An adaptive prolog programming language with machine learning

2012 ◽  
Author(s):  
Benjie Lu ◽  
Zhiqing Liu ◽  
Hui Gao
Keyword(s):  
Machine Learning ◽  
Programming Language ◽  
Prolog Programming

ПРИМЕНЕНИЕ МОДЕЛИ СИНТАКСИЧЕСКИ УПРАВЛЯЕМОГО ПЕРЕВОДА АРИФМЕТИЧЕСКИХ ВЫРАЖЕНИЙ В ПРОЦЕССЕ ОБУЧЕНИЯ СТУДЕНТОВ

2020 ◽  
Author(s):  
Igor Oblomov ◽  
Vyacheslav Rzhavin ◽  
Natalia Pervova ◽  
Alina Gerasimova
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Learning Process ◽  
Functional Programming ◽  
Syntactic Analysis ◽  
Simple Arithmetic ◽  
Functional Programming Languages ◽  
Source Data ◽  
Prolog Programming ◽  
Arithmetic Expressions

В статье рассматривается модель синтаксически управляемого перевода простых арифметических выражений и ее использование в процессе обучения. Атрибутно-транслируемая грамматика предполагает перевод последовательности актов в последовательность действий, которые, в свою очередь, будут являться исходными данными для следующих этапов трансляции. Раскрываются основные моменты обучения студентов декларативному языку программирования Пролог, делается упор на обработку множества символов действия. Дальнейшие исследования предполагают разработку моделей синтаксического анализа с помощью средств императивных и функциональных языков программирования с целью получения и анализа объективных оценок эффективности полученных моделей в процессе обучения будущих специалистов.This article discusses the model of syntactically controlled translation of simple arithmetic expressions and its use in the learning process. The attribute-translated grammar involves the translation of a sequence of acts into a sequence of actions, which will be the source data for the next stages of translation. The article reveals the main points of teaching students the Prolog programming language, focuses on the processing of many action symbols. Further research involves the development of models of syntactic analysis by means of imperative and functional programming languages in order to obtain and analyze the objective estimates of the effectiveness of the obtained models in the training of future specialists.

Programming Language Support for Implementing Machine Learning Algorithms

2020 ◽  
pp. 402-421
Author(s):  
Anitha Elavarasi S. ◽  
Jayanthi J.
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Mathematical Optimization ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
Machine Learning Applications ◽  
And Control

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.

scholarly journals An approach to assessment of dynamic software variability in mobile applications development

2018 ◽  
Keyword(s):  
Machine Learning ◽  
Programming Language ◽  
Mobile Applications ◽  
Product Line ◽  
Number Of Children ◽  
Java Programming ◽  
Complexity Metrics ◽  
Programming Tools ◽  
Dynamic Software ◽  
Code Complexity

The article describes the approach to the assessment of code reuse in Dynamic Product Line lines (DSPL). Some existing mechanisms to realize software variability in DSPL, such as machine learning, adaptive configurations based on Java programming tools which allow developing DSPL, especially in mobile applications domain, have been reviewed. During the development, some methods for the implementation of the variability specific to the selected programming language have been tested. For each of these mechanisms, such as Weighted Methods per Class, Response for a Class, Depth of Inheritance Tree, Coupling Between Objects, Number of Children, the code complexity metrics have been calculated. Based on these results the code reusability extent can be estimated for each of given variation mechanisms.

scholarly journals Job Recommendation System Implementation in Python vs. C++

2019 ◽  
Vol 8 (4) ◽  
pp. 2299-2302
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Programming Language ◽  
Mathematical Description ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Learning Problem ◽  
Wide Range ◽  
Job Recommendation

Implementing a machine learning algorithm gives you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for the transformations on those structures. There are numerous micro-decisions required when implementing a machine learning algorithm, like Select programming language, Select Algorithm, Select Problem, Research Algorithm, Unit Test and these decisions are often missing from the formal algorithm descriptions. The notion of implementing a job recommendation (a classic machine learning problem) system using to two algorithms namely, KNN [3] and logistic regression [3] in more than one programming language (C++ and python) is introduced and we bring here the analysis and comparison of performance of each. We specifically focus on building a model for predictions of jobs in the field of computer sciences but they can be applied to a wide range of other areas as well. This paper can be used by implementers to deduce which language will best suite their needs to achieve accuracy along with efficiency We are using more than one algorithm to establish the fact that our finding is not just singularly applicable.

