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6th International Conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Joseph Kurien, Alena Thomas Mathew, Sharon Ann Mana, Suja Cherukullapurath School of Computing Sathyabama Institute of Science And Technology Chennai India Sathyabama Institute of Science And Technology Department of Computer science Engineering Chennai India

ISBN: (纸本)9781665483285

In recent years, it is observed that most organizations face a huge challenge when it comes to monitoring details pertaining to their assets in their organization such as quantity count, working conditions, maintenance status, depreciation. It aids in the tracking of all assets' locations, quantities, functioning conditions, depreciation, and maintenance status. Assets of an organization are kept in records by means of inventory. A full list of all assets in the company will be available, indicating their description, inventory number, relevant location, operating condition, and all other data. This paper reflects on a system that will be used to store data of the Organization's mobile assets, devices, fixtures, inventory, maintenance activities etc. The system is created using various frontend technologies such as PHP, React, Bootstrap etc., backend using PHP and MySQL along with a double security feature. It can be accessed from anywhere with stable internet connectivity and valid login credentials. © 2022 IEEE.

关键词： Asset management

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Blockchain in Circular Economy: A Short Survey 4

Blockchain in Circular Economy: A Short Survey

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alnajdawi, Ruwaida Elhoseny, Mohamed Clark, Andrew Gaber, Tarek School of Science Engineering & Environment The University of Salford United Kingdom The College of Computing and Informatics The University of Sharjah United Arab Emirates The University of Salford United Kingdom

ISBN: (纸本)9781839539954

Resource consumption is increasing globally. Global resource consumption has surged by over 65%, contributing to 70% of greenhouse gases (GHGs), raising concerns for future generations. Circular economy offers a strategic approach that perpetuates materials and regenerates nature, particularly with key materials, to potentially reduce GHGs by 40% by 2050. However, circular economy implementation faces challenges, notably in tracking material lifecycles. Due to the complexity of tracking material lifecycles, technology is needed, most notably, blockchain. This paper will review blockchain applications in the circular economy through existing literature. A total of 27 publications were reviewed where future research of the publications were also revealed. © The Institution of engineering & Technology 2023.

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A Preference-Based Indicator Selection Hyper-Heuristic for Optimization Problems 9th

A Preference-Based Indicator Selection Hyper-Heuristic for ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Anwar, Adeem Ali Younas, Irfan Liu, Guanfeng Zhang, Xuyun School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW Australia Odyssey Analytics 5757 Woodway Drive HoustonTX77057 United States

ISBN: (纸本)9783031466601

Heuristics have been effective in solving computationally difficult optimization issues, but because they are often created for certain problem domains, they perform poorly when the challenges are significantly altered. The currently available techniques are either designed to address single- or multi-objective optimization issues solely, or they perform poorly with the same parameters. The multi-domain approach known as hyper-heuristics (HHs) can be used to solve optimization issues with minor variations. Motivated by the notion of utilizing the benefits of low-level heuristics (LLHs) in order to obtain well-distributed and convergent optimum solutions along with taking into account the shortcomings of the work completed in many-objective HHs. For many-objective optimization problems, this paper develops a high-level selection approach that employs indicators by preference and offers a unique selection hyper-heuristic called Preference-based Indicator Selection Hyper-heuristic (PBI-HH). In order to establish fairness between exploration and exploitation, the method makes use of a randomization mechanism and a greedy strategy to address a significant problem faced by HHs. Three well-known many-objective evolutionary algorithms are combined in the unique technique that is being proposed. The efficacy of the proposed strategy is assessed by contrasting it with cutting-edge HHs. PBI-HH performs better or equal to the state-of-the-art HHs on 155 out of 160 cases employing the HV indicator and has the optimal μ norm mean values across all datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Multiobjective optimization

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MAPIFY: Map Your Day-to-Day Activities using Web Application

MAPIFY: Map Your Day-to-Day Activities using Web Application

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Advances in computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Abhishek Kumar Singh S Janarthanan Sushant Upadhyay Siddhant Verma School of Computing Science and Engineering Galgotias University India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Nowadays, everyone has become so busy in their life that they sometimes forget their meetings, events, and many day-to-day activities. We are dependent on others for information to get somewhere which we often forget. Many of the people face problem of depending on others to get anywhere on the planet for their crucial activities such as important meetings, sport activities, or kind of event that is to be happen in future. Some of them also don’t remember all the activities and places to visit on. So, we have come up with the solution. The proposed solution to the existing problem is MAPIFY, a front-end web application developed by us which is an excellent example that one should include in their lifestyle. This application will keep the record of your various events in the local storage API of the web browser. This will make sure their location to be mapped on a map so that one can easily track it down, making life easier. It will keep record of all the activities that one should want to schedule along with location of that venue on the map. Also, there is no limit to these records, so one should not fear of storage run out, Since, we have used local storage Web API to solve the problem users face in case of weak internet connection which often leads to refresh of the web page which in turn leads to data losing. This mapify project will make sure to keep the record of the events with the location so that one can easily follow this to keep their day-to-day activities well-arranged and manage their time easily.

