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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Vrbaski, Mira Bolic, Miodrag Majumdar, Shikharesh University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

An approach to optimizing a large-scale microservice deployment for a critical notification system is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization of CPU, RAM and storage, and meeting the system notification deadlines. To the best of our knowledge, this is the first work dealing with multi-objective optimization for microservice notification applications where the notification load is variable and depends on other priorly executed microservice in request processes. Furthermore, best of our knowledge, this is the first work that is based on multi-objective optimization, analyzing and proposing a Pareto front and optimal list from preselected VM candidates. First, the paper formulates multi-objective optimization problems for microservice-based large-scale notification system applications and provides the mathematical deployment model. Then, based on the expected workload and required multi-objective criteria, the paper proposes a set of algorithms that provide the Pareto front optimal solution for preselecting a Cloud Service provider's VM type. In the end, the paper presents a case study that demonstrates the use of the proposed technique. © 2022 IEEE.

关键词： Multiobjective optimization

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Implementation and Analysis of Image Compression Using Ramanujan's Sum 13

Implementation and Analysis of Image Compression Using Raman...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Das, Kunal Radha, D. Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

In today's world, there are many types of data. The text files, the images, the audio, and the videos are all data and have been integrated into everyone's life. But the storage requirement of data is huge as all data is stored whether they are useful data or useless data. data compression is the process of compressing the data such as an image, text, or audio, in such a way that the file is easier to store, takes less space, can be transferred over the network with less bandwidth, etc. Image Compression, especially, has evolved in many aspects. A new mathematical concept has started gaining traction. The paper shares an implementation of Ramanujan's Sum-based transformation on bitmap images and tries to compress it with available algorithms. The image is transformed with Ramanujan's Sum Matrix, then quantized, and then compressed. Then the results are compared with the existing algorithms available, in terms of storage space, compression percentage, and Peak Signal to Noise Ratio PSNR. The findings are pretty much fascinating, that how a simple change in the mathematical equation can make a difference in the results of compression. © 2022 IEEE.

关键词： Image compression

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms

Machine Learning Techniques for Identifying Child Abusive Te...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Mohammad Kamal Uddin Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention.

关键词： Deep learning Support vector machines Radio frequency Measurement Machine learning algorithms Social networking (online) Prevention and mitigation Computational modeling Natural language processing Long short term memory

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Modelling Weave effect in PCBs using 2D cross-sectional analysis

Modelling Weave effect in PCBs using 2D cross-sectional anal...

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Khilkevich, Victor Hinaga, Scott Missouri University of Science and Technology Dept. of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory RollaMO United States Cisco Systems Inc San JoseCA United States

ISBN: (纸本)9798350360394

Printed circuit board dielectric substrates are composite materials produced by embedding fiber glass fabrics into epoxy resin. Because of this the medium in the PCB transmission lines is inhomogeneous which often leads to degradation of the signal integrity performance of the lines, particularly due to the differential skew. The detrimental effect of the fiber weave can be modeled relatively accurately using full-wave analysis, but at a high computational cost. Alternative modelling techniques are less demanding, but often lack accuracy. This article investigates a possibility of using the 2D cross-sectional analysis for the fiber weave effect modeling, which considerably decreases the computational cost of modeling while retaining the accuracy inherent to the field solvers. © 2024 IEEE.

关键词： System-on-chip

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and Grid Computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 IEEE.

关键词： Smart contract

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Implementation of an AI-based Competitive Cheerleading Coaching Assistant System

Implementation of an AI-based Competitive Cheerleading Coach...

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2023 IET International Conference on engineering Technologies and Applications, ICETA 2023

作者： Yang, Chun-Hung Tu, Jiun-Yu Wu, Dong-Shing Zhuang, Ya-Ting Tsai, Pei-Ying Hsu, Chen-Kai Dept. of Electronic Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan Dept. of Computer Science and Information Engineering Southern Taiwan University of Science and Technology No.1 Nantai St. Yungkang Dist. Tainan City710301 Taiwan

ISBN: (纸本)9781839539848

Competitive cheerleading is a highly complex and intense sport characterized by intricate motions. The complexity and high intensity of the sport often lead to various sports injuries. Therefore, the role of coaches is crucial in the training process of competitive cheerleading. However, due to the insufficient number of coaches, there are inevitably challenges in providing individual attention to all team members during training. To alleviate the workload of coaches and enhance the effectiveness of self-directed training for team members, this study has developed a cheerleading coach assistant system with artificial intelligence technology. This system demonstrates high judgment accuracy and significantly improves the standardization of basic motions for competitive cheerleaders, reducing the possibility of sports injuries. © The Institution of engineering & Technology 2023.

