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

作者： Dandale, M.N. Mazharunnisa Daniel, D. J. Joel Devadass Priya, R. Sathya Walid, Md. Abul Ala Thulasimani, T. Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities Maharashtra Nagpur India Koneru Lakshmaiya Educational Foundation Kl University Kl Business School Vijayawada Andhra Pradesh Vaddeswaram India VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Knowledge Institute of Technology Department of Cse Tamilnadu Salem India Department of Computer Science and Engineering Bangladesh Bannari Amman Institute of Technology Mathematics Tamilnadu Erode India

ISBN: (纸本)9798350396638

Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous researches are less efficient, slowly working algorithms when classification is performed in a large set of databases. The existing methods could be doing better while comparing the error factors, and in the cross-verification process, they have made inappropriate results, leading to wrong classifications. Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous research on email automation and invoice process automation have needed to improve classification model efficiency and they have less efficient, slowly working algorithms when doing classification in a large set of databases. In this work, the Random Forest algorithm is used for classification, and the Quest method is used to segment texts in emails and invoices, both of which can be automated more effectively. The results of existing categorization algorithms have been less than ideal, especially when used to huge datasets, and are often completely inaccurate. The suggested method outperforms previous ML/AI approaches because it produces highly accurate outcomes with little resource investment. There are a number of benefits to utilizing RPA with AI, such as cost reduction, increased output, and streamlined operations. The advantages of this automation, challenges that must be met, and potential answers to those questions are discussed in this study. © 2023 IEEE.

关键词： Support vector machines

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Pollutant correlation analysis in measurements at four classrooms in four Norwegian schools 17

Pollutant correlation analysis in measurements at four class...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin Dep. Energy and Process Engineering Trondheim Norway Dep. Industrial Economics and Technology Management Trondheim Norway Technical University of Denmark Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Demand-controlled ventilation (DCV) is often used in school buildings to save energy by modulating the airflow rates based on the demand. However, other pollutants and indoor air parameters may affect health and are not necessarily correlated to CO2. In this study investigation of correlations between PM2.5, formaldehyde, absolute humidity, CO2 and temperature are performed. Measurements of these parameters were performed between two months and one year, positioned in the supply air in four CO2-based DCV classrooms at four different schools. The time series of measurements were de-trended to avoid overestimating cross-correlation functions. From the measurements we have concluded the need to measure: 1) pollutants related to human activities (CO2) 2) pollutants that infiltrate from processes like combustion or traffic outdoors (PM2.5), and 3) parameters related to other "non-combustion-related activities" indoors, heat sources and moisture loads (temperature). Formaldehyde from building materials seems to be mostly degassed in these older than one-year buildings, but this study should be repeated on newer buildings. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Formaldehyde

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

arXiv

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arXiv 2024年

作者： Varotto, Matteo Heinrichs, Florian Schürg, Timo Tomasin, Stefano Valentin, Stefan Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time. © 2024, CC BY-NC-ND.

关键词： 5G mobile communication systems

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A Lightweight Encryption Method for Preserving E-Healthcare Data Privacy Using Dual Signature on Twisted Edwards Curves 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Farhat, Suryya Kumar, Manoj Vaish, Arti Dewangan, Bhupesh Kumar Choudhury, Tanupriya Kotecha, Ketan Department of Applied Science and Humanities ADGITM Delhi110053 India Department of Mathematics and Statistics Gurukula Kangri Vishwavidyalaya Haridwar249404 India Department of Computer Science and Engineering OP Jindal University Raigarh496109 India Lavale Campus Maharashtra Pune412115 India Pune412115 India Research Professor CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Due to covid-19 pandemic, research in e-healthcare system is gaining popularity because in most of the cases e-healthcare system does not require to present patient physically at doctor’s door. The reason behind this is that e-healthcare system eliminates the necessity for patient to travel at healthcare centres for doctor’s prescription or to collect their diseases diagnostic reports. In the present paper, we have proposed an algorithm, which may be capable to provide integrity for e-healthcare data and preserve its privacy also. The proposed scheme combines dual signature with EdDSA. Dual signature looks similar to double signature but it is quite different;it is a technique of coupling to different values of varied natures preserving them anonymous to varied objects securely. From recent research, it has been proved that elliptic curve cryptography is more suitable for resource constraint (lightweight) device when compared with RSA. Being an improvement of ECDSA, EdDSA signature generation is egregious and affordable. Moreover, EdDSA signature verification, being computationally very hard task, cannot be carried out by e-healthcare devices. Although edge computing devices having no operational constraints can easily perform this task. These characteristics make our proposal more elegant, perky, and efficient strategy for e-healthcare system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Performance assessment of low-cost sensors under representative indoor air conditions 17

