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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Deepak, Shashikant Kharbas, Vikash Kumar Murugan, R. Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350305463

Improvements in near-field Synthetic Aperture Radar (SAR) imaging are among the most notable developments in SAR imaging overall. To improve the quality and consistency of automated near-field SAR imaging, this study presents a unique strategy based on deep learning techniques. DeepSARNet, the suggested technique, is meant to be more effective than conventional approaches in terms of picture quality, interpretability, and speed. In this research, we introduce the strategy, evaluate its efficacy in comparison to six made-up classic approaches, and offer our findings. Automatic focus correction using a convolutional neural network (CNN) is the foundation of DeepSARNet, successfully minimizing focus defects inherent to near-field SAR imagery. The second part, a convolutional neural network (CNN)-based Near-Field Scatterer Removal (NFSR) technique, is designed to eliminate near-field scatterer artifacts for clearer, easier-tounderstand final pictures. Finally, the Object Detection and Classification (ODC) method integrates region proposal networks with CNNs to provide reliable results in object recognition. When compared to conventional techniques, DeepSARNet produces pictures with superior clarity and interpretability in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Moreover, DeepSARNet needs less calibration work, shortens processing time, and uses less resources than competing solutions, making it a prime candidate for use as a fully automated, efficient SAR imaging solution. The authors propose the DeepSARNet technique, which leverages deep learning capabilities, to address these ongoing issues with near-field SAR imaging. It improves picture quality, lessens artifacts, and increases automation, making it a useful asset in fields including disaster relief, ecological surveying, and military preparation. Research into improving and expanding these approaches in the realm of SAR imaging is warranted, since the results highlight the

关键词： Remote sensing

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Reimagining Architecture and Protocols for the Edge Computing Revolution in Web3

Reimagining Architecture and Protocols for the Edge Computin...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Loonkar, Shweta Ahmed, Zahid Kavitha, R. Atlas SkillTech University Department of Isme Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

In the context of Web3.0, the rapid pace of technological innovation has resulted in the emergence of novel computing paradigms. This is a natural result of the exponential growth of technology. Within the ecosystem of Web 3.0, the major emphasis of this research is on rethinking architecture and protocols in order to be ready for the revolution in edge computing. To improve the efficacy, security, and scalability of data processing and transmission, DECABI provides a dynamic edge computing architecture with integrated blockchain characteristics. The study accelerates data transfers without compromising their integrity by using dynamic resource allocation, encrypted data transmission, and a consensus protocol for edge computing (CPEC). The suggested method has much better performance than the alternatives. This exemplifies how the method has the ability to completely alter the way edge computing is carried out in the Web3.0 era. Countless simulations and exhaustive studies confirmed the existence of this option. This study lays the groundwork for a more stable and secure Web3.0 ecosystem, and it also makes a substantial contribution to the current conversation about the future of computer architectures and protocols. © 2024 IEEE.

关键词： Data integrity

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Design and Implementation of Embedded Qinghai Tourism Customer Service Question-and-Answer Robot Based on ChatGPT 24

Design and Implementation of Embedded Qinghai Tourism Custom...

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9th International Conference on Cyber Security and information Engineering, ICCSIE 2024

作者： Wu, Jing Guo, Zhenxin Wang, Zhicheng Zhang, Haotian Kang, Xaolin Ma, Xiaoguang Department of Computer and Information Science Qinghai Institute of Technology China Foshan Graduate School Northeastern University China School of Computer Science and Application Qinghai University China

ISBN: (纸本)9798400718137

The application of big data technology has brought new impetus and possibilities to the development of tourism. The design of an embedded Q&A robot system for Qinghai tourism customer service based on ChatGPT system aims to provide an intelligent customer service solution to help users obtain information about Qinghai tourism and answer questions. The system uses ChatGPT as the core dialogue model, combines the knowledge and data in the field of Qinghai tourism, and realizes the intelligent answer to user questions. This study application in the field of natural language processing, and analyzes the design requirements and implementation scheme of embedded travel question answering system in detail. The function module includes user login and registration, travel tickets, hotel information query, scenic spot ticket price query and other essential basic function modules of the tourism website. Whether the design is reasonable or not will also directly affect the user experience. The results show that ChatGPT embedded in Qinghai tourism Q&A system can not only provide more intelligent and accurate answers, but also enhance the interactivity and practicability of the system, and provide users with more convenient and personalized tourism information services. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Machine Learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management 3

Machine Learning and Data Mining for Predictive Modeling of ...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Gobi, N. Singh, B.P. Yadav, Surendra Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery. © 2023 IEEE.

关键词： Data mining

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing 3

Real-Time Data Streams: The Future of Predictive Maintenance...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Goyal, Manish Kumar Haripriya Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling. © 2023 IEEE.

关键词： Cost effectiveness

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An Improved Quantum Inspired Evolutionary Algorithm for the Solution of Minimum Spanning Tree 10

An Improved Quantum Inspired Evolutionary Algorithm for the ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Amrit Bansal, Sulabh Manipal University Jaipur Department of Computer Application Jaipur India Manipal University Jaipur Department of Information Technology Jaipur India

ISBN: (纸本)9789380544472

The Minimal Spanning Tree (MST) is a well-known combinatorial optimization problem that exists in various real-world applications, such as scheduling and pricing of flights, planning truck routes, recommending movies, and providing driving directions. The MST can be solved by various methods, including Prim's and Kruskal's algorithms. In this work, a Quantum Inspired Evolutionary Algorithm (QIEA-MST) is presented as a probabilistic algorithm for solving the problem of finding the minimum cost-spanning tree of a connected weight undirected graph. QIEAs are evolutionary algorithms that use some operators based on the theory of Quantum Computing. These algorithms are population-based stochastic search approaches, where a population of solutions is improved iteratively in order to generate new and better solutions gradually through a process of evolution. The proposed QIEA has been tailored to the MST problem, enabling to solve in reduced iterations. The results of the proposed approach are the same as the well-known optimal approaches of Prim's and Kruskal's. This work represents an intermediate outcome of ongoing research, and future publications will present the computational complexity of the methods and their implementation on various versions of the problem. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Evolutionary algorithms

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Designing Architecture for Container-As-A-Service (CaaS) in Cloud Computing Environment: A Review 3rd

Designing Architecture for Container-As-A-Service (CaaS) in ...

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3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021

作者： Malviya, Anshita Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application MMMUT Gorakhpur Gorakhpur India

ISBN: (纸本)9789811928277

Nowadays, every company is trying to become a tech company as we have seen that software is consuming the entire world. So, how the company approaches software development and serves their customers show how efficient their employees are or how agile their organization is. Therefore, it is of utmost importance for the companies to make correct decision about the architectural choices early in the software development life cycle. It has been observed that containers are taking place of virtual machines in the IT industry because they do not require to incorporate guest operating system in each instance like virtual machines, and they simply support the property and resources of host OS. This paper presents a survey on containers, and its architecture and how containers are better than virtual machines for software development. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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