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Priority queueing system with many types of requests and restricted processor sharing

Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12651-12662页

作者： D’Apice, Ciro Dudin, Alexander Dudin, Sergey Manzo, Rosanna Dipartimento di Scienze Aziendali - Management and Innovation Systems University of Salerno Via Giovanni Paolo II 132 Salerno Fisciano84084 Italy Department of Applied Mathematics and Computer Science Belarusian State University 4 Nezavisimosti Ave. Minsk220030 Belarus Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 Salerno Fisciano84084 Italy

A priority queueing model with many types of requests and restricted processor sharing is considered. A novel discipline of requests admission and service is proposed. This discipline assumes restriction of the bandwidth (capacity) of the server and the number of requests that can receive service in the system at the same time. This discipline is some kind of realistic hybrid of the traditional discipline of service in a multi-server system and the discipline of the limited processor sharing. The requests of the highest priority can push out from the service the low priority requests. Therefore, the important problem is fitting of the number of requests that can receive service at the same time to the bandwidth of the server. This problem is solved via construction and analysis of a multi-dimensional Markov chain describing operation of the system under any fixed set of the system parameters. © 2022, The Author(s).

关键词： Bandwidth

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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StyleGAN-∞: Extending StyleGAN to Arbitrary-Ratio Translation with StyleBook

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IEEE Transactions on Visualization and computer Graphics 2024年

作者： Dai, Yihua Xiang, Tianyi Deng, Bailin Du, Yong Cai, Hongmin Qin, Jing He, Shengfeng South China University Of Technology School Of Computer Science And Engineering Guangzhou China Singapore Management University School Of Computing And Information Systems Singapore Cardiff University School Of Computer Science And Informatics United Kingdom Ocean University Of China Faculty Of Information Science And Engineering Qingdao China Hong Kong Polytechnic University School Of Nursing Hong Kong Hong Kong

Although pre-trained large-scale generative models StyleGAN series have proven to be effective in various editing and translation tasks, they are limited to pre-defined fixed aspect ratio. To overcome this limitation, we propose StyleGAN-∞, a model that enables pre-trained StyleGAN to perform arbitrary-ratio conditional synthesis. Our key insight is to distill the expressive StyleGAN features into a StyleBook, such that an arbitrary-ratio condition can be translated to other forms by properly assembling pre-defined StyleBook vectors. To learn and leverage the StyleBook, we employ a network with three distinct stages, each corresponding to StyleBook extraction, StyleBook correspondence learning, and arbitrary-ratio synthesis. Extensive experiments on various conditional synthesis tasks, like super-resolution, sketch synthesis, and semantic synthesis, demonstrate superior performances over state-of-the-art image-to-image translation methods. Moreover, our model can easily generate megapixel images in diverse modalities by taking advantage of different pre-trained StyleGAN models. © 1995-2012 IEEE.

关键词： Generative adversarial networks

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SIS epidemics coupled with evolutionary social distancing dynamics

SIS epidemics coupled with evolutionary social distancing dy...

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American Control Conference (ACC)

作者： Keith Paarporn Ceyhun Eksin Department of Computer Science The University of Colorado Colorado Springs Department of Industrial and Systems Engineering Texas A & M University

A major factor contributing to the difficulties in epidemic forecasting is the unpredictable nature of the population behavior that can either mitigate or exacerbate the spread of a disease. In this paper, we consider a game-theoretic framework for modeling the disease prevalence dependent response of the population behavior in a susceptible-infected-susceptible (SIS) epidemiological model. Our behavioral response model is based on replicator dynamics, where the individuals’ underlying payoffs dynamically change in response to the prevalence of the disease. The coupled dynamics highlight the interplay between the epidemic state and distancing behaviors. We establish a critical threshold on the incentive parameters for which below the threshold, the state in which the disease is endemic and the population does not cooperate with the recommended public health measures is globally asymptotically stable (GAS). Above the threshold, we find through extensive numerical simulations that a variety of dynamical outcomes emerge. For some parameters, an interior equilibrium in which the endemic state is mitigated and a fraction of the population socially distancing is stable. For other parameters, a stable limit cycle about this interior state emerges. The arising rich set of dynamics demonstrate the potential of the modeling framework for epidemic forecasting.

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AI-Powered Hybrid Smart Parking: Optimizing Parking Management Across Diverse Applications in Smart Cities

AI-Powered Hybrid Smart Parking: Optimizing Parking Manageme...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Shalini, M.K. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Department of Computer Science Sheshadripuram College karnataka Bangalore560020 India Department of Studies in Computer Science Manasagangothri Campus University of Mysore karnataka Mysuru570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore karnataka Mysuru570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University karnataka Mysuru570006 India

An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and IoT technologies to enhance the user experience, streamline operations, and improve efficiency. It dynamically allocates parking spaces based on real-time demand, user preferences, and contextual factors. The system accurately predicts parking demand, optimizes space allocation and provides personalized recommendations, reducing congestion and waiting times. The hybrid smart parking algorithm combines machine learning techniques with domain-specific insights to prioritize parking allocation in diverse environments. The study emphasizes the importance of leveraging advanced technologies to address complex urban challenges, such as parking management, and aims to pave the way for sustainable, efficient, and user-centric parking solutions in smart cities. and Random Forest with an overall average score of 0.9400. Machine learning has become crucial for optimizing parking management systems, especially in densely populated cities. To address this challenge, advanced predictive models have been developed to anticipate parking duration based on slot availability, peak hours, and traffic conditions. The Random Forest model outperforms Logistic Regression, Random Forest, and K-Nearest Neighbors in predicting parking length, achieving high accuracy and performance metrics. It maintains user satisfaction and low operational costs, making it a recommended system for further implementation in parking management tasks. The classification report shows a respectable performance with 50% accuracy and a good recall for open slots. The optimization procedure was effective and did not reveal any notable areas for improvement. The optimized route (Slot2, Slot1, Slot3) provides an efficient parking sequence, with high accuracy (95%), good user satisfaction (90%) and suitabili

