categorized scientific picas is ultra-modern high-priced, or impossible. Area edition is a way to generalize a version educated on a selected dataset to a brand new area, wherein the model turned into not directly educated. Generative adverse Networks (GANs) were used to generate realistic artificial photographs. It has enabled the opportunity present day the usage of GANs to bridge the domain hole via generating artificial medical photos, which could then be used for education fashions. On this paper;we evaluate the latest progress in making use of area edition the use of GANs for scientific photosynthesis, with an emphasis on applications including pathology, scientific imaging, and optical acceptable imaging. We talk about the benefits of ultra-modern using GANs for area adaptation and present processes for each present day of the above programs. We also speak about various challenges and future directions in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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One-Shot Classification Is Enough for Automatic Label Mapping 27th

One-Shot Classification Is Enough for Automatic Label Mappi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lin, Xiaowen Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Ürümqi China Xinjiang Key Laboratory of Multilingual Information Technology Ürümqi China

ISBN: (纸本)9783031781681

In recent years, prompt-based learning has achieved some success in various natural language tasks. In text classification tasks, the construction of prompt templates and label mapping have a significant impact on the results. Therefore, many methods for constructing label mappings have emerged, such as manual and automatic construction. However, manual label construction is time-consuming, expensive, and has low scalability, while automatic label construction requires a large number of samples and other limitations. Therefore, we have explored a automatic label mapping method that only requires one sample. On the basis of only requiring one sample, we achieved good experimental results. In this paper, we propose an effective one-shot method for automatically constructing label mapping, ALaM. Our method is based on statistical algorithms and label semantic information for one to many label mapping. Our experiments demonstrate that ALaM achieves competitive performance on the GLUE benchmark without human effort or external resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion 6

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Song, Hao Lei, Yuxia Zi, Kangli Quan, Fuyuan Zhang, Tianhang Li, Qi School of Computer Science Qufu Normal University Shandong Rizhao China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9% over the best-performing baseline models. © 2024 IEEE.

关键词： Knowledge graph

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Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4458-4469页

作者： Chiu, Sheng-Min Liou, Yow-Shin Chen, Yi-Chung Lee, Chiang Shang, Rong-Kang Chang, Tzu-Yin Zimmermann, Roger National University of Singapore School of Computing 119077 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Chung Hsing University Taichung402202 Taiwan National Science and Technology Center for Disaster Reduction New Taipei City23143 Taiwan

The accurate and rapid transfer of complex urban spatiotemporal data is crucial for urban computing tasks such as urban planning and public transportation deployment for smart-city applications. Existing works consider auxiliary data or propose end-to-end models to process complex spatiotemporal information into more complex deep features. However, the latter is incapable of decoupling spatiotemporal knowledge, which means these end-to-end models lack modularity and substitutability. A general modular framework that can automatically capture simple representations of complex spatiotemporal information is required. In this article, we thus propose a universal framework for the transfer of spatiotemporal knowledge based on a radial basis function (RBF). We termed this approach spatial-temporal RBF transfer framework (STRBF-TF). The proposed STRBF-TF generates simple RBF representations of spatiotemporal flow distribution with an RBF transfer block and also leverages a channel attention mechanism. Moreover, we propose two RBF kernel initializers suitable for the source and the target domains, respectively. The framework retains important spatiotemporal knowledge in simple representations for the reconfiguration of spatiotemporal feature distribution for fast and accurate transfer. We conducted cross-domain learning experiments on a large real-world telecom dataset. The results demonstrate the efficiency and accuracy of the proposed approach, as well as its suitability for real-world applications. © 2020 IEEE.

关键词： Radial basis function networks

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm 21

A Novel Method to Predict Chronic Kidney Disease using Optim...

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21st International Learning and technology Conference, L and T 2024

作者： Preethi, I. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Dhaou, Imed Ben Vistas School of Computing Sciences Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andhra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

关键词： Classification (of information)

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Automatic Detection of Leaf Diseases in Hibiscus Plants Using Live Image Dataset with User Interface

Automatic Detection of Leaf Diseases in Hibiscus Plants Usin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sriram, Suthir Jaishwal, Richa Kumari Regmi, Shudarsan Nivethitha, V. Thangavel, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India College of Information and Technology United Arab Emirates University United Arab Emirates

ISBN: (纸本)9798331542344

The agriculture sector globally faces significant challenges due to plant diseases, with ensuring crop loss and economic barriers. Hibiscus plants, grown as both ornamental and medicinal plants, are affected by diseases that can damage the various parts of the plant and reduce their growth and yield. In this research, we suggest an approach to automatically identify hibiscus plant diseases, using deep-learning strategies. Our proposed work involves the development of a Convolutional Neural Network model trained on an extensive dataset that included images of hibiscus leaves infected with plant diseases, as well as images of healthy hibiscus leaves. Our model leveraged Transfer learning and it showed promise in the detection of the disease and the type of disease in the hibiscus leaves. The significant advantage of this approach is the early detection of hibiscus plant diseases, which is an opportunity for hibiscus disease management and loss reduction and improved health of the plant. The novelty of our approach lies in its focus on the distinct visual markers of hibiscus plant diseases, such as discoloration, shrinkage, and fungal spores like Anthracnose and leaf rust, which are critical in determining plant health. Also, it facilitates early detection, enabling timely intervention with minimal pesticide use. This ensures healthier plantations and contributes to sustainable cultivation practices. Furthermore, the insights gained from this study can serve as a blueprint for addressing plant diseases in other agricultural species, thereby advancing global food security. Our model achieved a high test accuracy of 97.21%, with a precision of 96.88%, recall of 96.45%, and an F1 score of 96.66%. Additionally, the model demonstrated robust performance in distinguishing between healthy and diseased leaves, as evidenced by a ROC-AUC score of 98.50%, underscoring its effectiveness in disease detection for hibiscus plants. © 2024 IEEE.

关键词： Medicinal chemistry

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Learning in a digital fabrication course on building tangible artefacts

Learning in a digital fabrication course on building tangibl...

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2024 International Design Society Conference, Design 2024

作者： Nanjappan, Vijayakumar Georgiev, Georgi V. Casakin, Hernan Soomro, Sohail Ahmed Center for Ubiquitous Computing University of Oulu Finland School of Computer Science and Information Technology University College Cork Ireland Ariel University Israel Sukkur IBA University Pakistan

This paper examines how students' ideas evolve into physical prototypes within a digital fabrication design course. Examining the materials used, customization approaches, iterations, and team dynamics of 26 student projects reveals interplays between ideas, available tools, materials and constraints. Findings show the predominance of techniques, design preferences, concept refinement, and teamwork challenges. The implications highlight the value of hands-on iteration for alignment with reality and the need to support collaboration skills alongside technical prototype development. © 2024 Proceedings of the Design Society. All rights reserved.

关键词： 3D printing

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