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

作者： Ganguly, Swastick Jathin, S. Mohak, M. Sanjay, M. Somashekhar, B.M. Shobharani, N. Amrita School Of Computing Department Of Computer Science Mysuru Campus India Gitam School Of Technology Gitam University Dept Of Computer Science And Engineering Bengaluru Campus India

ISBN: (纸本)9798350371567

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability. © 2024 IEEE.

关键词： Cotton

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MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocation using Grey Wolf Optimization 5

MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocati...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Gandhimathi, P. Kiruthiga, S. BIndu, K.V. Shanthi, H.J. Velmurugan, V. Sangeethaa, S.N. Srm Madurai College for Engineering and Technology Pottapalayam Sivagangai District630612 India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science &engineering Chennai India Puduvoyal India Department: Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India Vel Tech Rangarajan Dr Sagunlatha R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Bannari Amman Institute of Technology India

ISBN: (纸本)9798350389609

Cloud computing is a cutting-edge technology that has gained immense popularity in the present era. The burgeoning demand for services has necessitated the development of a framework for efficiently allocating resources to incoming requests. This approach not only enhances the network's efficiency but also reduces costs significantly. The convergence of cloud and edge computing has emerged as a burgeoning field of study in the computing industry over the past few years. The exponential rise in the number of customers and requests for cloud data centers (CDCs) has highlighted the critical need for robust servers and energy-efficient mechanisms. As CDCs expand to meet growing demands, they face significant challenges related to energy consumption, environmental impact, and operational costs. Employing suitable algorithms for resource allocation in CDCs is crucial in order to minimize energy consumption. The objective of this research endeavor was to devise an ingenious approach for dynamically allocating resources in cloud networks by leveraging the power of the Gray Wolf Optimization algorithm. The proposed work performs Multi-Cloud Heuristic Resource Allocation (MCHeRA) using Grey Wolf Optimization (GWO) algorithm and produces 61% of computational resource utilization, 79% of CPU utilization, 64% memory utilization, 74% bandwidth utilization. The algorithm that has been put forth is meticulously assessed and contrasted with its counterparts. The outcome of the simulation manifests the remarkable efficacy of the novel MCHeRA in comparison to alternative algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Retinal Fundus Image Synthesis using Image Segmentation and DCGAN 10

Retinal Fundus Image Synthesis using Image Segmentation and ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumar, Arun Juliet, Sujitha Anishin Raj, M.M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Muthoot Institute of Technology and Science Department of Cse Kerala India

ISBN: (纸本)9798350353068

Training high-quality deep learning models in the ophthalmic domain requires many good-quality labeled training images. Unlike natural images, which are readily available, there is a scarcity of good quality labeled medical images. This work, address the problem for the ophthalmic domain by proposing a methodology to generate good-quality synthetic fundus images using available real images. This work utilizes image segmentation to separate important components of a fundus image, such as the vessel network and optic disc, then combine them with the extracted background to form low-quality fundus images. These images, along with the real image set, are used to train a Deep Convolutional Generative Adversarial Network (DCGAN). The resulting GAN model will be capable of generating high-quality fundus images. The synthetic images generated are of good quality and, at the same time, different from the training set used initially. These images could be employed in tandem with both the preliminary synthetic image and the authentic image to train a classification model. Moreover, they can provide a foundation for transfer learning in the classification of eye diseases. © 2024 IEEE.

关键词： Generative adversarial networks

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Triboelectric-electromagnetic hybrid generator with swing-blade structures for effectively harvesting distributed wind energy in urban environments

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Nano Research 2023年第9期16卷 11621-11629页

作者： Yangyang Yan Jinzhi Zhu Yuejun Zhang Zhaohui Wang Lin Jiang Zhong Lin Wang Jianyang Zhu Tinghai Cheng Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China Georgia Institute of Technology AtlantaGA 30332-0245USA Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

The wind energy in cities cannot be exploited effectively because natural wind is unstable and ***,a triboelectricelectromagnetic hybrid generator with swing-blade structures(SBS-TEHG)was designed to effectively harvest intermittent and continuous wind energy in an urban ***,the spring structure and base were considered to realize the maximum output performance of triboelectric ***,the computational fluid dynamics method was applied to optimize the structure of the SBS-TEHG to improve its aerodynamic *** starting wind speed of the SBS-TEHG was 2 m/s,and its energy conversion efficiency was 9.04%,159%higher than that of the SBS-TEHG without guide plates at 4 m/*** results demonstrated that the SBS-TEHG lit 105 light-emitting diodes(LEDs)under the intermittent-wind harvesting mode at a wind frequency of 1 Hz when the single swing blade operated,while a wireless PM_(2.5)&PM_(10)sensor was powered by the SBS-TEHG after a period of operation under the continuous-wind harvesting *** findings of this study provide a novel solution for lowspeed wind energy harvesting in cities and demonstrate the potential of SBS-TEHG as a distributed energy source.

