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A MODIFIED WEAK GALERKIN FINITE ELEMENTMETHOD FOR SINGULARLY PERTURBED PARABOLIC CONVECTION-DIFFUSION-REACTION PROBLEMS

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Journal of Computational Mathematics 2023年第6期41卷 1246-1280页

作者： Suayip Toprakseven Fuzheng Gao Faculty of Engineering Department of Computer ScienceArtvin Coruh UniversityArtvin08100Turkey School of Mathematics Shandong UniversityJinan 250100China

In this work,a modified weak Galerkin finite element method is proposed for solving second order linear parabolic singularly perturbed convection-diffusion *** key feature of the proposed method is to replace the classical gradient and divergence operators by the modified weak gradient and modified divergence operators,*** apply the backward finite difference method in time and the modified weak Galerkin finite element method in space on uniform *** stability analyses are presented for both semi-discrete and fully-discrete modified weak Galerkin finite element *** order of convergences are obtained in suitable *** have achieved the same accuracy with the weak Galerkin method while the degrees of freedom are reduced in our *** numerical examples are presented to support the theoretical *** is theoretically and numerically shown that the method is quite stable.

关键词： The modified weak Galerkin finite element method Backward Euler method Parabolic convection-diffusion problems Error estimates

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Classification and Health Prediction in Plants Using Deep Convolutional Neural Networks 3

Classification and Health Prediction in Plants Using Deep Co...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Jha, Narendra Kumar Shukla, Praveen Kaumar School of Engineering Babu Banarasi das University Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350336238

Plants are one of the core components of human life and its surrounding environment. Various diseases not only diminish the ecological relevance of plants and the products they produce but also have an impact on their economic value. The main goal of this study is to identify plant kinds and design a suitable and effective method for judging a plant's healthiness based on pictures of its leaves in order to give a workable system for an instant and economical solution to this problem. The analysis of both biotic and abiotic elements that affect a plant's general health is known as plant pathology. Farmers must identify the issue quickly in order to take appropriate measures and stop additional losses. Consequently, it is recommended for this study to use a Deep Convolutional Neural Network (DCNN) to categorise damaged leaves. A genuine dataset of 4503 images of the 12 diverse tree leaves that were gathered at the 'Shri Mata Vaishno Devi University in Katra, J&K, India', is used to verify this work. Both healthy and unhealthy leaf photos are taken in the dataset that belongs to the 12 different plants type. The result found after testing the model was quite good. The suggested DCNN model will have greater classification accuracy and able to classify the plant type of the leaves as well as the model can also predict whether the leaf is healthy or unhealthy. © 2023 IEEE.

关键词： Image classification

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A Decentralized Blockchain-Based Architecture for Healthcare Industry 1

A Decentralized Blockchain-Based Architecture for Healthcare...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Pandey, Shraiyash De, Abhik Kumar Choudhary, Shrishti Asim, Mohammad Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350330915

The most prevalent problems in healthcare in India are related to doctors' referral processes, data transmission between health facilities, and patient portals for accessing their medical records. Furthermore, the preservation and distribution of Electronic Medical Records (EMR) is a critical necessity for health organizations since they hold all confidential health data about patients. However, because most Indian health institutions employ centralized outsourced systems, they have issues with EMR security and interoperability. In this paper, we present a Blockchain-based technological architecture for EMR management in Indian health institutions. Due to Blockchain's properties such as privacy, security, transparency, and immutability, we have implemented it in the architecture to address several issues within healthcare. © 2023 IEEE.

关键词： Architecture Blockchain Healthcare

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Harmonic Harmony: Advancing Musical Instrument Classification through CNN-SVM 4

Harmonic Harmony: Advancing Musical Instrument Classificatio...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Chaudhary, Preeti Verma, Aditya Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350349900

This research intends to improve the field of Music Information Retrieval (MIR) by creating and comparing a Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches for the precise identification of five distinct musical instruments. The process of categorizing is quite intricate because of the subtle timbre characteristics coming from the musical sounds. We have done a detailed study evaluation of the model. To evaluate each class of instrument, we have employed precision, recall, F1-score and accuracy metrics. The precision for the model in Class 1 was 89.5, the recall was 93.76, the F1 score was 91.58, and the accuracy was 97. Class 2 precision was 91.12%, recall was 94.2%, F1 score was 92.64%, and accuracy was 97%. The third class performed outstandingly with a precision of 93.28%, recall of 97.71%, Fl-score of 95.44%, and accuracy of 98%. The precision rate of class 4 was 94.53%, the recall rate was 86.18%, the F1-score was 90.16%, and the accuracy rate was 96%. Class 5 got 95.38% precision, 92.94% recall, 94.14% F1 score, and an accuracy of 98%. In order to evaluate the overall effectiveness of the model, we calculated the macro average (precision: The results displayed precision (91.63%), recall: 92.96%, F1-score: 92.79%), weighted average (precision: 92.85%, recall: 92.77%, F1-score: 92.74%), and micro average (92.77%). These measures give an overall picture of the model's reliability. The confusion matrix gives another proof, and the fact is that the model accuracy is 92.77033344%. This demonstrates the potential of the CNN-SVM technology for a highly accurate instrument classification across various musical styles. © 2024 IEEE.

关键词： Convolutional neural networks

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Applications of Topological Data Analysis with Morphology Operators on Hypergraphs

Applications of Topological Data Analysis with Morphology Op...

