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4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： He, Yangfan Yin, Siyuan Xiamen University Malaysia School of Computing and Data Science Selangor Malaysia

ISBN: (纸本)9798350314496

Food image classification plays a crucial role in computer vision research and has immense theoretical significance, along with numerous potential applications in various fields such as smart health, smart retail, and smart home. While several studies have been conducted on this topic, there is a lack of consensus on the optimal approach to achieve high accuracy. In this study, the author propose an improved Convolutional Neural Network (CNN) architecture for food image classification. This study aims to enhance the accuracy of food image classification by examining the influence of different optimizers, loss functions, and activation functions on the performance of four neural networks, including VGG19, MobileNet V2, DenseNet, and a traditional CNN architecture, using the ChineseFoodNet dataset. Besides, the paper will improve the performance of the selected model by utilizing data augmentation techniques and transfer learning. The findings of this study can help researchers and practitioners develop more robust and accurate models, facilitating their application in areas such as smart health and smart homes. It also has practical implications for the development of more accurate food image classification models in computer vision field. © 2023 IEEE.

关键词： Image classification

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Optimization of data Protection in IoT Field of Cloud computing Using Compiler Privacy Calculation Method 8

Optimization of Data Protection in IoT Field of Cloud Comput...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Huang, Jingjia Huang, Xincheng School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350310931

This paper aims to enhance cloud computing security and mitigate privacy risks by investigating optimized compiler models. Through extensive literature review and analysis, we have selected a compiler model based on the Porcupine privacy computing approach and a component called Quill, which translates plain text specifications into equivalent implementations in homomorphic cryptography. Concurrently, we propose additional optimization strategies that improve the efficiency and cost effectiveness of homophobic encryption. Our work signifies a step forward in the realm of secure, private cloud computing © 2023 IEEE.

关键词： Cloud computing

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Big data analytics for privacy through ND-homomorphic encryption

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Journal of Control and Decision 2023年第1期10卷 64-71页

作者： S.Saravanan N.Poornima Department of CSE SRM Institute of Science and TechnologyChennaiIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurIndia

Rapidly rising the quantity of Big data is an opportunity to flout the privacy of people. Whenhigh processing capacity and massive storage are required for Big data, distributed networkshave been used. There are several people involved in these activities, the system may contributeto privacy infringements frameworks have been developed for the preservation of privacy atvarious levels (e.g. information age, information the executives and information preparing) asfor the existing pattern of huge information. We plan to frame this paper as a literature surveyof these classifications, including the Privacy Processes in Big data and the presentation of theAssociate Challenges. Homomorphic encryption is particularised aimed at solitary single actionon the ciphered information. Homomorphic enciphering is restrained to an honest operation onthe encoded data. The reference to encryption project fulfils many accurate trading operationson coded numerical data;therefore, it protects the written in code-sensible information evenmore.

关键词： Security and confidentiality vast statistics data encryption nondeterministic fully homomorphic encryption

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Optimized Energy Distribution in Smart Grid System Using Hybrid Machine Learning Techniques 8

Optimized Energy Distribution in Smart Grid System Using Hyb...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Daorui, Ding Chang, Hong Durga Kumar, Burra Venkata School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350310931

Smart Grid systems are becoming increasingly important with the rapid advent of industrial parks and residential areas. The aim of the Smart Grid system is to modernize and enhance the efficiency, reliability, and flexibility of electrical power grids. It integrates network, smart sensors, and time series prediction machine learning algorithms into the existing grid transmission system to collect the data and make predictions for optimizing decisions. With the help of big data techniques, Smart Grid systems can provide on-demand power supplies. However, most of the proposed prediction algorithms for Smart Grid system highly rely on pre-defined parameters and influential factors, such kind of models fail to dynamically update themselves to cater to the changing world especially when emergencies happen. In this paper, we introduce Dynamic Bayesian Network (DBN) concept and propose a new prediction architecture Smart Grid+ to improve the systems' self-adaptability and resilience to power outages, faults, and disruptions. Because of DBN's ability of dynamical expansion and posterior probability calculation from prior data, our proposed Smart Grid+ responds to emergencies more quickly and predicts the electricity load more precisely. And we believe it can better apply to the requirements of Smart Grid system and has a promising future. © 2023 IEEE.

