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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

Big data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Exploration of Teaching Method of Students’ Independent Proposition

Exploration of Teaching Method of Students’ Independent Pro...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Zhang, Jin Wang, Lizhi Quan, Weihong Li, Haoran Gong, Xiaoli College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9789811581540

Aiming at obsolescence and deficiencies of the question bank in the current teaching of computer major courses, we propose a teaching method of student independent proposition, which can not only enrich the question bank as well as ensure the novelty of the exercises, but also effectively obtain feedback data of teaching quality based on the answers to exercises. In this paper, we first discuss the teaching significance of independent proposition and some key issues such as quality management encountered in practice. Then, we propose solutions to the problems of correctness checking and difficulty grading based on some natural language processing and deep learning technologies. Finally, to prove the effectiveness of our approach, we introduce the specific application in teaching practice, including the construction and use of the online education system based on the artificially constructed question bank. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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A Study on a Reduced Indicator System for the network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： network security

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Three-level Compact Caching for Search Engines Based on Solid State Drives 23

Three-level Compact Caching for Search Engines Based on Soli...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Rui Sun, Pengyu Tong, Jiancong Zang, Ruirui Qian, Heng Pan, Yu Stones, Rebecca J. Wang, Gang Liu, Xiaoguang Li, Yusen College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Baidu Inc Beijing China

ISBN: (纸本)9781665494571

The query processing in search engines can be classified into either disk I/O operations and CPU computation operations. Conventional search engine caching methods mostly aimed at reducing disk I/O operations. However, with the new trend of using solid state drives (SSDs) instead of hard disk drives for search engine storage, the bottleneck of query processing shifts from disk I/O to computation. In this paper, we design a three-level compact caching structure suited to SSD-based search engines, incorporating caches for (a) doculets, partial documents which contain previously encountered snippets, (b) fragments, compact data structures containing snippet metadata, and (c) ranked docID lists, which index the top-k documents relevant to a query. This three-level compact caching method aims at reducing the bottleneck of repetitive CPU computation operations. Moreover, by considering different levels of caches as a whole system, the proposed three-level compact caching eliminates the redundancy among cache items. This increases the efficiency of cache space utilization. Experimental results indicate an improvement in query response latency by a factor of around 2.6 when using static caching and 1.3 under dynamic caching. © 2021 IEEE.

关键词： Costs Solid state drives Smart cities Query processing Redundancy Search engines Metadata

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Multi-scale fusion residual encoder-decoder approach for low illumination image enhancement

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 63-72页

作者： Pan Xiaoying Wei Miao Wang Hao Jia Fengzhu School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'an 710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'an 710121China School of Software Northwestern Polytechnical UniversityXi'an 710072China

The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise,which leads to a reduction in image quality.A multi-scale fusion residual encoder-decoder(FRED)was proposed to solve the *** directly learning the end-to-end mapping between light and dark images,FRED can enhance the image’s brightness with the details and colors of the original image fully restored.A residual block(RB)was added to the network structure to increase feature diversity and speed up network ***,the addition of a dense context feature aggregation module(DCFAM)made up for the deficiency of spatial information in the deep network by aggregating the context’s global multi-scale *** experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM).For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively,a satisfactory visual quality is obtained under the enhancement of low-light.

关键词： image enhancement low illumination feature fusion residual network

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Design and Implementation of Rumor Governance System Based on Blockchain 2

Design and Implementation of Rumor Governance System Based o...

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2nd International Conference on Computer science and Blockchain, CCSB 2022

作者： Li, Yutong Song, Jiazhen Wang, Jingrong Lu, Haori Nie, Peng College of Cyber Science Nankai University Tianjin300350 China Nankai University College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

ISBN: (纸本)9798350331882

Nowadays, the network rumor is constantly breeding and spreading. It becomes a big difficulty to trace the source and refute the rumors with high efficiency. For this, we designed and implemented an intelligent rumor detection, source tracing and accurate rumor refuting system. This system has three main functions. First, the system provides credible evidence for the tracing and accountability of rumors with the immutable nature of blockchain. Second, we propose a new intelligent rumor detection model that does not involve manual work, that is, by looking for opposite semantic pairs. Third, a new accurate rumor refuting scheme is proposed, which can help break through the information cocoons. The system provides a standardized and effective management platform for the collaborative governance of online rumors in the new era and the improvement of the environment for online public opinion. It also facilitates the development of the Clear Action and the crackdown on online rumors. © 2022 IEEE.

关键词： Smart contract

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Critical (P5, dart) -Free Graphs 16th

Critical (P5, dart) -Free Graphs

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Xia, Wen Jooken, Jorik Goedgebeur, Jan Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

ISBN: (纸本)9783031496134

Given two graphs H1 and H2, a graph is (H1, H2) -free if it contains no induced subgraph isomorphic to H1 nor H2. A dart is the graph obtained from a diamond by adding a new vertex and making it adjacent to exactly one vertex with degree 3 in the diamond. In this paper, we show that there are finitely many k-vertex-critical (P5, dart) -free graphs for k≥ 1. To prove these results, we use induction on k and perform a careful structural analysis via Strong Perfect Graph Theorem combined with the pigeonhole principle based on the properties of vertex-critical graphs. Moreover, for k∈ { 5, 6, 7 } we characterize all k-vertex-critical (P5, dart) -free graphs using a computer generation algorithm. Our results imply the existence of a polynomial-time certifying algorithm to decide the k-colorability of (P5, dart) -free graphs for k≥ 1 where the certificate is either a k-coloring or a (k+ 1 ) -vertex-critical induced subgraph. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Polynomial approximation

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The Research of Retinopathy Image Recognition Method Based on Vit 5

The Research of Retinopathy Image Recognition Method Based o...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Xu, Zongyu Xu, Xuebin Huang, Zihao School of Computer Science and Technology Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China

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Track Irregularity Prediction Based on DWT-DLSTM Model

Track Irregularity Prediction Based on DWT-DLSTM Model

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Meng, Haining Zhang, Jiawei Zheng, Yi Ji, Wenjiang Tong, Xinyu Hei, Xinhong School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Lab Network Computer and Security Technology Xi'an China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The long-term operation of high-speed railway will lead to track irregularity that will cause random vibration of the track system and affect driving safety. The accurate prediction of track irregularity is of great significance to the quality of high-speed railway. In this paper, we proposed a DWT-DLSTM model to predict the track irregularity for high-speed railway. Firstly, the track irregularity time series data is denoised through the discrete wavelet transform (DWT). Then the deep long short-term memory (DLSTM) neural network is adopted to predict the denoised data. Finally, the experiment results show that the proposed DWT-DLSTM model outperforms other traditional models and obtain more accurate prediction results for track irregularity. © 2022 IEEE.

关键词： Discrete wavelet transforms

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