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Industrial Electronics: Developments & applications (ICIDeA), IEEE International Conference on-

作者： S. Rahamat Basha Pratibha C. Kaladeep Yalagi Balaram Puli Pandian Sundaramoorthy Rajesh Daruvuri S Vinayagapriya Department of computer science and engineering Malla reddy college of engineering and technology Department of Computer Science and Engineering Walchand Institute of Technology Solapur Pratibhayalagi Engineering and Data Science Everest Computers Inc Application Developer EL CIC-1W-AMI IBM Technical Account Manager Cloud Data and AI Google Dept of ece St.josephs college of engineering

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Precise detection of plant diseases is essential in agriculture to maximize crop management and yield. The present study presents an innovative approach that integrates convolutional neural networks (CNNs) with local binary patterns (LBPs) in order to enhance the categorization of plant leaf diseases. The intricacy of plant disease images complicates the evaluation of their many features by conventional approaches. To solve this, the suggested technique utilizes the strengths of both CNNs and LBPs. The CNN component is highly effective in extracting discriminative features from raw pictures using hierarchical representations, which are crucial for differentiating between different illness categories. Simultaneously, the LBP descriptor enhances its usefulness by acquiring intricate texture information from specific areas of the images, so providing more effectiveness in distinguishing small differences resulting from various diseases. Utilizing a feature fusion approach, the merging of CNN and LBP features merges spatial and textural data, resulting in a more thorough representation of the input images. The fusion described herein significantly improves the precision of plant disease classification. The analysis of benchmark datasets of plant leaf diseases showed that this integrated method outperforms the use of CNNs or LBPs individually in terms of classification accuracy. The research findings indicate that the suggested approach, which combines deep Convolutional Neural Networks (CNNs) with Linear Bayesian Processes (LBPs), provides a more efficient approach for detecting plant leaf illnesses. This, in turn, makes a substantial contribution to crop management and agricultural productivity.

关键词： Productivity Plant diseases Accuracy Merging Crops Benchmark testing Feature extraction Spatial databases Bayes methods Convolutional neural networks

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Cash Flow Forecasting Based on Wavelet Transform and Neural Networks

Cash Flow Forecasting Based on Wavelet Transform and Neural ...

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International Conference on computer science and engineering (UBMK)

作者： Zümra Söylen Farida Mammadova Mandana Fasounaki Ayşe İrem Özmen Gökhan İnce Department of Computer Engineering Istanbul Technical University İstanbul Türkiye R&D Department MDP Group İstanbul Türkiye Department of Computer Engineering & AI and Data Science Application and Research Center Istanbul Technical University İstanbul Türkiye

Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics.

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Blockchain Technology and Artificial Intelligence's Future applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, ai can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and ai are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Investigation of sex-related functional connectivity alterations in autism using class imbalance mitigation approach 46

Investigation of sex-related functional connectivity alterat...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Namgung, Jong Young Mun, Jongmin Park, Yeong Jun Kim, Jaeoh Park, Bo-Yong Inha University Department of Data Science Incheon Korea Republic of University of Southern California Marshall School of Business Los Angeles United States Inha University Department of Computer Engineering Incheon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of

ISBN: (纸本)9798350371499

Autism spectrum disorder is primarily diagnosed in males, leading to the lack of understanding of brain disorganization in female individuals with autism. To fill the gap, we applied a Gaussian mixture model-based oversampling technique to the functional connectivity data to adjust for the sex imbalance in autism. Leveraging a dimensionality reduction technique, we generated a low-dimensional principal component (i.e., gradient) and assessed its between-group differences between the sexes. We observed significant sex-related differences in sensorimotor, attention, and default mode networks, which were associated with higher-order cognitive control processes. Transcriptomic association analysis provided a potential biological underpinning, specifying gene enrichment in the cortex, thalamus, and striatum. Finally, symptom severity prediction analysis suggested that the functional gradient was only associated with symptoms in female individuals with autism. © 2024 IEEE.

关键词： Dimensionality reduction

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(ai)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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Beyond Normal: On the Evaluation of Mutual Information Estimators 37

Beyond Normal: On the Evaluation of Mutual Information Estim...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Czyż, Pawel Grabowski, Frederic Vogt, Julia E. Beerenwinkel, Niko Marx, Alexander Department of Biosystems Science and Engineering ETH Zurich Switzerland ETH AI Center ETH Zurich Switzerland Institute of Fundamental Technological Research Polish Academy of Sciences Poland Department of Computer Science ETH Zurich Switzerland SIB Swiss Institute of Bioinformatics Switzerland

ISBN: (纸本)9781713899921

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology. However, mutual information estimators are typically evaluated on simple families of probability distributions, namely multivariate normal distribution and selected distributions with one-dimensional random variables. In this paper, we show how to construct a diverse family of distributions with known ground-truth mutual information and propose a language-independent benchmarking platform for mutual information estimators. We discuss the general applicability and limitations of classical and neural estimators in settings involving high dimensions, sparse interactions, long-tailed distributions, and high mutual information. Finally, we provide guidelines for practitioners on how to select appropriate estimator adapted to the difficulty of problem considered and issues one needs to consider when applying an estimator to a new data set. © 2023 Neural information processing systems foundation. All rights reserved.

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Multi-agent Scheduling for Shared Manufacturing Systems

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Procedia computer science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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