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Unmasking Pneumonia with Deep Learning: Enhancing Medical Diagnosis through Advanced AI Models 14

Unmasking Pneumonia with Deep Learning: Enhancing Medical Di...

引用

14th international conference on cloud computing, Data Science and engineering, Confluence 2024

作者： Puri, Naman Ashhad, Abdul Kumar, Nitish Amity University Department of Computer Science Engineering Noida India

ISBN: (纸本)9798350344837

Pneumonia has emerged as a global health concern, with a higher mortality rate compared to Covid-19. In 2019, over two million people succumbed to this infectious disease, including nearly 600,000 infants under the age of 5. The traditional method of disease identification, reliant on manual examination by radiologists, is plagued by its unreliability due to suboptimal accuracy. Advancements in computational resources, particularly the enhanced computing capabilities of GPUs, have opened opportunities to train highly complex models. This research undertakes a comparative study of various deep learning models for the purpose of pneumonia detection. The primary objective is to perform binary image classification for pneumonia recognition using three distinct models: a baseline Sequential model built from scratch using TensorFlow, VGG16 Autoencoder, and Keras ViT. The aim is to assess their efficacy on the test set. The dataset comprises 5856 Chest X-Ray images, both anterior and posterior views, categorized as Normal or Pneumonic. © 2024 IEEE.

关键词： Diagnosis

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A Survey on Spectrum Handoff Techniques with Research Challenges and Issues 13

A Survey on Spectrum Handoff Techniques with Research Challe...

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13th international conference on cloud computing, Data Science and engineering, Confluence 2023

作者： Babjan, Patan Rajendran, V. Vels Institute of Science Technology and Advanced Studies Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665462631

The exceptionally quick development in worldwide mobile data traffic there is a prompt need to have components to use the accessible spectrum to the possible extent. Cognitive radio (CR) is a likely innovation to tackle the spectrum shortage issue for future wireless frameworks. Spectrum sensing, spectrum mobility (handoff), spectrum decision, and spectrum sharing are utmost parts in the CR. Spectrum handoff is a basic part of cognitive radio systems to offer strong assistance for secondary users. There is a lot of literature available about spectrum sensing, however, there is limited research available about spectrum handoff technique (to the best of authors' knowledge). As a result, the majority of this study focuses on the spectrum handoff approach, including its forms, literature, system model research difficulties, and obstacles. This study may be very valuable for scholars interested in cognitive radio, especially spectrum handoff technology. © 2023 IEEE.

关键词： Cognitive radio

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Inverse multiserver parallel outsourcing computing scheme based on three-step decomposition matrix 5

Inverse multiserver parallel outsourcing computing scheme ba...

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5th international conference on Computer communication and Network Security, CCNS 2024

作者： Ding, Yan Wang, Na Hu, Mengdie Zhang, Tianpeng Information Engineering University China

ISBN: (纸本)9781510681842

With the rapid development of information technology such as cloud computing and Internet of Things, outsourcing computing has received more and more attention. Outsourcing computing provides a way for resource-constrained clients to solve complex computing problems. Matrix inversion is a basic mathematical operation widely used in scientific and engineering fields. However, there are some problems in the existing matrix inversion outsourcing computing, such as privacy security challenges, the limitation of parallel computing in multi-server mode and poor verification efficiency. In order to solve these problems, we propose a three-step decomposition based matrix inversion multi-server parallel computing algorithm, and designs a corresponding matrix inversion multi-server security verifiable computing scheme. In this scheme, the matrix partitioning and matrix LU decomposition techniques are used to decompose the matrix inversion process into three steps: matrix LU decomposition, triangular matrix inversion and triangular matrix multiplication. At the same time, the number of servers is extended from two to multiple, which improves the efficiency of server parallel computation. Security analysis and simulation experiments show that the proposed scheme can improve the efficiency of outsourcing computation while meeting the security goals. © 2024 SPIE.