关键词： Logistic regression

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Markov Persuasion Processes with Endogenous Agent Beliefs

arXiv

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arXiv 2023年

作者： Iyer, Krishnamurthy Xu, Haifeng Zu, You Industrial and Systems Engineering University of Minnesota United States Department of Computer Science University of Chicago United States

We consider a dynamic Bayesian persuasion setting where a single long-lived sender persuades a stream of "short-lived" agents (receivers) by sharing information about a payoff-relevant state. The state transitions are Markovian conditional on the receivers’ actions, and the sender seeks to maximize the long-run average reward by committing to a (possibly history-dependent) signaling mechanism. Such problems are common in platform markets, where the platform seeks to achieve desirable long-term revenue and welfare outcomes by influencing the actions of users. While most previous studies of Markov persuasion consider exogenous agent beliefs that are independent of the chain, we study a more natural variant with endogenous agent beliefs that depend on the chain’s realized history. A key challenge to analyzing such settings is to model the agents’ partial knowledge about the history information. To address this challenge, we analyze a Markov persuasion process (MPP) under various information models that differ in the amount of information the receivers have about the history of the process. Specifically, we formulate a general partial-information model where each receiver observes the history with an periods lag (for ≥ 0). Our technical contribution starts with analyzing two benchmark models, i.e., the full-history information model (i.e., = 0) and the no-history information model (i.e., = ∞). We establish an ordering of the sender’s payoff as a function of the informativeness of agent’s information model (with no-history as the least informative), and develop efficient algorithms to compute optimal solutions for these two benchmarks. For the information model with general , we present the technical challenges in finding an optimal signaling mechanism, where even determining the right dependency on the history becomes difficult. Restricting the dependence on the history to a given length, we formulate the sender’s problem as a bilinear optimization program. To bypass th

关键词： Information theory

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Alzheimer’s Disease Risk Genes Mining Based on a Supervised Machine Learning Method and PPI Network Construction

Alzheimer’s Disease Risk Genes Mining Based on a Supervised...

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International Conference on computer Supported Cooperative Work in Design

作者： Qiushi Zhang Lang Ao Junfeng Liu Yang Xi Dandan Chen School of Computer Science Northeast Electric Power University Jilin China College of Intelligent Systems Science and Engineering & School of Automation Engineering Harbin Engineering University & Northeast Electric Power University Harbin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Although genome-wide association studies (GWAS) have identified multiple Alzheimer’s disease (AD) risk genes by many novel machine learning methods, their ability to explain the "missing heritability" remains insufficient. One possible reason is that these methods typically discard single-nucleotide polymorphisms (SNPs) with marginal main effects during the dimensionality reduction process. Another reason could be the reliance on case-control study designs, which are less statistically informative than the quantitative trait (QT). In addition, synapse plays an important role in the pathogenesis of AD, but only a few genes related to synapse have been reported based on these methods. Thus, different approaches are necessary to better understand and explain the "missing heritability" of AD. By using CSF T-tau/Aβ 42 ratio as QT, this paper performed genome-wide SNP-SNP interactions based on a supervised machine learning method. A total of 467 statistically significant SNP-SNP interaction pairs were found to have marginal main effects, and explained a high-level variance of T-tau/Aβ 42 . To further mining AD risk genes that related to synapses, the Protein–Protein Interaction (PPI) network construction method was used. As a result, a subnetwork with 5 genes were found, which might have effects on both synaptic and cognitive functions within AD patients.

关键词： Proteins Pathology Federated learning Pathogenesis Genomics Machine learning Sensitivity and specificity

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An Optimal Approach to Allocation of Human Resources using LSTM

SSRN

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SSRN 2023年

作者： Yarramsetti, Sarojini Elizabeth, J. Ruby Department of Computer Science and Business Systems Nehru Institute of Engineering & Technology Tamilnadu Coimbatore641 105 India

Human resource allocation is a way of appropriate allocation and reasonable placement of human *** resources can maintain the best combination with other resources at any time. Nevertheless, the irregularities in management teams and the balanced differences of talent quality have a great effect on the balanced development of an enterprise. Based on this, this paper presents the establishment of a Long Short Term Memory(LSTM) model to realize the allocation of human resources and the balanced development of task management. Firstly, LSTM, based on the recurrent neural network, is established. Then, the human resources data is analyzed to calculate the matching degree between the human resources and posts. Finally, Event Based Scheduler was carried out for personnel scheduling then according to the matching degree score between the human resources and posts, the optimal balanced allocation result of the human resources was done. Experimental results show that our method can bring significant improvements to personnel position matching and effectively enhance the efficiency of human resource allocation. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

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Frontiers of Information Technology & Electronic engineering 2023年第10期24卷 1375-1389页

作者： Zhen LIANG Taoran WU Wanwei LIU Bai XUE Wenjing YANG Ji WANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of Software Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Key Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical *** of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given ***-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this ***,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation *** alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great *** is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

关键词： Robust neural networks Training method Drawdown risk Global robustness training Monotonically decreasing robustness

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