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irispublishersagriculture · 4 years ago

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Iris Publishers - World Journal of Agriculture and Soil Science (WJASS)

Assessing the Risks of Spatial Spread of the New Coronavirus COVID-19 by Models

Authored by Fawzy ZF

After the outbreak of COVID-19 in China, COVID-19 has also erupted in other countries in the world. Among the countries where new pneumonia outbreaks, Spain, Italy, France and Germany are more serious [1]. As of April 27, Spain, Italy, France and Germany have each accumulated diagnosed 229842 cases, 199414 cases, 165,842 cases, 158758 cases, the new crown pneumonia spread, and various measures of everyday life and people’s social normal operation had not Estimated impact [2].

In fact, there are some urgent problems to be solved regarding the spread of COVID -19. Can existing interventions effectively control COVID-19? Can you elaborate on the changes and development characteristics of each epidemic situation? Can you combine the conclusions found in the comparison of the city / region, actual national population, medical level, traffic conditions, geographic location, customs and culture, and anti-epidemic measures? What mathematical model can we build to solve the problem?

COVID-19 is a new coronavirus discovered in December 2019. The epidemic data is not sufficient, and clinical methods such as clinical trials are still in the exploration stage. So far, the epidemic situation data is difficult to apply directly to the existing mathematical model. The problems to be solved are how effective the existing emergency response is and how to invest medical resources more scientifically in the future. On this basis, this article aims to study the shortcomings of this part [3-5].

Methods

Data

We obtained epidemiological data from the Aminer website, the People’s Republic of China from January 22 to April 3, and Spain, Italy, France, Germany from February 15 to April 27. This includes data such as cumulative confirmed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, and existing confirmed cases. The relevant input is shown in the figure

The model

Based on the collected epidemic data, we tried to find the propagation law of COVID-19 and proposed effective prevention and control methods.

There are generally three methods for systematically studying the spread of infectious diseases. One is to establish a dynamic model of infectious diseases. The second is statistical modeling

Based on Logistic estimated square law

The traditional SEIR model cannot describe the different developments of the epidemic well. After analyzing the actual situation and the existing data, we have established a more effective infectious disease transmission model. According to the using statistical methods such as random processes and time series analysis. The third is to use data mining technology to obtain information in the data and discover the epidemic law of infectious diseases. Using the collected data from various countries, this article mainly uses the third method.

In this paper, the growth model of COVID-19 transmission is established, and the prediction effect of the mathematical model on the spread of COVID-19 epidemic is compared.

actual situation of the epidemic, we will analyze the relevant data indicators of the five countries (cumulatively diagnosed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, existing confirmed cases) to adapt to the current situation of the new coronary pneumonia epidemic in the world propagation

As can be seen from the data graph, the change in cumulative death toll in Italy over time is a non-linear process. Considering the shape of the scatter plot and the model generally involving the Logistic curve model, here we use the Logistic curve model for fitting. The basic form of the logistic curve model is:

y = 1 / (a + be ^ (-t))

Therefore, we need to transform this nonlinear process into a linear model after data processing.

Take x0 = e ^ (-t), y0 = 1 / y; Then the original model is converted to a linear model y0 = a + bx0.

Simulation

Since COVID-19 has been developing in Italy for a long period of time, and the cumulative number of confirmed cases is relatively large, the data is more convincing, so here we take the cumulative number of confirmed cases in Italy from February 15th to May 3rd. The nonlinear model becomes a linear model, and matlab is used for fitting linear regression analysis. Matlab source code is as follows [6-9]:

x = [1: 1: 27];

y=[3,3,21,229,655,1701,3089,5883,10149,17660,27980,4103 5,59138,74386,92472,105792,119827,132547,143626,156363,1 65155,175925,183957,192994,199414,205436 , 210717];

plot (x, y, ‘r *’);

xlabel (‘time’)

ylabel (‘population’) x0 = exp (-x);

y0 = 1. / y;

f = polyfit (x0, y0,1);

y_fit = 1 ./ (f (1). * exp (-0.338. * x) + f (2));

plot (x, y_fit * 1000);

hold on

plot (x, y, ‘r *’);

xlabel (‘time’)

ylabel (‘population’)

Results

Logistic model estimates

On the basis of the cumulative number of confirmed cases in Italy from February 15th to May 3rd, we used Matlab to establish a Logistic model and performed linear regression analysis. Using the above processing, we can get the predicted cumulative number of confirmed cases in Italy as shown in Figure 6.

As shown in Figure 6, we can conclude that the Logistic model has a good fitting effect on the actual cumulative number of confirmed cases, thus providing reference value for departments and hospitals at all levels to effectively intervene and prevent the spread of new coronavirus in the next few days.

Discussion

The spread of COVID-19 is affected by many complex factors. In the early stage of the transmission of COVID-19, it is difficult to establish a Logistic model and parameter estimation and obtain a fairly accurate simulation result, but the initial estimated parameters such as the growth rate of the confirmed cases and the possible cumulative maximum confirmed cases can be obtained through existing data. It is helpful to solve important parameters such as infection rate and recovery rate, which will help us to grasp the transmission trend of COVID-19 more accurately.

Limitations

• Promotion of the model: The SEIR model based on 2019-nCoV can be established. The SEIR model is superior to the logistic model in trend prediction, but due to the many parameters to be considered, the calculation error is greater than the logistic model [10-19].

• A dynamic growth rate model based on 2019-nCoV can be established. The dynamic growth rate model has a good fitting effect but has a certain error.

• You can also optimize on the value of r. The methods of optimizing r are: 1. Perform grid optimization; 2. Perform bipartite optimization; You can optimize on the value of K and update in real time.

• After the turning point of the epidemic situation, that is, the fitting effect of the reducer and the saturation period is poor, and even a large error occurs [20-23]

To read more about this article: https://irispublishers.com/wjass/fulltext/assessing-the-risks-of-spatial-spread-of-the-new-coronavirus-covid-19-by-models.ID.000603.php

Indexing List of Iris Publishers: https://medium.com/@irispublishers/what-is-the-indexing-list-of-iris-publishers-4ace353e4eee

Iris publishers google scholar citations: https://scholar.google.co.in/scholarhl=en&as_sdt=0%2C5&q=irispublishers&btnG=

#agriculture #journal of agriculture #Agriculture and Soil Science #Inter National Agriculture Science #Food science #soil science

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gayatrisc · 5 years ago

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Top 8 Python Libraries for Data Science

Python language is popular and most commonly used by developers in creating mobile apps, games and other applications. A Python library is nothing but a collection of functions and methods which helps in solving complex data science-related functions. Python also helps in saving an amount of time while completing specific tasks.

Python has more than 130,000 libraries that are intended for different uses. Like python imaging library is used for image manipulation whereas Tensorflow is used for the development of Deep Learning models using python.

There are multiple python libraries available for data science and some of them are already popular, remaining are improving day-by-day to reach their acceptance level by developers

Read: HOW TO SHAPE YOUR CAREER WITH DATA SCIENCE COURSE IN BANGALORE?

Here we are discussing some Python libraries which are used for Data Science:

1. Numpy

NumPy is the most popular library among developers working on data science. It is used for performing scientific computations like a random number, linear algebra and Fourier transformation. It can also be used for binary operations and for creating images. If you are in the field of Machine Learning or Data Science, you must have good knowledge of NumPy to process your real-time data sets. It is a perfect tool for basic and advanced array operations.

2. Pandas

PANDAS is an open-source library developed over Numpy and it contains Data Frame as its main data structure. It is used in high-performance data structures and analysis tools. With Data Frame, we can manage and store data from tables by performing manipulation over rows and columns. Panda library makes it easier for a developer to work with relational data. Panda offers fast, expressive and flexible data structures.

Translating complex data operations using mere one or two commands is one of the most powerful features of pandas and it also features time-series functionality.

3. Matplotlib

This is a two-dimensional plotting library of Python a programming language that is very famous among data scientists. Matplotlib is capable of producing data visualizations such as plots, bar charts, scatterplots, and non-Cartesian coordinate graphs.

It is one of the important plotting libraries’ useful in data science projects. This is one of the important library because of which Python can compete with scientific tools like MatLab or Mathematica.

4. SciPy

SciPy library is based on the NumPy concept to solve complex mathematical problems. It comes with multiple modules for statistics, integration, linear algebra and optimization. This library also allows data scientist and engineers to deal with image processing, signal processing, Fourier transforms etc.

If you are going to start your career in the data science field, SciPy will be very helpful to guide you for the whole numerical computations thing.

5. Scikit Learn

Scikit-Learn is open-sourced and most rapidly developing Python libraries. It is used as a tool for data analysis and data mining. Mainly it is used by developers and data scientists for classification, regression and clustering, stock pricing, image recognition, model selection and pre-processing, drug response, customer segmentation and many more.

6. TensorFlow

It is a popular python framework used in deep learning and machine learning and it is developed by Google. It is an open-source math library for mathematical computations. Tensorflow allows python developers to install computations to multiple CPU or GPU in desktop, or server without rewriting the code. Some popular Google products like Google Voice Search and Google Photos are built using the Tensorflow library.

7. Keras

Keras is one of the most expressive and flexible python libraries for research. It is considered as one of the coolest machine learning Python libraries offer the easiest mechanism for expressing neural networks and having all portable models. Keras is written in python and it has the ability to run on top of Theano and TensorFlow.

Compared to other Python libraries, Keras is a bit slow, as it creates a computational graph using the backend structure and then performs operations.

8. Seaborn

It is a data visualization library for a python based on Matplotlib is also integrated with pandas data structures. Seaborn offers a high-level interface for drawing statistical graphs. In simple words, Seaborn is an extension of Matplotlib with advanced features.

