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arXiv 2023年

作者： Yang, Qihui Saldaña, Joan Scoglio, Caterina Department of Electrical and Computer Engineering Kansas State University ManhattanKS66506 United States Department of Computer Science Applied Mathematics and Statistics Universitat de Girona Girona Catalonia17003 Spain

The Markovian approach, which assumes exponentially distributed interinfection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual’s infectiousness depends on its viral load and varies over time. In this paper, we present a Susceptible-Infected-Recovered-Vaccinated-Susceptible epidemic model incorporating non-Markovian infection processes. The model can be easily adapted to accurately capture the generation time distributions of emerging infectious diseases, which is essential for accurate epidemic prediction. We observe noticeable variations in the transient behavior under different infectiousness profiles and the same basic reproduction number R0. The theoretical analyses show that only R0 and the mean immunity period of the vaccinated individuals have an impact on the critical vaccination rate needed to achieve herd immunity. A vaccination level at the critical vaccination rate can ensure a very low incidence among the population in the case of future epidemics, regardless of the infectiousness profiles. © 2023, CC BY-NC-ND.

关键词： Cell proliferation

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Stability Criteria for Neural Networks with Stochastic Impulsive Intensity and Impulsive Density

Stability Criteria for Neural Networks with Stochastic Impul...

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Neuromorphic Computing (ICNC), International Conference on

作者： Hao Deng Jingxin Liu Jun Peng Yingxu Wang Fei Chang School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning China School of Mathematics and Physics Sciences Chongqing University of Science and Technology Chongqing China Chongqing Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM·AI*) Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.

关键词： Sufficient conditions Stability Neuromorphic engineering Neural networks Stability criteria Control theory Time-varying systems Numerical stability

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Improving Smart Agriculture Through the Use of Machine Learning to Offer Tailored Crop Forecasts

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 74-83页

作者： Patil, R.N. Maroor, Jnaneshwar Pai Dhanke, Jyoti A. Chunduru, Madhava Rao Singh, Mangal Jangir, Pradeep Upadhyay, Shrikant Department of Mechanical Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department Of Computer Science And Engineering Koneru Lakshmaiah Educational Foundation Vaddeswaram AP Guntur District India Department of Electronics and Telecommunication Engg. Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

It is not possible to identify crop illnesses by examining each disease independently. Users may only get forecasts for the most anticipated illnesses with the use of a robust analytic methodology. This study presents a method that can collect, analyze, and identify crop health information all on one platform using an IoT and machine learning-based approach. Food security and the provision of essential raw materials for several industries are both supported by agriculture, which is the most important and basic vocation. Improved crop yields, thanks to new innovative agricultural practices, mean less water wasted and more money in the bank. An intelligent irrigation system that uses a machine learning algorithm to forecast a crop’s water needs is the suggested model. One such rapidly evolving invention is the Internet of Things (IoT), which is now spreading its wings across all geographies. Technological innovation is reaching a new level of practicality in agriculture with the rise of embedded PCs like Arduino. Soil quality monitoring using Arduino, several sensors, and an Android app is shown and accomplished in this study. Temperature, soil moisture level, ammonia, and carbon content limit soil quality in this study. Arduino is used as both a worker and an information processing device, and it is in charge of sensor security. The terminal device is an Android phone. Producers may improve agricultural yields by collecting and analyzing data on weather, soil, seed, and yields of crops, moisture, and gusts of wind in specific areas. Furthermore, a flask-built GUI was used to present crop forecasts derived from a bespoke recommender system. Because of its adaptable design, the system may one day be able to discover the suggested items of other areas. This article uses targeted agricultural data to create five machine-learning approaches that are comparable to one another and to compare and contrast them. © The Author(s), under exclusive licence to Springer Nature Swit

关键词： Decision navigation systems Internet of Things (IoT) Irrigation systems Machine learning algorithms Sensors

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Combating Misinformation/ Disinformation in Online Social Media: A Multidisciplinary View

Combating Misinformation/ Disinformation in Online Social Me...

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作者： Barni, Mauro Fang, Yi Liu, Yuhong Robinson, Laura Sasahara, Kazutoshi Vincent, Subramaniam Wang, Xinchao Wu, Zhizheng Department of Information Engineering and Mathematics University of Siena Italy Department of Computer Science and Engineering Santa Clara University United States Department of Sociology Santa Clara University United States Department of Innovation Science Tokyo Institute of Technology Japan Markkula Center for Applied Ethics Santa Clara University United States Department of Electrical and Computer Engineering National University of Singapore Singapore School of Data Science Chinese Univ. of Hong Kong Shenzhen China

Recently, the viral propagation of mis/disinformation has raised significant concerns from both academia and industry. This problem is particularly difficult because on the one hand, rapidly evolving technology makes it much cheaper and easier to manipulate and propagate social media information. On the other hand, the complexity of human psychology and sociology makes the understanding, prediction and prevention of users' involvement in mis/disinformation propagation very difficult. This themed series on "Multi-Disciplinary Dis/Misinformation Analysis and Countermeasures"aims to bring the attention and efforts from researchers in relevant disciplines together to tackle this challenging problem. In addition, on October 20th, 2021, and March 7th 2022, some of the guest editorial team members organized two panel discussions on "Social Media Disinformation and its Impact on Public Health During the COVID-19 Pandemic,"and on "Dis/Misinformation Analysis and Countermeasures - A Computational Viewpoint."This article summarizes the key discussion items at these two panels and hopes to shed light on the future directions. © 2022 M. Barni, Y. Fang, Y. Liu, L. Robinson, K. Sasahara, S. Vincent, X. Wang and Z. Wu.