Performance assessment of low-cost sensors under representat...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin NTNU Dep. Energy and Process Engineering Trondheim Norway NTNU Dep. Industrial Economics and Technology Management Trondheim Norway DTU Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Recently many indoor air quality (IAQ) low-cost sensors (LCS) have arrived in the market. Being cost-effective encourages sensor installation in many different situations for IAQ-monitoring and sometimes for control of HVAC systems. However, very often these sensors are delivered with incomplete information and there are many doubts about their performance, reliability, and response to aging or drift. In this article, an evaluation of the performance of eight equal in-house mounted IAQ stations is presented. Pre-calibrated LCS measuring CO2, temperature, RH, and particulate matter were connected to a RaspberryPi. Some tests were repeated with 6 months of difference and some with one year. In general, all stations responded similarly to the events to which they were exposed, the inter-sensor correlations values were higher than 0.95. LCS reacted similarly to the sources (trends) but with quantitative disparities. Sensor calibration is needed but when included, the tested LCS seem fit for IAQ measurements. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Carbon dioxide

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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Blockchain Empowered Secure Federated Learning for Consumer IoT Applications in Cloud-Edge Collaborative Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Kumar, Mohit Samriya, Jitendra Kumar Walia, Guneet Kaur Verma, Prabal Wu, Huaming Gill, Sukhpal Singh National Institute of Technology Department of Information Technology Jalandhar India Indian Institute of Information Technology Computer Science and Engineering Sonepat India National Institute of Technology Department of Information Technology Srinagar India Tianjin University Center for Applied Mathematics Tianjin China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

The growing number of consumer Internet of Things (IoT) gadgets, including smart homes, fitness trackers, connected appliances, and home security systems, is transforming the way we live our daily lives. This has led to the emergence of a collaborative cloud-edge paradigm to leverage resources and services near the end-user, thereby providing prompt response to delay-sensitive real-time applications. Nevertheless, the tremendous amount of data generated by various IoT devices and sent over the network is always an open security challenge. The introduction of Federated Learning (FL) addresses the security and data privacy shortcomings of traditional centralised machine learning. Despite FL's use for data privacy, it must overcome a number of significant challenges, such as privacy concerns, communication overhead, stragglers, and heterogeneity. To solve these challenges, this paper proposes a novel technique for enhancing security in IoT-enabled edge cloud computing networks, utilising blockchain-driven FL and Gaussian Bayesian transfer convolutional neural network architectures for data analysis. Blockchain-driven FL ensures the security and privacy of consumer IoT applications. In comparison to state-of-the-art works, the experimental results achieved throughput of up to 89%, latency of 71%, training accuracy of 91%, validation accuracy of 96%, and network security of 92%. © 1975-2011 IEEE.

关键词： Federated learning

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Improved Compact Genetic Algorithms with Efficient Caching

arXiv

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arXiv 2025年

作者： Dutta, Prasanta Mukhopadhyay, Anirban Physics and Applied Mathematics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering University of Kalyani West Bengal Kalyani741235 India

Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to those of classical GAs. However, cGAs have a tendency to repeatedly generate the same chromosomes as they approach convergence, resulting in unnecessary evaluations of identical chromosomes. This article introduces the concept of caching in cGAs as a means of avoiding redundant evaluations of the same chromosomes. Our proposed approach operates equivalently to cGAs, but enhances the algorithm’s time efficiency by reducing the number of function evaluations. We also present a data structure for efficient cache maintenance to ensure low overhead. The proposed caching approach has an asymptotically constant time complexity on average. The proposed method further generalizes the caching mechanism with higher selection pressure for elitism-based cGAs. We conduct a rigorous analysis based on experiments on benchmark optimization problems using two well-known cache replacement strategies. The results demonstrate that caching significantly reduces the number of function evaluations required while maintaining the same level of performance accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Genetic algorithms

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Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks

arXiv

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arXiv 2025年

作者： Gao, Mars Liyao Williams, Jan P. Kutz, J. Nathan Computer Science & Engineering University of Washington SeattleWA United States Mechanical Engineering University of Washington SeattleWA United States Applied Mathematics University of Washington SeattleWA United States Electrical and Computer Engineering University of Washington SeattleWA United States

Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collection procedures. In this paper, we present Sparse Identification of Nonlinear Dynamics with SHallow REcurrent Decoder networks (SINDy-SHRED), a method to jointly solve the sensing and model identification problems with simple implementation, efficient computation, and robust performance. SINDy-SHRED uses Gated Recurrent Units to model the temporal sequence of sparse sensor measurements along with a shallow decoder network to reconstruct the full spatio-temporal field from the latent state space. Our algorithm introduces a SINDy-based regularization for which the latent space progressively converges to a SINDy-class functional, provided the projection remains within the set. In restricting SINDy to a linear model, a Koopman-SHRED model is generated. SINDy-SHRED (i) learns a symbolic and interpretable generative model of a parsimonious and low-dimensional latent space for the complex spatio-temporal dynamics, (ii) discovers new physics models even for well-known physical systems, (iii) achieves provably robust convergence with an observed globally convex loss landscape, and (iv) achieves superior accuracy, data efficiency, and training time, all with fewer model parameters. We conduct systematic experimental studies on PDE data such as turbulent flows, real-world sensor measurements for sea surface temperature, and direct video data. The interpretable SINDy and Koopman models of latent state dynamics enable stable and accurate long-term video predictions, outperforming all current baseline deep learning models in accuracy, training time, and data requirements, including Convolutional LSTM, PredRNN, ResNet, and SimVP. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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