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2023 Asia Conference on Power, Energy Engineering and computer technology, PEECT 2023

作者： Zhang, Zhe Gao, Jing Wang, Yongsheng Guan, ShiJie Luo, Jidong Inner Mongolia University of Technology School of Data Science and Application Hohhot China Inner Mongolia Agricultural University School of Computer and Information Hohhot China Inner Mongolia DongRun Energy Technology Co. Ltd Hohhot China

ISBN: (纸本)9798350313376

Wind power data receive wind volatility and have strong non-smoothness, making it difficult to achieve high accuracy in wind power prediction. To address this challenge, this paper proposes a wind power multi-step prediction model combining VMD (Variational Modal Decomposition), EMD (Empirical Modal Modal Decomposition), Hurst analysis and temporal entropy values. Firstly, the first decomposition of the historical wind power data is carried out by VMD decomposition;then Hurst analysis is performed on the components of the first decomposition, and the components with low regularity are decomposed twice using EMD decomposition;then the components of the second decomposition are further filtered using permutation entropy, and the components with high entropy values are compared with the high The components with high entropy, high Hurst and low entropy of the secondary decomposition are formed into high randomness irregular component, regular component and low randomness low regularity component;for the three types of components, BP neural network is used to predict them respectively, and they are reorganized into wind power prediction values. The experiments prove that the model proposed in this paper has higher prediction accuracy and faster running time than the current mainstream models, and can achieve more efficient wind power prediction. © 2023 IEEE.

关键词： Wind power

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Unsupervised anomaly detection algorithm for sensor data streams based on GAT-GAN 6

Unsupervised anomaly detection algorithm for sensor data str...

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6th International Conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Su, Yuhang Ma, Jun Fan, Jinyu Chen, Bohang Qinghai Normal University School of Physics and Electronic Information Engineering Qinghai Xining810016 China The College of Computer Qinghai Normal University The Computer Technology and Application College Qinghai University Qinghai Xining810016 China Qinghai Normal University The College of Computer Qinghai Xining810016 China

ISBN: (纸本)9781510682962

Detecting data flow anomalies within Wireless Sensor Networks (WSNs) and reaching the server can promptly identify faults and issue maintenance alerts, thereby ensuring safe and reliable operation of the system. However existing methods can utilize single variables in data streams to perform most anomaly detection tasks, they often ignore the correlation between variables, resulting in reduced detection performance. Therefore, our approach constructs a generative adversarial network model based on Graph Attention Network (GAT) and employs a dual autoencoding structure in both the generator and discriminator to learn the latent representation of the entire time series. The results of ablation experiments and comparison experiments on two real datasets with baseline models show that GAT-GAN outperforms the former in detecting data anomalies. © 2024 SPIE.

关键词： Gallium nitride

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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks 15

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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

作者： Pattanaik, Ashutosh Shweta Ahmed, Zahid School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility © 2024 IEEE.

关键词： Cellular Contextualization Machine Learning Non-Orthogonal Spectrum-Sharing Successor

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Assessing the Robustness of Machine Learning Algorithms against Emerging Privacy and Security Threats in Medical applications 15

Assessing the Robustness of Machine Learning Algorithms agai...

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

作者： Rao, Srikrishna Baskar Goswami, Sarbojeet Goyal, Manish Kumar School of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India ARKA JAIN University Department of optometry Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

Using gadgets to realize algorithms in medical packages can provide new insights and talents, including new approaches to diagnose illnesses or reveal treatments. Rising privacy and security threats pose potential dangers to those structures that can restrict their efficacy and average performance. To make sure tool mastering algorithms are resilient to those threats, it is critical to assess their robustness. This summary offers an assessment of techniques to evaluate the robustness of tool-getting-to-know algorithms against emerging privacy and protection threats in medical programs. First, we check the kinds of threats and discuss how they'll affect the general performance of machine studying algorithms. Secondly, we compare a selection of strategies for measuring the robustness of gadgets and gaining knowledge of algorithms, which include static, dynamic, and adverse check strategies. Ultimately, we speak about techniques for mitigating threats in scientific packages, which provides for incorporating relaxed layout thoughts in tool studying structures and utilizing smoke assessments and strain tests. Through this assessment, we aim to provide more facts on the techniques for assessing the robustness of gadget-studying algorithms against rising privacy and security threats in scientific programs. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation 7

Magnetic Type Classification in Sunspot Group Based on Semi-...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Liu, Junhong Li, Baoping Luo, Zihui School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665478571

Sunspot group, known as active solar regions, is the main sources of solar storms. The morphological and magnetic characteristics of solar active regions play a very important role in solar storm forecasting and is well described by Mount Wilson Sunspot Classification Scheme. The development of convolutional neural network methods in the field of image processing makes efficient magnetic type classification possible. In this paper, we propose a method based on semi-supervised learning and knowledge distillation for magnetic type classification in sunspot group. On the sunspot magnetic type classification dataset, our method achieves 95.14% total classification accuracy, 97.4%, 94.43% and 85.71% F1-scores of Alpha, Beta, and Beta-x types respectively. © 2022 IEEE.

