scholarly journals Application of machine learning technologies for Drone Network in logistics and port activities of Russia

2021 ◽  
pp. 149-157
Author(s):  
О.М. Михов ◽  
Н.В. Шаталова ◽  
О.В. Бородина ◽  
Ю.И. Васильев
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
Scientific Article ◽  
Learning Technology ◽  
Foreign Companies ◽  
International Trends ◽  
Logistics Enterprises ◽  
The Cost ◽  
Network Technologies ◽  
Transport Logistics

Cтатья посвящена проведению исследовательского анализа особенностей практического использования беспилотных дронов и квадрокоптеров. Актуальность исследования обусловлена тем, что технологии Drone Network позволяют решить основную проблему логистики – провести оптимизацию финансовых расходов, путем сокращения затрат на реализацию цепочек поставок. Это помощь интеграции логистики, дронов и технологии машинного обучения. В рамках статьи рассмотрены теоретические аспекты понятия технологии «Drone Network». Проанализирован зарубежный опыт и международные тенденции в использовании беспилотных дронов при формировании цепочки поставок логистики морских предприятий. Рассмотрены ключевые преимущества, которые предоставляют данные технологии в совершенствовании транспортной логистики компаний. Проанализированы перспективы развития технологий Drone Network на территории Российской Федерации. Рассмотрены основные проблемы, препятствующие их практическому применению российскими компаниями. Проанализированы недостатки, с которыми сталкиваются организации в рамках использования технологий беспилотных дронов в логистике. Описаны перспективы развития технологий Drone Network в международном и российском рынке. Проанализированы перспективы применения беспилотных дронов, управляемых технологиями машинного обучения, в рамках развития портовой деятельности, внутрипортовой логистики и для поиска бедствующих кораблей. The scientific article is devoted to the research analysis of the features of the practical use of unmanned drones and frame copters in the framework of the transport logistics of goods and orders by foreign companies. The relevance of the study is due to the fact that Drone Network technologies allow solving the main problem of logistics - to optimize financial costs by reducing the cost of implementing supply chains. Perhaps this is helping the integration of logistics, drones and machine learning technology. The article discusses the theoretical aspects of the concept of the "Drone Network" technology. Analyzed foreign experience and international trends in the use of unmanned drones in the formation of the supply chain of logistics enterprises. The key advantages that these technologies provide in improving the transport logistics of companies are considered. The prospects for the development of Drone Network technologies on the territory of the Russian Federation are analyzed. The main problems that hinder their practical application by Russian companies are considered. The paper analyzes the shortcomings faced by organizations in the use of unmanned drone technologies in logistics. The prospects for the development of Drone Network technologies in the international and Russian markets are described. The prospects for the use of unmanned drones controlled by machine learning technologies in the development of port activities, intra-port logistics and for the search for distressed ships are analyzed.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals SCIENTIFIC BASIS OF PROJECT-BASED LEARNING TECHNOLOGY

2020 ◽  
Vol 75 (1) ◽  
pp. 366-370
Author(s):  
Z. Osmanova ◽  
◽  
Zh. Seysenbayeva ◽  
Keyword(s):  
Design Method ◽  
Preschool Education ◽  
Scientific Basis ◽  
Learning Technologies ◽  
Project Based Learning ◽  
Educational Process ◽  
Scientific Article ◽  
Learning Technology ◽  
Self Confidence ◽  
The Individual

The scientific article analyzes the importance of studying the technology of project-based learning. It is concluded that the technology of project-based learning has a direct impact on the humanization of the individual as a person, forming the intellectual, professional, moral, spiritual, civic and other qualitative qualities of the student. The role of the student in the formation of personality is determined. Briefly about the history of project-based learning. The design method consists in finding, finding and solving students ' problems in any situation, organizing independent work, activities. As the advantages of project training, it is proposed to show the results of the knowledge obtained on the basis of logical thinking of the student, increasing his self-confidence and capabilities, working on the project and realizing the responsibility assigned to them. The goals and objectives of project-based learning are defined, and the ways of their application in the educational process are outlined. The types of project-based learning technologies used in preschool education are offered. Scientific research related to the technology of project-based learning is updated, updated, supplemented and developed over time.

scholarly journals Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research

2020 ◽  
Vol 6 (3) ◽  
pp. 27-32
Author(s):  
Artur S. Ter-Levonian ◽  
Konstantin A. Koshechkin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Technologies ◽  
Learning Technology ◽  
Preclinical Research ◽  
Processing Technologies ◽  
Drug Synergy ◽  
Positive Effects ◽  
The Past ◽  
The Right

Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.

scholarly journals Technology Readiness Levels for Machine Learning Systems

2021 ◽  
Author(s):  
Alexander Lavin ◽  
Ciarán Lee ◽  
Alessya Visnjic ◽  
Siddha Ganju ◽  
Dava Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Engineering ◽  
Particle Physics ◽  
Basic Research ◽  
Medical Diagnostics ◽  
Learning Technologies ◽  
Technology Readiness ◽  
Learning Technology ◽  
Learning Development ◽  
Testing Standards

