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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

引用

15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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A Custom CNN Model for Tomato Leaf Disease Detection 3

A Custom CNN Model for Tomato Leaf Disease Detection

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Garba, Ahmad Ahmad Jain, Vishal Singla, Kanika Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331518592

Tomato cultivation is playing a vital role both in universal agriculture and economy. However, the presence of various types of diseases poses a significant threat to the tomato plants and result in substantial reduction in crop yield. Therefore, detecting and managing these diseases at an early age is very crucial in order to minimize damages. Due to technological advancements, Convolutional Neural Networks have become known as an effective tool for recognition of plant diseases because of their remarkable capability of recognizing and classifying of images. This research work proposed the use of a Convolutional Neural Network(CNN) model that is built from scratch. The proposed model is made up of 14 layers and showed promising result with high accuracy of 97.91%, a precision of 96.51%, with 97.47% and 96.91% for recall and F1-score respectively. The proposed custom model also outperforms a transfer learning model and other deep learning models which indicates how effective and robust the model is when it comes to tomato disease detection. © 2024 IEEE.

关键词： Fruits

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Revolutionizing Plant Disease Detection: A Comprehensive Review of Machine Learning Techniques and Applications

Revolutionizing Plant Disease Detection: A Comprehensive Rev...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Gupta, Sanket Jain, Suresh Bandhu, Kailash Chandra Medi-Caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Being one of the greatest sectors in the world and a source of food, agricultural products, raw materials, and employment for billions of people, agriculture has a tremendous effect on the Indian economy as well as on the global economy. But crop diseases that can happen at any time at any stage of crop farming have a big influence on agriculture, lowering crop yields and lowering the quality of the goods that are collected. Plant diseases occasionally even result in the extinction of entire plant species. In the field of managing agricultural and plant diseases, artificial intelligence (AI) is a powerful tool that supports farmers and researchers in identifying, diagnosing, preventing, and treating illnesses more successfully. This review will evaluate several previously published studies on the detection of plant diseases using deep learning and other machine learning methods, which will provide an advantage to upcoming researchers. A variety of performance measurements were also reviewed to evaluate different datasets on different plant diseases, architectures, and methodologies. Further, we will review well-known ML and DL approaches, including support vector machines (SVM), convolutional neural networks (CNN), artificial neural networks (ANN), k-mean clustering, Stochastic Gradient Descent (SGD), etc., to see how they perform differently. At last, we will conclude which is the better option for disease detection between DL and ML and which algorithm is more accurate in predicting the disease so that farmers can make appropriate solutions to protect them. We will also discuss the future scope of these modern technologies in the fields of agriculture and detecting disease in its primary stages. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of satellite images with VGG19 and Convolutional Neural Network (CNN) 2

Classification of satellite images with VGG19 and Convolutio...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Chowdhury, Md Tanvir Rahman, Habibur Sumon, Monjurul Islam Talha, Abu East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350371314

This work presents the application procedure VGG19, MobileNetV2, and CNN models for images Classification in remote sensing applications. This strategy employs pre-trained models, using transfer learning Feature extraction. The dataset includes classes like 'Cloudy,' 'desert,' 'green_field' and 'water.' Data augmentation techniques are involved to improve model generalization. The Models pass through training and evaluation with various considerations Setup, and class delivery are illustrated. Confusion matrix and precision-recall curves are analyzed. In addition, the Convolutional Neural Network (CNN) is used, focusing Data preparation, amplification and model design. The study presents the training, evaluation and comparison of two models version, shows effective image classification. The test result show a accuracy of 97.51% with consistently high Precision, recall, and F1-score across classes ('cloudy,' 'desert,' 'green_area,' and 'water'), resulting in aggregates accuracy of 96%. These results underscore its effectiveness Transfer learning and CNN-based techniques in robust images classification is the critical step from data exploration to model deployment considerations are included, providing a comprehensive framework for remote sensing imagery classification. © 2024 IEEE.

关键词： Remote sensing

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NeuroSense: Smartwatch-based Early Detection Framework for Alzheimer’s Disease 22

NeuroSense: Smartwatch-based Early Detection Framework for A...

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Dixit, Aarju Das, Debasis Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331510183

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with an annual global economic impact of approximately $1 trillion. Early diagnosis is crucial to mitigate disease progression, yet current detection methodologies lack consistency and accuracy in delineating disease stages. To address these challenges, we introduce NeuroSense, a novel smartwatch-based early detection framework for AD. NeuroSense employs advanced activity recognition algorithms to passively monitor ambulatory patterns, behaviors, and sleep cycles via Inertial Measurement Unit (IMU) and photoplethysmogram (PPG) sensors from smartwatches (i.e., Apple Watch and Fitbit), including metrics like walking speed, sleep posture, and heart rate variability. NeuroSense leverages enhanced shallow neural networks integrating cross-sectional entropy to dynamically adjust feature weights, improving sensitivity to subtle variations in AD biomarkers. Additionally, the SqueezeNet architecture has been optimized by introducing deeper fire modules and expanding filter sizes, enabling high-resolution feature extraction with reduced computational overhead. These models are further refined with a cloud computing infrastructure, allowing model updation, ensuring adaptive learning based on continuous data influx. To validate its efficacy, NeuroSense was tested on AI-generated datasets, achieving a detection accuracy of 94.4%, substantially higher than existing approaches. This novel framework provides a robust, scalable, and non-invasive solution for continuous AD monitoring, representing a significant advancement in the early detection and management of neurodegenerative disorders. ©2024 IEEE.

