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Laurent Bloch. "Un langage pour enseigner la programmation, Scheme ou Python ?" Bulletin 1024, no. 20 (November 2022): 85–95. http://dx.doi.org/10.48556/sif.1024.20.85.
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Philippot, Alexandre, Stéphane Lecasse, Bernard Riera, and François Gellot. "Développement d’un connecteur logiciel pour l’apprentissage de l’automatisme." J3eA 21 (2022): 2056. http://dx.doi.org/10.1051/j3ea/20222056.
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L’apprentissage de l’automatisme s’adresse aujourd’hui à un public d’étudiants ayant pour la plupart avant tout un background informatique. Ils/elles passent par des phases d’apprentissage de langages compilés et/ou interprétés. Il est compliqué alors pour eux de passer d’une réflexion informatique avec un langage venant du monde de l’IT (Information Technology) vers la programmation d’Automates Programmable Industriel (API), au comportement cyclique, synchrone et aux langages normalisés (IEC 61131-3) issu du monde de l’OT (Operational Technology). Ce papier présente une proposition de mise en place d’un connecteur logiciel entre ces deux mondes aux travers de l’utilisation d’un logiciel de simulation de Parties Opératives Factory I/O (realgames.co) et du langage Python.
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KENOUFI, Abdelouahab. "Probabilist Set Inversion using Pseudo-Intervals Arithmetic." TEMA (São Carlos) 15, no. 1 (2014): 097. http://dx.doi.org/10.5540/tema.2014.015.01.0097.
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<pre><!--StartFragment-->In this paper, we present how to use an interval arithmetic framework based on free algebra construction, in order to build better defined inclusion function for interval semi-group and for its associated vector space. One introduces the <span>psi</span>-algorithm, which performs set inversion of functions and exhibits some numerical examples developed with the python programming langage<!--EndFragment--></pre>.
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Jovanović, S., and S. Weber. "Modélisation et accélération de réseaux de neurones profonds (CNN) en Python/VHDL/C++ et leur vérification et test à l’aide de l’environnement Pynq sur les FPGA Xilinx." J3eA 21 (2022): 1028. http://dx.doi.org/10.1051/j3ea/20220028.
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Nous présentons un ensemble de travaux pratiques qui seront dispensés au sein du Master EEA - Électronique Embarquée à l’université de Lorraine dans le cadre des modules Modélisation SystemC et Conception VLSI. Ces TP sont destinés à initier les étudiants à la compréhension, modélisation et conception des réseaux de neurones convolutifs dans des langages de description de matériel au niveau RTL (VHDL, le module Conception VLSI) et dans un langage de haut niveau (C++/SystemC, le module Modélisation SystemC). Ils sont organisés autour d’un ensemble d’outils de modélisation et de synthèse de Mentor Graphics (Modelsim, Catapult HLS) et spécifiques aux plateformes FPGA Xilinx et à l’environnement Pynq pour la simulation, test et vérification.
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Graillet, Olivia, Frédéric Alicalapa, Pierre-Olivier Lucas de Peslouan, Denis Genon-Catalot, and Jean-Pierre Chabriat. "Approche pédagogique pour l’étude d’autoconsommation photovoltaïque au niveau Master avec utilisation de l’API de SolarIO en langage Python." J3eA 23 (2024): 0002. http://dx.doi.org/10.1051/j3ea/20240002.
