Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
DataMelt
DataMelt (sometime termed DMelt) is a software program for scientific computation, data analysis and data visualization. The program is used for statistical data analysis, curve fitting, data-mining, machine learning, numeric computations and interactive scientific plotting in 2D and 3D. DataMelt is designed for scientists, engineers and students. DataMelt is multiplatform since it is written in Java, thus it runs on any operating system where the Java virtual machine can be installed. DataMelt also uses high-level programming languages, such as Jython, Apache Groovy, JRuby, but Java coding can also be used to call DataMelt numerical and graphical libraries. DataMelt is an attempt to create a data-analysis environment using open-source packages with a coherent user interface and tools competitive to commercial programs. The idea behind the project is to incorporate open-source mathematical and numerical software packages with GUI-type user interfaces into a coherent program in which the main user interface is based on short-named Java/Python classes. This was required to build an analysis environment using Java scripting concept. A typical example will be shown below. Scripts and Java code (in case of the Java programming) can be run either in a GUI editor of DataMelt or as batch programs. The graphical libraries of DataMelt can be used to create applets. All charts (or "Canvases") used for data representation can be embedded into Web browsers. DataMelt can be used for analysis of large numerical data volumes, data mining, statistical data analysis and mathematics are essential. The program can be used in natural sciences, engineering, modeling and analysis of financial markets. While the program falls into the category of open source software, it is not completely free for commercial usage (see below), no source code is available on the home page, and all documentation and even bug reporting requires "membership".
  • 1.1K
  • 14 Nov 2022
Topic Review
International Trade Theory
International trade theory is a sub-field of economics which analyzes the patterns of international trade, its origins, and its welfare implications. International trade policy has been highly controversial since the 18th century. International trade theory and economics itself have developed as means to evaluate the effects of trade policies.
  • 6.6K
  • 14 Nov 2022
Topic Review
Simple API for Grid Applications
The Simple API for Grid Applications (SAGA) is a family of related standards specified by the Open Grid Forum to define an application programming interface (API) for common distributed computing functionality.
  • 223
  • 14 Nov 2022
Topic Review
Sports Analytics
Sports analytics are a collection of relevant, historical, statistics that can provide a competitive advantage to a team or individual. Through the collection and analyzation of these data, sports analytics inform players, coaches and other staff in order to facilitate decision making both during and prior to sporting events. The term "sports analytics" was popularized in mainstream sports culture following the release of the 2011 film, Moneyball, in which Oakland Athletics General Manager Billy Beane (played by Brad Pitt) relies heavily on the use of analytics to build a competitive team on a minimal budget. There are two key aspects of sports analytics — on-field and off-field analytics. On-field analytics deals with improving the on-field performance of teams and players, including questions such as "which player on the Red Sox contributed most to the team's offense?" or "who is the best wing player in the NBA?", etc. Off-field analytics deals with the business side of sports. Off-field analytics focuses on helping a sport organization or body surface patterns and insights through data that would help increase ticket and merchandise sales, improve fan engagement, etc. Off-field analytics essentially uses data to help rightsholders take decisions that would lead to higher growth and increased profitability. As technology has advanced over the last number of years data collection has become more in-depth and can be conducted with relative ease. Advancements in data collection have allowed for sports analytics to grow as well, leading to the development of advanced statistics and machine learning, as well as sport specific technologies that allow for things like game simulations to be conducted by teams prior to play, improve fan acquisition and marketing strategies, and even understand the impact of sponsorship on each team as well as its fans. Another significant impact sports analytics have had on professional sports is in relation to sport gambling. In depth sports analytics have taken sports gambling to new levels, whether it be fantasy sports leagues or nightly wagers, bettors now have more information at their disposal to help aid decision making. A number of companies and webpages have been developed to help provide fans with up to the minute information for their betting needs.
  • 1.0K
  • 14 Nov 2022
Topic Review
Government Censorship of Telegram Messenger
Telegram Messenger application has been blocked by multiple countries.
  • 10.4K
  • 14 Nov 2022
Topic Review
Network Threat Detection with ML/DL in SDN-Based Platforms
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network.
  • 799
  • 14 Nov 2022
Topic Review
Komi (Go)
Komi (コミ) in the game of Go are points added to the score of the player with the white stones as compensation for playing second. Black's first move advantage is generally considered to equal somewhere between 5 and 7 points by the end of the game. Standard komi is 6.5 points under the Japanese and Korean rules; under Chinese, Ing and AGA rules standard komi is 7.5 points; under New Zealand rules standard komi is 7 points. Komi typically applies only to games where both players are evenly ranked. In the case of a one-rank difference, the stronger player will typically play with the white stones and players often agree on a simple 0.5-point komi to break a tie ("jigo") in favour of white, or no komi at all. Komidashi (コミ出し) is the more complete Japanese language term. The Chinese term is 贴目 (tiē mù) and the Korean term is Deom (덤).
  • 1.6K
  • 14 Nov 2022
Topic Review
CryptoLocker
The CryptoLocker ransomware attack was a cyberattack using the CryptoLocker ransomware that occurred from 5 September 2013 to late May 2014. The attack utilized a trojan that targeted computers running Microsoft Windows, and was believed to have first been posted to the Internet on 5 September 2013. It propagated via infected email attachments, and via an existing Gameover ZeuS botnet. When activated, the malware encrypted certain types of files stored on local and mounted network drives using RSA public-key cryptography, with the private key stored only on the malware's control servers. The malware then displayed a message which offered to decrypt the data if a payment (through either bitcoin or a pre-paid cash voucher) was made by a stated deadline, and it threatened to delete the private key if the deadline passes. If the deadline was not met, the malware offered to decrypt data via an online service provided by the malware's operators, for a significantly higher price in bitcoin. There was no guarantee that payment would release the encrypted content. Although CryptoLocker itself was easily removed, the affected files remained encrypted in a way which researchers considered unfeasible to break. Many said that the ransom should not be paid, but did not offer any way to recover files; others said that paying the ransom was the only way to recover files that had not been backed up. Some victims claimed that paying the ransom did not always lead to the files being decrypted. CryptoLocker was isolated in late May 2014 via Operation Tovar, which took down the Gameover ZeuS botnet that had been used to distribute the malware. During the operation, a security firm involved in the process obtained the database of private keys used by CryptoLocker, which was in turn used to build an online tool for recovering the keys and files without paying the ransom. It is believed that the operators of CryptoLocker successfully extorted a total of around $3 million from victims of the trojan. Other instances of encryption-based ransomware that have followed have used the "CryptoLocker" name (or variations), but are otherwise unrelated.
  • 651
  • 14 Nov 2022
Topic Review
Computer Vision and Convolutional Neural Networks
Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture.
  • 1.5K
  • 14 Nov 2022
Topic Review
Starlink Project
The Starlink Project, referred to by users as Starlink and by developers as simply The Project, was a United Kingdom astronomical computing project which supplied general-purpose data reduction software. Until the late 1990s, it also supplied computing hardware and system administration personnel to UK astronomical institutes. In the former respect, it was analogous to the United States IRAF project. The project was formally started in 1980, though the funding had been agreed, and some work begun, a year earlier. It was closed down when its funding was withdrawn by the Particle Physics and Astronomy Research Council in 2005. In 2006, the Joint Astronomy Centre released its own updated version of Starlink and took over maintenance; the task was passed again in mid-2015 to the East Asian Observatory. The latest version was released on 2018 July 19. Part of the software is relicensed under the GNU GPL while some of it remain under the original custom licence.
  • 343
  • 13 Nov 2022
  • Page
  • of
  • 371
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Video Production Service
Feedback