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International Conference on Mathematics of Intelligent Computing and Data science, ICMICDS 2022

作者： Ramkumar, P.B. Dhanya, P.M. Department of Mathematics Rajagiri School of Engineering and Technology Kerala Kakkanad India Department of Computer Science and Business Systems Rajagiri School of Engineering and Technology Kerala Kakkanad India

ISBN: (纸本)9789819615049

A hypergraph is a type of graph, H=(V,E) where V is the set of nodes and E is the set of hyperedges;where one edge consists of may nodes. Morphological operators are nonlinear operators which can be applied on images, graphs, hypergraphs etc. Dilation and erosion are morphological operators and other operators like opening, closing can be obtained by repeated application of these operators. Topological space consists of a set X together with topology τ defined on X, which satisfies the axioms namely Nullset, arbitrary union of opensets and finite intersection of opensets. Such a topological space S=(X,T) is compatible with a hypergraph H if X=VH∪EH where VH and EH are the vertex set and edge set respectively. The morphological operators induces several topology on hypergraph with its compatible topological space S=(X,T). Neighborhood of a node is defined in many ways which can be considered as open sets in the corresponding topological space. The purpose of this paper is to apply topological properties on hypergraphs using morphological operators. Separation axiom and closure function are illustrated using hypergraph structure. The work is extended to crime analysis, text processing and patient disease analysis. Topological feature extraction is done on Barcodes created from crime hypergraphs. This gives a better visualization of crime analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph theory

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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Performance Analysis of Machine Learning Models for Liver Disease Patient Classification

Performance Analysis of Machine Learning Models for Liver Di...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Behera, Ranjit Kumar Behera, Dayal Kumar Swetanisha, Subhra Jena, Ajay Kumar Silicon University Department of Computer Science & Engineering Bhubaneswar India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Trident Academy of Technology Department of CSE Odisha Bhubaneswar India KIIT University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Liver disease is a significant global health concern, necessitating the development of accurate and efficient diagnostic tools. This study presents a comparative analysis of various machine learning classifiers, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), XGBoost, and Ensemble Models, for the classification of liver disease patients. The challenge of class imbalance in the dataset is addressed using Synthetic Minority Over-sampling Technique (SMOTE), ensuring a more balanced and representative training process. Missing data is handled with the K-Nearest Neighbors (KNN) Imputer, while Recursive Feature Elimination (RFE) is employed to select the most relevant features, optimizing the model's performance. Experimental results demonstrate the effectiveness of these approaches, with a detailed comparison of each classifier's performance in terms of accuracy and F1-score. This study provides valuable insights into the strengths and limitations of different machine learning classifiers for liver disease classification, contributing to the advancement of predictive modeling in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Native Nest: An E-Commerce Platform for Promoting Tribal Products and Culture 8

Native Nest: An E-Commerce Platform for Promoting Tribal Pro...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rajendrakumar, S. Chowta, Yashasvi Tadikonda, G Harshavardhan Reddy, Sathi Lakshmi Priyatha Karthik, Mukkanti Venkata Sai Shanmugam, Selvanayaki Kolandapalayam Thangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemical Engineering and Materials Science Coimbatore641112 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore641112 India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350376425

This research study proposes a new online platform, Native Nest, to address the challenges faced by the commerce industry in Ooty District. This initiative provides a sustainable platform for registered tribal artisans to connect with buyers, promoting their cultural heritage and products. The platform features a user-friendly interface, adjustable layout, and reliable infrastructure, ensuring a seamless experience for both clients and administrators. QR codes strategically placed throughout Ooty facilitate easy access to the platform, expanding its reach to tourists and locals. Native Nest offers detailed product information and enables online purchasing, eliminating the need for middlemen and providing a convenient shopping experience. By empowering tribal communities and promoting their cultural heritage, Native Nest contributes to the socio-economic development of the region. This initiative enables cultural exchange and expands the market share of tribal arts and expressions, creating a more comprehensive and sustainable marketplace. © 2024 IEEE.

关键词： Marketplaces

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A retailer’s inventory model for deteriorating items under power pattern demand with shortages partially backlogged in both crisp and fuzzy environments

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International Journal of Industrial and Systems engineering 2025年第1期49卷 96-118页

作者： Patra, Sourav Kumar Paikray, Susanta Kumar Tripathy, Rudra Mohan Department of Mathematics Veer Surendra Sai University of Technology Odisha Burla768018 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India

An inventory predicament can be resolved with numerous techniques, starting from the trial-and-error manner of mathematical and simulation methods. Mathematical methods always serve as powerful tools for minimising total inventory costs. In this paper, we have considered a retailer’s inventory problem in order to determine an optimal strategy that minimises the total inventory cost under various constraints. Here, the constraints include constant deterioration, power-pattern demand, permissible shortages, partial backlog, different inventory costs, and inherent imprecision of various expenses concerning the current scenario. Subsequently, we develop the mathematical model of the problem together with its solving policy in a crisp as well as fuzzy environments. Moreover, we provide several numerical illustrations to validate our findings. Finally, we present several managerial insights for inventory managers based on the sensitivity analysis of associated parameters. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms 9

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kannappan, A. Kolagani, Nagesh Kandaswamy, Vijay Anand Vishnupriyan, J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350377972

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition Communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life. © 2024 IEEE.

关键词： Ant colony optimization

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