Matplotlib is used for basic plotting such as bars, pies, lines, scatter plots and Seaborn is used for a variety of visualization patterns with few syntaxes and less complexity.

With the development of data science and machine learning, Python data science libraries are also advancing day by day. If you are interested in learning python libraries in-depth, get NearLearn’s Best Python Training in Bangalore with real-time projects and live case studies. Other than python, we provide training on Data Science, Machine learning, Blockchain and React JS course. Contact us to get to know about our upcoming training batches and fees.

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ijcmcrjournal · 4 years ago

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Assessing the Risks of Spatial Spread of the New Coronavirus COVID-19 by Bin Z* in International Journal of Clinical Studies & Medical Case Reports

Abstract

With the spread of the new coronavirus around the world, governments of various countries have begun to use the mathematical modeling method to construct some virus transmission models assessing the risks of spatial spread of the new coronavirus COVID-19, while carrying out epidemic prevention work, and then calculate the inflection point for better prevention and control of epidemic transmission. This work analyzes the spread of the new coronavirus in China, Italy, Germany, Spain and France and explores the quantitative relationship between the growth rate of the number of new coronavirus infections and time.

Background: In December 2019, the first Chinese patients with pneumonia of unknown cause is China admitted to hospital in Wuhan, Hubei Jinyintan, since then, COVID-19 in the rapid expansion of China Wuhan, Hubei, in a few months time, COVID-19 is Soon it spread to a total of 34 provincial-level administrative regions in China and neighbouring countries, and Hubei Province immediately became the hardest hit by the new coronavirus. In an emergency situation, we strive to establish an accurate infectious disease retardation growth model to predict the development and propagation of COVID-19, and on this basis, make some short-term effective predictions. The construction of this model has Relevant departments are helpful for the prevention and monitoring of the new coronavirus, and also strive for more time for the clinical trials of Chinese researchers and the research on vaccines against the virus to eliminate the new corona virus as soon as possible.

Methods: Collect and compare and integrate the spread of COVID-19 in China, Italy, France, Spain and Germany, record the virus transmission trend among people in each country and the protest measures of relevant government departments. According to the original data change law, establish a Logistic growth model.

Findings: Based on the analysis results of the Logistic model, the Logistic model has a good fitting effect on the actual cumulative number of confirmed cases, which can bring a better effect to the prediction of the epidemic situation and the prevention and control of the epidemic situation.

Interpretation: In the early stage of the epidemic, due to inadequate anti-epidemic measures in various countries, the epidemic situation in various countries spread rapidly. However, with the gradual understanding of COVI D -19, the epidemic situation began to be gradually controlled, thereby retarding growth.

Keywords: New coronavirus; Logistic growth model; Infection prediction and Infection prediction

Introduction

After the outbreak of COVID-19 in China, COVID-19 has also erupted in other countries in the world. Among the countries where new pneumonia outbreaks, Spain, Italy, France and Germany are more serious [1]. As of April 27, Spain, Italy, France and Germany have each accumulated diagnosed 229842 cases, 199414 cases, 165,842 cases, 158758 cases, the new crown pneumonia spread and various measures of everyday life and people's social normal operation had not Estimated impact [2].

In fact, there are some urgent problems to be solved regarding the spread of COVID-19. Can existing interventions effectively control COVID-19? Can you elaborate on the changes and development characteristics of each epidemic situation? Can you combine the conclusions found in the comparison of the city / region, actual national population, medical level, traffic conditions, geographic location, customs and culture and anti-epidemic measures? What mathematical model can we build to solve the problem?

COVID-19 is a new coronavirus discovered in December 2019. The epidemic data is not sufficient, and clinical methods such as clinical trials are still in the exploration stage. So far, the epidemic situation data is difficult to apply directly to the existing mathematical model. The problems to be solved are: how effective the existing emergency response is and how to invest medical resources more scientifically in the future. On this basis, this article aims to study the shortcomings of this part [3-5].

Methods

Data: We obtained epidemiological data from the Aminer website, the People's Republic of China from January 22 to April 3, and Spain, Italy, France, Germany from February 15 to April 27. This includes data such as cumulative confirmed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, and existing confirmed cases. The relevant input is shown in the figure (Figure 1,2,3,4 and 5).

The model: Based on the collected epidemic data, we tried to find the propagation law of COVID-19 and proposed effective prevention and control methods.

There are generally three methods for systematically studying the spread of infectious diseases. One is to establish a dynamic model of infectious diseases. The second is statistical modeling using statistical methods such as random processes and time series analysis. The third is to use data mining technology to obtain information in the data and discover the epidemic law of infectious diseases. Using the collected data from various countries, this article mainly uses the third method.

In this paper, the growth model of COVID-19 transmission is established, and the prediction effect of the mathematical model on the spread of COVID-19 epidemic is compared.

Based on Logistic estimated square law: The traditional SEIR model cannot describe the different developments of the epidemic well. After analyzing the actual situation and the existing data, we have established a more effective infectious disease transmission model. According to the actual situation of the epidemic, we will analyze the relevant data indicators of the five countries (cumulatively diagnosed cases, cumulative deaths, newly diagnosed cases per day, cumulative number of cured cases, existing confirmed cases) to adapt to the current situation of the new coronary pneumonia epidemic in the world propagation (Table 1).

y = 1 / (a + be ^ (-t))

Therefore, we need to transform this nonlinear process into a linear model after data processing.

Take x0 = e^ (-t), y0 = 1/y; Then the original model is converted to a linear model y0= a+bx0.

y = 1/(a + be ^ (-t))

Therefore, we need to transform this nonlinear process into a linear model after data processing.

Take x0 = e ^ (-t), y0 = 1/y; Then the original model is converted to a linear model y0 = a + bx0.

Simulation: Since COVID-19 has been developing in Italy for a long period of time, and the cumulative number of confirmed cases is relatively large, the data is more convincing, so here we take the cumulative number of confirmed cases in Italy from February 15th to May 3rd The nonlinear model becomes a linear model and matlab is used for fitting linear regression analysis. Matlab source code is as follows [6-9]:

x = [1: 1: 27];

y = [3,3,21, 229,655, 1701,3089,5883,10149,17660,27980,41035,59138,74386,92472,105792,119827,132547,1436

26,156363,165155,175925,183957,192994,199414,205436,210717];

Plot (x, y, 'r*');

xlabel ('time')

ylabel ('population')

x0 = exp (-x);

y0 = 1 / y;

f = polyfit (x0, y0,1);

y_fit = 1 / (f (1). * exp (-0.338. * x) + f (2));

plot (x, y_fit * 1000);

hold on

plot (x, y, 'r *');

xlabel ('time')

ylabel ('population')

Results

Logistic Model Estimates: On the basis of the cumulative number of confirmed cases in Italy from February 15th to May 3rd, we used Matlab to establish a Logistic model and performed linear regression analysis. Using the above processing, we can get the predicted cumulative number of confirmed cases in Italy as shown in (Figure 6).

Discussion

Limitations

Promotion of the model: The SEIR model based on 2019-nCoV can be established. The SEIR model is superior to the logistic model in trend prediction, but due to the many parameters to be considered, the calculation error is greater than the logistic model [10-19].

A dynamic growth rate model based on 2019-nCoV can be established. The dynamic growth rate model has a good fitting effect, but has a certain error.

You can also optimize on the value of r. The methods of optimizing r are:

1. Perform grid optimization;

2. Perform bipartite optimization; You can optimize on the value of K and update in real time.

After the turning point of the epidemic situation, that is, the fitting effect of the reducer and the saturation period is poor, and even a large error occurs [20-23].

For more information about Journal :

https://ijclinmedcasereports.com/

https://ijclinmedcasereports.com/pdf/IJCMCR.RW.ID.00003.pdf https://ijclinmedcasereports.com/ijcmcr-rw-id-00003/

#clinical studies #Covid 19 #Coronavirus #Logistic growth model #IJCMCR

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itsrahulpradeepposts · 5 years ago

Text

Why Python is used in data science? How data science courses help in a successful career post COVID pandemic?

Data science has tremendous growth opportunities and is one of the hot careers in the current world. Many businesses are thriving for skilled data scientists. Data science requires many skills to become an expert – One of the important skills is Python programming. Python is a programming language widely used in many fields. It is considered as the king of the coding world. Data scientists extensively use this language and even beginners find it easy to learn the Python language. To learn this language, there are many Python data science courses that guide and train you in an effective way.

What is Python?

Python is an interpreted and object-oriented programming language. It is an easily understandable language whose syntaxes can be grasped by a beginner quickly. It was found by Guido in 1991. It is supported in operating systems like Linux, Windows, macOS, and a lot more. The Python is developed and managed by the Python software foundation.

The second version of Python was released in 2000. It features list comprehension and reference counting. This version was officially stopped functioning in 2020. Currently, only the Python version 3.5x and later versions are supported.

Why Python is used in data science?

Python is the most preferred programming language by the data scientists as it effectively resolves tasks. It is one of the top data science tools used in various industries. It is an ideal language to implement algorithms. Python’s scikit-learn is a vital tool that the data scientist find it useful while solving many machine learning tasks. Data science uses Python libraries to solve a task.

Python is very good when it comes to scalability. It gives you flexibility and multiple solutions for different problems. It is faster than Matlab. The main reason why YouTube started working in Python is because of its exceptional scalability.

Features of Python language

Python has a syntax that can be understood easily.

It has a vast library and community support.

We can easily test codes as it has interactive modes.

