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EXTREME LEARNING MACHINE FOR cloud-BASED BREAST CANCER DIAGNOSIS

EXTREME LEARNING MACHINE FOR CLOUD-BASED BREAST CANCER DIAGN...

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2024 international conference on Sustainable computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Malviya, Ashwini Batra, Raman Vyshnavi, A. Department of uGDX ATLAS SkillTech University Maharashtra Mumbai India Department of Mechanical Engineering Noida Institute of Engineering & Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9798350355338

Breast cancer is one of the leading causes of death for women worldwide, which emphasises how important it is to discover the disease early and treat it quickly to reduce the dangers involved. Machine learning combined with cloud computing has become a major player in the field of illness diagnosis, especially in areas where access to healthcare facilities is scarce. This novel method functions as a supplementary reader to improve the precision of illness diagnosis, providing assistance to radiologists. Furthermore, cloud-based solutions ease the difficulties experienced by those living in distant locations by enabling telehealth offerings and remote diagnostics. The potential of neural network training (ANN), a widely used machine learning approach, in the detection of illness has attracted interest. Lastly, a method based on cloud computing is suggested to diagnose breast cancer remotely via ELM. When compared to cutting-edge technology for disease detection, the suggested cloud-based ELM approach performs better;this was especially confirmed using the Wisconsin Diagnosed Breast Cancer Diagnostics (WBCD) dataset. Analytical comparisons show how the cloud-based ELM performs better than other approaches, proving its effectiveness in improving diagnostic precision. This research establishes ELM as a reliable option for breast cancer detection by highlighting its capabilities in standalone and cloud settings. © 2024 IEEE.

关键词： Contrastive Learning

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Towards Hybrid computing with Scalable Mass Parallelism for High-Performance cloud computing of Digital Twins 8

Towards Hybrid Computing with Scalable Mass Parallelism for ...

引用

8th international conference on Information, Control, and communication Technologies, ICCT 2024

作者： Zatuliveter, Yury Fishchenko, Elena V.A. Trapeznikov Institute of Control Sciences of the RAS Moscow Russia

ISBN: (纸本)9798331517564

In order to improve the computing performance of the cloud implementations of digital twin models, the possibilities of hybridization of cloud servers using single-chip accelerating general-purpose computers with mass multi-core parallelism are investigated. Such accelerating computers belong to the class of Multi-Processor Systems-on-a-Chip (MPSoC). The variants for system-engineering integration of mass multi-core parallelism of accelerating computer of class MPSoC into a virtual environment of cloud computing resources are considered. On the example of the requirements for the creation of digital twins of nuclear power plants (NPPs), the possibilities of using hybrid computing systems based on the proposed single-chip accelerator with mass multi-core parallelism of the MPSoC class are considered. The proposed multi-SIMD architecture of such accelerators is based on the scalable multiprocessor architecture of accelerating computers PS-2000 for the eponymous hybrid computing complexes, which were produced in a large industrial series in the absence of world analogues. The high efficiency of architecture PS-2000 had been confirmed by the wide practice of application in various fields. At the conceptual level, it is shown that the use of the proposed single-chip accelerating computers with in-chip integration of mass multicore parallelism into the virtual environment of cloud servers opens up opportunities for cloud systems to expand the scaling ranges of computing performance of cloud servers by orders of magnitude. It is necessary when building digital twins of technogenic systems with increased security and computing performance requirements, for example, for NPP. © 2024 IEEE.

关键词： Nuclear power plants

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Optimizing Resource Costs With Machine Learning Techniques 2

Optimizing Resource Costs With Machine Learning Techniques

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Balamanigandan, R. Mahaveerakannan, R. Saraswathi, S. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

The number of SMEs opting for cloud computing rises, efficient utilisation of the cloud has become a crucial issue because of the rental nature of the resources. Current approaches tend to be more traditional, targeting only individual components such as the amount of computing capacity, which may not adequately capture or mitigate the factors that define most actual cloud workloads and their unpredictable behaviour. In light of this, this article presents a new strategy that applies anomaly detection learning algorithms and particle swarm optimization to achieve the right cloud resource configuration at the least cost possible. The suggested idea works independent of a specific context in a loop fashion and distinguishes exceptional cases through an anomaly detection strategy in the loop process and at the same time learns the usage patterns. More importantly, it adapts to changes in system utilisation, as well as the pricing models offered by various cloud providers. Realistic costs as well as system data using the Microsoft Azure proved significant with highlighting 85% costs shaving within 10 months. This research supports the effectiveness of our integrated solution for solving multifaceted issues related to resource management in cloud environments for SMEs. © 2025 IEEE.

