scholarly journals Development of Kubeflow Components in DevOps Framework for Scalable Machine Learning Systems

2021 ◽  
Vol 9 (VI) ◽  
pp. 640-645
Author(s):  
Chandana EP
Keyword(s):  
Machine Learning ◽  
Cloud Infrastructure ◽  
Management Platform ◽  
Distributed Training ◽  
Container Management ◽  
The World ◽  
Architecture Style ◽  
And Performance ◽  
Development And Validation ◽  
Multi Cloud

Now-a-days, in the world of enterprise, machine learning workloads have become mainstream. However, there is an abundance of choices that can be made around multi-cloud infrastructure and machine learning toolkits, making it complex to balance their costs and performance. Microservices architecture has been the preferred architecture style for a few years now and there’s been rapid growth in its adoption, never failing to provide exceptionally testable & maintainable services. To have a lot more simplified services management, deployment and to orchestrate tools, Kubernetes is recommended. Kubeflow, a known and widely adopted open source container management platform that manages machine learning stack on Kubernetes. This paper discusses the development and validation of Kubeflow components such as PyTorch, TensorFlow, & Notebook Servers. It includes PodDefault functionalities for notebooks and container builder API to build docker images using Kaniko. Using Helm, Kubeflow upgrade operation is performed to enhance the configured resources whenever required for the distributed training jobs & workloads. Hence, providing data scientists a scalable platform to run machine learning workloads without having to worry about resources, costs, time, and portability.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Cyber Threats Detection and Mitigation Using Machine Learning

2020 ◽  
pp. 132-149 ◽  
Author(s):  
Vaishnavi Ambalavanan ◽  
Shanthi Bala P.
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Dominant Role ◽  
Virtual Space ◽  
System Efficiency ◽  
Technology Infrastructure ◽  
Protection Mechanism ◽  
Cyber Threats ◽  
The World ◽  
And Performance

Cyberspace plays a dominant role in the world of electronic communication. It is a virtual space where the interconnecting network has an independent technology infrastructure. The internet is the baseline for the cyberspace which can be openly accessible. Cyber-security is a set of techniques used to protect network integrity and data from vulnerability. The protection mechanism involves the identification of threats and taking precaution by predicting the vulnerabilities in the environment. The main cause of security violation will be threats, that are caused by the intruder who attacks the network or any electronic devices with the intention to cause damage in the communication network. These threats must be taken into consideration for the mitigation process to improve the system efficiency and performance. Machine learning helps to increase the accuracy level in the detection of threats and their mitigation process in an efficient way. This chapter describes the way in which threats can be detected and mitigated in cyberspace with certain strategies using machine learning.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

Development and Validation of a Cognitive Reserve Index in HIV

2021 ◽  
pp. 1-9
Author(s):  
Navaldeep Kaur ◽  
Lesley K. Fellows ◽  
Marie-Josée Brouillette ◽  
Nancy Mayo
Keyword(s):  
Cognitive Reserve ◽  
Study Entry ◽  
Video Gaming ◽  
Everyday Functioning ◽  
Data Set ◽  
Multiple Indicators ◽  
And Performance ◽  
Competitive Games ◽  
Development And Validation

Abstract Objectives: In the neuroHIV literature, cognitive reserve has most often been operationalized using education, occupation, and IQ. The effects of other cognitively stimulating activities that might be more amenable to interventions have been little studied. The purpose of this study was to develop an index of cognitive reserve in people with HIV, combining multiple indicators of cognitively stimulating lifetime experiences into a single value. Methods: The data set was obtained from a Canadian longitudinal study (N = 856). Potential indicators of cognitive reserve captured at the study entry included education, occupation, engagement in six cognitively stimulating activities, number of languages spoken, and social resources. Cognitive performance was measured using a computerized test battery. A cognitive reserve index was formulated using logistic regression weights. For the evidence on concurrent and predictive validity of the index, the measures of cognition and self-reported everyday functioning were each regressed on the index scores at study entry and at the last follow-up [mean duration: 25.9 months (SD 7.2)], respectively. Corresponding regression coefficients and 95% confidence intervals (CIs) were computed. Results: Professional sports [odds ratio (OR): 2.9; 95% CI 0.59–14.7], visual and performance arts (any level of engagement), professional/amateur music, complex video gaming and competitive games, and travel outside North America were associated with higher cognitive functioning. The effects of cognitive reserve on the outcomes at the last follow-up visit were closely similar to those at study entry. Conclusion: This work contributes evidence toward the relative benefit of engaging in specific cognitively stimulating life experiences in HIV.

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