The errors that arise can be easily understood and cleared quickly.

It is free software, and it can be downloaded online. Even there are free online Python compilers available.

The code can be extended by adding modules. These modules can also be implemented in other languages like C, C++, etc.

It offers a programmable interface as it is expressive in nature.

We can code Python anywhere.

The access to this language is simple. So we can easily make the program working.

The different types of Python libraries used for data science

1.Matplotlib

Matplotlib is used for effective data visualization. It is used to develop line graphs, pie charts, histograms efficiently. It has interactive features like zooming and planning the data in graphics format. The analysis and visualization of data are vital for a company. This library helps to complete the work efficiently.

2.NumPy

NumPy is a library that stands for Numerical Python. As the name suggests, it does statistical and mathematical functions that effectively handles a large n-array. This helps in improving the data and execution rate.

3.Scikit-learn

Scikit- learn is a data science tool used for machine learning. It provides many algorithms and functions that help the user through a constant interface. Therefore, it offers active data sets and capable of solving real-time problems more efficiently.

4.Pandas

Pandas is a library that is used for data analysis and manipulation. Even though the data to be manipulated is large, it does the manipulation job easily and quickly. It is an absolute best tool for data wrangling. It has two types of data structures .i.e. series, and data frame. Series takes care of one-dimensional data, and the data frame takes care of two-dimensional data.

5.Scipy

Scipy is a popular library majorly used in the data science field. It basically does scientific computation. It contains many sub-modules used primarily in science and engineering fields for FFT, signal, image processing, optimization, integration, interpolation, linear algebra, ODE solvers, etc.

Importance of data science

Data scientists are becoming more important for a company in the 21st century. They are becoming a significant factor in public agencies, private companies, trades, products and non-profit organizations. A data scientist plays as a curator, software programmer, computer scientist, etc. They are the central part of managing the collection of digital data. According to our analysis, we have listed below the major reasons why data science is important in developing the world’s economy.

Data science helps to create a relationship between the company and the client. This connection helps to know the customer’s requirements and work accordingly.

Data scientists are the base for the functioning and the growth of any product. Thus they become an important part as they are involved in doing significant tasks .i.e. data analysis and problem-solving.

There is a vast amount of data travelling around the world and if it is used efficiently, it results in the successful growth of the product.

The resulting products have a storytelling capability that creates a reliable connection among the customers. This is one of the reasons why data science is popular.

It can be applied to various industries like health-care, travel, software companies, etc.

Big data analytics is majorly used to solve the complexities and find a solution for the problems in IT companies, resource management, and human resource.

It greatly influences the retail or local sellers. Currently, due to the emergence of many supermarkets and shops, the customers approaching the retail sellers are drastically decreased. Thus data analytics helps to build a connection between the customers and local sellers.

Are you finding it difficult to answer the questions in an interview? Here are some frequently asked data science interview questions on basic concepts

Q. How to maintain a deployed model?

To maintain a deployed model, we have to

Monitor

Evaluate

Compare

Rebuild

Q. What is random forest model?

Random forest model consists of several decision trees. If you split the data into different sections and assign each group of data a decision tree, the random forest models combine all the trees.

Q. What are recommendation systems?

A recommendation system recommends the products to the users based on their previous purchases or preferences. There are mainly two areas .i.e. collaborative filtering and content-based filtering.

Q. Explain the significance of p-value?

P-value <= 0.5 : rejects the null-hypothesis

P-value > 0.5 : accepts null-hypothesis

P-value = 0.5 : it will either except or deny the null-hypothesis

Q. What is logistic regression?

Logistic regression is a method to obtain a binary result from a linear combination of predictor variables.

Q. What are the steps in building a decision tree?

Take the full data as the input.

Split the dataset in such a way that the separation of the class is maximum.

Split the input.

Follow steps 1 and 2 to the separated data again.

Stop this process after the complete data is separated.

Best Python data science courses

Many websites provide Data Science online courses. Here are the best sites that offer data science training based on Python.

GreatLearning

Coursera

EdX

Alison

Udacity

Skillathon

Konvinity

Simplilearn

How data science courses help in a successful career post-COVID-19 pandemic?

The economic downfall due to COVID-19 impacts has lead to upskill oneself as the world scenarios are changing drastically. Adding skills to your resume gives an added advantage of getting a job easily. The businesses are going to invest mainly in two domains .i.e. data analysis of customer’s demand and understanding the business numbers. It is nearly impossible to master in data science, but this lockdown may help you become a professional by indulging in data science programs.

Firstly, start searching for the best data science course on the internet. Secondly, make a master plan in such a way that you complete all the courses successfully. Many short-term courses are there online that are similar to the regular courses, but you can complete it within a few days. For example, Analytix Labs are providing these kinds of courses to upskill yourself. So this is the right time where you are free without any work and passing time. You can use this time efficiently by enrolling in these courses and become more skilled in data science than before. These course providers also give a data science certification for the course you did; this will help to build your resume.

Data science is a versatile field that has a broad scope in the current world. These data scientists are the ones who are the pillars of businesses. They use various factors like programming languages, machine learning, and statistics in solving a real-world problem. When it comes to programming languages, it is best to learn Python as it is easy to understand and has an interactive interface. Make efficient use of time in COVID-19 lockdown to upskill and build yourself.

#data science #greatlearning #python #online course #career

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juniperpublishers-biostatistics · 6 years ago

Text

Prediction of Strong Ground Motion Using Fuzzy Inference Systems Based on Adaptive Networks

Authored by Mostafa Allameh Zadeh*

Abstract

Peak ground acceleration (PGA) estimates have been calculated in order to predict the devastation potential resulting from earthquakes in reconstruction sites. In this research, a training algorithm based on gradient descent were developed and employed by using strong ground motion records. The Artificial Neural Networks (ANN) algorithm indicated that the fitting between the predicted strong ground motion by the networks and the observed PGA values were able to yield high correlation coefficients of 0.78 for PGA. We attempt to provide a suitable prediction of the large acceleration peak from ground gravity acceleration in different areas. Methods are defined by using fuzzy inference systems based on adaptive networks, feed-forward neural networks (FFBP)by four basic parameters as input variables which influence an earthquake in regional studied. The affected indices of an earthquake include the moment magnitude, rupture distance, fault mechanism and site class. The ANFIS network — with an average error of 0.012 — is a more precise network than FFBP neural networks. The FFBP network has a mean square error of 0.017 accordingly. Nonetheless, these two networks can have a suitable estimation of probable acceleration peaks (PGA) in this area.

Keywords: Adaptive-network-based fuzzy inference systems; Feed-forward back propagation error of a neural network; Peak ground acceleration; Rupture distance

Abbreviations: PGA: Peak ground acceleration; ANN: Artificial Neural Networks; FFBP: Feed-Forward Neural Networks; FIS: Fuzzy Inference System; Mw: Moment Magnitude

Introduction

Peak ground acceleration is a very important factor that must be considered in any construction site in order to examine the potential damage that can result from earthquakes. The actual records by seismometers at nearby stations may be considered as a basis. But a reliable method of estimation may be useful for providing more detailed information of the earthquake’s characteristics and motion [1]. The peak ground acceleration parameter is often estimated by the attenuation of relationships and also by using regression analysis. PGA is one of the most important parameters, often analyzed in studies related to damages caused by earthquakes [2]. It is mostly estimated by the attenuation of equations and is developed by a regression analysis of powerful motion data. Powerful motions relating to a ground have basic effects on the structure of that region [3]. Peak ground acceleration is mostly estimated by attenuation relationships [4]. The input variables in the constructed artificial neural network model are the magnitude, the source-to-site distance and the site’s conditions. The output is the PGA. The generalization capability of ANN algorithms was tested with the same training data. Results indicated that there is a high correlation coefficient (R2) for the fitting that is between the predicted PGA values by the networks and those of the observed ones. Furthermore, comparisons between the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Developed ANN models can be conservatively utilized to achieve a better understanding of the input parameters and their influence, and thus reach PGA predictions.

Kerh & Chaw [1] used software calculation techniques to remove the lack of certainties in declining relations. They used the mixed gradient training algorithm of Fletcher-Reeves’ back propagation error [5]. They applied three neural network models with different inputs including epicentric distance, focal depth and magnitude of the earthquakes. These records were trained and then the output results were compared with available nonlinear regression analysis. The comparisons demonstrated that the present neural network model did have a better performance than that of the other methods. From a deterministic point of view, determining the strongest level of shaking- that can potentially happen at a site- has long been an important topic in earthquake science. Also, the maximum level of shaking defines the maximum load which ultimately affects urban structures.

From a probabilistic point of view, knowledge of the greatest ground motions that can possibly occur would allow a meaningful truncation of the distribution of ground motion residuals, and thus lead to a reduction in the computed values pertaining to probabilistic seismic hazard analyses. Particularly, it points to the low annual frequencies that exceed norms which are considered for critical facilities [6,7]. Empirical recordings of ground motions that feature large amplitudes of acceleration or velocity play a key role in defining the maximum levels of ground motion, which outline the design of engineering projects, given the potentially destructive nature of motions. They also provide valuable insights into the nature of the tails that further distribute the ground motions.

Feed-forward, back propagation error in neural networks

Artificial neural networks are a set of non-linear optimizer methods which do not need certain mathematical models in order to solve problems. In regression analysis, PGA is calculated as a function of earthquake magnitude, distance from the source of the earthquake to the site under study, local condition of the site and other characteristics that are linked to the earthquake source such as slippery length and reverse, normal or wave propagation. In non-linear regression methods, non-linear relations which exist between input and output parameters are expressed as estimations, through statistical calculations within a specified relationship [8]. One of the most popular neural networks is the back propagation algorithms. It is particularly useful for data modeling and the application of predictions [9] (Equations 1, 2 and 3). It is a supervised learning technique which was first described by Werbos [10] and further developed by Rumelhart et al. [11]. Furthermore, its most useful function is for feed forward neural networks where the information moves in one direction only, forward, beginning from the input nodes through to the hidden nodes, and then to the output nodes. There are no cycles or loops in the network.