关键词： Resource allocation

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cloud-ENHANCED WIRELESS SENSOR NETWORKS FOR AIR QUALITY MONITORING

CLOUD-ENHANCED WIRELESS SENSOR NETWORKS FOR AIR QUALITY MONI...

引用

2024 international conference on Sustainable computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Shreenidhi, H.S. Agrawal, Trapty Kale, Kiran Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Karnataka Bangalore India

ISBN: (纸本)9798350355338

A growing trend in environmental monitoring involves the use of inexpensive wireless sensors to create densely dispersed networks that provide higher spatial resolution than traditional air quality control systems. In this study, a novel air quality monitoring system-a wireless sensors network, or WSN, made up of a dispersed sensor network connected to a cloud system-is presented. The sensor nodes use low-power ZigBee motes to send field measurements in real time to the cloud via a dedicated gateway. An optimally calibrated cloud computing framework has been instituted to effectively retain, oversee, handle, and present the data originating from the sensor system. Optimised chemical and contaminant identification is made possible by using artificial intelligence tools in the cloud for sophisticated data handling and analysis. This system is unique in that it is small, light, and energy-efficient, allowing for the deployment of several nodes for thorough air quality evaluations in various locations. The effectiveness of the method in identifying common volatile organic chemicals including the compounds benzene, toluene, ethane, and xylene to is validated by a practical example in a laboratory environment. Principal component analysis, perceptron networks with backpropagation learning, and help vector computation methods are all used in the data processing. The results show that the volatile organic compound concentrations may be distinguished and quantified with good accuracy. cloud computing, air quality, chemical sensors, and wireless sensor networks are among the key themes. © 2024 IEEE.

关键词： Zigbee

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Optimal Divisible Load Scheduling for Load Balancing in cloud computing 5

Optimal Divisible Load Scheduling for Load Balancing in Clou...

引用

5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Ramya, R.S. Durai, R.S.Vishnu Vijayakumar, R. Dhivyashree, M. Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350379945

cloud computing is a significant technological advancement that finds application in different research domains, facilitating on-demand access to resources. Throughout this process, the cloud encounters many challenges related to fault tolerance, availability, scalability, and overhead reduction. across these challenges, load balancing across multiple nodes emerges as a significant concern. Ensuring equitable distribution of loads across several nodes is a crucial prerequisite for enhancing resource utilization and optimizing task response time. This research introduces a unique approach to address the aforementioned challenges by proposing a DLS methodology that use a divisible load scheduling algorithm. The algorithm exhibits dynamic behavior as it operates on data stored in two distinct centers and processes multiple requests. The experimental findings demonstrate that the suggested methodology exhibits strong performance, as shown by favourable values of several performance criteria such as throughput, reaction time, migration time, and resource utilization, when compared to conventional techniques. © 2024 IEEE.

关键词： Scheduling algorithms

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Real-time Agrochemical Management Solutions using cloud computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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8th international conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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Implementation and deployment of the integration of IoT devices in the development of GIS in the cloud platform

Implementation and deployment of the integration of IoT devi...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Krishna Kumari, R. Gireesh, N. Bhavani, R. Sathish, K. Senthil, G.A. Praveena, S. Department of Mathematics Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Information Technology Agni College of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9781032669663

The technological infrastructure provided by cloud providers, and used as a deployment platform, allows GIS to take advantage of its high storage and processing capacities;while the use of IoT devices allows the automation of the data collection process. However, the current implementations are carried out in an ad hoc manner, without providing solutions that facilitate their design, implementation and reuse. In this work, we propose a development approach that, based on models that describe at a high level of abstraction, both the system architecture and the interaction between its services and IoT devices, allows guiding the implementation and deployment activities in cloud environments. This has been illustrated with the design and implementation of a GIS application analyse the collected spatial data with air quality sensors and were deployed on the Google cloud platform. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Air quality

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Efficient Handoff Detection for Computation Offloading using Machine Learning and Bio-Inspired Optimization Techniques in Mobile Edge computing 13

Efficient Handoff Detection for Computation Offloading using...