关键词： Bayesian networks

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Reasoning with Trees: Faithful Question Answering over Knowledge Graph 31

Reasoning with Trees: Faithful Question Answering over Knowl...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Shen, Tiesunlong Wang, Jin Zhang, Xuejie Cambria, Erik School of Information Science and Engineering Yunnan University Kunming China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761964

Recent advancements in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks. This study introduces Reasoning with Trees (RwT), a novel framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability. RwT reformulates knowledge graph question answering (KGQA) as a discrete decision-making problem, leveraging Monte Carlo Tree Search (MCTS) to iteratively refine reasoning paths. This approach mirrors human-like reasoning by dynamically integrating the LLM's internal knowledge with external KG information. We propose a real-data guided iteration technique to train an evaluation model that assesses action values, improving the efficiency of MCTS. Experimental results on two benchmark KGQA datasets demonstrate that RwT significantly outperforms existing state-of-the-art methods, with an average performance improvement of 9.81%. Notably, RwT achieves these improvements without requiring complete LLM retraining, offering a more efficient and adaptable approach to enhancing LLM reasoning capabilities. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Improving quantitative stock trading prediction based on MAD using Q-learning technology

Improving quantitative stock trading prediction based on MAD...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Zhao, Pengcheng Yin, Siyuan Kumar, Burra Venkata Durga Yew, Teh Jia Xiamen University Malaysia School of Computing and Data Science Selangor Malaysia

ISBN: (纸本)9798350322309

Stocks are a proof of shares in a corporate enterprise and represent the ownership of the stockholder in the joint stock company. Stock trading is a dynamic and complicated project, stock history data is a non-linear and highly noisy time series. Considering the risky nature of stock trading, it is essential to provide an optimal prediction method to ensure stock trading security. The accuracy of traditional stock trading prediction is insufficient, so this study attempts to use reinforcement learning models for stock trading change prediction under big data. This paper proposes the median absolute deviation method (MAD) and Q-learning model to build a more effective prediction model. The simulation results based on NASDAQ Composite (^IXIC) data show that the new method can better help predict stocks. The method has some limitations and currently uses a combination of traditional econometric models and reinforcement learning models, which have some efficiency issues. However, the research in this paper provides a good extension to the theoretical aspects of stock prediction analysis and can provide investors with new research methods and perspectives. © 2023 IEEE.

关键词： Forecasting

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Analysis of Sentiment Towards Artificial Intelligent Industry Using Hybrid Natural Language Processing Technique 8

Analysis of Sentiment Towards Artificial Intelligent Industr...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Chang, Hong School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350310931

Artificial Intelligence (AI) has revolutionized various aspects of human life and transformed how people live, work, and interact. However, the development of AI also poses potential risks and ethical concerns. In this report, we aim to analyze the sentiment toward the AI industry using hybrid natural language processing techniques. To achieve our aim, we propose a model that draws upon a survey of related work. data collection involves gathering user-generated data from social media platforms. We then use hybrid natural language processing techniques to analyze the sentiment toward the AI industry. Our analysis reveals that the sentiment towards the AI industry is generally positive, with many people recognizing its potential benefits. However, there are also concerns about the potential risks and ethical implications of AI development. Some leading figures in the AI industry have expressed concerns about the potential misuse of AI and the need for ethical guidelines. In conclusion, our analysis highlights the transformative effects of AI on various industries and the potential risks associated with its development. We recommend that policymakers and industry leaders work together to develop ethical guidelines for the development and use of AI. This will help to ensure that the benefits of AI are maximized while minimizing the potential risks and ethical concerns. © 2023 IEEE.

关键词： Sentiment analysis

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Deep Learning-Based Emotion Recognition using CNN, LSTM for Multimodal Text, Speech, and Facial Analysis 5

Deep Learning-Based Emotion Recognition using CNN, LSTM for ...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Firdou, Talat Binti Ashra, Mohammed Kalpan, A.V. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350389609

Emotion detection is a critical field in AI as well as computer vision, seeking to recognize or detect human emotions from facial expressions. Although this area has been greatly advanced through Convolutional Neural Networks (CNNs), current methods using only single-modal data often exhibit reduced accuracy. In this work, we confront these challenges with a hybrid deep learning method for disparate modalities of information such as speech, video, and text. We pre-process and then use CNN feature extractors for the FER2013 data set. The hybrid deep learning model, hence proposed, achieved a weighted accuracy of 79.3% on FER2013, overcoming the conventional unimodal methods. We are using transformers library with the pre-trained RoBERTa model for text. For speech emotion features, we are using TESS dataset then using a CNN and LSTM (Long Short-Term Memory) model. The CNN extracts feature from the speech, while the LSTM is used for temporal analysis to detect emotions. Once these multimodal features have been obtained, our work would be to fuse in order to get the final emotion recognition using Deep Neural Networks (DNNs). The process increases the precision and reliability of the recognition systems, helping to accumulate even more detailed intuition for different emotional states for a combined approach. © 2024 IEEE.

关键词： Convolutional neural networks

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