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Multi-Disease Prediction Based on Deep Learning: A Survey

Computer Modeling in engineering & sciences 2021年第8期128卷 489-522页

作者： Shuxuan Xie Zengchen Yu Zhihan Lv College of Data Science Software Engineering Qingdao UniversityQingdao2660712China

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in themedical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Amongthem, the hot deep learning field has shown greater potential in applications such as disease prediction and drugresponse prediction. From the initial logistic regression model to the machine learning model, and then to thedeep learning model today, the accuracy of medical disease prediction has been continuously improved, and theperformance in all aspects has also been significantly improved. This article introduces some basic deep learningframeworks and some common diseases, and summarizes the deep learning prediction methods correspondingto different diseases. Point out a series of problems in the current disease prediction, and make a prospect for thefuture development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates thehigh correlation between deep learning and the medical field in future development. The unique feature extractionmethods of deep learning methods can still play an important role in future medical research.

关键词： Deep learning disease prediction Internet of Things COVID-19 precision medicine

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Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions 2

Multimodal Hybrid Approach for Fine-Grained Classification o...

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2nd International Conference on Intelligent Perception and Computer Vision, CIPCV 2024

作者： Kang, Kai Xu, Jianyu An, Shilong Chen, Jilin Wang, Renfang Wang, Han Ocean University of China Faculty of Information Science and Engineering Qingdao China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo China Qingdao Municipal Center For Disease Control Prevention Qingdao China

ISBN: (纸本)9798350360097

Among the spectrum of dermatological conditions, melanoma stands as the most lethal, emanating from melanocytes-cells responsible for producing melanin, the skin's pigment. Distinctively more aggressive than its counterparts, melanoma possesses a higher propensity to metastasize, rendering it one of the most formidable types of skin cancer. Given its rapid invasion and propensity for widespread metastasis, prompt and accurate diagnosis, followed by immediate intervention, is imperative. Fine-grained classification tasks are particularly pertinent in this context, aiming to discern between closely resembling subclasses, a challenge akin to differentiating subtle visual nuances among similar-appearing skin pathologies. Consequently, fine-grained classification emerges as a critical tool in the nuanced diagnosis, early detection, and ongoing surveillance of skin diseases. In response, we have developed a innovative network, termed Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions (MultiFG), adept at handling a variety of skin conditions. This model proficiently extracts and processes metadata while concurrently capturing visual details through a Convolutional Neural Network (CNN). This is followed by the integration of visual and metadata insights within a Transformer architecture, facilitating enhanced fusion and refined fine-grained analysis. Our proposed methodology's efficacy is substantiated through rigorous testing on two distinct datasets, where it has demonstrated promising results. © 2024 IEEE.

关键词： Convolutional neural networks

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Water body extraction in Nansi Lake Basin by integrating surface object types and spectral features 6

Water body extraction in Nansi Lake Basin by integrating sur...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Xing, Jun Cui, Binge Li, Peng Wang, Xiaohui Song, Lijuan Big Data College Qingdao Huanghai University Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Marine Geosciences Institute of Estuarine and Coastal Zone Ocean University of China Qingdao China

ISBN: (纸本)9781510688018

Water body extraction is the preliminary step to define the range of remote sensing for water quality, which is of great significance to define the monitoring boundary of conventional water pollutants in Nansi Lake Basin. The original Sentinel-2 L2A multispectral image was processed with super resolution, and its spatial resolution of 13 bands was unified to 10m. According to the difference of spectral features of surfaces objects, characteristics of NDWI, NDWI3, MNDWI, NDPI and other water indexes were analyzed. By comparing effects of water body extraction with four water body indexes, it was found that the applicability of different water indexes in different surface object types was different. The remote sensing image of Nansi Lake Basin is mainly divided into five surface object types, including vegetation, towns, small rivers, mountains and shadows. The EM algorithm was used to classify surface objects of remote sensing images and complete the definition of five surface object types. In different areas of surface object types, the water index with better applicability was respectively used to extract water body. The results showed that the method of water body extraction integrating surface object types and spectral characteristics is highly efficient, and can obtain better accuracy of water body extraction than the one using only a single water index, and effect of water body extraction for large-scale remote sensing images is effectively improved. © 2025 SPIE.

关键词： Lakes

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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

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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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DiaSet: An Annotated dataset of Arabic Conversations 30

DiaSet: An Annotated Dataset of Arabic Conversations

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Israeli, Abraham Naaman, Aviv Makhoul, Rawaa Maduel, Guy Ejmail, Amir Jubran, Julian Karain, Dana Lisnyansky, Dina Fine, Shai Bar, Kfir The Data Science Institute Reichman University Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9782493814104

We introduce DiaSet, a novel dataset of dialectical Arabic speech, manually transcribed and annotated for two specific downstream tasks: sentiment analysis and named entity recognition. The dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. Our dataset incorporates authentic conversations between YouTube influencers and their respective guests. Furthermore, we have enriched the dataset with simulated conversations initiated by inviting participants from various locales within the said regions. The participants were encouraged to engage in dialogues with our interviewer. Overall, DiaSet consists of 644.8K tokens and 23.2K annotated instances. Uniform writing standards were upheld during the transcription process. Additionally, we established baseline models by leveraging some of the pre-existing Arabic BERT language models, showcasing the potential applications and efficiencies of our dataset. We make DiaSet publicly available for further research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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A Survey of LLM datasets:From Autoregressive Model to AI Chatbot

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Journal of Computer science & Technology 2024年第3期39卷 542-566页

作者：杜非马新建杨婧如柳熠罗超然王学斌姜海鸥景翔 National Key Laboratory of Data Space Technology and System Beijing 100195China Advanced Institute of Big Data Beijing 100195China Fu Foundation School of Engineering and Applied Science Columbia UniversityNY 10027U.S.A. School of Software and Microelectronics Peking UniversityBeijing 100091China CCF IEEE

Since OpenAI opened access to ChatGPT,large language models(LLMs)become an increasingly popular topic attracting researchers’attention from abundant ***,public researchers meet some problems when developing LLMs given that most of the LLMs are produced by industries and the training details are typically *** datasets are an important setup of LLMs,this paper does a holistic survey on the training datasets used in both the pre-train and fine-tune *** paper first summarizes 16 pre-train datasets and 16 fine-tune datasets used in the state-of-the-art ***,based on the properties of the pre-train and fine-tune processes,it comments on pre-train datasets from quality,quantity,and relation with models,and comments on fine-tune datasets from quality,quantity,and *** study then critically figures out the problems and research trends that exist in current LLM *** study helps public researchers train and investigate LLMs by visual cases and provides useful comments to the research community regarding data *** the best of our knowledge,this paper is the first to summarize and discuss datasets used in both autoregressive and chat *** survey offers insights and suggestions to researchers and LLM developers as they build their models,and contributes to the LLM study by pointing out the existing problems of LLM studies from the perspective of data.

关键词： large language model(LLM) autoregressive model AI chatbot natural language processing(NLP)corpora OpenAI

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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