In (1), one instance of iteration is written for the back propagation algorithm. Where Xk is a vector of current weights and biases, gk is the current gradient and a is the learning rate.

In (2), where F is the performance function of error (mean square error),'t' is the target and 'a' is the real output

In (3), 'a' is the net output,(n) is the net input and 'f' is the activation function of the neuron model

In (4), the error of energy is calculated by the least squares estimate for back-propagation learning algorithm. Where N is the number of training patterns, m is the number of neurons in the output layer. And tjk is the target value of processing the neuron. Therefore, this algorithm changes synaptic weights along with the negative gradient of the error energy function. Furthermore, it mostly benefits feed-forward neural networks where the information moves in only one direction, forward, beginning from the input nodes, through to the hidden nodes, and then to the output nodes. There are no cycles or loops in the network. The basic back-propagation algorithm adjusts the weights in the steepest direction of descent wherein the performance function decreases most rapidly. This network is a general figure of a multi-layer Prospectron network with one or several occasions of connectivity. Theoretically, it can prove every theorem that can be proven by the feed-forward network. Also, problems can be solved more accurately by testing general feed-forward networks.

Results of FFBP neural network

In Figure 1, testing the output of feed-forward neural networks against the true output is demonstrated. In Figure 2, the correlation coefficient of training, testing and validating general feed-forward neural networks is shown. In Table 1, testing the output of a feed-forward network against its true output has been compared. In Figure 3, training and validating the error graph against the feed-forward neural network is shown. Mean square error versus epoch is shown in Figure 4 with the aim of training and checking the general feed-forward

network. The sensitivity factor was obtained by training the feed-forward network (Figure 5). The sensitivity factor for input parameters is shown in Table 2. The performance error function was obtained by testing the FFBP neural network (Table 3).

Data processing

The datasets of records by large amplitude considered in this study involves one sets of accelerogram selected based on their value of PGA. These records are described below in terms of the variables generally considered to control the behavior of ground motions in general i.e. Magnitude, Rupture distance, style of faulting and site classification. The dataset includes recordings from events with Moment Magnitude ranging from (5.2-7.7) and rupture distance from (0.3-51.7 km). The SC values in the models were used as (1 to 5) for S-Wave velocity (For (1), Vs>1500 m/s and (5) Vs<180m/s). One Model was developed for each ANN method. This model was developed for estimation of maximum PGA values of the three components. The Focal Mechanism values in this model were used as (1 to 5) that (1: Strike Slip, 2:Reverse, 3:Normal, 4:Reverse oblique and 5: Normal oblique). A program includes MATLAB Neural Network toolbox was coded to train and test the models for each ANN method. All recordings from crustal events correspond to rupture distances shorter than 25km. The horizontal dataset shows a predominance of records from strike-slip and reverse earthquakes. Ground motions recorded on early strong-motions instruments often required a correction to be applied to retrieve the peak motions, Filtering generally eliminates the highest frequencies for motions recorded on modern accelerographs, and thus reduces the observed PGA values. The training of networks was performed using 60 sets of data. Testing of networks was done using 14 datasets that were randomely selected among the whole data. As shown in Figure 6 (a,b), the Mw and RD values of test and train data varied in the range of (5.2-7.7) and (0.3-52 km), respectively, the fault mechanism values were given in the Figure 6c. Figure 6d illustrated the site conditions of train and test data. As seen in this figure the site conditions were commonly soft and stiff soil types. Figure 6e showed the maximum PGA of records of the three components. In ANFIS model, training and the testing of records are shown in Figures 7a,b. Final decision surfaces are shown in Figure 7c. Final quiver surfaces are shown in Figure 7d.

Adaptive network based fuzzy inference system

The fuzzy logic appeared parallel to the growth in evolution of neural networks theory. The definition of being fuzzy can be found in human decision-making. These definitions can be searched by methods related to processing information [12].ANFIS is one of hybrid neuro-fuzzy inference expert systems and it works like the Takagi-Sugeno-type fuzzy inference system, which was developed by Jang [13]. ANFIS has a similar structure to a multilayer feed-forward neural network, but the links in an ANFIS can only indicate the flow direction of signals between nodes. No weights are associated with the links [14]. ANFIS

architecture consists of five layers of nodes. Out of the five layers, the first and the fourth layers consist of adaptive nodes while the second, third and fifth layers consist of fixed nodes. The adaptive nodes are associated with respective parameters, while the fixed nodes are devoid of any parameters [15-17]. For simplicity, we assume that the fuzzy inference system under consideration has two inputs x, y and one output called z. Supposing that the rule base contains two fuzzy if-then rules(6 and 7) of the Takagi & Sugenos [18], then the type-1 ANFIS structure can be illustrated as in Figure 6.

Rule 1: If (x is A1) and (y is B1) then (f = plx + qly + r1) (5)

Rule 2: If(x is A2) and (y is B2) then (f2 = p2 x + q2 y + r2) (6)

Where x and y are the inputs, A, Bi are the fuzzy sets and fi is the output within the fuzzy region specified by the fuzzy rule. Then pi , qiand r are the design parameters that are determined during the training process, in which a circle indicates fixed nodes, whereas a square indicates adaptive nodes.

The node functions which are in the same layer are of the same function family as described below:

In Figure 8, layer (1), every node (i) is a square node with a node function like this: O1i=μAi(x)

The outputs of this layer constitute the fuzzy membership grade of the inputs, which are presented as:

Where x and y are the inputs that enter node (i), A is a linguistic label and m (x),mBi (y) can adapt any fuzzy membership function.(a,b and c) are the parameters of the membership function. As the values of these parameters change, the bell shaped function varies accordingly. In layer 2 (Figure 8), every node is a circle node labeled n. The outputs of this layer can be presented as a firing strength of rule. In layer 3, every node is a circle node labeled N. The 'th’ node calculates the ratio of the ‘ith ’ rules' firing strength to the sum of all rules belonging to the firing strength. For convenience, outputs of this layer will be termed as normalized firing strengths. In layer 4, the defuzzification layer is an adaptive node with one node. The output of each node in this layer is simply a first order polynomial.

Where Wi is the output of layer 3, {pi * * ri} is the parameter set. Parameters in this layer will be referred to as consequent parameters. In layer 5,the summation neuron is a fixed node which computes the overall output as the summation of all incoming signals. The single node in this layer is a circle node labeled E that computes the overall output as the summation of all incoming signals.

Functionally, there are almost no constraints on the node functions of an adaptive network except in the case of a piecewise differentiability. Structurally, the only limitation of network configuration is that it should be of the feed-forward type. Due to minimal restrictions, the applications of adaptive networks are immediate and immense in various areas. In this section, we propose a class of adaptive networks which are functionally equivalent to fuzzy inference systems. The targeted architecture is referred to as ANFIS, which stands for Adaptive Network-based Fuzzy Inference System. ANFIS utilizes a strategy of hybrid training algorithm to tune all parameters. It takes a given input/output data set and constructs a fuzzy inference system which has membership function parameters that are tuned, or adjusted, using a back-propagation algorithm in combination with the least-squares type of method (NAZMY .T.M, 2009). Fuzzy inference systems are also known as fuzzy- rule-based systems, fuzzy models, fuzzy associative memories or fuzzy controllers, when used as controllers. Basically, a fuzzy inference system is comprised of five functional blocks.

a. A rule base containing a number of fuzzy if-then rules.

b. A database which defines the membership functions of the fuzzy sets used in the fuzzy rules.

c. A decision-making unit which performs inference operations on the rules.

d. A fuzzification interface which transforms the crisp inputs into degrees of match with linguistic values.

e. A defuzzification interface which transform the fuzzy results of the inference into a crisp output.

Usually the rule base and database are jointly referred to as the knowledge base. The steps of fuzzy reasoning performed by fuzzy inference systems are:

a. To compare the input variables with the membership functions on the premise part so as to obtain the membership values. (That is the fuzzification step).

b. To combine multiplications or minimizations of the membership values on the premise part so as to yield the firing strength of each rule.

c. To generate the qualified consequence— either fuzzy or crisp — of each rule depending on the firing strength.

d. To aggregate the qualified consequences so as to produce a crisp output. (That is the defuzzification step).

Results ofANFIS network for maximum PGA simulation

In this research, an adaptive neuro-fuzzy inference method was applied to simulate non-linear mapping among acceleration peak conditions. The neuro-fuzzy model included an approximate fuzzy reasoning through a sugeno fuzzy inference system (FIS). The input space was fuzzified by a grid-partitioning technique. A hybrid learning algorithm was selected in order to adapt the model's parameters. Furthermore, a linear-nonlinear regression analyses and neural network model were employed to observe the relative performances. Based on our findings, it can be concluded that the neuro-fuzzy control system exhibits a superior performance, compared to the other employed methods [19,20]. In the developed ANFIS model, input-space fuzzification was carried out via the grid-partitioning technique. Fuzzy variables were divided into four triangular membership functions *i>*2> x3* 3. The 625 fuzzy ‘if-then’ rules were set up where in the fuzzy variables were connected by the T-Norm (AND) apparatus. First order sugeno FIS was selected for the approximate reasoning process. The adjustment of independent parameters was made according to the batch mode based on the hybrid learning algorithm. The ANFIS model was trained for 50epochs until the observed error ceased to fluctuate. The resultant neuro-fuzzy Simulink model structure is illustrated in Figure 9.

