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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yihe Zhao Jun Yao Jian Wang Xuetao Xie Sergey V. Ablameyko College of Science China University of Petroleum (East China) Qingdao China College of Petroleum Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, we introduce an innovative network structure based on PINNs, specifically designed to address fluid-structure interaction (FSI) problems in porous media, referred to as M-PINN. Our approach is inspired by fully coupled traditional numerical methods, leveraging a multi-task learning (MTL) framework with soft parameter sharing to enhance the interactions across different physical fields. This alignment leads to an improved representation of multi-physics coupling phenomena. Experiments on the constant pressure production problem demonstrate that our method provides better performance than both traditional and sequential PINN approaches.

关键词： Couplings Inverse problems Partial differential equations Neural networks Production Media Multitasking Mathematical models Information technology Information exchange

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Design and Security Analysis of Isoidentity based Isobeta Cryptosystem 1

Design and Security Analysis of Isoidentity based Isobeta Cr...

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

作者： Bhosale, Nilesh Pohane, Rupesh Meshram, Siddhant Meshram, Akshaykumar Adak, Malabika Reddy, K.T.V. Department of Applied Mathematics Yeshwantrao Chavan College of Engineering M.S. Nagpur441110 India Department of Mathematics Suryodaya College of Engineering and Technology M.S. Nagpur India Computer Science Department New York University Room 305 251 Mercer Street New YorkNY10012 United States M. S. Wardha442004 India

ISBN: (纸本)9781665460842

Digital data protection frameworks inclusive of cryptographic mechanisms and signature crypto mechanisms are essential for stable data communication in open network systems. An extensive open-key folder is not necessary for ID-based crypto mechanisms like ID-based crypto mechanisms, the ID- basedsignature mechanisms, else ID-basedkey circulation mechanisms are between the enhanced an opposing for create stable and protected digital data communication for enormous-scale open network systems recommended by Shamir. This article suggests an isoidentity based isobeta crypto mechanism build on the generalized isodiscrete isologarithm isoproblem and isointeger isofactorization isoproblem that is one of the initial implementations of Shamir's for security norms. Also, we examine the security adverse a conjuration of certain clients in the suggested mechanism, and demonstrate the probability of establishing a higher secure mechanism. © 2022 IEEE

关键词： Cryptography

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Development of Virtual Reality Platform through Human computer Interaction using Artificial Intelligence

Development of Virtual Reality Platform through Human Comput...

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applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： S Saranya B Channarayapriya U Harshavardhini A.Sunitha Nandhini J. Revathi R Venkatesan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Deparment of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Mathematics Kongu Engineering College Erode India Deparment of Information Technology Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments.

关键词： Human computer interaction Deep learning Technological innovation Heuristic algorithms Virtual environments Collaboration Real-time systems

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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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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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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： R. Jayanthi A. Hepzibah Christinal R. Hephzibah T. Shekinah Chandrajit Bajaj D. Abraham Chandy Department of Mathematics Karunya University Coimbatore India Department of Computer Science and Engineering Loyola – ICAM College of Engineering and Technology Chennai Computational Applied Mathematics Chair in Visualization University of Texas Austin Department of Electronics and Communication Engineering Karunya University Coimbatore India

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers – Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes – for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes.

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Unsupervised Feature Selection via Multi-Structure Learning and Indicator Matrix

Unsupervised Feature Selection via Multi-Structure Learning ...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Zebiao Hu Xiaoyu Gao Ziwei Bian Jian Wang Sergey V. Ablameyko College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Science China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Traditional feature selection methods usually only consider a specific geometric structure of the original data, which cannot reveal the most essential underlying structure of the data. Apart from this, a regularization constraint with sparsity is often imposed on the mapping matrix, which inevitably leads to additional parameter setting issues. To address these challenges, a novel unsupervised feature selection via multi-structure learning and indicator matrix (UFSMI) is proposed in this study. More concretely, we construct a unified framework that incorporates multi-structure learning and feature selection. UFSMI first explores the global and local geometric structure of high dimensional data in subspace to excavate the intrinsic structure of data, effectively reducing the interference of redundant features. Then, an indicator matrix for feature selection that can explicitly indicate which features should be selected is in-troduced without imposing any regularization constraints. Finally, the most useful subset of features can be obtained based on the indices of the non-zero elements in the indicator matrix. A series of experiments are conducted to demonstrate the effectiveness and superiority of the proposed method on nine real-word high dimensional datasets by comparing with existing state-of-the-art feature selection methods.

关键词： Representation learning Interference Feature extraction Excavation Market research Computational intelligence

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Open set label noise learning with robust sample selection and margin-guided module

arXiv

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arXiv 2025年

作者： Zhao, Yuandi Xia, Qianxi Sun, Yang Wen, Zhijie Ma, Liyan Ying, Shihui College of Air Traffic Management Civil Aviation University of China Tianjin300300 China Department of Mathematics College of Sciences Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai University Shanghai200072 China

In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we introduce a method based on Robust Sample Selection and Margin-Guided Module (RSS-MGM). Firstly, unlike the prior clean sample selection approach, which only select a limited number of clean samples, a robust sample selection module combines small loss selection or high-confidence sample selection to obtain more clean samples. Secondly, to efficiently distinguish open set label noise and closed set ones, margin functions are designed to filter open-set data and closed set data. Thirdly, different processing methods are selected for different types of samples in order to fully utilize the data's prior information and optimize the whole model. Furthermore, extensive experimental results with noisy labeled data from benchmark datasets and real-world datasets, such as CIFAR-100N-C, CIFAR80N-O, WebFG-469, and Food101N, indicate that our approach outperforms many state-of-the-art label noise learning methods. Especially, it can more accurately divide open set label noise samples and closed set ones. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

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