scholarly journals RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

2021 ◽  
Vol 15 (2) ◽  
pp. 199-209
Author(s):  
Ade chandra Saputra ◽  
Ahmadi Ahmadi ◽  
Ariesta Lestari
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Programming Language ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Public Places ◽  
The Face ◽  
Haar Cascade ◽  
Analysis Design

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

Scientific programming with Python

2019 ◽  
pp. 17-37
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Programming Language ◽  
High Level ◽  
Mathematical Operations ◽  
Considerable Support

This chapter offers a brief introduction to scientific programming with Python with an emphasis on some mathematical operations that will form the basis of many algorithms. This will specifically include working with matrices and convolutions. Python is a high-level programming language similar to Matlab and R that has gained increasing popularity in the machine learning community. The main reason this book uses Python is that it is freely available and now provides considerable support for machine learning with packages such as sklearn and Keras that are discussed and utilized in this book. Some familiarity with programming concepts is assumed, and the chapter concentrates on a brief introduction to the specific environment and supporting libraries used throughout as well as some basic operations such as convolutions that will be important in later algorithms.

AN EVENT-BASED REAL-TIME LOGIC FOR THE SPECIFICATION AND ANALYSIS OF REAL-TIME SYSTEMS

1993 ◽  
Vol 02 (01) ◽  
pp. 71-91 ◽  
Author(s):  
HORNG-YUAN CHEN ◽  
JEFFREY J.P. TSAI ◽  
YAODONG BI
Keyword(s):  
Real Time ◽  
Temporal Logic ◽  
Programming Language ◽  
Target System ◽  
Real Time Systems ◽  
Testing And Debugging ◽  
Time Logic ◽  
Prolog Programming ◽  
Event Based ◽  
Time Systems

Research on real-time systems now focuses on formal approaches to specify and analyze the behavior of real-time systems. Temporal logic is a natural candidate for this since it can specify properties of event and state sequences. However, “pure” temporal logic cannot specify “quantitative” aspect of time. The concepts of eventuality, fairness, etc. are essentially “qualitative” treatment of time. The pure temporal logic makes no reference to absolute time. For real-time systems, the pure qualitative specification and analysis of time are inadequate. In this paper, we present a modification of temporal logic—Event-based Real-time Logic (ERL), based on our event-based conceptual model. The ERL provides a high-level framework for specifying timing properties of real-time systems, and it can be implemented using Prolog programming language. In our approach to testing and debugging of real-time systems, the ERL is used to specify both expected behavior (specification) and actual behavior (execution traces) of the target system and to verify that the target system achieves the specification. In this paper, a method is presented to implement the ERL using Prolog programming language for testing and debugging real-time systems.

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

scholarly journals Prophecy on Programming Language using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 3699-3706
Author(s):  
Komal Bhaskar Thube
Keyword(s):  
Machine Learning ◽  
Programming Language ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computer Language ◽  
Decision Tree Classifier ◽  
Development Environment ◽  
Tree Classifier ◽  
Develop Software ◽  
Neighbor Classifier

A programming language is a computer language developers use to develop software programs, scripts, or other sets of instruction for computers to execute. It is difficult to determine which programming language is widely used. In our work, I have analyzed and compared the classification results of various machine learning models and find out which programming language is widely used by developers. I have used Support Vector Machine (SVM), K neighbor classifier (KNN),Decision Tree Classifier(CART) for our comparative study. My task is to analyze different data and to classify them for the efficiency of each algorithm in terms of accuracy, precision, recall, and F1 Score. My best accuracy was 94.29% percent which was found using SVM. These techniques are coded in python and executed in Jupyter NoteBook, the Scientific Python Development Environment. Our experiments have shown that SVM is the best for predictive analysis and from our study that SVM is the well-suited algorithm for the prediction of the most widely used programming language.

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