Toward Rapid Development and Deployment of Machine Learning Pipelines across Cloud-Edge

2021 ◽  
pp. 99-128
Author(s):  
Anirban Bhattacharjee ◽  
Yogesh Barve ◽  
Shweta Khare ◽  
Shunxing Bao ◽  
Zhuangwei Kang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rapid Development

Machine Learning Arrives in Archaeology

2021 ◽  
Vol 9 (2) ◽  
pp. 186-191
Author(s):  
Simon H. Bickler
Keyword(s):  
Machine Learning ◽  
Rapid Development ◽  
Rapid Identification ◽  
First Century ◽  
Traditional Methods ◽  
Archaeological Practice ◽  
The World ◽  
Archaeological Work ◽  
Archaeological Analysis

OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.

scholarly journals Use of Augmented Reality to Create an iOS App with Watson Studio

2021 ◽  
Vol 4 ◽  
pp. 98-100
Author(s):  
Semen Gorokhovskyi ◽  
Yelyzaveta Pyrohova
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Augmented Reality ◽  
New Technologies ◽  
Higher Education Institutions ◽  
Rapid Development ◽  
Easy Access ◽  
Smart Devices ◽  
The Real ◽  
The World

With the rapid development of applications for mobile platforms, developers from around the world already understand the need to impress with new technologies and the creation of such applications, with which the consumer will plunge into the world of virtual or augmented reality. Some of the world’s most popular mobile operating systems, Android and iOS, already have some well-known tools to make it easier to work with the machine learning industry and augmented reality technology. However, it cannot be said that their use has already reached its peak, as these technologies are at the stage of active study and development. Every year the demand for mobile application developers increases, and therefore more questions arise as to how and from which side it is better to approach immersion in augmented reality and machine learning. From a tourist point of view, there are already many applications that, with the help of these technologies, will provide more information simply by pointing the camera at a specific object.Augmented Reality (AR) is a technology that allows you to see the real environment right in front of us with a digital complement superimposed on it. Thanks to Ivan Sutherland’s first display, created in 1968 under the name «Sword of Damocles», paved the way for the development of AR, which is still used today.Augmented reality can be divided into two forms: based on location and based on vision. Location-based reality provides a digital picture to the user when moving through a physical area thanks to a GPS-enabled device. With a story or information, you can learn more details about a particular location. If you use AR based on vision, certain user actions will only be performed when the camera is aimed at the target object.Thanks to advances in technology that are happening every day, easy access to smart devices can be seen as the main engine of AR technology. As the smartphone market continues to grow, consumers have the opportunity to use their devices to interact with all types of digital information. The experience of using a smartphone to combine the real and digital world is becoming more common. The success of AR applications in the last decade has been due to the proliferation and use of smartphones that have the capabilities needed to work with the application itself. If companies want to remain competitive in their field, it is advisable to consider work that will be related to AR.However, analyzing the market, one can see that there are no such applications for future entrants to higher education institutions. This means that anyone can bring a camera to the university building and learn important information. The UniApp application based on the existing Swift and Watson Studio technologies was developed to simplify obtaining information on higher education institutions.

scholarly journals Applications of Machine Learning in Ambulatory ECG

Hearts ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 472-494
Author(s):  
Joel Xue ◽  
Long Yu
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Ambulatory Ecg ◽  
Event Prediction ◽  
The People ◽  
Important Diagnostic Tool ◽  
Beat Detection ◽  
Applications Of Machine Learning

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.

scholarly journals Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyang Bai ◽  
Xuesheng Zhang
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Technological Development ◽  
Rapid Development ◽  
Evidence Theory ◽  
Predictive Analysis ◽  
Learning Technology ◽  
World Cup ◽  
Python Programming

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.

Research on enterprise financial economics early warning based on machine learning method

2021 ◽  
pp. 1-11
Author(s):  
Jian Yi
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Economics ◽  
Rapid Development ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Combined Model ◽  
Svm Algorithm ◽  
The Stability

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

scholarly journals Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Detection System ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

scholarly journals Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1771
Author(s):  
Muhammad Ashfaq Khan ◽  
Juntae Kim
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Rapid Development ◽  
Research Problem ◽  
Vital Role ◽  
Attack Detection ◽  
Detection Methods ◽  
Malicious Attacks

Recently, due to the rapid development and remarkable result of deep learning (DL) and machine learning (ML) approaches in various domains for several long-standing artificial intelligence (AI) tasks, there has an extreme interest in applying toward network security too. Nowadays, in the information communication technology (ICT) era, the intrusion detection (ID) system has the great potential to be the frontier of security against cyberattacks and plays a vital role in achieving network infrastructure and resources. Conventional ID systems are not strong enough to detect advanced malicious threats. Heterogeneity is one of the important features of big data. Thus, designing an efficient ID system using a heterogeneous dataset is a massive research problem. There are several ID datasets openly existing for more research by the cybersecurity researcher community. However, no existing research has shown a detailed performance evaluation of several ML methods on various publicly available ID datasets. Due to the dynamic nature of malicious attacks with continuously changing attack detection methods, ID datasets are available publicly and are updated systematically. In this research, spark MLlib (machine learning library)-based robust classical ML classifiers for anomaly detection and state of the art DL, such as the convolutional-auto encoder (Conv-AE) for misuse attack, is used to develop an efficient and intelligent ID system to detect and classify unpredictable malicious attacks. To measure the effectiveness of our proposed ID system, we have used several important performance metrics, such as FAR, DR, and accuracy, while experiments are conducted on the publicly existing dataset, specifically the contemporary heterogeneous CSE-CIC-IDS2018 dataset.

User Model of Personalized Search Engine for Product Design Based on Machine Learning

2011 ◽  
Vol 460-461 ◽  
pp. 747-753
Author(s):  
Ying Shi Kang ◽  
Hai Ning Wang
Keyword(s):  
Machine Learning ◽  
Product Design ◽  
Search Engine ◽  
Interaction Design ◽  
Hot Spot ◽  
Rapid Development ◽  
Web Design ◽  
Internet Technology ◽  
Web Pages ◽  
Personalized Search

With the rapid development of internet technology, focusing on the product design of individual users, emphasizing the interaction design for Web and improving the user experience have become an inevitable trend of Web design, and also the hot spot of the design of personalized search engine. This paper proposed an optimized algorithm for building user models for product design websites. In order to show the design dimensions of Web pages presented by a browser, a concept of freshness is presented in this algorithm. By analyzing the user behavior of browsing Web pages, the model was updated using methods of machine learning. At last, the performance and effectiveness of this algorithm was analyzed and estimated through the simulation experiment.

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