The input space contains four parameters- moment magnitude (Mw), rupture distance, fault mechanism and site class. The output contains vertical components of PGA, including 40 records from different regions of the world, 24 records for training, 6 records for checking and 10 records for testing the selected ANFIS network. Sixty training data and sixty checking data pairs were obtained at first. The one used here contains 625 rules, with four membership functions being assigned to each input variable, having total number of 3185 fitting parameters which are composed of 60 premise parameters and 3125 consequent parameters. This section presents the simulation results of the proposed type-3 ANFIS with both batch (off-line) and pattern (on-line) learning. In the first example, ANFIS is used to model highly nonlinear functions, where by results are compared with the neural network approach and also with relevant earlier work. In the second example, the FIS name is PGA1 and the FIS type is sugeno. We used the 'and-or' method for input partitioning. Furthermore, we used ‘wtsum’ and 'wtaver' functions for defuzzification. The ranges of input and output variables- in other words, the target variables- are Mw=(5-8), R=(1.50-80 km), fault mechanism type =(1-5),site class=(1-5) and target range (PGA)=(0.5-2.50). The number of MFs={5 5 5 5}, the MF type=Trimf and G-bell MFs (Figure 9 $10). The result of this simulation is LSE: 0.002 and the final epoch error equals to 0.0000002.

Results of the ANFIS network

The input MFs for initial fuzzy inference system and the MFs of trained FIS are shown in Figure 11 & 12. The rule base for the designed ANFIS is shown in Figure 13. Finally, a trained FIS structure is created from the initial FIS by using the ANFIS GUI editor, which is depicted in Figure 14. Also, by testing the results, one can interpret Table 4. Fuzzy parameters used for training ANFIS are shown in Table 5. Also two membership functions for ANFIS training are shown in Figure 10.

Discussion on Results

Empirical recordings have had a significant influence on the estimation of the maximum physical ground motions that can be possible. Peak ground acceleration is an important factor which needs to be investigated before testing devastation potentials that can result from earthquakes in rebuilding sites. One of the problems that deserve attention by seismologists is the occurrence of earthquakes where of the ground motion acceleration peak unexpectedly appears to be more than 1g (Figure 15-18). Valuable data on some earthquakes have been used by Strasser [6] to investigate the earthquakes' physical processes and their consequences.

Figure 15a &b shows the acceleration and velocity traces of the horizontal components falling into this category for which the recordings were available. Spectra of pseudo-acceleration response, pertaining to damping by 5%, are also shown. All the examined traces are characterized by a very pronounced peak in the short-period (T<0.3s) range of the spectrum. The peak velocities that are associated with these recordings are less than 50cm/s. Slip distribution of focal mechanism for tohoko earthquake in Japan are shown in Figure 15c. The results of Gullo and Ercelebi's [2] research (2007) indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R2). Furthermore, comparisons between the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression method (Table 6). The developed ANN models can be used conservatively so as to establish a good understanding of the influence of input parameters for the PGA predictions.

In Strasser and Bommer's [6] research, a dataset of recordings was examined. It was characterized by the recordings' large amplitudes of PGA (1g) (Figure 15). A number of physical processes have been proposed in the literature to explain these large ground motions, which are commonly divided into source, path and site related effects. While it is often a matter of convention whether these are considered to be predominantly linked to ground motion generation (source effects) or propagation (path and site effects), particularly in the nearsource region, it is important to distinguish between factors that are event-specific, station-specific and record-specific, in terms of implications for ground motion predictions and thus seismic hazard assessment. This is because only site-specific effects can be predicted for certain, in advance. In the present paper, the ANN algorithm indicated that the fitting between the predicted PGA values by the networks and the observed PGA values could yield high correlation coefficients of 0.851for PGA ( 3). Moreover, comparisons between the correlations obtained by the ANN and the regression method demonstrated that the ANN algorithm performed better than the regressions. The Levenberg-Marquart gradient method which we applied on the training algorithm contributed dominantly to fitting the results well.

It had the potential to carry out training very quickly. Moreover, the network models developed in this paper offer new insights into attenuation studies for the purpose of estimating the PGA. In this study, ANFIS and FFBP models were developed to forecast the PGA in different regions of the world. The results of two models and the observed values were compared and evaluated based on their training and validation performance (Figures 2 & 4). The results demonstrated that ANFIS and FFBP models can be applied successfully to establish accurate and reliable PGA forecasting, when comparing the results of the two networks. It was observed that the value of R belonging to the FFBP models is high (0.78) (Figure 3). Moreover, the LSE values of the ANFIS model — which is 0.012 — were lower than that of the FFBP model (Table 4). Therefore, the ANFIS model could be more accurate than the FFBP model. However, a significant advantage is evident when predicting the PGAvia ANFIS, compared to the FFBP model (Figures 16 & 17). The simulations show that the ANFIS network is good for predicting maximum peak ground acceleration in some regions of the world. Finally, the minimum testing error- obtained for the ANFIS network- is 0.002 and the ultimate epoch error is 0.012 (Table 4). This conclusion shows that the ANFIS network can be suitable and useful for predicting values of peak ground acceleration for future earthquakes. PGA-predicted values versus record numbers for three neural networks are shown in Figure 18 & 19.

Conclusion

In this study, FFBP neural networks and ANFIS were trained so as to estimate peak ground acceleration in an area. The input variables in the ANN model were the magnitude, the rupture distance, the focal mechanism and site classification. The output was the PGA only. In the end, the minimum testing error was obtained for the ANFIS network, which equaled 0.002, and the mean square error for the FFBP neural network equaled 0.017. This conclusion shows that the ANFIS network can be suitable and useful in predicting peak ground acceleration for future earthquakes.

Acknowledgement

I am very grateful to the editors and anonymous reviewers, for their suggestions aimed at improving the quality of this manuscript. I also appreciate professors Strasser and Bommer for granting necessary data to this work.

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#Adaptive-network #fuzzy inference systems #neural network #Rupture distance #Moment Magnitude

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thecoroutfitters · 6 years ago

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What Eigenvalue Calculator Is – and What it Is Not

The eigenvalues correspond to the quantity of variation in the variables that is explained by means of a component so the very first component ought to be the one utilized in computing the very first principal component. All these solvers follow precisely the same general idea. Think about the harmonic oscillator Find the overall solution utilizing the system technique.

From time to time, the solution need not be exceedingly accurate. Such a processor can very easily access a substantial database. For some basic systems, a closed-form analytical solution might be available.

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The key is to care for the intricate eigenvalue as a real one. If this operator acts on an overall wavefunction the outcome is typically a wavefunction with a very different shape. In addition, in the equation below, you will see that there is just a little difference between covariance and variance.

Determinants are simple, the eigenvalues are simply the diagonal entries and the eigenvectors are only elements of the conventional basis. The associated eigenvectors are now able to be found. Eigenvalues may also be calculated in an optimised method.

Component analysis is just one of the vital strategies that is utilised to lessen dimension space without losing valuable info. Dissertation is a segment that’s major you’ll need to attain within your program. Because of this, search engines utilize static high quality measures like PageRank as merely one of many elements in scoring an internet page on a query.

In addition, this page typically only deals with the most general instances, there will probably be special cases (for instance, non-unique eigenvalues) that aren’t covered in any way. This list gives a number of the minors from the matrix above. Let’s look at a good example.

All About Eigenvalue Calculator

On-line calculator is completely free and is an effortless method of problem solving. With the aid of a graphing calculator, we can readily address this dilemma. Pressing `Calculate’ again will bring about a new random first guess and ought to bring about a solution.

However, for large, finding these zeros are mathcalculator.org/eigenvalue-calculator/ sometimes a daunting issue. It makes the lives of individuals using matrices easier. Within the next page, we’ll examine the problem of locating eigenvectors.

You ought to know about these various kinds of tools and their working mechanisms. This function has to be represented by a finite amount of information, for example by its value at a finite number of points during its domain, though this domain is a continuum. Then you ought to have to locate the polynomial and the features of the polynomial equation.

The structure of a few of these securities could be complex and there can be less available information than other sorts of debt securities. Its existence lets you calculate the typical value of the definite integral. Within the next section we introduce several properties which make it simpler to calculate determinants.

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Matlab recognises the input is an intricate number and does some of the job for you. There’s currently no choice to carry out this normalization based on anything besides all selected variables. Encoding this matrix on a computer is likely to take a significant lot of memory!

The Unexpected Truth About Eigenvalue Calculator

Then you’ve got to bring another step. In truth, it’s a royal pain. We obtain the exact same result!

Therefore, the outcomes of this step may be used for other linear systems where the matrix has an identical structure. The system of Lagrange multipliers can be utilized to lower optimization problems with constraints to unconstrained optimization issues. To fix a system of linear equations using Gauss-Jordan elimination you must do the next steps.

The start of 20 century is referred to as the golden age of control engineering. If you don’t will need to manage zero-value elements, skipping them are able to massively accelerate execution on sparse layouts. The majority of the moment, all you will need is to understand how long it will take to address your system, and hopefully, what is the most proper solver.

You merely recognize an underlying pattern. When you expand this, I strongly advise that you expand along the very first row. Unique types can be mixed together in one matrix.

The key is to use parametrization to create a true function of a single variable, and after that apply the one-variable theorem. Euler’s Formula makes it simple. This completely free online Modulo Calculator makes it simple to figure out the modulo of any 2 numbers.