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L’île de La Réunion, qui fait partie des Zones Non Interconnectées (ZNI), dépend actuellement à 62% des importations d’énergies fossiles pour la production d’électricité. Afin de contribuer à son autonomie énergétique, il est nécessaire de développer les sources d’énergies renouvelables locales. Dans ce contexte, l’unité de recherche ENERGY-Lab et la Faculté des Sciences et Technologies de l’Université de La Réunion proposent le cursus « Master Energie ». L’un des objectifs du Master est de permettre aux étudiants d’acquérir des compétences pouvant répondre aux problématiques énergétiques actuelles. A La Réunion, le secteur des installations photovoltaïques est particulièrement actif en raison d’un fort potentiel solaire, lié à son climat subtropical. La démarche pédagogique présentée dans ce document est ainsi composée d’activités en lien avec l’optimisation de l’autoconsommation d’une centrale photovoltaïque : établir un plan de sobriété énergétique, affiner la précision du dimensionnement de la centrale PV et simuler des flux de puissances. Les différents outils et technologies utilisés (tableurs, programmation Python et G LabVIEW, API, bases de données) ont été choisis pour s’adapter à la fois à un contexte scientifique et industriel.
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Akeel Hussein Alaasam, Hussein, Ahmed Ali Talib Al-Khazaali, Ali Hussein Aleiwi, and Doaa Wahhab Ibrahim. "Learn Land Features Using Python Language." BIO Web of Conferences 97 (2024): 00111. http://dx.doi.org/10.1051/bioconf/20249700111.
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Python has emerged as an essential programming language for research due to continuous technological advancements that emphasize its role in streamlining scientific workflows. This article elucidates Python's burgeoning impact on researchers across disciplines. Tracing Python's origins and applications within the earth sciences contextualizes its versatility. While acquiring proficiency in Python exceeds this article's scope, discussions detail its utilities for earth science data analysis, visualization, management, and rapid computations. With Python expertise, researchers can engineer customized software with domain-specific tools to advance all earth science spheres. Ultimately, this article underscores Python's position as a vital programming language for contemporary academic research through its flexibility and specialization for scientific use cases.
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Gujar, Advait. "C vs Python: A Cursory Look with Industry Opinion." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 55–64. http://dx.doi.org/10.22214/ijraset.2023.56446.
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In this paper, the author explores the characteristics and applications of C & Python programming languages across various industries, drawing insights from interviews with professionals. The study includes a comparative analysis of C and Python based on execution time, code readability, and length. C, favored for its speed and applicability in game development and embedded systems, has complexities such as large code size and lack of cross-platform support. In contrast, Python excels in artificial intelligence, machine learning, and web scraping due to its simplicity and extensive libraries. The article emphasizes the influence of programming communities on language popularity, noting Python's widespread adoption due to its concise syntax and strong community support. Industry experts concur on C's complexity and time-intensive nature but acknowledge its effectiveness. Python's ease of learning has made it the world's most widely used language, prompting non-coding sectors to encourage Python education.
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Peta, Saphalya. "Python- An Appetite for the Software Industry." International Journal of Programming Languages and Applications 12, no. 4 (2022): 1–14. http://dx.doi.org/10.5121/ijpla.2022.12401.
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Python is a scripting language that's high- positioned, interpreted, interactive, and object- oriented. Python is intended to be a veritably accessible programming language. It generally uses English terms rather than punctuation, and it has smaller syntactical structures than other languages. Python is a must-have skill for scholars and working professionals who want to become exceptional software masterminds, especially if they work in the web development field. It's a freshman-friendly scripting language. Some of the crucial features of Python programming language are- It supports OOP as well as functional and structured programming methodologies. It can be used as a scripting language or collected into bytecode for large-scale operations. It allows dynamic type verification and provides veritably high-position dynamic data types. It facilitates scrap collection by itself. Numerous different programming languages have been impacted by Python's design and gospel. Some of those languages are Boo, Cobra, CoffeeScript, Go, Swift, Ruby, etc. Some of the advantages of Python programming language are straightforward, free, simple to use, and largely compatible, object- acquainted, has multitudinous libraries, has erected in data structures, has a wide range of uses, boosts productivity and speed, and simple to understand. One of the most extensively used programming languages is Python. It's an open- source language. Python's