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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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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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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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Simulation model optimization for bonnet polishing considering consistent contact area response

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Frontiers of Mechanical Engineering 2024年第4期19卷 87-105页

作者： Yanjun HAN Haiyang ZHANG Menghuan YU Jinzhou YANG Linmao QIAN School of Mechanical Engineering Southwest Jiaotong UniversityChengdu 610031China Institute of Data Science Maastricht UniversityMaastricht 6229ENthe Netherlands

Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes,but model complexity often poses challenges to the practical implementation and efficiency of these *** paper presents an innovative method for optimizing simulation model parameters,focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model ***,controllable and uncontrollable factors in bonnet simulations are analyzed,and then a simplified contact model is developed and applied under constant force *** characterize the bonnet's contact performance,a contact area response curve is introduced,which can be obtained through a series of single spot contact ***,a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and *** average deviation of the contact area under different conditions was reduced from 22.78%before optimization to 3.43%after optimization,preliminarily proving the effectiveness of the proposed simulation optimization ***,orthogonal experiments are further conducted to validate the proposed *** comparison between the experimental and predicted material removal functions reveals a high consistency,validating the accuracy and effectiveness of the proposed optimization method based on consistent contact *** research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.

关键词： bonnet polishing simulation contact area tool influence function optimization

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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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Remaining useful life prediction of cutting tools with an optimal feature construction method of DiCCA 15

Remaining useful life prediction of cutting tools with an op...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Yin, Chen Sun, Tongda Dong, Yining Centre for Systems Informatics Engineering School of Data Science Hong Kong Institute for Data Science City University of Hong Kong Kowloon Hong Kong School of Data Science City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9798350354010

Remaining useful life (RUL) prediction plays an essential role in the prognostic health management of cutting tools, which aims to ensure workpiece surface quality and extend tool life. Although deep learning methods have exhibited great performance in the field of tool RUL prediction, establishing an effective deep learning model usually requires extensive training data, and collecting substantial tool failure data in the actual milling process is time-consuming and expensive. Alternatively, constructing robust tool degradation features and developing a lightweight prediction model might be more practical and deployment-friendly. Therefore, this paper introduces an optimal tool degradation feature extraction method with a tailored dimension reduction algorithm named dynamic-inner canonical correlation analysis (DiCCA), where the temporal dependency and dynamic information hidden in the sequential tool degradation process are captured for the RUL prediction of cutting tools. Experimental studies are performed with a milling dataset, and the results indicate that the optimal tool degradation feature extracted by the proposed DiCCA can explicitly reflect the degradation process of cutting tools, and accurate RUL prediction can be achieved through a lightweight BP model. © 2024 IEEE.

关键词： Prediction models

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A Study of data Leakage Prevention Techniques in Remote Collaborative Work Application 24

A Study of Data Leakage Prevention Techniques in Remote Coll...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Xu, Wenfang Zhao, Shusheng School of Data Science and Computer Engineering Xiamen Institute of Technology China

ISBN: (纸本)9798400718137

Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protection measures have increasingly become the focus of attention, posing a severe challenge to enterprise data security. To address this challenge, this paper proposes an innovative comprehensive data leakage prevention technical solution. This solution integrates cloud services and kernel technology and creates a set of data security protection systems for the remote collaboration working environment. This technical solution adopts a series of effective technical means, including asymmetric encryption, one-way data flow control, dynamic digital watermarking technology, injection attack defense, and program anti-hanging mechanism, significantly enhancing the security of data in the collaborative office system. The constructed framework includes multiple modules such as user management, service support, kernel driver, and user terminal. This technical solution can be integrated with enterprise remote collaboration office software, providing a higher level of security guarantee for the remote office environment, effectively curbing the risk of sensitive data leakage, and also providing solid technical support for the healthy and stable development of remote collaboration work. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

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Movie Reviews using Sentiment Analysis with Natural Language API 8

Movie Reviews using Sentiment Analysis with Natural Language...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Athish, V.P. Rajeswari, D. Sree Nandha, S. Srm Institute of Science and Technology School of Computing Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350347791

Sentiment Analysis is the process of analyzing sentiment of a piece of text by identifying polarity or writer's attitude towards any topic or product such as positive, neutral or negative. This paper uses sentiment analysis on movie reviews in twitter data, which is the most dominant public social media and its data is accessible using their own Twitter API. Words and misspellings make sentiment analysis for twitter data, harder due to the existence of slang words and bad spelling. This work uses Hadoop for the various features it provides over the API. The proposed work uses the knowledge base approach using lexica analyzer for sentiment analysis of movie reviews. The assigned score for movies is 100. The proposed work get all the positive, negative and neutral comments to judge "how good"or "how bad"the movie is. © 2023 IEEE.

关键词： Sentiment analysis

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