There’s no immediate geometric or intuitive interpretation of multiplying both matrices in reverse purchase. It is an easy algorithm that does not compute matrix decomposition, and hence it can be utilized in instances of large sparse matrices. Eigen value of symmetric matrix is apparently very beneficial for many practical issues.

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jobdxb · 6 years ago

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Math Statistician required

Location: Dubai - United Arab Emirates Salary: Experience: 1 Year Shift Timings: Morning Shift Job Type: Full-Time Description: BSc in Science in mathematics A Bachelor's Degree in Statistics, Math A strong background in mathematical and statistical modelling Fundamentals Sciences Analyzes and reports quality analytics Develops testing procedures and control groups to determine optimal program performance Utilizes statistical software to mine and analyze data to support quality initiatives Prepares detail and summary level reports including written interpretation of analytic results Understanding of the systems engineering lifecycle to include independent testing and data verification Ability to work independently as well as part of a team Willingness to learn, solve problems and perform in a dynamic work environment Previous experience working as a Quant Analyst, Data Analyst, Data Scientist or Big Data Analyst - desirable Degree in maths / statistics / machine learning / econometrics essential Experience using C++ or Python is highly regarded, although Matlab & R experience will also be considered Strong algorithmic coding skill and solid knowledge of data structures Experience with statistical forecasting essential Understanding of machine / statistical learning techniques such as non-linear regression, kernel regression, support vector machines (SVM), neural networks, classification trees and similar techniques very beneficial Develop analytic frameworks to support research effort in demand forecasting to enhance company's capability in making strategic product decision in a regulated any industry and highly competitive market environment. Demonstrated experience and success interpreting research requests, developing analysis plans, performing high-level statistical analysis, statistical sampling, and computing required samples sizes.

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mobileexpressnow · 8 years ago

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KOTLIN, Python, and React Native among the Top 10 Programming Languages to Look Out For in 2018

In our Mobile app development industry, if there is anything that grows at par with the continuous app entries in the stores and the frequent updates, it is the increasing number of programming languages to support the mushrooming.

Based on the usability and ranking factors, I have listed down 10 Programming Languages that will define the next year.

Let’s cut to the chase:

Here is the list of Top 10 Programming Languages that will dominate the app development market in the year 2018.

Swift 4.0

Java 8

KOTLIN

React Native

Python

Node.Js

Haskell

MATLAB

JavaScript

1. Swift 4.0

Swift 4 is based on the strong points of Swift 3, providing better stability and robustness, offering source code compatibility with the Swift 3 language. It has brought in enhancements to the library, and have additional features like serialization and archival. Taking iPhone app development companies to the next level.

The new version has been introduced with new workflow features and complete API for the Swift Manager Package

Features –

It is now possible to develop a number of packages before you tag your official release. Also, it easier now to work on branch of packages at the same time.

The package products are now formalized, which makes it possible to have a closer look at what the libraries that are published to the clients by the package.

To negate the effect of hostile manifests, Swift package now appears in sandbox that prevent file system modification and network access.

Swift, in comparison to Objective-C is gaining popularity with each passing day (as you can see in the image below), and is expected to completely surpass Objective-C iOS app development language soon.

2. Java 8

Java 8 is an upgrade to Java’s programming model and is a coordinated advancement of the Java language, JVM, and libraries. The language, which is used for Android app development includes features promoting ease of use, productivity, security, improved performance, and improved polyglot programming.

Features –

Virtual Extension Methods and Lambda Expression

One of the most noteworthy features of the Java SE 8 language is its implementation of the Lambda expressions and the various related supporting features for both the platform and Java programming language.

Time and Date API

The new API allows developers to manage time and date in a much cleaner, easier to understand, and natural way.

Nashhorn JavaScript Engine

A fresh high performance, lightweight implementation of the JavaScript engine has been integrated to the JDK and has been made available to the Java applications through existing APIs.

Improved Security

This has replaced the present hand-maintained document of the caller sensitive methods with the mechanism, which accurately identifies the methods and allow the callers to be discoverable reliably.

3. Kotlin

The now official Google programming language, Kotlin is used for developing multi-platform applications. With the help of the language, one can create apps for JVM, Android, Native, and Browser. Since the announcement of it becoming the official language, Kotlin has been adopted by a number of companies for their apps. Since it’s very new in the industry, we recently wrote an article to help make it easy for the developers to make the switch from Java to Kotlin.

Read – Kotlin for Android App Development – The Whys and Hows and Bonus Tips

Features –

Java Interoperability

Kotlin is 100% interoperable with Java, making it easy for the Java developers to learn the language. The platform gives the developers an option to paste their code and it converts the Java code into Kotlin’s.

Zero Runtime Overhead

The language has concentrated extensions to the Java library. Most of its functions are in-line which simply become inline code.

Null Safety

Kotlin eliminates the side-effects of code’s null reference. The language does not compile the codes that returns or assigns a null.

Extension Functions

Developers can add methods in classes without bringing any changes to their source code. One can add the methods on the per user basis in the classes.

4. React Native

React Native is the framework, which uses React to define the user interface for the native devices. With the help of React Native one can build applications, which runs on both iOS and Android devices using JavaScript.

Features –

Code Reuse

The language gives you the freedom to use the same code for both iOS and Android.

Live Reload

It allows you to see the most recent change that you have made to the code, immediately.

Strong Performance

The language makes use of the Graphics Processing Unit, which makes it well tuned for mobile apps in terms of the speed advantage it offers.

Modular Architecture

Its interface helps developers in looking into someone else’s project and building upon it. It gives the benefit of flexibility as it takes less time for the developers to understand the programming logic and edit it.

5. Python

It is a general purpose language that has a variety of uses ranging from mathematical computing, such as – NumPy, SymPy, and Orange; Desktop Graphical UI – Panda3D, Pygame and in Web Development – Bottle and Django.

Python is known for its clean syntax and short length of code, and is the the most wanted programming language.

Features –

Easy to Learn

The language has a simple and elegant syntax which is much easier to write and read as compared to the other programming languages like C#, Java, and C++. For a newbie it is every easy to start with Python solely because of its easy syntax.

Open Source

The developers can freely use the language, even for their commercial uses. Other than using and distributing the software that are written in it, you can also make changes in the source code.

Portable

Python can be moved from one platform to another and run in them without any changes.

It can run seamlessly on platforms including Mac OS X, Windows, and Linux.

Standard Libraries

Python has standard libraries which save developers’ time in writing all the code themselves. Suppose you want to connect MySQL database on the web server, now instead of writing the whole code by yourself, you can make use of the MySQLdb library.

6. R

It is an open source program which is used to perform statistical operations. R is a command line driven program, meaning that developers enter command at the prompt and every command is implemented one at a time.

Features –

R supports object oriented programming with the generic functions and procedural programming with functions.

It can print the analysis reports in form of graph in both hardcopy and on-screen.

Its programming features consist of exporting data, database input, viewing data, missing data, variable labels, etc.

Packages form an element of R programming language. Thus, they are helpful in collecting the sets of R functions in a particular unit.

7. Node.Js

Node.js is the cross-platform, open-source JavaScript run-time environment for implementing JavaScript code on the server side.

It makes use of an event-focused, non-blocking I/O model, which makes it efficient and lightweight, ideal for data-concentrated real-time apps that can run across series of distributed devices.

Features –

Event Driven

All APIs in the Node.js library are event driven, meaning the Node.js server doesn’t have to wait for the API to return data. Server moves to next API after calling it and the notification mechanism of the Node.js events help servers in getting a response from the last API call.

Fast

Built on Google Chrome’s V8 JavaScript engine, the language’s library code execution’s speed is very fast.

Scalable

Node.js make use of one thread program, which can offer service to a large number of requests than its traditional servers such as Apache HTTP Server.

Zero Buffering

The Node.js application don’t buffer any data. They output all the data in portions.

8. Haskell

Haskell is a functional programming language. It is a first commercial language to enter the functional programming domain. It is a mix of a number of generalizable functions which define what a program is supposed to do., allowing the lower layers handle the mundane details such as iteration.

As compared to other similar programming languages, Haskell offers support for –

Lazy Evaluation

Monadic side-effects

Syntax based on the layout

Type classes

Pure functions by default

On the top of it, Huskell is one of the top 15 loved programming languages according to Stack Overflow Developer Survey.

9. MATLAB

The proprietary programming language allows plotting of data and functions, matrix manipulations, development of user interfaces, implementation of algorithms, and interfacing with the programs written in the other languages that includes C++, C, C#, Fortran, Java, and Python.

It is one of the most superior language in the programs used for scientific and mathematical purposes. According to statistics Google Trends, this language will continue to remain in the market.

Features –

Offers interactive environment for design, iterative exploration, and problem solving.

Provide library of functions for fourier analysis, optimization, numerical integration, and Linear algebra among others.

Give development tool for bettering the code quality, maintainability, and maximizing their performances.

Provide function for integration of MATLAB algorithms with the external languages and applications like Java, C, .NET, and Microsoft Excel.

10. JavaScript

It allows developing applications for mobile, desktop and web, as well as build interactive websites. When compared to Python or Java, JavaScript is easier to learn and implement because of all of the accessible UI features. It has many convenient and flexible libraries, among which React.js, Angular.js, and Vue.js are the most trending ones.

JavaScript is one of the most used programming languages by developers, ranking on the top with 62.5% in the <a href=”http://ift.tt/2hvE0Qp; rel=”nofollow”>Stack Overflow Developer Survey</a> (as you can see in the graph given below).

Features-

Universal Support

All modern web browsers support JavaScript, thanks to built-in interpreters.

