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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Zhao, Yizhu Zeng, Jun Chongqing University School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9781728173979

To solve the problem of information overload, the recommendation system has developed in various fields. Faced with a variety of forms and rich masses of books, the book recommendation system based on various recommendation methods is applied in the library. In the traditional book recommendation system, there are some problems, such as single recommendation mode, lack of pertinence, too centralized recommended books and so on. In order to solve these problems, this paper proposes a personalized book recommendation system. Through user expression recognition to obtain user preferences, according to user preferences to recommend books to user. Facial expression recognition is realized by using convolution neural network model. This kind of recommendation method has real-time and authenticity. The book recommendation system based on facial expression recognition can improve users' sense of use when applied to library robots. © 2020 IEEE.

关键词： Face recognition

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weipeng Lv Changkun Jiang Jianqiang Li College of Computer Science and Software Engineering and the National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task.

关键词： Proteins Deep learning Protein engineering Neurons Predictive models Network architecture Feature extraction

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Influent property forecasting of sewage treatment based on big data analysis approach 3

Influent property forecasting of sewage treatment based on b...

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3rd International Conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2021

作者： Cheng, Zhiwei Xie, Zhihong Deng, Yongsheng Wang, Xiaodan Information Engineering Institute Chongqing Vocational and Technical University of Mechatronics Chongqing402760 China School of Big Data and Software Engineering Chongqing College of Mobile Communication Chongqing401520 China

Precise influent property forecasting is very important to maintain the stable operation of sewage treatment procedure. A big data analysis method of combining the wavelet packet transform (WPT) and adaptive network-based fuzzy inference system (ANFIS) is reported to solve this problem. In this approach, the WPT is used to decompose the influent property data in different cycles. The time sub-series, which are results of wavelet coefficients reconstruction, are employed to establish the forecasting system. The forecasting sub-results of each cycle are eventually integrated into an overall forecasting result. Furthermore, chaos theory is introduced to obtain the input structure of the multi-cycle regression models. The reported approach is verified by the historical influent property. A back propagation neural network and the standard ANFIS are used for a comparison test. The results demonstrate that the reported method has best ability in the peer models. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

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IEEE International Conference on big data

作者： Mark Junjie Li Wenzhu Cai Yigang Lin Sunjie Huang Joshua Zhexue Huang Patrick Xiaogang Peng College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Academy of Inspection and Quarantine

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing.

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VARIATIONAL PRINCIPLE FOR NON-ADDITIVE NEUTRALIZED BOWEN TOPOLOGICAL PRESSURE

arXiv

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arXiv 2023年

作者： Qu, Congcong Xu, Lan College of Big Data and software Engineering Zhejiang Wanli University Zhejiang Ningbo315107 China Department of Mathematics and Physics Suzhou Vocational University Jiangsu Suzhou215104 China

Ovadia and Rodriguez-Hertz [OH23] defined the neutralized Bowen open ball as (Formula Presented). Yang, Chen and Zhou [YCZ23] introduced the notion of neutralized Bowen topological entropy of subsets by replacing the usual Bowen ball by neutralized Bowen open ball. And they established variational principles for this notion. In this note, we extend this result to the non-additive neutralized Bowen topological pressure and established the variational principle for non-additive potentials with tempered distortion. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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A case study of industrial data analysis: Gearbox temperature prediction of wind turbines using ensemble deep learning regression 2

A case study of industrial data analysis: Gearbox temperatur...

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2nd IEEE International Conference on big data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2021

作者： Cheng, Zhiwei Deng, Yongsheng Wang, Xiaodan Xie, Zhihong Chongqing Vocational and Technical University of Mechatronics School of Information Engineering Chongqing402760 China School of Big Data and Software Engineering Chongqing College of Mobile Communication Chongqing401520 China

ISBN: (纸本)9780738131221

The damage and effectiveness of gearboxes may directly lead to the shutdown of wind turbines (WTs) and increase the unplanned maintenance cost. The variation and distribution of gearbox temperature can reflect its working condition. The predicting gearbox temperature has emerged as an essential method for guaranteeing the safety, availability, and efficiency of WTs. A method called ensemble deep learning regression (EDLR) is proposed for gearbox temperature prediction. Using random forest method, the collected features are then selected as main impacting features. Long short-term memory is sequentially employed for the temperature prediction. The proposed approach is investigated with real data from a wind farm. The experimental results reveal that EDLR has excellent regression performance in terms of the adopted criteria and that it can accurately predict the gearbox temperature. © 2021 IEEE.

关键词： Wind turbines

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CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fujia Zhou Wei Zhang Guangxu Zhu Hang Li Qingjiang Shi School of Software Engineering Tongji University Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks.

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Assessing the Risk of Currency Digitalization in Kazakhstan

Assessing the Risk of Currency Digitalization in Kazakhstan

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Talgat Baimaganbetov Mariia Sodnomova Elvira Aitmukhanbetova Research and Innovation Center “Big Data and Blockchain Technologies” Astana IT University Astana Kazakhstan Astana IT University Astana Kazakhstan Department of Software Engineering Astana IT University Astana Kazakhstan

Every year, new technologies in every industry change the model and structure of processes in human life. Including the economy, which interacts so closely with us. After the advent of blockchain technology, many years have passed to test the viability of digital currency and digital wallets. Many countries are already introducing digital currencies at the state level. Since 2021, a study has begun on the need to introduce a national digital currency. A pilot project with basic functions was also developed and test launches of the digital tenge were carried out.

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F2NAS: Flexible Federated Neural Architecture Search in Green Edge Computing

F2NAS: Flexible Federated Neural Architecture Search in Gree...

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IEEE International Conference on Communications (ICC)

作者： Zebo Zhao Chao Qiu Yunfeng Zhao Xiaofei Wang Haipeng Yao Xiuhua Li F. Richard Yu College of Intelligence and Computing Tianjin University Tianjin China Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid growth of edge computing calls for fine-tuned deep neural network (DNN) deployment that emphasizes energy-efficient implementation, due to the resource constraints of edge devices. Traditional Federated Learning-based Neural Architecture Search (FL-based NAS) has been instrumental in the complexities of this deployment, particularly in addressing constraints posed by device heterogeneity, limited resources, and privacy preservation. However, it is hindered by issues such as suboptimal aggregation of homogeneous neural blocks, significant knowledge waste in disregarding heterogeneous neural blocks, and excessive communication energy consumption. This paper introduces F 2 NAS in green edge computing, a novel energy-efficient approach that addresses these limitations by ensuring flexible and energy-efficient model design and training for edge devices. Firstly, F 2 NAS introduces an innovative aggregation strategy that enhances the integration of homogeneous neural blocks by using inter-block distances to optimize weight allocation. Further, it employs a unique parameter extraction technique that recaptures valuable insights from previously overlooked heterogeneous neural blocks. Finally, F 2 NAS meticulously calibrates communication energy consumption by balancing loss function and model interaction, setting and refining an upper limit for model communication. Experimental results reveal F 2 NAS enhances model accuracy by 2.8% to 4.7%, simultaneously reducing the energy consumption by nearly 50% through optimizing the communication cost.

关键词： Training Energy consumption Privacy Accuracy Computational modeling Refining Energy efficiency

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