关键词： Schedules Planets Geology Web pages Libraries Browsers Faces

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Effective Strategies for Resource Allocation and Scheduling in Cloud computing 1

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Jananee, M. Suresh, A. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu SRM Nagar Kattankulathur Chennai603203 India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Cloud computing, the newest paradigm in distributed systems, and opens up vast possibilities for efficiently meeting demands without investing in costly infrastructure. In particular, IaaS clouds offer an accessible, flexible, and scalable infrastructure for the deployment of these scientific applications by giving users access to a virtually endless pool of resources that can be obtained, configured, and used on demand and charged on a pay-as-you-go basis. The main issue with cloud computing is the lack of resources. For cloud providers, managing resources is a challenging issue. To help they manage their resources efficiently and affordably, researchers offer their insightful recommendations. This paper proposes a number of methods for cutting down on scheduling-related expenses and lag time, as well as for making the most of available resources, meeting strict deadlines, and minimizing energy consumption—all without breaching service-level agreements. Comparisons of proposed and established methods have been found to yield positive outcomes in experiments. The utilization rate has been identified in existing system is 88, but the proposed system rate is 88.82. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Energy utilization

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Knapsack Encryption with Elliptic Curve Cryptography Based Secured Wireless Network 2

Knapsack Encryption with Elliptic Curve Cryptography Based S...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Arun, V. Kuppusamy, P.G. Naveen, G. Santhuja, P. School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Department of Electronics and Communication Engineering Andhra Pradesh Puttur India Jagannath Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350347579

This research study discusses about the prospective future research areas while presenting the state-of-the-art in wireless network cryptography. Elliptic Curve Cryptography (ECC) offers a safe way for interacting nodes to exchange keys. In order to implement ECC, the message is first converted into an affine point on the Elliptic Curve (EC), and then the knapsack method is applied to the ECC-encrypted message across the finite field. This approach presents Knapsack Encryption with ECC Algorithm (KECC) for improving data security in wireless networks. The data is encrypted by using the knapsack algorithm and the ECC algorithm. The necessary encrypted data is forwarded to the receiver via in-between nodes. As a result, it avoids the man-in-the-middle attack. Experimental results prove that the KECC strategy increases the forward ratio. In addition, it reduces the key generation, encryption, as well as decryption computational time. © 2023 IEEE.

关键词： Wireless networks

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Comparative Analysis of Machine Learning Algorithms in Thyroid Disease Prediction 23th

Comparative Analysis of Machine Learning Algorithms in Thyro...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Preethiya, T. Tyagi, Amit Kumar Pandiarajan, T. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India Department of Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Thyroid disease is estimated to be the most common illness in the twenty-first century. The thyroid hormones are produced by a butterfly-shaped gland in the throat. The crucial function these hormones play in bodily metabolism, mental health, and general well-being. In this study, we present a method for accurately analysing thyroid function in an unbalanced dataset using Machine Learning (ML) algorithms. This dataset's accuracy score is ambiguous because most target variable classes are 1. Performance measurements are accuracy and recall, but when class distribution is asymmetrical, the F1-score balances the other parameters. The performance is evaluated using the F1-score, which is one of the best output metrics for unbalanced classification tasks. Simulation results shows that the Decision Tree (DT) algorithm outperforms the other algorithms with an F1-score of 0.9957, the Area Under Curve (AUC) score of 0.9917 and the kappa coefficient of 0.978 in the disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Revealing distribution discrepancy by sampling transfer in unlabeled data 24

Revealing distribution discrepancy by sampling transfer in u...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhilin Zhao Longbing Cao Xuhui Fan Wei-Shi Zheng School of Computing Macquarie University Australia and School of Computer Science and Engineering Sun Yat-sen University China School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9798331314385

There are increasing cases where the class labels of test samples are unavailable, creating a significant need and challenge in measuring the discrepancy between training and test distributions. This distribution discrepancy complicates the assessment of whether the hypothesis selected by an algorithm on training samples remains applicable to test samples. We present a novel approach called Importance Divergence (I-Div) to address the challenge of test label unavailability, enabling distribution discrepancy evaluation using only training samples. I-Div transfers the sampling patterns from the test distribution to the training distribution by estimating density and likelihood ratios. Specifically, the density ratio, informed by the selected hypothesis, is obtained by minimizing the Kullback-Leibler divergence between the actual and estimated input distributions. Simultaneously, the likelihood ratio is adjusted according to the density ratio by reducing the generalization error of the distribution discrepancy as transformed through the two ratios. Experimentally, I-Div accurately quantifies the distribution discrepancy, as evidenced by a wide range of complex data scenarios and tasks.

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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Computer Systems science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS 11

TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS

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11th International Conference on Learning Representations, ICLR 2023

作者： Guo, Ruchi Cao, Shuhao Chen, Long Department of Mathematics University of California Irvine United States Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City United States

A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. The new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks. This research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Inverse problems

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