scholarly journals Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 196
Author(s):  
Nancy A Angel ◽  
Dakshanamoorthy Ravindran ◽  
P M Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Yuh-Chung Hu
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Best Practice ◽  
Machine Learning Algorithms ◽  
Open Research ◽  
Rigorous Research ◽  
Management Of Resources ◽  
Speed Accuracy ◽  
And Storage ◽  
Critical Constraints

Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

scholarly journals A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1274
Author(s):  
Daniel Bonet-Solà ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Multimedia Retrieval ◽  
Natural Environments ◽  
K Nearest Neighbor ◽  
Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing

2020 ◽  
Vol 17 (8) ◽  
pp. 3765-3769
Author(s):  
N. P. Ponnuviji ◽  
M. Vigilson Prem
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Distributed Denial Of Service ◽  
Detection Techniques ◽  
Network Bandwidth ◽  
Ddos Attack

Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threats and vulnerabilities in the protocols legacy in the form of bugs that pave way for intrusion in different ways by the attackers. The most common among attacks is the Distributed Denial of Service (DDoS) attack. This attack targets the cloud’s performance and cause serious damage to the entire cloud computing environment. In the DDoS attack scenario, the compromised computers are targeted. The attacks are done by transmitting a large number of packets injected with known and unknown bugs to a server. A huge portion of the network bandwidth of the users’ cloud infrastructure is affected by consuming enormous time of their servers. In this paper, we have proposed a DDoS Attack detection scheme based on Random Forest algorithm to mitigate the DDoS threat. This algorithm is used along with the signature detection techniques and generates a decision tree. This helps in the detection of signature attacks for the DDoS flooding attacks. We have also used other machine learning algorithms and analyzed based on the yielded results.

scholarly journals Stock Market Prediction using Machine Learning and Cloud Computing

2019 ◽  
Vol 8 (09) ◽  
pp. 24847-24850
Author(s):  
Nirbhay Narkhede
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cloud Computing ◽  
Crucial Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computational Power ◽  
The World ◽  
A Company ◽  
Risky Business

In the world with increasing globalization , where money places a crucial role in determining the expansion and earnings of a company trading places a very crucial role. Multiple companies invest millions and billions of dollars in other countries with an expectation to make profits. In such a risky business Predicting the movement of the market can help companies or individual in making good decisions and can prevent severe loses. In this research paper we will discuss how we can use the computational power of the computer on cloud along with the machine learning algorithms to predict the closing values of the stocks which is a big challenge otherwise. For this purpose we will use Python as our programming language which supports a lot of ML based Libraries. The models we will be using are SVM(Support Vector Machine) , Linear Regression , Random Forest, XGBoost ,LSTM for deep learning

scholarly journals Heterogeneous Network Representation Learning

2020 ◽  
Author(s):  
Yuxiao Dong ◽  
Ziniu Hu ◽  
Kuansan Wang ◽  
Yizhou Sun ◽  
Jie Tang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Heterogeneous Network ◽  
Representation Learning ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Relational Properties ◽  
Open Research ◽  
Different Types ◽  
Graph Neural Networks

Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machine learning algorithms for addressing corresponding network tasks. To learn expressive embeddings, current research developments can fall into two major categories: shallow embedding learning and graph neural networks. After a thorough review of the existing literature, we identify several critical challenges that remain unaddressed and discuss future directions. Finally, we build the Heterogeneous Graph Benchmark to facilitate open research for this rapidly-developing topic.

scholarly journals A Review of Machine Learning Algorithms for Cloud Computing Security

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1379
Author(s):  
Umer Ahmed Butt ◽  
Muhammad Mehmood ◽  
Syed Bilal Hussain Shah ◽  
Rashid Amin ◽  
M. Waqas Shaukat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Storage ◽  
Response Times ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Information Storage ◽  
Future Research ◽  
Computer Algorithms ◽  
Advantages And Disadvantages

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

scholarly journals Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 180
Author(s):  
Haiyang Jiang ◽  
Mingshu He ◽  
Yuanyuan Xi ◽  
Jianqiu Zeng
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Shopping Mall ◽  
Training Dataset ◽  
Shopping Malls ◽  
Location Services ◽  
Proposed Model ◽  
And Behavior ◽  
Speed Accuracy

Machine learning (ML)-based methods are increasingly used in different fields of business to improve the quality and efficiency of services. The increasing amount of data and the development of artificial intelligence algorithms have improved the services provided to customers in shopping malls. Most new services are based on customers’ precise positioning in shopping malls, especially customer positioning within shops. We propose a novel method to accurately predict the specific shops in which customers are located in shopping malls. We use global positioning system (GPS) information provided by customers’ mobile terminals and WiFi information that completely covers the shopping mall. According to the prediction results, we learn some of the behavior preferences of users. We use these predicted customer locations to provide customers with more accurate services. Our training dataset is built using feature extraction and screening from some real customers’ transaction records in shopping malls. In order to prove the validity of the model, we also cross-check our algorithm with a variety of machine learning algorithms. Our method achieves the best speed–accuracy trade-off and can accurately locate the shops in which customers are located in shopping malls in real time. Compared to other algorithms, the proposed model is more accurate. User preference behaviors can be used in applications to efficiently provide more tailored services.

scholarly journals Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Piotr Nawrocki ◽  
Patryk Osypanka
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Resource Configuration ◽  
Demand Prediction ◽  
Novel Approach ◽  
Cloud Resource ◽  
Management Of Resources ◽  
System Responsiveness ◽  
System Characteristics

AbstractPredicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.

scholarly journals Aflatoxin detection on direction of the 4.0 age at 3.0 costs

2019 ◽  
Vol 7 (7) ◽  
pp. 338-346 ◽  
Author(s):  
Mariana Matulovic ◽  
Flávio José de Oliveira Morais ◽  
Angela Vacaro de Souza ◽  
Cleber Aalexandre de Amorim ◽  
Luiz Fernando Sommaggio Coletta
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Environmental Variables ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Toxic Metabolite ◽  
Monitoring And Control ◽  
Carcinogenic Effects ◽  
And Control

Articulate the most diverse and sophisticated technologies, such as Remote Sensing, Big Data, Cloud Computing, Internet of Things, 3D Printing, among others, is part of universe 4.0, whether industrial or agricultural. Focusing on agricultural context, this paper proposes a low-cost 4.0 device to perform the monitoring and control of certain environmental variables for the detection of aflatoxins in peanut crops. Aflatoxins are toxic metabolite of fungi genus Aspergillus that can cause toxic and carcinogenic effects in humans and animals. The device developed was able to monitor temperature and humidity variations helping the aflatoxins identification. The equipment portability allows its use in silos with encapsulation via Additive Manufacturing, besides the aflatoxin prediction from Machine Learning algorithms.

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