demand is growing, and its operations are expanding in virtually every assiduity. It's abundant in every way. It has a wide range of capabilities. Python is a popular programming language. It's also developing a strong request in the IT sector. Python is in high demand across the globe. Python helps you negotiate more in lower time. Python has a large community that supports and meets the requirements of inventors. Python is therefore one of the most popular programming languages. It's a veritably reliable and effective programming language. Python programmers are in high demand because Python is being used in a variety of sectors. Python is an extensively used computer language that was created nearly 25 years ago. Python is useful in a variety of fields, including web development, desktop app development, machine literacy, big data, data analysis, and robotics. Clean syntax, extremely clear law, a wide range of uses, packages that help apply features, and a cool community that helps grow this excellent language are just a many of the reasons why people like this language and why it's well suited for different tasks. The Python programming language has a bright future. The advanced technologies like Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Science, etc and world-notorious companies similar as Amazon, Google, Apple, Deloitte, Microsoft, Netflix, and Accenture have the Python programming language as their backbone which states that Python is in demand and AN APPETITE FOR THE SOFTWARE INDUSTRY! A standard and scientific procedure of an Empirical Exploration Methodology (Survey) was conducted to check the statement stated by the author where 900 repliers from colourful corridors of the globe shared their thoughts. From the check, it was concluded that 99.8% of the respondents feel that Python is one of the in- demand programming languages for the digital assiduity in the present time.
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Patel, Aryan. "Mojo: A Python-based Language for High-Performance AI Models and Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26529.
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Python has become a popular language for AI model development due to its elegant and flexible programming capabilities, extensive tool ecosystem, and high-performance libraries like Numpy and PyTorch. However, Python's execution speed remains a challenge, especially for performance-critical inner loops. To address this, Python programmers often rely on wrappers for C, FORTRAN, or Rust code, leading to a "two-language" approach that introduces complexities in deployment and debugging. This research paper introduces Mojo, a promising solution to the Python performance issue, which is essentially Python++ and built on top of MLIR (Multi-Level Intermediate Representation). Mojo is a rigorously designed superset of Python that allows seamless integration of high-performance implementations by switching to a faster "mode." This paper discusses the key features of Mojo, its deployment advantages, and its comparison with other alternatives in the AI and ML development landscape.
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Lazebna, Nataliia. "ENGLISH-LANGUAGE BASIS OF PYTHON PROGRAMMING LANGUAGE." Research Bulletin Series Philological Sciences 1, no. 193 (2021): 371–76. http://dx.doi.org/10.36550/2522-4077-2021-1-193-371-376.
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The dynamic nature of the Python programming language, the accumulation of a certain linguosemiotic basis indicates the similarity of this language with the English language, which is the international one and mediates human communication in both real and virtual worlds. In this study, the English language is positioned as the linguistic basis of Python language of programming, which is widely used in industry, research, natural language processing, textual information retrieval, textual data processing, texts corpora, and more. English language, its lexical features, text representation and interaction with logical and functional basis in the context of Python programming language are considered further in this research. Thus, the unity of verbal units and symbols in the modern English-language digital discourse indicates both the order and variability of the constituents therein. The functionality of linguosemiotic elements produces a network of relationships, where each of these integrated elements can produce from a word or symbol a holistic set of units, which are extrapolated in the English-language digital discourse and mediates human communication with a machine. An overview of the basic properties of Python language, such as values, types, expressions, and operations are in focus of the study. Though users understand the responses of Python interpreter, there is a need to follow certain instructions and codes. To facilitate work with this programming language and prescribed English-language commands, it is necessary to involve linguists to cooperate with programmers to invent a certain logical and reasonable principle of Python commands operation.
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Alder, Denis, José Natalino Silva, João Olegário Pereira de Carvalho, Jose Do Carmo Lopes, and Ademir R. Ruschel. "La stratégie de modélisation empirique « cohort » et son application pour l¿aménagement de la forêt de Tapajós, Pará, Amazonie brésilienne." BOIS & FORETS DES TROPIQUES 314, no. 314 (2012): 17. http://dx.doi.org/10.19182/bft2012.314.a20486.