关键词： Sports

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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THE IMPACT OF SCANNER DOMAIN SHIFT ON DEEP LEARNING PERFORMANCE IN MEDICAL IMAGING: AN EXPERIMENTAL STUDY

arXiv

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arXiv 2024年

作者： Guo, Brian Lu, Darui Szumel, Gregory Gui, Rongze Wang, Tingyu Konz, Nicholas Mazurowski, Maciej A. Dept. of Computer Science Dept. of Electrical and Computer Engineering Dept. of Radiology Dept. of Biostatistics & Bioinformatics Duke University NC United States

Purpose: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in the performance of deep neural networks which are trained on data acquired by one scanner and tested on another. This significant practical issue is well-acknowledged, however, no systematic study of the issue is available across different modalities and diagnostic tasks. Materials and Methods: In this paper, we present a broad experimental study evaluating the impact of scanner domain shift on convolutional neural network performance for different automated diagnostic tasks. We evaluate this phenomenon in common radiological modalities, including X-ray, CT, and MRI. Results: We find that network performance on data from a different scanner is almost always worse than on same-scanner data, and we quantify the degree of performance drop across different datasets. Notably, we find that this drop is most severe for MRI, moderate for X-ray, and quite small for CT, on average, which we attribute to the standardized nature of CT acquisition systems which is not present in MRI or X-ray. We also study how injecting varying amounts of target domain data into the training set, as well as adding noise to the training data, helps with generalization. Conclusion: Our results provide extensive experimental evidence and quantification of the extent of performance drop caused by scanner domain shift in deep learning across different modalities, with the goal of guiding the future development of robust deep learning models for medical image analysis. © 2024, CC BY.

关键词： computerized tomography

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12th International Conference on Cloud Computing, data science and engineering, Confluence 2022

作者： Bhatia, Madhulika Dhir, Saru Tanwar, Poonam Khan, Amit Amity University Dept. Of Computer Science And Engineering Noida India AmityMRIIRS Faculty Of Engineering And Technology Faridabad India

ISBN: (纸本)9781665437011

Image recognition is one of the world's fastest growing technologies is Image recognition. Its use in security, monitoring systems has made it the matter of talk in the recent times. There are plentiful of imaging applications which require image similarity as a part of their procedure. Though the applications are quite different, but the process is same. To discover the steps which are crucial in recognizing similar images is one main challenge in image recognition. Preprocessing and feature extraction have been given the importance by past studies. However, the role of similarity measure is still not clear. This paper provides the results which indicates that for recognizing similar images the similarity measures are important. This paper proposes one of the popular similarity metrics called cosine similarity. © 2022 IEEE.

关键词： Image recognition

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High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle Communication

High Isolation Dual Port MIMO Antenna for Vehicle-to-Vehicle...

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2023 IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Sharma, Preeti Singh, Prabhakar Tiwari, Rakesh N. Kumar, Pradeep Galgotias University Physics Division School of Basic Sciences Greater Noida India Greater Noida Institute of Technology Dept. of Applied Science Greater Noida India Madanapalle Institute of Technology & Science Dept. of Electronics and Communication Engineering Madanapalle India University of KwaZulu-Natal Dept. of Electrical Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350336917

This research paper reports a very thin 2-port multiple-input multiple-output (MIMO) antenna with a small footprint of 14× 10× 0.58mm3. The proposed design works between the frequencies of 8.73 to 15.86 GHz. A modified hexagonal patch is used along with the defected ground plane to obtain wide bandwidth. A conventional decoupling strip technique is utilized to improve the isolation between the ports and it is found >19dB across the whole operating band. In addition to that, 'H'-shaped slot has been incorporated into the grounding plane to enhance impedance matching. The realized gain and radiation patterns of the designed antenna are calculated and found quite acceptable. Furthermore, the diversity parameters of the antenna are computed with envelope correlation coefficient (ECC) 9.97dB across the entire operating frequency range. Finally, the results of MIMO antenna are compared with the calculated data for the purpose of practical validation. © 2023 IEEE.

关键词： Defects

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