关键词： Social networking (online)

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The Role of Predictive Maintenance Optimization Techniques in Enhancing Industrial Productivity

The Role of Predictive Maintenance Optimization Techniques i...

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International Conference on Optimization and Applications (ICOA)

作者： Mohamed Er-Ratby Abdessamad Kobi Youssef Sadraoui Moulay Saddik Kadiri LASTI Laboratory of Science and Engineering Techniques National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

The convergence between advanced technology and industrial production holds significant potential to enhance operational efficiency, reduce maintenance costs, and minimize unexpected disruptions. Artificial intelligence, in particular, plays a crucial role in predicting machine failures, thereby contributing to the optimization of operational performance and maximizing overall company output. However, current challenges in maintenance and production are numerous. High maintenance costs are often a major concern for companies, as they can significantly eat into profit margins. Unexpected downtime, caused by sudden machine failures, leads to substantial production losses, affecting not only revenue but also customer satisfaction and the company's reputation. Furthermore, the increasing complexity of industrial systems makes the management of maintenance and production even more difficult, necessitating more advanced and integrated solutions. This article explores the various applications of artificial intelligence in the manufacturing industry, highlighting its impact on business performance. It also examines the steps necessary for implementing predictive maintenance, from data collection and processing to the establishment of predictive models. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize overall company output.

关键词： Productivity Manufacturing industries Costs Focusing Companies Predictive models Maintenance Artificial intelligence Optimization Predictive maintenance

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Nash Equilibrium of Joint Day-ahead Electricity Markets and Forward Contracts in Congested Power Systems

arXiv

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arXiv 2023年

作者： Banaei, Mohsen Buygi, Majid Oloomi Raouf-Sheybani, Hani Ebrahimy, Razgar Madsen, Henrik Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark Department of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Engineering Quchan University of Technology Quchan Iran

Uncertainty in the output power of large-scale wind power plants (WPPs) can face the electricity market players with undesirable profit variations. Market players can hedge themselves against these risks by participating in forward contracts markets alongside the day-ahead markets. The participation of market players in these two markets affects their profits and also the prices and power quantities of each market. Moreover, limitations in the transmission grid can affect the optimal behavior of market players. In this paper, a Cournot Nash equilibrium model is proposed to study the behavior of market players in the forward contract market and the day-ahead electricity market in a congested power system with large-scale integration of WPPs. The proposed method is applied to a test system, and the results are discussed. © 2023, CC BY.

关键词： Power markets

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Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach 8

Identification of Building Damages using Satellite Images ba...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Chavhan, Ganesh K. Singh, Sumeshwar Sahebrao Doke, Ankush Pilli, Daniel Kailasavalli, S. Vyankatesh Ghamande, Manasi K. J. Somaiya College of Arts Commerce and Science Department of Geography Ahmednagar Maharashtra Kopargaon India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Mahatma Phule Nutan Maha Vidyalaya Department of Geography Maharashtra Mirajgaon India Klbs Koneru Lakshmaiah Educational Foundation Mba Department Andhra Pradesh Vijayawada India Psna College of Engineering and Technology Mathematics Tamilnadu Dindigul India Vishwakarma Institute of Information Technology Engineering and Applied Science Maharashtra Pune India

ISBN: (纸本)9798350396638

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably. © 2023 IEEE.

关键词： Principal component analysis

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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Conflict-free Connection Number and Independence Number of a Graph

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Acta Mathematicae Applicatae Sinica 2021年第2期37卷 278-286页

作者： Jing WANG Meng JI School of Computer Engineering and Applied Mathematics Changsha UniversityChangsha 410022China Hunan Province Key Laboratory of Industrial Internet Technology and Security Changsha UniversityChang-sha 410022China College of Mathematical Science Tianjin Normal UniversityTianjin 300007China

An edge-colored graph G is conflict-free connected if any two of its vertices are connected by a path,which contains a color used on exactly one of its *** conflict-free connection number of a connected graph G,denoted by cf c(G),is defined as the minimum number of colors that are required in order to make G conflict-free *** this paper,we investigate the relation between the conflict-free connection number and the independence number of a *** firstly show that cf c(G)≤α(G)for any connected graph G,and give an example to show that the bound is *** this result,we prove that if T is a tree with?(T)≥(α(T)+2)/2,then cf c(T)=?(T).

关键词： edge-coloring conflict-free connection number independence number tree

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Hierarchical Superpixel Relation Graph Combined with Convolutional Sparse Coding for Self-Supervised Hyperspectral Image Denoising

Hierarchical Superpixel Relation Graph Combined with Convolu...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhongshun Jiang Qipeng Qian Yi Qiu Yuntao Qian College of Computer Science Zhejiang University Hangzhou China Department of Applied Mathematics University of Arizona Tucson AZ USA College of Engineering Huzhou University Huzhou China

Self-supervised methods have recently been widely used for hyperspectral image (HSI) denoising. As only a single noisy HSI required to be restored is used for learning, effectively exploring the spatial-spectral joint information for HSI representation becomes to be a more critical problem. In this paper, we propose a self-supervised denoiser based on a blind-spot network and a new feature extraction method that combines convolutional neural network (CNN) and graph neural network (GNN). The features from the convolutional backbone network are fed into the GNN as graph nodes. With hierarchical superpixel segmentation of HSI, the feature nodes with contextual and hierarchical relationships are feature-enhanced by the powerful feature interaction capability of GNN. Experimental results show that our method is competitive compared with state-of-the-art methods.

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