Abstract The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics.

scholarly journals Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review

2018 ◽  
pp. 1-16 ◽  
Author(s):  
Catherine H. Saunders ◽  
Curtis L. Petersen ◽  
Marie-Anne Durand ◽  
Pamela J. Bagley ◽  
Glyn Elwyn
Keyword(s):  
Machine Learning ◽  
Patient Education ◽  
English Language ◽  
Development Stage ◽  
Learning Technologies ◽  
Rapid Review ◽  
Learning Technology ◽  
Patient Centeredness ◽  
Education Material

PurposeClear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular across industries for automated tasks, like analyzing language and spotting readability issues. With the experience of patients with cancer in mind, we reviewed whether anyone has proposed, modeled, or applied machine learning technologies for the assessment of patient education materials and explored the utility of this application.MethodsWe systematically searched the literature to identify English-language articles published in peer-reviewed journals or as conference abstracts that proposed, used, or modeled the use of machine learning technology to assess patient education materials. Specifically, we searched MEDLINE, Web of Science, CINAHL, and Compendex. Two reviewers assessed study eligibility and performed study screening.ResultsWe identified 1,570 publications in our search after duplicate removal. After screening, we included five projects (detailed in nine articles) that proposed, modeled, or used machine learning technology to assess the quality of patient education materials. We evaluated the utility of each application across four domains: multidimensionality (2 of 5 applications), patient centeredness (1 of 5 applications), customizability (0 of 5 applications), and development stage (theoretical, 1 of 5 applications; in development, 3 of 5 applications; complete and available, 1 of 5 applications). Combining points across each domain, the mean utlity score across included projects was 1.8 of 5 possible points.ConclusionGiven its potential, machine learning has not yet been leveraged substantially in the assessment of patient education materials. We propose machine learning systems that can dynamically identify problematic language and content by assessing the quality of patient education materials across a range of flexible, customizable criteria. Assessment may help patients and families decide which materials to use and encourage developers to improve materials overall.

scholarly journals Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

2021 ◽  
Vol 11 (1_suppl) ◽  
pp. 23S-29S
Author(s):  
Zamir A. Merali ◽  
Errol Colak ◽  
Jefferson R. Wilson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Spine Surgery ◽  
Human Performance ◽  
Automated Analysis ◽  
Learning Technologies ◽  
Current Status ◽  
Learning Technology ◽  
Spinal Disorders ◽  
Pubmed Database

Study Design: Narrative review. Objectives: We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. Methods: A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. Results: Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. Conclusion: Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom

2020 ◽  
pp. 1-12
Author(s):  
Heping Lu
Keyword(s):  
Machine Learning ◽  
Distance Education ◽  
Learning Algorithm ◽  
Learning Technologies ◽  
Learning Technology ◽  
Expression Recognition ◽  
Computer Algorithms ◽  
The Status ◽  
Education Today ◽  
Intelligent Classroom

Educational information system is a hot topic in education today, and informatization is not only reflected in teaching methods. With the development of computer vision and deep learning technologies and the gradual maturity of related hardware, the application of computer algorithms and intelligent identification in distance education has become a norm. This research studies the entrepreneurial model of distance intelligent classrooms, uses machine learning technology as the basis, and combines intelligent image recognition technology to identify the status and expression of students in distance education classrooms. Moreover, this paper has carried out a more detailed study of face detection and expression recognition technology and tried to apply it to classroom teaching evaluation, which has shown certain feasibility in experiments. At the end of this article, the system was tested and analyzed with the collected data, which verified the feasibility and accuracy of the system.

scholarly journals Enhancement of Virtual Private Network Security Using Machine Learning Technologies

2021 ◽  
Vol 2021 (2) ◽  
pp. 1-4
Author(s):  
Zaman Nagy ◽  
Mousa Wali
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Network Performance ◽  
Performance Metrics ◽  
Virtual Private Network ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Training Stage ◽  
Private Network

Network security concerns and approaches are expanded in the recent days due to the network developments and network user’s expansion. In this paper, virtual private network is suggested for network security after fulfilment of other network performance metrics such as throughput and time delay. Furthermore, in order to prevent any malicious activities with those connections under one virtual private network, artificial intelligence attack predictor is implemented. Using of various machine learning algorithms such as Naïve Bays and Random Forest, attacks can be predicted and then blocked. Another approach is used for attacks prediction called as Artificial Neural Network which represents a deep learning technology to predict any attach by learning the attacks behaviors during the training stage.

scholarly journals Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiangming Wang ◽  
Baobao Dong
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Learning Technologies ◽  
Practical Significance ◽  
Learning Technology ◽  
Enterprise Strategy ◽  
Mechanism Study ◽  
Smart Healthcare ◽  
Medical Analysis

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

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