关键词： Sleep research

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IoT-Enhanced Healthcare Infrastructure: Towards Efficient Resource Management 2

IoT-Enhanced Healthcare Infrastructure: Towards Efficient Re...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Mishra, Tilak Kumar Chinmay, Ayes Pattanaik, Anmol Department of Computer Science and Engineering Odisha India

ISBN: (纸本)9798350344882

The Internet of Things (IoT) has brought about significant transformations in several sectors, including healthcare. The use of IoT technology inside healthcare infrastructure holds promise for optimising resource allocation and enhancing the overall quality of healthcare provision. The objective of this study is to examine the function of the IoT in hospital infrastructure and assess its influence on the optimisation of resource allocation. In this paper, we present a comprehensive analysis of healthcare systems that use IoT technology. We examine the possible advantages and obstacles associated with these IoT-enabled healthcare systems. In addition, we provide case studies that exemplify IoT applications in the context of hospital resource management. In conclusion, the next sections will delve into prospective avenues and probable research prospects within this field. © 2023 IEEE.

关键词： Internet of things

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An Analysis of Image Captioning Models using Deep Learning

An Analysis of Image Captioning Models using Deep Learning

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Goel, Navya Arora, Aditi Kashyap, Priyanshu Varshney, Sagar Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350323887

Developing a self-descriptive software for describing and explaining the activities seen in an image has become a necessary and interesting field of research in Deep Learning and Artificial Intelligence. The process of formulating precise description for an image that is syntactically as well as semantically correct is known as Captioning. The task commences with classifying the attributes of an image and extracting feature vectors using various CNN models. In the proposed work, we have demonstrated the working of three different CNN models i.e. Xception, VGG-16 and ResNet50 along with observing the impressive accuracy achieved by each model respectively. The dataset used in our proposed work is Flickr_8k dataset having 8091 images. The features extracted from these three models individually are fed to LSTM (Long Short Term Memory) model, an example of RNN model and further using this for sentence formation. We have generated captions using each model and verified the accuracy achieved using visual metric and BLEU (BiLingual Evaluation Understanding) metric. The three systems grant the good quality result and captions, on comparing the BLEU scores which are, 0.79 for Xception Model, 0.75 for VGG-16 and, 0.84 for ResNet50. ResNet50 is proven to be best for classification and feature extraction with 84% accurate captions on 50 epochs and also facilitates the benefit of overcoming the vanishing gradient problem. We believe this technology can help visually impaired people to recognize and interpret the correlation of activities happening in the image. © 2023 IEEE.

关键词： Long short-term memory

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Recognition of Facial Emotions Using CNN Architecture and FER2013

Recognition of Facial Emotions Using CNN Architecture and FE...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Thomas, Bernard Bhatt, Adityavikram Singh, Shailendra Narayan Amity University ASET Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350378092

Humans are social and emotional beings. With the rapid normalization of work-from-home and hybrid work environments, most people are having a difficult time truly expressing their emotions, which when not addressed inevitably leads to drastic mental problems. In order to address or prevent these mental problems, detection and recognition of facial emotions serve as an initial step. This paper presents a facial emotion recognition model working on the CNN (convolutional neural network) architecture, trained and tested using the FER2013 dataset, achieving an accuracy of 74% of detecting seven emotions - Happy, Neutral, Fear, Sadness, Anger, Disgust, and Surprise. © 2024 IEEE.

关键词： Convolutional neural networks

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Addressing Bias in Text-to-Image Generation: A Review of Mitigation Methods 3

Addressing Bias in Text-to-Image Generation: A Review of Mit...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Prerak, Shah Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350391565

Although text-to-image algorithms are quite good at converting text to images, they may use biased training data. This study looks into several definitions of bias and methods for calculating and mitigating it in models that convert text to images. We evaluate previous research on gender, skin tone, and geo-cultural bias in this survey. It shows the positive and negative aspects of different methods for evaluation and mitigation of bias. We discovered that: (1) To prevent misunderstandings when talking about "bias,"it is crucial to make sure that definitions of bias are based on societal harms and to recognize the similarities and differences across suggested definitions;(2) Different sets of handmade prompts are commonly used, but there are no common assessment frameworks or metrics, and there are no unifying criteria for evaluation;and (3) It is necessary to conduct an extensive study to find bias mitigation methods to provide fair images because current mitigation techniques are unable to completely address biases. We identify future research directions that support precise definitions, evaluations, and mitigation of bias based on the present constraints. By highlighting the significance of researching biases in T2I systems, we want to inspire more research into the comprehensive understanding and management of biases, with the ultimate goal of creating equitable and reliable T2I technologies for all. © 2024 IEEE.

关键词： Adversarial machine learning

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Relation Between Electrical Grid Congestion and Bus Characteristics

Relation Between Electrical Grid Congestion and Bus Characte...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Verdaasdonk, Flin Vlasiou, Maria Hoogsteen, Gerwin Hurink, Johann University of Twente Department of Electrical Engineering Mathematics Computer Science Netherlands

ISBN: (纸本)9789531842976

Electrical grids are increasingly congested, which causes stability and safety risks. To get more insights in how congestion can be managed, this paper analyses whether bus characteristics exist that have a consistent influence on grid loading. To study this, a metric for quantifying bus influence on grid loading is introduced and applied in two case studies. The first case study investigates if there are characteristics that correlate with congestion consistently, regardless of grid topology. The results from this case study suggest that none of the evaluated characteristics consistently correlate with grid loading. These results imply that topology should be explicitly considered in congestion management. The second case study investigates the variance in the correlation between bus characteristics and influence, within a single topology, over a variety of conditions. The results of the second case study suggest that this correlation is robust to reasonable changes in bus loads. © 2024 IEEE.

关键词： Sensitivity analysis

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