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Spectral Galerkin Approximation of Fractional Optimal Control Problems with Fractional Laplacian

Advances in Applied Mathematics and Mechanics 2023年第6期15卷 1631-1654页

作者： Jiaqi Zhang Yin Yang Zhaojie Zhou School of Mathematics and Computational Science Xiangtan UniversityHunan Key Laboratory for Computation and Simulation in Science and EngineeringKey Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan 411105HunanChina School of Mathematics and Computational Science Xiangtan UniversityNational Center for Applied Mathematics in HunanHunan International Scientific and Technological Innovation Cooperation Base of Computational ScienceXiangtan 411105HunanChina School of Mathematics and Statistics Shandong Normal UniversityJinan 250014ShandongChina

In this paper spectral Galerkin approximation of optimal control problem governed by fractional elliptic equation is *** deal with the nonlocality of fractional Laplacian operator the Caffarelli-Silvestre extension is *** first order optimality condition of the extended optimal control problem is derived.A spectral Galerkin discrete scheme for the extended problem based on weighted Laguerre polynomials is developed.A priori error estimates for the spectral Galerkin discrete scheme is *** experiments are presented to show the effectiveness of our methods and to verify the theoretical findings.

关键词： Fractional Laplacian optimal control problem Caffarelli-Silvestre extension weighted Laguerre polynomials

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Collaborative Edge Server Placement for Maximizing QoS with Distributed Data Cleaning

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IEEE Transactions on Services computing 2025年第3期18卷 1321-1335页

作者： Liang, Yuzhu Yin, Mujun Wang, Wenhua Liu, Qin Wang, Liang Zheng, Xi Wang, Tian Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519000 China Huaqiao University College of Computer Science and Technology Xiamen361021 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China Macquarie University School of Computing NSW2109 Australia

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O(ln m), where m represents the number of users' tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios. © 2008-2012 IEEE.

关键词： Data accuracy

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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A Data-Driven Approach in predicting Heart Disease Detection in earlier stage 2

A Data-Driven Approach in predicting Heart Disease Detection...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nagarjuna, N. Kavitha, S. Lakshmi, H.N. SRM Institute of Science and Technology School of Computing Dept. of Computing Tamilnadu Kattankulathur India CVR College of Engineering Dept. of CSIT Hyderabad India CVR College of Engineering Dept. of Emerging Technologies Hyderabad India

ISBN: (纸本)9798350365269

In the healthcare field accumulation of vast amounts of data, known as big data, that contains valuable insights important for decision-making is available. The Data-driven approaches consistently proves their effectiveness by highlighting the pivotal roles of data analysis in error detection, valuable information identification, healthcare variables assumption and validation, and correlation establishment among health-related System. The proposed system Heart-Risk predict (HRP) serves as a data analysis tool by using statistical modeling techniques. The proposed novelistic model focuses on prediction of heart disease and risk factors using the k-means grouping algorithm by employing data analytics and visualization tools, which is further tested on publicly available datasets. Some of basic health related attributes like patient age, chest pain category, blood pressure, glucose levels and heart rate aid in early-stage chest pain type of determination. Through preprocessing methods and classifier-based evaluations, the current study showcases heart disease prediction accuracy at a higher level. Furthermore, the proposed system integrates the Tabu search mechanism and the K-means algorithm in addressing traditional method limitations. This integration enhances efficacy in handling large datasets by reducing computational complexity and give improvement in prediction accuracy. Thorough experimental validation depict system's efficacy and superiority over existing methods by offering a promising implications for early heart disease detection and proactive management, thus improvising public health outcomes. © 2024 IEEE.

关键词： Data visualization

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Prevention Against DDoS 12

Prevention Against DDoS

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12th International Conference on Computer Communication and Informatics, ICCCI 2022

作者： Venkatesh, K. Subashini, B. Kumar, P. Vinoth School of Computing College of Engineering Srm Institute of Science and Engineering Department of Networking and Communications Kattaknulathur India School of Computing College of Engineering Srm Institute of Science and Engineering India

ISBN: (纸本)9781665480352

The current time is totally reliant upon Internet. Web gives a worldwide data source to every one of its clients;thus the availability of web is of much significance. The Distributed Denial of Service (DDoS) assaults have become an enormous scope issue or inconvenience for the clients of PC frameworks which are associated with the Internet. DDoS aggressors take over on the optional casualty frameworks and thus utilizing them to wage an organized huge scope assault against target frameworks. As of now the new countermeasures are being made to forestall DDoS assaults to help the frameworks that are casualties of such an assault. The aggressors are constantly building up the new programming and adjusting more established DDoS assault instruments to evade or keep away from these new countermeasures. In this paper our principle center is around the DDoS assault which squares and damages the organization accessibility by flooding the objective with high measure of illicit traffic usurping its data transfer capacity, overburdening it to stop authentic traffic to get past. Different strategies to turn away and decrease these assaults alongside their benefits and inconveniences are additionally talked about. © 2022 IEEE.

关键词： Denial-of-service attack

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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A Sentimental Analysis Approach for Personalized Drug Recommendations Using Machine Learning 1

A Sentimental Analysis Approach for Personalized Drug Recomm...

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1st IEEE International Conference on computing and Data science, ICCDS 2024

作者： Vijayaraj, A. Murugan, V.P. Jebakumar, R. Gaurav, N.V. Santhosh, V. Shai Kumar, R. R.M.K. Engineering College Department of Information Technology Chennai India Panimalar Engineering College Department of Mathematics Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350365337

Since the virus that causes corona disease was discovered, it has gotten more difficult to find medical professionals with the appropriate licenses, including doctors, nurses, diagnostic equipment, and medications. Due to the intense grieving, a substantial number of people in the medical industry pass away. Because of the scarcity, a lot of individuals began self-medicating without first consulting a doctor, which made the health problem worse. There are numerous well-established applications for machine learning, and recent years have witnessed a growth in the pace and breadth of computerization-related research and development. The goal of this research is to develop a method for recommending medications that might greatly reduce the obligations of specialists. In this work, we built a drug recommendation system that makes estimations about the emotional of patient reviews using a variety of different vectorization techniques, such as using a Bow, The TF-ID Word 2 Vector, or Manually Feature Analysis. The predicted emotions were graded using accuracy, remembrance, flscore, exactness, and the area beneath the slope (AUC). The results show that, when weighed against the other models, the classification technique Linear SVC + The TF -I vector process has the greatest accuracy. © 2024 IEEE.

关键词： Recommender systems

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Effective anomaly detection in hybrid wireless IoT environment through machine learning model: a survey

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International Journal of Mobile Network Design and Innovation 2023年第4期10卷 175-181页

作者： Shanmuganathan, V. Annamalai, Suresh Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India

Wireless hybrid environment has seamlessly integrated inside all smart homes and in smart environments. Assaults and Anomalies are probably going to happen in open platform of IoT frameworks and which can result easily by sending fake alarms and the inability to appropriately identify basic occasions. To address that, IoT frameworks must be outfitted with peculiarity recognition preparing notwithstanding the necessary occasion identification capacity. This is a key component that empowers unwavering quality and productivity in IoT. An effective assault and oddity ready framework are a lot of important in IoT based climate, since these are power starving gadgets and convey extremely delicate information's. A proficient K-nearest neighbour (KNN) and Random Forest algorithm-based sensor information inconsistency recognition is anticipated in an edge gadget in this paper. The sensor hardware failure and software failures are identified in the IoT based environment and effective identification was achieved through KNN and Random Forest algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Anomaly detection

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