Dynamic

Just like many other scripting languages, JavaScript is dynamically typed. Here, a type is linked with each value and not just with each expression. Moreover, JavaScript includes an eval function that performs statements provided as strings at run-time.

Imperative and Structured

This programming language supports almost all the structured programming syntax from C, except scoping (right now, it had only function scoping with var).

Prototype-based (Object-oriented)

JavaScript is nearly object-based with an object considered as an associative array, combined with a prototype. Each string in case of JavaScript serves the name for an object property, with two ways to specify the name. A property can be added, deleted or rebound at run-time, and most of the properties of an object can be computed using a for…in loop.

From ease of development to the richness of the end application, there are a number of reasons why the world continues to see advancements programming languages – making them newer and better.

Learning and using the ones mentioned in the article will definitely help you win the rat race to delivering top ranking apps.

The post KOTLIN, Python, and React Native among the Top 10 Programming Languages to Look Out For in 2018 appeared first on Appinventiv Official Blog - Mobile App Development Company.

0 notes

olumina · 8 years ago

Text

KOTLIN, Python, and React Native among the Top 10 Programming Languages to Look Out For in 2018

Based on the usability and ranking factors, I have listed down 10 Programming Languages that will define the next year.

Let’s cut to the chase:

Here is the list of Top 10 Programming Languages that will dominate the app development market in the year 2018.

Swift 4.0

Java 8

KOTLIN

React Native

Python

Node.Js

Haskell

MATLAB

JavaScript

1. Swift 4.0

The new version has been introduced with new workflow features and complete API for the Swift Manager Package

Features –

It is now possible to develop a number of packages before you tag your official release. Also, it easier now to work on branch of packages at the same time.

The package products are now formalized, which makes it possible to have a closer look at what the libraries that are published to the clients by the package.

To negate the effect of hostile manifests, Swift package now appears in sandbox that prevent file system modification and network access.

2. Java 8

Features –

Virtual Extension Methods and Lambda Expression

Time and Date API

The new API allows developers to manage time and date in a much cleaner, easier to understand, and natural way.

Nashhorn JavaScript Engine

A fresh high performance, lightweight implementation of the JavaScript engine has been integrated to the JDK and has been made available to the Java applications through existing APIs.

Improved Security

This has replaced the present hand-maintained document of the caller sensitive methods with the mechanism, which accurately identifies the methods and allow the callers to be discoverable reliably.

3. Kotlin

Read – Kotlin for Android App Development – The Whys and Hows and Bonus Tips

Features –

Java Interoperability

Zero Runtime Overhead

The language has concentrated extensions to the Java library. Most of its functions are in-line which simply become inline code.

Null Safety

Kotlin eliminates the side-effects of code’s null reference. The language does not compile the codes that returns or assigns a null.

Extension Functions

Developers can add methods in classes without bringing any changes to their source code. One can add the methods on the per user basis in the classes.

4. React Native

Features –

Code Reuse

The language gives you the freedom to use the same code for both iOS and Android.

Live Reload

It allows you to see the most recent change that you have made to the code, immediately.

Strong Performance

The language makes use of the Graphics Processing Unit, which makes it well tuned for mobile apps in terms of the speed advantage it offers.

Modular Architecture

5. Python

Python is known for its clean syntax and short length of code, and is the the most wanted programming language.

Features –

Easy to Learn

Open Source

The developers can freely use the language, even for their commercial uses. Other than using and distributing the software that are written in it, you can also make changes in the source code.

Portable

Python can be moved from one platform to another and run in them without any changes.

It can run seamlessly on platforms including Mac OS X, Windows, and Linux.

Standard Libraries

6. R

Features –

R supports object oriented programming with the generic functions and procedural programming with functions.

It can print the analysis reports in form of graph in both hardcopy and on-screen.

Its programming features consist of exporting data, database input, viewing data, missing data, variable labels, etc.

Packages form an element of R programming language. Thus, they are helpful in collecting the sets of R functions in a particular unit.

7. Node.Js

Node.js is the cross-platform, open-source JavaScript run-time environment for implementing JavaScript code on the server side.

It makes use of an event-focused, non-blocking I/O model, which makes it efficient and lightweight, ideal for data-concentrated real-time apps that can run across series of distributed devices.

Features –

Event Driven

Fast

Built on Google Chrome’s V8 JavaScript engine, the language’s library code execution’s speed is very fast.

Scalable

Node.js make use of one thread program, which can offer service to a large number of requests than its traditional servers such as Apache HTTP Server.

Zero Buffering

The Node.js application don’t buffer any data. They output all the data in portions.

8. Haskell

As compared to other similar programming languages, Haskell offers support for –

Lazy Evaluation

Monadic side-effects

Syntax based on the layout

Type classes

Pure functions by default

On the top of it, Huskell is one of the top 15 loved programming languages according to Stack Overflow Developer Survey.

9. MATLAB

It is one of the most superior language in the programs used for scientific and mathematical purposes. According to statistics Google Trends, this language will continue to remain in the market.

Features –

Offers interactive environment for design, iterative exploration, and problem solving.

Provide library of functions for fourier analysis, optimization, numerical integration, and Linear algebra among others.

Give development tool for bettering the code quality, maintainability, and maximizing their performances.

Provide function for integration of MATLAB algorithms with the external languages and applications like Java, C, .NET, and Microsoft Excel.

10. JavaScript

Features-

Universal Support

All modern web browsers support JavaScript, thanks to built-in interpreters.

Dynamic

Imperative and Structured

This programming language supports almost all the structured programming syntax from C, except scoping (right now, it had only function scoping with var).

Prototype-based (Object-oriented)

From ease of development to the richness of the end application, there are a number of reasons why the world continues to see advancements programming languages – making them newer and better.

Learning and using the ones mentioned in the article will definitely help you win the rat race to delivering top ranking apps.

The post KOTLIN, Python, and React Native among the Top 10 Programming Languages to Look Out For in 2018 appeared first on Appinventiv Official Blog - Mobile App Development Company.

0 notes

shuying877 · 8 years ago

Text

Image Processing Engineer job at Rapsodo Pte. Ltd Singapore

Rapsodo develops sports electronics products using imaging technologies. We are a young ambitious start up developing complex camera systems and solving difficult problems with intellectual property developed in house. Our products touching the lives of tens of thousands of people around the world. We are on a growth path and always looking for smart, ambitious people to join our team and be a part of our journey.

The ideal candidate for the Image Processing Algorithm Engineer position is willing to bring their skill to the team to develop state of the art measurement algorithm using imaging sensors with passion and drive.

MS/M.Eng/PhD in Computer Science or 3+ years of industry and/or research experience in relevant field

Concentration(s) in: Computer Vision, Machine Learning, Image Processing, and/or Computer Graphics

Excellent skills developing in C/cplusplus.

Experience with OpenCV.

The ability to communicate technical information clearly and succinctly to both technical and non-technical team.

Highly Desirable:

Strong skills developing in MATLAB

Expertise in image classification, segmentation, and feature extraction

Strong understanding of linear algebra, optimization, probability and statistics

Experience with software architecture and/or API design, complemented by robust integration skills

A background in Parallel programming

From http://www.startupjobs.asia/job/31059-image-processing-engineer-architect-job-at-rapsodo-pte-ltd-singapore

from https://startupjobsasiablog.wordpress.com/2017/07/18/image-processing-engineer-job-at-rapsodo-pte-ltd-singapore-2/

0 notes

ameliamike90 · 8 years ago

Text

Image Processing Engineer job at Rapsodo Pte. Ltd Singapore

MS/M.Eng/PhD in Computer Science or 3+ years of industry and/or research experience in relevant field

Concentration(s) in: Computer Vision, Machine Learning, Image Processing, and/or Computer Graphics

Excellent skills developing in C/cplusplus.

Experience with OpenCV.

The ability to communicate technical information clearly and succinctly to both technical and non-technical team.

Highly Desirable:

Strong skills developing in MATLAB

Expertise in image classification, segmentation, and feature extraction

Strong understanding of linear algebra, optimization, probability and statistics

Experience with software architecture and/or API design, complemented by robust integration skills

A background in Parallel programming

StartUp Jobs Asia - Startup Jobs in Singapore , Malaysia , HongKong ,Thailand from http://www.startupjobs.asia/job/31059-image-processing-engineer-architect-job-at-rapsodo-pte-ltd-singapore Startup Jobs Asia https://startupjobsasia.tumblr.com/post/163129066654

0 notes

startupjobsasia · 8 years ago

Text

Image Processing Engineer job at Rapsodo Pte. Ltd Singapore

MS/M.Eng/PhD in Computer Science or 3+ years of industry and/or research experience in relevant field

Concentration(s) in: Computer Vision, Machine Learning, Image Processing, and/or Computer Graphics

Excellent skills developing in C/cplusplus.

Experience with OpenCV.

The ability to communicate technical information clearly and succinctly to both technical and non-technical team.

Highly Desirable:

Strong skills developing in MATLAB

Expertise in image classification, segmentation, and feature extraction

Strong understanding of linear algebra, optimization, probability and statistics

Experience with software architecture and/or API design, complemented by robust integration skills

A background in Parallel programming

StartUp Jobs Asia - Startup Jobs in Singapore , Malaysia , HongKong ,Thailand from http://www.startupjobs.asia/job/31059-image-processing-engineer-architect-job-at-rapsodo-pte-ltd-singapore

0 notes

olumina · 8 years ago

Text

Top 10 Trendiest Programming Languages of 2018

Based on the usability and ranking factors, I have listed down 10 Programming Languages that will define the next year.

