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 Classification of Tennis Video Types Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xun Gong ◽  
Fucheng Wang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Video Data ◽  
Learning Technology ◽  
Video Content ◽  
Video Classification ◽  
The World ◽  
Spatial Detection

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

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 Computational classification of animals for a highway detection system

2021 ◽  
Vol 58 ◽  
pp. e174951
Author(s):  
Denis Sato ◽  
Adroaldo José Zanella ◽  
Ernane Xavier Costa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Road Safety ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

Classification of Polarity of Opinions Using Unsupervised Approach in Tourism Domain

2016 ◽  
Vol 3 (4) ◽  
pp. 68-78 ◽  
Author(s):  
Mahima Goyal ◽  
Vishal Bhatnagar
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Web Portals ◽  
Traditional Methods ◽  
Recent Growth ◽  
Machine Learning Approach ◽  
Unsupervised Approach

The recent growth of e-commerce websites has paved a way for the users to express their opinions on these web portals which, in turn, makes the customers review these comments before buying any product or service. The comprehensive reading of these large number of reviews is cumbersome and tiring. The purpose of this paper is to perform the analysis on the tourism domain reviews to decide whether the document is positive or negative. The traditional methods use a machine learning approach, but the authors are using an unsupervised dictionary based approach to classify the opinions. The scores of the opinions are extracted using Sentiwordnet, a popular dictionary for calculating the sentiment.

The Value of Interaction for Russia, the USA and China Facing the Information Warfare

2013 ◽  
Vol 3 (4) ◽  
pp. 1-9
Author(s):  
Vasilyeva Inna
Keyword(s):  
United States ◽  
Rapid Development ◽  
The United States ◽  
World Politics ◽  
First Century ◽  
Information Warfare ◽  
The World ◽  
The Usa ◽  
Open Dialogue ◽  
World Information

The rapid development of informatization worldwide, specifically in the USA, China and Russia, and its penetration into all spheres of the vital interests of an individual, society and the state, have caused, besides doubtless advantages, the emergence of a number of significant problems. The urgent necessity to protect the information along with being protected from it has become one of them. The geopolitical confrontation and information warfare between the United States and China will be a major factor in world politics in the twenty-first century. This increasing tendency is pushing Russia towards further increasing the development of information warfare along with ensuring national security; the formation of an open dialogue between civilizations; and resistance to the threat of conflict in the field of information. Exhaustion of natural resources of the planet, their consumption and growth of the population do not contribute to the reduction of information warfare. Therefore, the positions of Russia, the United States and China will be strengthened due to deeper integration into the world information space. This paper has highlighted the quintessence of what causes the great thirst towards reaching information comfort and leadership, along with information warfare terrifying serious threats and modern global geopolitical tendencies.

scholarly journals Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

2020 ◽  
Author(s):  
Rahul Kumar ◽  
Ridhi Arora ◽  
Vipul Bansal ◽  
Vinodh J Sahayasheela ◽  
Himanshu Buckchash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Rapid Development ◽  
Healthcare Systems ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Deep Feature ◽  
The World ◽  
Chest X Ray

ABSTRACTAccording to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

scholarly journals Classification of Teachers and Lecturers Engagement on Webinar during the Pandemic using the Utilization of Big Data

2021 ◽  
Vol 2 (3) ◽  
pp. 673-684
Author(s):  
Ida Afriliana ◽  
Nurohim
Keyword(s):  
Big Data ◽  
Fuzzy Inference ◽  
Rapid Development ◽  
Inference System ◽  
Face To Face ◽  
Neuro Fuzzy ◽  
The World ◽  
Training Error ◽  
Online Seminar

The Pandemic had a big impact on education in Indonesia and also in the world. In early 2020, during this pandemic, face-to-face meetings have turned into virtual or online meetings for both the learning process and seminars or workshops. The rapid development of technology supports this change in the world of education, this can be seen from the number of online seminars conducted to improve the competence of lecturers or teachers. The development of this online seminar allows the circulation of information that is increasingly large, fast, and almost unlimited by time and space. This causes a large amount of information to be scattered in the virtual world in various fields. With this very fast information technology, trillions of bytes of data are created every day from various sources such as on social media, especially those related to applications that are often used in website-based seminar media. This is called unstructured big data. In this study, big data will be implemented to classify educators' engagement of online seminar participants during an early pandemic. The activity stages in big data management and data processing support are acquired, accessed, analytical, and applied. The method for this study is the Adaptive Neuro-Fuzzy Inference System (ANFIS) to classify the engagement of teachers and  lecturers an online seminar.  The results of the training error obtained from ANFIS are 0.273482 with the ANFIS structure 4-12-12-12-1 or 4 inputs, 12 hidden layers, and 1 output.

Russian Citizens' Representations of the Country's Position in the Geopolitical Space of the Commonwealth of Independent States, Europe, and the World

2003 ◽  
Vol 8 (4) ◽  
pp. 238-251
Author(s):  
Victor F. Petrenko ◽  
Olga V. Mitina ◽  
Kirill A. Bertnikov
Keyword(s):  
Well Being ◽  
Multidimensional Space ◽  
Commonwealth Of Independent States ◽  
Repertory Grids ◽  
Implicit Model ◽  
The World ◽  
Public Consciousness ◽  
Independent States ◽  
Invariant Factors

The aim of this research was the reconstruction of the system of categories through which Russians perceive the countries of the Commonwealth of Independent States (CIS), Europe, and the world as a whole; to study the implicit model of the geopolitical space; to analyze the stereotypes in the perception of different countries and the superposition of mental geopolitical representations onto the geographic map. The techniques of psychosemantics by Petrenko, originating in the semantic differential of Osgood and Kelly's “repertory grids,” were used as working tools. Multidimensional semantic spaces act as operational models of the structures of consciousness, and the positions of countries in multidimensional space reflect the geopolitical stereotypes of respondents about these countries. Because of the transformation of geopolitical reality representations in mass consciousness, the commonly used classification of countries as socialist, capitalist, and developing is being replaced by other structures. Four invariant factors of the countries' descriptions were identified. They are connected with Economic and Political Well-being, Military Might, Friendliness toward Russia, and Spirituality and the Level of Culture. It seems that the structure has not been explained in adequate detail and is not clearly realized by the individuals. There is an interrelationship between the democratic political structure of a country and its prosperity in the political mentality of Russian respondents. Russian public consciousness painfully strives for a new geopolitical identity and place in the commonwealth of states. It also signifies the country's interest and orientation toward the East in the search for geopolitical partners. The construct system of geopolitical perception also depends on the region of perception.

Sign in / Sign up

Export Citation Format

Share Document