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Topic Review
Windows 2.0
Windows 2.0 is an obsoleted 16-bit Microsoft Windows GUI-based operating environment that was released on December 9, 1987, and the successor to Windows 1.0. This product's family includes Windows 2.0, a base edition for 8086 real mode, and Windows/386 2.0, an enhanced edition for i386 protected mode.
  • 610
  • 08 Oct 2022
Topic Review
Deep Learning in Causality Mining
Deep learning models for causality mining (CM) can enhance the performance of learning algorithms, improve the processing time, and increase the range of mining applications.
  • 609
  • 08 Nov 2021
Topic Review
Clip (Command)
The clipboard is a buffer that some operating systems provide for short-term storage and transfer within and between application programs. The clipboard is usually temporary and unnamed, and its contents reside in the computer's RAM. The clipboard provides an application programming interface by which programs can specify cut, copy and paste operations. It is left to the program to define methods for the user to command these operations, which may include keybindings and menu selections. When an element is copied or cut, the clipboard must store enough information to enable a sensible result no matter where the element is pasted. Application programs may extend the clipboard functions that the operating system provides. A clipboard manager may give the user additional control over the clipboard. Specific clipboard semantics vary among operating systems, can also vary between versions of the same system, and can sometimes be changed by programs and by user preferences. Windows, Linux and macOS support a single clipboard transaction.
  • 609
  • 01 Nov 2022
Topic Review
BLAST
In bioinformatics, BLAST (basic local alignment search tool) is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a query sequence with a library or database of sequences, and identify library sequences that resemble the query sequence above a certain threshold. Different types of BLASTs are available according to the query sequences. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. The BLAST algorithm and program were designed by Stephen Altschul, Warren Gish, Webb Miller, Eugene Myers, and David J. Lipman at the National Institutes of Health and was published in the Journal of Molecular Biology in 1990 and cited over 50,000 times.
  • 608
  • 17 Oct 2022
Topic Review
Central Line (Geometry)
In geometry, central lines are certain special straight lines that lie in the plane of a triangle. The special property that distinguishes a straight line as a central line is manifested via the equation of the line in trilinear coordinates. This special property is related to the concept of triangle center also. The concept of a central line was introduced by Clark Kimberling in a paper published in 1994.
  • 608
  • 27 Sep 2022
Topic Review
Human Behavior Analysis by Data
The goal of this study was to conduct a literature review of current approaches and techniques for identifying, understanding, and predicting human behaviors through mining a variety of sources of textual data with a focus on enabling classification of psychological behaviors regarding emotion, cognition, and social empathy. 
  • 608
  • 05 Aug 2021
Topic Review
Semantic Search and SemSime
This paper presents SemSime, a semantic similarity method for searching over a setof digital resources previously annotated by means of concepts from a weighted reference ontology.It is based on a frequency approach for weighting the ontology, and refines both the user request and the annotations of the digital resources with rating scores. Such scores are High, Medium, and Low and, in the user request, indicate the preferences assigned by the user to each of the concepts representing the searching criteria whereas, in the annotations of the digital resources, they represent the levels of quality associated with each concept in describing the resources. The experiment we have performed shows that SemSime outperforms the previous semantic search method SemSim.
  • 608
  • 29 Oct 2020
Topic Review
Approaches to Predict Pedestrian Dynamics
Identifying the factors that control the dynamics of pedestrians is a crucial step toward modeling and building various pedestrian-oriented simulation systems. Several approaches have been proposed by researchers to predict pedestrians’ movement characteristics using different methods and techniques. Based solely on experimental evidence, researchers isolate the factors that influence the interactions between pedestrians in single-file movement. With artificial neural networks, one can approximate the fitting function that describes pedestrians’ movement without having modeling bias. The analysis is focused on the distances and range of interactions across neighboring pedestrians.
  • 607
  • 23 Aug 2022
Topic Review
H2O
H2O is open-source software for big-data analysis. It is produced by the company H2O.ai. H2O allows users to fit thousands of potential models as part of discovering patterns in data. The H2O software runs can be called from the statistical package R, Python, and other environments. It is used for exploring and analyzing datasets held in cloud computing systems and in the Apache Hadoop Distributed File System as well as in the conventional operating-systems Linux, macOS, and Microsoft Windows. The H2O software is written in Java, Python, and R. Its graphical-user interface is compatible with four browsers: Chrome, Safari, Firefox, and Internet Explorer.
  • 607
  • 30 Nov 2022
Topic Review
Deep Learning-Based Methods for Crop Disease Estimation
Deep learning methods such as U-Net, SegNet, YOLO, Faster R-CNN, VGG and ResNet have been used extensively for crop disease estimation using Unmanned Aerial Vehicle (UAV)  imagery. The basic building block of the deep learning architecture is basically the success of convolutional neural networks (CNN). The deep learning models implemented for crop disease estimation using UAV imagery can be categorized into classification-based, segmentation-based and detection-based approaches. Segmentation-based models attempt to classify each pixel in an image into different categories such as healthy vs. diseased pixels, whereas classification-based models look into overall images and classify the image into pre-defined disease classes.
  • 606
  • 16 May 2023
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