关键词： Inverse problems

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FSD-Inference: Fully Serverless Distributed Inference with Scalable cloud communication 40

FSD-Inference: Fully Serverless Distributed Inference with S...

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40th IEEE international conference on Data engineering, ICDE 2024

作者： Oakley, Joe Ferhatosmanoglu, Hakan University of Warwick Department of Computer Science United Kingdom

ISBN: (纸本)9798350317152

Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for dataintensive applications and machine learning (ML) workloads. Traditional 'server-ful' platforms enable distributed computation via fast networks and well-established inter-process communication (IPC) mechanisms such as MPI and shared memory. In the absence of such solutions in the serverless domain, parallel computation with significant IPC requirements is challenging. We present FSD-Inference, the first fully serverless and highly scalable system for distributed ML inference. We explore poten-tial communication channels, in conjunction with Function-as-a-Service (FaaS) compute, to design a state-of-the-art solution for distributed ML within the context of serverless data-intensive computing. We introduce novel fully serverless communication schemes for ML inference workloads, leveraging both cloud-based publish-subscribe/queueing and object storage offerings. We demonstrate how publish-subscribe/queueing services can be adapted for FaaS IPC with comparable performance to object storage, while offering significantly reduced cost at high parallelism levels. We conduct in-depth experiments on benchmark DNNs of various sizes. The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism. © 2024 IEEE.

关键词： Deep neural networks

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A Multi-attention Based CNN-BiLSTM Intrusion Detection Model for In-vehicle Networks 21

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and communications, ISPA/BDcloud/SocialCom/SustainCom 2023

作者： Gao, Kai Huang, Hao Liu, Linhong Du, Ronghua Zhang, Jinlai Changsha University of Science & Technology College of Automotive and Mechanical Engineering Changsha410114 China

ISBN: (纸本)9798350329223

The development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. This paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area Network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. The model achieved a detection accuracy of 99.992%. The reduction of features effectively simplifies the model and improves its computation efficiency. © 2023 IEEE.

关键词： Anomaly detection

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Enhancing cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and Data Protection in cloud Platforms 5

Enhancing Cloud Security with AI: Developing Robust, Scalabl...

引用

5th international conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Abusalih Rahumath Ali, AbuFaizur Rahman Saravanan, K. Abirami, B.Bala Pandi, V.Samuthira Aroulanandam, Vijay Vasanth Parthiban, S. Enterprise Solution Architect Tekskills Inc. PrincetonNJ08540 United States R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India School of Computing SRM Institute of Science & Technology Networking and Communications Department Tamil Nadu Kattankulathur India SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of CSE Chennai Thandalam India

ISBN: (纸本)9798331530013

As cloud computing continues to gain popularity, guaranteeing the security of cloud environments has become an issue of the utmost importance for businesses all over the world. With a particular emphasis on the development of solutions that are both robust and scalable for the purpose of threat mitigation and data protection, this study investigates the application of artificial intelligence (AI) in the process of improving cloud security. Security frameworks that are driven by artificial intelligence are able to proactively discover, evaluate, and respond to new threats in real time. This is accomplished by employing machine learning algorithms and advanced analytics. With the purpose of demonstrating the efficacy of various artificial intelligence approaches in tackling common vulnerabilities such as data breaches, unauthorized access, and distributed denial of service attacks, we investigate a variety of AI methodologies, such as anomaly detection, behavior analysis, and automated incident response. In addition, we analyze the significance of a multilayered security approach, which involves integrating artificial intelligence tools with conventional security measures in order to develop an all-encompassing defense plan. The purpose of this article is to present case studies that illustrate successful implementations of artificial intelligence-enhanced security solutions on cloud platforms. These case studies demonstrate the influence that these solutions have on increasing security posture and operational efficiency. Ultimately, the findings of this study highlight the significant role that artificial intelligence plays in the transformation of cloud security practices. This paves the way for cloud infrastructures that are more robust and secure in the face of changing cyber threats. © 2024 IEEE.