Let’s cut to the chase:

Here is the list of Top 10 Programming Languages that will dominate the app development market in the year 2018.

Swift 4.0

Java 8

KOTLIN

React Native

Python

Node.Js

Haskell

MATLAB

JavaScript

1. Swift 4.0

The new version has been introduced with new workflow features and complete API for the Swift Manager Package

Features –

It is now possible to develop a number of packages before you tag your official release. Also, it easier now to work on branch of packages at the same time.

The package products are now formalized, which makes it possible to have a closer look at what the libraries that are published to the clients by the package.

To negate the effect of hostile manifests, Swift package now appears in sandbox that prevent file system modification and network access.

2. Java 8

Features –

Virtual Extension Methods and Lambda Expression

Time and Date API

The new API allows developers to manage time and date in a much cleaner, easier to understand, and natural way.

Nashhorn JavaScript Engine

A fresh high performance, lightweight implementation of the JavaScript engine has been integrated to the JDK and has been made available to the Java applications through existing APIs.

Improved Security

This has replaced the present hand-maintained document of the caller sensitive methods with the mechanism, which accurately identifies the methods and allow the callers to be discoverable reliably.

3. Kotlin

Read – Kotlin for Android App Development – The Whys and Hows and Bonus Tips

Features –

Java Interoperability

Zero Runtime Overhead

The language has concentrated extensions to the Java library. Most of its functions are in-line which simply become inline code.

Null Safety

Kotlin eliminates the side-effects of code’s null reference. The language does not compile the codes that returns or assigns a null.

Extension Functions

Developers can add methods in classes without bringing any changes to their source code. One can add the methods on the per user basis in the classes.

4. React Native

Features –

Code Reuse

The language gives you the freedom to use the same code for both iOS and Android.

Live Reload

It allows you to see the most recent change that you have made to the code, immediately.

Strong Performance

The language makes use of the Graphics Processing Unit, which makes it well tuned for mobile apps in terms of the speed advantage it offers.

Modular Architecture

5. Python

Python is known for its clean syntax and short length of code, and is the the most wanted programming language.

Features –

Easy to Learn

Open Source

The developers can freely use the language, even for their commercial uses. Other than using and distributing the software that are written in it, you can also make changes in the source code.

Portable

Python can be moved from one platform to another and run in them without any changes.

It can run seamlessly on platforms including Mac OS X, Windows, and Linux.

Standard Libraries

6. R

Features –

R supports object oriented programming with the generic functions and procedural programming with functions.

It can print the analysis reports in form of graph in both hardcopy and on-screen.

Its programming features consist of exporting data, database input, viewing data, missing data, variable labels, etc.

Packages form an element of R programming language. Thus, they are helpful in collecting the sets of R functions in a particular unit.

7. Node.Js

Node.js is the cross-platform, open-source JavaScript run-time environment for implementing JavaScript code on the server side.

It makes use of an event-focused, non-blocking I/O model, which makes it efficient and lightweight, ideal for data-concentrated real-time apps that can run across series of distributed devices.

Features –

Event Driven

Fast

Built on Google Chrome’s V8 JavaScript engine, the language’s library code execution’s speed is very fast.

Scalable

Node.js make use of one thread program, which can offer service to a large number of requests than its traditional servers such as Apache HTTP Server.

Zero Buffering

The Node.js application don’t buffer any data. They output all the data in portions.

8. Haskell

As compared to other similar programming languages, Haskell offers support for –

Lazy Evaluation

Monadic side-effects

Syntax based on the layout

Type classes

Pure functions by default

On the top of it, Huskell is one of the top 15 loved programming languages according to Stack Overflow Developer Survey.

9. MATLAB

It is one of the most superior language in the programs used for scientific and mathematical purposes. According to statistics Google Trends, this language will continue to remain in the market.

Features –

Offers interactive environment for design, iterative exploration, and problem solving.

Provide library of functions for fourier analysis, optimization, numerical integration, and Linear algebra among others.

Give development tool for bettering the code quality, maintainability, and maximizing their performances.

Provide function for integration of MATLAB algorithms with the external languages and applications like Java, C, .NET, and Microsoft Excel.

10. JavaScript

Features-

Universal Support

All modern web browsers support JavaScript, thanks to built-in interpreters.

Dynamic

Imperative and Structured

This programming language supports almost all the structured programming syntax from C, except scoping (right now, it had only function scoping with var).

Prototype-based (Object-oriented)

From ease of development to the richness of the end application, there are a number of reasons why the world continues to see advancements programming languages – making them newer and better.

Learning and using the ones mentioned in the article will definitely help you win the rat race to delivering top ranking apps.

The post Top 10 Trendiest Programming Languages of 2018 appeared first on Appinventiv Official Blog - Mobile App Development Company.

0 notes

mobileexpressnow · 8 years ago

Text

Top 10 Trendiest Programming Languages of 2018

Based on the usability and ranking factors, I have listed down 10 Programming Languages that will define the next year.

Let’s cut to the chase:

Here is the list of Top 10 Programming Languages that will dominate the app development market in the year 2018.

Swift 4.0

Java 8

KOTLIN

React Native

Python

Node.Js

Haskell

MATLAB

JavaScript

1. Swift 4.0

The new version has been introduced with new workflow features and complete API for the Swift Manager Package

Features –

It is now possible to develop a number of packages before you tag your official release. Also, it easier now to work on branch of packages at the same time.

The package products are now formalized, which makes it possible to have a closer look at what the libraries that are published to the clients by the package.

To negate the effect of hostile manifests, Swift package now appears in sandbox that prevent file system modification and network access.

2. Java 8

Features –

Virtual Extension Methods and Lambda Expression

Time and Date API

The new API allows developers to manage time and date in a much cleaner, easier to understand, and natural way.

Nashhorn JavaScript Engine

A fresh high performance, lightweight implementation of the JavaScript engine has been integrated to the JDK and has been made available to the Java applications through existing APIs.

Improved Security

This has replaced the present hand-maintained document of the caller sensitive methods with the mechanism, which accurately identifies the methods and allow the callers to be discoverable reliably.

3. Kotlin

Read – Kotlin for Android App Development – The Whys and Hows and Bonus Tips

Features –

Java Interoperability

Zero Runtime Overhead

The language has concentrated extensions to the Java library. Most of its functions are in-line which simply become inline code.

Null Safety

Kotlin eliminates the side-effects of code’s null reference. The language does not compile the codes that returns or assigns a null.

Extension Functions

Developers can add methods in classes without bringing any changes to their source code. One can add the methods on the per user basis in the classes.

4. React Native

Features –

Code Reuse

The language gives you the freedom to use the same code for both iOS and Android.

Live Reload

It allows you to see the most recent change that you have made to the code, immediately.

Strong Performance

The language makes use of the Graphics Processing Unit, which makes it well tuned for mobile apps in terms of the speed advantage it offers.

Modular Architecture

5. Python

Python is known for its clean syntax and short length of code, and is the the most wanted programming language.

Features –

Easy to Learn

Open Source

The developers can freely use the language, even for their commercial uses. Other than using and distributing the software that are written in it, you can also make changes in the source code.

Portable

Python can be moved from one platform to another and run in them without any changes.

It can run seamlessly on platforms including Mac OS X, Windows, and Linux.

Standard Libraries

6. R

Features –

R supports object oriented programming with the generic functions and procedural programming with functions.

It can print the analysis reports in form of graph in both hardcopy and on-screen.

Its programming features consist of exporting data, database input, viewing data, missing data, variable labels, etc.

Packages form an element of R programming language. Thus, they are helpful in collecting the sets of R functions in a particular unit.

7. Node.Js

Node.js is the cross-platform, open-source JavaScript run-time environment for implementing JavaScript code on the server side.

It makes use of an event-focused, non-blocking I/O model, which makes it efficient and lightweight, ideal for data-concentrated real-time apps that can run across series of distributed devices.

Features –

Event Driven

Fast

Built on Google Chrome’s V8 JavaScript engine, the language’s library code execution’s speed is very fast.

Scalable

Node.js make use of one thread program, which can offer service to a large number of requests than its traditional servers such as Apache HTTP Server.

Zero Buffering

The Node.js application don’t buffer any data. They output all the data in portions.

8. Haskell

As compared to other similar programming languages, Haskell offers support for –

Lazy Evaluation

Monadic side-effects

Syntax based on the layout

Type classes

Pure functions by default

On the top of it, Huskell is one of the top 15 loved programming languages according to Stack Overflow Developer Survey.

9. MATLAB

It is one of the most superior language in the programs used for scientific and mathematical purposes. According to statistics Google Trends, this language will continue to remain in the market.

Features –

Offers interactive environment for design, iterative exploration, and problem solving.

Provide library of functions for fourier analysis, optimization, numerical integration, and Linear algebra among others.

Give development tool for bettering the code quality, maintainability, and maximizing their performances.

Provide function for integration of MATLAB algorithms with the external languages and applications like Java, C, .NET, and Microsoft Excel.

10. JavaScript

Features-

Universal Support

All modern web browsers support JavaScript, thanks to built-in interpreters.

Dynamic

Imperative and Structured

This programming language supports almost all the structured programming syntax from C, except scoping (right now, it had only function scoping with var).

Prototype-based (Object-oriented)

From ease of development to the richness of the end application, there are a number of reasons why the world continues to see advancements programming languages – making them newer and better.

Learning and using the ones mentioned in the article will definitely help you win the rat race to delivering top ranking apps.

The post Top 10 Trendiest Programming Languages of 2018 appeared first on Appinventiv Official Blog - Mobile App Development Company.

0 notes