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13th IEEE international conference on communication Systems and Network Technologies, CSNT 2024

作者： Joshi, Vaishali Patil, Kishor Electronics and Telecommunication Sinhgad Academy of Engineering Kondhwa. Pune India

ISBN: (纸本)9798350305463

With the rise of smartphones and the Internet of Things (IoT), more and more calculations are being performed on mobile devices, which rapidly depletes their power reserves. To save the battery of mobile device offloading the tasks to cloud is one of the solutions but, delay-sensitive applications, such as virtual gaming, augmented reality, etc., are negatively affected by the solution of offloading the task to a centralized cloud. For maximum efficiency in both power consumption and latency, it is desirable to locate cloud resources at the Edge of the mobile Network. Quality of Experience (QoE) is affected if mobility of end user is not addressed. Handoff detection is crucial for resolving mobility issues in mobile edge computing. In this work, we offer a machine learning and bio-inspired optimization-based method for detecting handoffs. © 2024 IEEE.

关键词： Computation offloading

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The Computer Network in Construction BIM in the Context of cloud computing 15

The Computer Network in Construction BIM in the Context of C...

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15th IEEE international conference on Computational Intelligence and communication Networks, CICN 2023

作者： Meng, Fei Guo, Shangzhi Liu, Yinying Chongqing Institute of Engineering Chongqing401320 China Chongqing University Chongqing400044 China

ISBN: (纸本)9798350324433

cloud computing is a product of information technology development, which neither belongs to an independent technology nor a specific application field, but integrates several information technologies. Its parallel computing, cloud storage, network communication, virtualization and load balancing characteristics give BIM technology a broader application prospect. The purpose of this paper is to study the application of computer networks in construction BIM based on the background of cloud computing. This paper starts from the study of the problems existing in the current development of BIM technology in the construction field, the connotation, development objectives and technical characteristics of BIM technology, and combines the cloud computing technology, which is gradually becoming popular in the current information field, with the study of BIM based on cloud computing. Through practical application, it is found that the collaborative engineering and construction management platform proposed in this paper is flexible, adaptable and convenient for integration with actual projects. © 2023 IEEE.

关键词： cloud computing

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A Case for Deploying Dynamic Neural Network on Edge-cloud Continuum Environment 8

A Case for Deploying Dynamic Neural Network on Edge-Cloud Co...

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8th IEEE international conference on Edge computing and communications, EDGE 2024

作者： Pozi, Muhammad Syafiq Mohd Sato, Yukinori Toyohashi University of Technology Department of Computer Science and Engineering Japan

ISBN: (纸本)9798350368499

As Deep Neural Network models become more accurate, the associated AI applications experience higher inference runtimes. This results in increased overall communication latency within the edge-cloud continuum, where the computing tasks are distributed. To address this, we propose a Dynamic Neural Network based framework to improve inference accuracy with optimal latency overhead within the edge-cloud continuum environment. The framework consists of 2 main components: Deep Neural Network offloading mechanism to the computing resources on edge-cloud continuum, and parameter selection for inference-exiting mechanism. Based on evaluations using the ImageNet dataset, our Dynamic Neural Network-based inference framework successfully improves inference accuracy while maintaining optimal latency overhead within the edge-cloud continuum. The framework surpasses the inference accuracy of single model-based approaches, where our cascaded model obtained 74.17% accuracy compared to single model accuracy, which is 73.95%. © 2024 IEEE.

关键词： Neural network models

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