关键词： cloud platforms

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Application of cloud Collaborative computing Model in the Design of Electric Power communication Information Security Management System

Application of Cloud Collaborative Computing Model in the De...

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2023 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2023

作者： Ye, Yuxuan Li, Lei Hu, Huanjun Wang, Shengwei Ning, Hao Information & Communication Branch of State Grid Hubei Electric Power Company Hubei Wuhan430077 China

ISBN: (纸本)9798350329124

As the technical support system of power grid dispatching command, data information transmission and intelligent substation construction, electric power urgently needs to plan and build a perfect, integrated, automated and unified data center basic platform. And promoting the research and innovation of cloud computing can accelerate the process of information construction, and then enhance the level of industrialization. With the goal of minimizing the average delay of the system, by introducing a continuously variable partition coefficient of cloud and edge tasks, the delay optimization problem is transformed into a convex optimization problem, and finally an optimized task partition strategy is obtained. The experimental results show that, compared with the traditional grid data processing method based on cloud center architecture, this method improves the speed of data processing, and at the same time avoids the delay of data uploading and waiting for data return, thus achieving a quick response to data. Properly loading the tasks of edge nodes into the cloud center can effectively reduce the system delay of power business and alleviate the network congestion. © 2023 IEEE.

关键词： Electric power transmission

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A Framework for Real-Time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th international conference on cloud computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Healthcare management in Bigdata with Blockchain and IOT integrated System 1

Healthcare management in Bigdata with Blockchain and IOT int...

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1st IEEE international conference on Advances in computing, communication and Networking, ICAC2N 2024

作者： Kaliyahan, Shalini Bhatnagar, Prachi Gupta, Shilpi Jain, Archana Malik, Suraj Gochhait, Saikat Sharma, Aditi Pandey, Mahima Shankar IIMT University Department of Computer Science and Application Uttar Pradesh Meerut India Meerut Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Meerut India IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

In 21st decade, healthcare is one of the biggest challenges, and the current scenario very flexible to adapt the effective execution technology solutions like internet of things (IoT). Through this a patient may access the treatment remotely and rapidly. Most suitable and effective applications need complex data of personal care, and reliability among the entire participated component or parties. Blockchain technology has been captured the industry domain with its computation and trusted model among participated parties. This paper presents a data storage decision framework for managing blockchain storage using on-chain and off-chain methods. There is a need for making balanced decisions between the very expensive on-chain storage methods and the less secure off-chain storage, towards optimizing blockchain storage costs. © 2024 IEEE.

关键词： cloud computing architecture

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Machine Learning Based Theoretical and Experimental Analysis of DDoS Attacks in cloud computing

Machine Learning Based Theoretical and Experimental Analysis...

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2023 IEEE international conference on Device Intelligence, computing and communication Technologies, DICCT 2023

作者： Suman, Om Prakash Kumar, Mohit Dr B R Ambedkar National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Dr B R Ambedkar National Institute of Technology Department of Information Technology Punjab Jalandhar India

ISBN: (纸本)9781665474924

Researchers frequently turn to cloud computing due to its services, applications, and advantages. It boosts the accessibility of resources over the internet, which leads to risk because data is transmitted over an unsecured network. Various cyber- attacks affect the confidentiality, integrity, and availability (CIA) of systems connected via the internet. The massive threat posed by Distributed Denial of Service (DDOS) keeps rising quickly. Due to their ability to pass as legitimate traffic, most DDoS attacks remain undetectable. Implementing intrusion detection systems (IDS) can help to partially resolve these challenges. Hence, the authors have focused on DDoS attacks in this paper and implemented the state of art Machine learning (ML) approaches to analyze the performance. The article will serve as a stepping stone for new researchers in the area of cloud security. © 2023 IEEE.

关键词： Machine learning

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