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Appliance of Machine Learning Algorithms in Prudent Clinical Decision-Making Systems in the Healthcare Industry
Published in Ashish Mishra, G. Suseendran, Trung-Nghia Phung, Soft Computing Applications and Techniques in Healthcare, 2020
T. Venkat Narayana Rao, G. Akhila
In the past, people obtained information manually from books. Now, everyone uses computers to write and store information. This information is stored in electronic form. Similarly, the uses of computers to store patients’ information has become commonplace in hospitals. Medical information of the patient includes various fields such as name, age, gender, type of disease and so on. This entire information is stored in the form of tables in databases. As the years pass, the patient's data increases year by year, resulting in a wealth of information. Doctors retrieve the useful information from this data based on certain attributes. This helps in proper decision making. The retrieval information is done using queries. A query is a request for the information from a database table. It plays a vital role in retrieving the information from the database. For example, if the doctor wants any information, he or she poses doubts in the form of a query to the database. The database then looks for the related data to the query and sends it to the doctor or user [5]. See Figure 9.8.
Web Databases
Published in Akshi Kumar, Web Technology, 2018
Structured Query Language (SQL) is a programming language used by database architects to design relational databases. In a SQL database like MySQL, Sybase, Oracle, or IBM DM2, SQL executes queries, retrieves data, and edits data by updating, deleting, or creating new records. SQL is a lightweight, declarative language that does a lot of heavy lifting for the relational database, acting like a database’s version of a server-side script. Some examples of popular SQL databases are: MySQL: The most popular open source database, excellent for CMS and blogsOracle: An object-relational DBMS (database management system) written in C++. Oracle has also released an Oracle NoSQL databaseIBM DB2: A family of database server products from IBM built to handle advanced “big data” analyticsMicrosoft SQL Server: A Microsoft-developed DBMS for enterprise-level databases that supports both SQL and NoSQL architecturesMicrosoft Azure: A cloud computing platform that supports any operating system and lets you store, compute, and scale data in one placeMariaDB: An enhanced, drop-in version of MySQLPostgreSQL: An enterprise-level, object-relational DBMS that uses procedural languages like Perl and Python, in addition to SQL-level code
Database querying using SQL
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Modern Data Science with R, 2021
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
SQL (Structured Query Language) is a programming language for relational database management systems. Originally developed in the 1970s, it is a mature, powerful, and widely used storage and retrieval solution for data of many sizes. Google, Facebook, Twitter, Reddit, LinkedIn, Instagram, and countless other companies all access large datastores using SQL.
GEOSS Platform data content and use
Published in International Journal of Digital Earth, 2023
Enrico Boldrini, Stefano Nativi, Jiri Hradec, Mattia Santoro, Paolo Mazzetti, Max Craglia
The other fundamental information type is the Discovery request (i.e. resource record), which was presently utilized for analyzing the GEOSS Platform utilization. Discovery request records are generated by the GEOSS users and/or clients (i.e. Machine-to-Machine interactions). These requests aim to search GEOSS for the Dataset objects that match a set of well-specified search clauses – i.e. the query constraints. As depicted in Figure 3, a stored GEOSS Discovery request is characterized by three query components: Query filters, constituted by a set of query constraints (i.e. a list of Dataset metadata elements and their respective occurrence values).Query metadata, a set of metadata elements that describe the query itself (e.g. the query timestamp and its originator IP address).Query results, a set of metadata elements that describe the query results (e.g. the data provider’s name and the title characterizing each matching Dataset).
Future home stories: participatory predicaments and methodological scaffolding in narrative speculation on alternative domestic lives
Published in Digital Creativity, 2022
Alexa Becker, Benedikt Haupt, Arne Berger, Christian Pentzold
Our review assessed the unsystematic array of participatory, design-oriented approaches geared toward domestic IoT. We especially focused on those revolving around fictional accounts and storytelling. Our collection of studies was based on a query of the ACM digital library and the Web of Science database. A set of 19 articles was analyzed to assess how story-based methods used for researching and designing future IoT-enhanced homes engaged with issues of storytelling, participation, and the ability of the devised procedures to spur the imagination of people to conceive visions of alternative future homes. Judging from the dates of publication, this is a niche yet nascent area of inquiry and design. The volume of pieces peaked in 2019; the earliest paper came out in 2009, but in most of the subsequent years, only one single article was published.
A survey of phishing attack techniques, defence mechanisms and open research challenges
Published in Enterprise Information Systems, 2022
The attacker uses the embedded objects (i.e. images, scripts, flash, ActiveX, etc.) to avoid phishing detection techniques. The various anti-phishing methods (Moghimi and Varjani 2016; Zhang, Hong, and Cranor 2007; Xiang and Hong 2009; Wenyin et al. 2010; Ramesh, Krishnamurthi, and Kumar 2014) take the decision based on the features of source content. Therefore, attacker adds these embedded objects in place of source code and textual content. Machine learning-based technique extract some features from the page source and HTML body of the webpage. If these embedded objects replace some HTML content, then machine learning-based anti-phishing technique cannot extract features from the webpage. Moreover, a correct search query forms the basis of search engine-based methods. If search query contains copyright field and if it is replaced with some image, then how can search engine-based method construct efficient query. Thus, detection of the phishing website that uses embedded objects is still an open challenge.