关键词： Solar energy

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Research on Abnormal Behavior Detection Method Based on User Access Sequence Analysis

Research on Abnormal Behavior Detection Method Based on User...

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computer applications and Information technology (ISCAIT), International Symposium on

作者： Yong Zhao Rong Ji School of Computer Science Beijing University of Technology Beijing China NORINCO Group Information Center Institute of Computer Application Technology Beijing China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

With the rapid development of enterprise informatization, traditional perimeter security protection technologies have gradually exposed their limitations in dealing with unknown threats. In response to this issue, this article proposes an anomaly behavior detection method based on user access sequence analysis. This method constructs a multi-level and multi-dimensional adaptive baseline model through deep data modeling and machine learning algorithms. By introducing sequence correlation analysis and spatiotemporal distribution feature analysis, comprehensive modeling was conducted from three dimensions: individual-level behavior patterns, position-level behavior patterns, and group-level behavior patterns, significantly improving the perception ability of covert network threats. The experimental results indicate that the multidimensional sequence baseline weighted scoring mechanism proposed in this study can effectively identify potential abnormal access behaviors. This provides a new technological support method for enterprise network security protection system.

关键词： Adaptation models Analytical models Sequences Machine learning algorithms Network security Data models Threat assessment Real-time systems Spatiotemporal phenomena Protection

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Applying Deep Neural Networks to Sentiment Analysis in Social Media 3

Applying Deep Neural Networks to Sentiment Analysis in Socia...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Meena, Y.R. Shrivastav, Amit Kumar Kharbas, Vikas Kumar School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Office of Registrar Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350381931

Deep Neural Networks (DNNs) have currently been applied to sentiment evaluation responsibilities in social media, attaining advanced overall performance compared to conventional machine mastering techniques. That is due to the truth that DNNs are capable of analyzing representations via non-linear function maps, permitting them to seize the complex semantic relationships in textual content correctly. DNNs are also perfect for natural language processing (NLP) obligations because they can generalize from sparse records and use distributed representations in huge-scale datasets. The maximum commonly used DNN architectures for sentiment evaluation in social media are recurrent neural networks (RNNs) and convolutional neural networks (CNNs). RNNs are especially nicely applicable to this project due to the fact that they are able to capture temporal and contextual data and capture the dynamics of language use. RNNs are also extra sturdy to noisy or unstructured entry, which is commonplace in social media information. Alternatively, CNNs are nicely suited to seize neighborhood patterns in textual content, consequently permitting them to study neighborhoods, which means from phrases or phrases. In order to observe DNNs to sentiment analysis in social media, these networks ought to be taught with relevant and correct labels. It requires the development of an excessively satisfactory categorized dataset for training and validation purposes, which can be quite exertion-intensive. Additionally, DNNs are required to enter statistics in a form that is suitable for education and can frequently be non-trivial to extract from social media resources. © 2024 IEEE.

关键词： Deep neural networks

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Quantitative analysis of sign language translation using artificial neural network model

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Procedia computer Science 2024年 245卷 998-1009页

作者： Fendy Wijaya Leonardo Dahendra Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system.

关键词： sign language Artificial Neural Network SIBI prediction quantitative analysis

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Hybrid Recommendation on Collaborative Filtering for Book Recommendation

Hybrid Recommendation on Collaborative Filtering for Book Re...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Keshav Gupta Sudeshna Chakraborty Kalyani Singh Dewan Imdadul Islam Himanshu Agrawal Shams Alam School of Computer Application and Technology Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

As the volume of available books grows exponentially, selecting the right book has become increasingly challenging for users. To address this, a hybrid recommendation system that incorporates both collaborative filtering (CF) and popularity-based filtering is proposed. The system aims to provide personalized recommendations by analyzing user preferences, while also leveraging the collective preferences of the broader user base. Collaborative filtering uses a user-item interaction matrix and applies cosine similarity to identify patterns in user ratings, predicting books that align with individual preferences. Popularity-based filtering complements this by considering books that are highly rated and frequently accessed by many users, ensuring that popular and widely appreciated books are recommended alongside personalized suggestions. Byintegrating both approaches, the system achieves a balance between personalization and popularity, enhancing the overall user experience. Experimental results indicate that this hybrid model not only improves recommendation accuracy but also provides users with diverse options that reflect both their interests and the wider reading trends. This makes the system particularly effective for recommending both niche and popular books.

关键词： Filtering Collaborative filtering Soft sensors Collaboration Machine learning Information processing Market research Feature extraction User experience Recommender systems

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