This slides answers questions like What Java Collections are?
What they are made up of? What are benefits of using them?
Read more at
data-structure-learning.blogspot.com
Project where data sets of different drivers with different driving behavior were classified with linear regression and machine learning to train and test data.
In this presentation, I am discussing, How to implement, a Decision Tree machine learning model, from Scratch, in Python. This implementation is, without using any machine learning libraries, like learn.
After completing this tutorial, you will not only learn the fundamentals of, Decision Tree algorithm but also, you will be able to implement, a Decision Tree model, from scratch.
Project where data sets of different drivers with different driving behavior were classified with linear regression and machine learning to train and test data.
In this presentation, I am discussing, How to implement, a Decision Tree machine learning model, from Scratch, in Python. This implementation is, without using any machine learning libraries, like learn.
After completing this tutorial, you will not only learn the fundamentals of, Decision Tree algorithm but also, you will be able to implement, a Decision Tree model, from scratch.
Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks. Or Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life.
#encapsulationinjava #java #javaprogramming
Encapsulation in java
Encapsulation in java is a mechanism of wrapping the data(variable) and code acting on the data (methods) together as a single unit.
Encapsulation is one of the four fundamental oop concepts.
It is also known as data hiding.
In this declare the variables as private.
Provide public setter and getter methods to modify and view the values of the variables.
VerXCombo: An interactive data visualization of popular library version combi...Au Gai
In large software systems, it is common practice to
adopt third-party libraries. Decisions by system maintainers to either update or introduce new third-party libraries can range from trivial to complex. For instance, incompatibility between internal library dependencies may complicate adoption. Therefore, system maintainers especially need adequate assurance of any candidate library release. Using the ‘wisdom of the crowd’, VerXCombo aims to assist system maintainers by mining popular library dependency patterns of similar systems. Through data interactions, VerXCombo leverages parallel sets to break-down large and complex dataset into distinguishable patterns of 1.)popular and 2.) latest library dependency release combinations.
Populating our tool with a maven library dependency dataset from over 4,000 Java Open Source projects, we demonstrate through a case scenario navigation and best fit combinations of the VerXCombo tool. A video highlighting the main features of the tool can be found at: http://goo.gl/wWPylL
Industrial IoT to Predictive Analytics: A Reverse Engineering Approach from S...Lokukaluge Prasad Perera
A novel mathematical framework to support industrial digitization of shipping is presented in this study. The framework supports a data flow path, i.e. from Industrial IoT (i.e. with Big Data) to Predictive Analytics, where digital models with advanced data analytics are introduced. The digital models are derived from ship performance and navigation data sets and a combination of such models facilitates towards the proposed Predictive Analytics. Since the respective data sets are used to derive the Predictive Analytics, this mathematical framework is also categorized as a reverse engineering approach. Furthermore, a data anomaly detection and recover procedure that is associated with the same framework to improve the respective data quality are also described in this study.
TOPS Technologies offer Professional Java Training in Ahmedabad.
Ahmedabad Office (C G Road)
903 Samedh Complex,
Next to Associated Petrol Pump,
CG Road,
Ahmedabad 380009.
http://www.tops-int.com/live-project-training-java.html
Most experienced IT Training Institute in Ahmedabad known for providing Java course as per Industry Standards and Requirement.
A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks. Or Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life.
#encapsulationinjava #java #javaprogramming
Encapsulation in java
Encapsulation in java is a mechanism of wrapping the data(variable) and code acting on the data (methods) together as a single unit.
Encapsulation is one of the four fundamental oop concepts.
It is also known as data hiding.
In this declare the variables as private.
Provide public setter and getter methods to modify and view the values of the variables.