关键词： triboelectric nanogenerator swing-blade structure low-speed wind energy computational fluid dynamics distributed energy source

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An Extraction Aggregation Strategy Using a Hierarchical Representation Model Based on Longformers and Transform

An Extraction Aggregation Strategy Using a Hierarchical Repr...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Hans, Nitish Hirishikesh, P. Victoria, A Helen Srm Institute of Science & Technology Department of Computing Technologies Chennai India Srm Institute of Science & Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350302820

The quantity of written content about medical information is staggering, and it keeps on increasing daily. Think about the internet, which is full of websites, news, constantly updated status, blogs, and a great deal more. Utilizing search to swiftly scroll through key findings is the most effective method for navigating material mainly because the results are not arranged in any specific fashion. A considerable percentage of this stuff needs to be summarized to emphasize the most significant information. Creating a custom summary with our own two hands of everything that can be effectively done by text summarization techniques. There is a critical need for this automated method. This project incorporates technologies that are capable of summarizing the text automatically. It implies paraphrasing phrases into a single thought. We propose a hierarchical document that represents an architecture for extraction aggregation that utilizes 'Longformer' as a set encoder and 'Transformer' as a text encoder to represent text documents properly. This model is referred to as the Long-Trans-Extr model. One of the benefits of using Longformer as a set encoder is that the model can take in lengthy documents with a maximum of 4096 tokens, which only adds a little amount of additional processing. This quick summarization tool can be used by doctors or medical practitioners for quickly analysing emergency or critical patients. © 2023 IEEE.

关键词： Extraction

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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How do Software Engineering Researchers Use GitHub? An Empirical Study of Artifacts & Impact 24

How do Software Engineering Researchers Use GitHub? An Empir...

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24th IEEE International Conference on Source Code Analysis and Manipulation, SCAM 2024

作者： Alrashedy, Kamel Binjahlan, Ahmed Georgia Institute of Technology School of Computer Science AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798331528508

Millions of developers share their code on open-source platforms like GitHub, which offer social coding opportunities such as distributed collaboration and popularity-based ranking. Software engineering researchers have joined in as well, hosting their research artifacts (tools, replication package & datasets) in repositories, an action often marked as part of the publication's contribution. Yet a decade after the first such paper-with-GitHub-link, little is known about the fate of such repositories in practice. Do research repositories ever gain the interest of the developer community, or other researchers? If so, how often and why (not)? Does effort invested on GitHub pay off with research impact? In short: we ask whether and how authors engage in social coding related to their research. We conduct a broad empirical investigation of repositories from published work, starting with ten thousand papers in top SE research venues, hand-Annotating their 3449 GitHub (and Zenodo) links, and studying 309 paper-related repositories in detail. We find a wide distribution in popularity and impact, some strongly correlated with publication venue. These were often heavily informed by the authors' investment in terms of timely responsiveness and upkeep, which was often remarkably subpar by GitHub's standards, if not absent altogether. Yet we also offer hope: popular repositories often go hand-in-hand with well-cited papers and achieve broad impact. Our findings suggest the need to rethink the research incentives and reward structure around research products requiring such sustained contributions. © 2024 IEEE.

关键词： Software engineering

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Advancing Virtual Reality Interaction through Real-Time Hand Gesture Recognition for Immersive Experiences 3

Advancing Virtual Reality Interaction through Real-Time Hand...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Singh, Vinayak Kumar Kalinathan, Lekshmi Marimuthu, Marimuthu Vadivel, Sengazhani Murugesan Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India VIT Business School Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350372120

Virtual Reality technology offers immersive experiences, but reliance on handheld controllers can limit immersion. This study explores real-time hand gesture recognition to enhance natural interaction in VR environments. Motivated by VR's diverse applications in gaming, education, and training, it seeks to improve engagement and realism. The study provides an overview of current hand gesture recognition techniques, including computer vision and deep learning methods. Understanding the state of real-time gesture recognition informs future research and development in VR interaction. It enables researchers, developers, and practitioners to make informed decisions about integrating and enhancing gesture recognition technologies in various VR applications © 2024 IEEE.

关键词： Virtual reality

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Multi-core-Aware VM Placement Algorithms Using Constraint Programming in Cloud Environment

Multi-core-Aware VM Placement Algorithms Using Constraint Pr...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Nagadevi, S. Vidhya, R. Abirami, G. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai603203 India

ISBN: (纸本)9789819746491

One of the key challenges in a cloud data enter is how do we place virtual machines on physical machines. Mapping virtual machines onto physical machines is known as virtual machine placement. The optimal virtual machine placement improves resource utilization and power consumption. The parameters such as resource wastage, overloaded physical machines and number of active physical machines are improved by properly mapping the virtual machines. The improper mapping between virtual machines and physical machines increases the overloaded and under-loaded physical machines. Due to this, the resource utilization is reduced and more number of active physical machines is introduced. Therefore, physical machine consolidation techniques are needed to rearrange the virtual machines from overloaded and under-loaded physical machines. To place a virtual machine on a physical machine, various resource constraints are checked to see whether the physical machine is having the resource capacity demanded by the virtual machine or not. If the physical machine is not having sufficient resource capacity, then the next physical machine is checked. In most of the existing research works on virtual machine placement algorithms, the resource demands of the virtual machines are compared against the total resource capacity of physical machines. It is a simplistic one-dimensional problem leads to suboptimal results like no dedicated cores and overloaded cores. Here, there is a possibility of assigning the virtual machines beyond the CPU capacity of the pCPU. Therefore, a lot of pCPUs will be overloaded, and hence, a lot of physical machines will be overloaded. Because of this, the virtual machines may not get enough resource to run the applications on it. So, the performance of the overall system is degraded. Also, the virtual machines are not assigned to a dedicated pCPU of a physical machine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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