VerXCombo: An interactive data visualization of popular library version combi...Au Gai
In large software systems, it is common practice to
adopt third-party libraries. Decisions by system maintainers to either update or introduce new third-party libraries can range from trivial to complex. For instance, incompatibility between internal library dependencies may complicate adoption. Therefore, system maintainers especially need adequate assurance of any candidate library release. Using the ‘wisdom of the crowd’, VerXCombo aims to assist system maintainers by mining popular library dependency patterns of similar systems. Through data interactions, VerXCombo leverages parallel sets to break-down large and complex dataset into distinguishable patterns of 1.)popular and 2.) latest library dependency release combinations.
Populating our tool with a maven library dependency dataset from over 4,000 Java Open Source projects, we demonstrate through a case scenario navigation and best fit combinations of the VerXCombo tool. A video highlighting the main features of the tool can be found at: http://goo.gl/wWPylL
Industrial IoT to Predictive Analytics: A Reverse Engineering Approach from S...Lokukaluge Prasad Perera
A novel mathematical framework to support industrial digitization of shipping is presented in this study. The framework supports a data flow path, i.e. from Industrial IoT (i.e. with Big Data) to Predictive Analytics, where digital models with advanced data analytics are introduced. The digital models are derived from ship performance and navigation data sets and a combination of such models facilitates towards the proposed Predictive Analytics. Since the respective data sets are used to derive the Predictive Analytics, this mathematical framework is also categorized as a reverse engineering approach. Furthermore, a data anomaly detection and recover procedure that is associated with the same framework to improve the respective data quality are also described in this study.
TOPS Technologies offer Professional Java Training in Ahmedabad.
Ahmedabad Office (C G Road)
903 Samedh Complex,
Next to Associated Petrol Pump,
CG Road,
Ahmedabad 380009.
http://www.tops-int.com/live-project-training-java.html
Most experienced IT Training Institute in Ahmedabad known for providing Java course as per Industry Standards and Requirement.
A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
Java collection classes and their usage.how to use java collections in a program and different types of collections. difference between the map list set, volatile keyword.
Java collection classes and their usage.how to use java collections in a program and different types of collections. difference between the map list set, volatile keyword.
Design patterns are optimized, reusable solutions to the programming problems that we encounter every day. A design pattern is not a class or a library that we can simply plug into our system; it's much more than that. It is a template that has to be implemented in the correct situation. It's not language-specific either. A good design pattern should be implementable in most—if not all—languages, depending on the capabilities of the language. Most importantly, any design pattern can be a double-edged sword— if implemented in the wrong place, it can be disastrous and create many problems for you. However, implemented in the right place, at the right time, it can be your savior.
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2. What are Collections?
• Collections aka containers are used to group objects into single entity.
• Once the objects are stored in a collection you can then perform operations on single object or multiple
objects (bulk operations).
• You can insert, update, delete, and retrieve objects into entity. Also we can query the entity for size and
other things.
3. What are Collections made up of?
• Collections are made up of 3 things. They are:
• Interfaces : Interfaces are abstract data types which represents the collections. Interfaces allow the
collection classes to be manipulated independently given that the implementing class adheres to the
contract of the interface.
• Implementations : Implementations are class that implements the collection interfaces. There are abstract
as well as concrete implementations of collection interfaces. This implementations are ready to use, highly
efficient, well tested and reusable data structures.
• Algorithms : On the core of the implementation of the interfaces there reside the powerful algorithm
implementations done on object. This includes searching sorting, shuffling, frequency and others. These
algorithms are finely tuned and suit the needs of many applications as per the data size.
4. Benefits of using Java Collections
• Tuned algorithms and quality
• There are several algorithms implemented in collections framework. We don’t know them but we use
them daily. For example Arrays.sort(int[]).
• These algorithms are finely tuned and suit the better needs of applications.
• Because of these implementations developers are freed from designing their own data structures and
test them for errors or memory leaks.
• Reduced Programming effort
• As the algorithms and data structures are implemented in collections developer can focus on other
important parts of application.
• Reduced learning effort
• It is very easy to learn collections because of its API names and documentation.
• Reusability
• Collections can be reused. Its interfaces and implementations are flexible enough to be reused given
that it adheres contract of interface.