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La stratégie de modélisation empirique est ici revue et présentée ainsi que son application à l'Amazonie orientale. Le modèle de croissance Cafogrom élaboré au cours de la période 1994-1998 a pu être testé grâce aux récentes mesures de 2003 et 2007 en Forêt nationale de Tapajós dans deux zones expérimentales dénommées km67 et km114 au long de l'autoroute BR 163 reliant Santarém à Cuiabá. Le modèle montre un accroissement annuel de la forêt avec un écart annuel de moins de 15 % au cours de la période 1981-2007 sur le km67 et avec la même précision sur km114, un site moins productif, mais avec un biais accru de sous-estimation d'environ 32 % en 26 ans. L'accroissement moyen annuel du volume des arbres de plus de 50 cm de diamètre (DBH) a été de 2,2 m3/ha/an en 26 ans, dont 1,2 m3/ha/an (54 %) pour les essences commerciales. Les parcelles étudiées sur le site km114, le moins productif, ont eu un accroissement moyen de 1,07 m3/ha/an au cours de vingt ans couvrant la période 1983-2003. En considérant les règles du gouvernement brésilien dont l'intensité maximale d'exploitation est de 30 m3/ha avec une rotation de passage en coupe de 35 ans (0,86 m3/ha/an), la viabilité de ce régime conservateur est confirmée à condition que l'exploitation comprenne une gamme variée d'espèces commerciales. La stratégie de mise à jour de Cafogrom est détaillée, elle devra être réécrite sous la forme d'une application en langage Python dans le cadre contextuel Myrlin/ Fmt (www.myrlin.org, www.eofmt.com).
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Galli, Massimiliano, Enric Tejedor, and Stefan Wunsch. "A New PyROOT: Modern, Interoperable and More Pythonic." EPJ Web of Conferences 245 (2020): 06004. http://dx.doi.org/10.1051/epjconf/202024506004.
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Python is nowadays one of the most widely-used languages for data science. Its rich ecosystem of libraries together with its simplicity and readability are behind its popularity. HEP is also embracing that trend, often using Python as an interface language to access C++ libraries for the sake of performance. PyROOT, the Python bindings of the ROOT software toolkit, plays a key role here, since it allows to automatically and dynamically invoke C++ code from Python without the generation of any static wrappers beforehand. In that sense, this paper presents the efforts to create a new PyROOT with three main qualities: modern, able to exploit the latest C++ features from Python; pythonic, providing Python syntax to use C++ classes; interoperable, able to interact with the most important libraries of the Python data science toolset.
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Wibowo, Firmansyah Rekso, and Muhammad Faisal. "Comparative Analysis of Sorting Algorithms: TimSort Python and Classical Sorting Methods." JISA(Jurnal Informatika dan Sains) 7, no. 1 (2024): 11–18. http://dx.doi.org/10.31326/jisa.v7i1.1785.
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The sorted() function within the Python programming language has emerged as the primary choice among developers for sorting operations. Consequently, this study offers a comparative analysis of various classical sorting algorithms and Python's built-in sorting mechanisms, with the objective of identifying the most time-efficient sorting algorithm. The analysis involves assessing the time complexity of each algorithm while handling data arrays ranging from 10 to 1,000,000 elements using Python. These arrays are populated with randomly generated numeric values falling within the range of 1 to 1000. The benchmark algorithms utilized encompass Heap Sort, Shell Sort, Quick Sort, and Merge Sort. A looping mechanism is applied to each algorithm, and their execution speeds are gauged utilizing the Python 'time.perf_counter()' library. The findings of this study collectively indicate that Python's standard algorithm, surpasses classic sorting algorithms, including Heapsort, Shellsort, Quicksort, and Mergesort, in terms of execution.
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Ye, Chengze, Zhuoyang Shen, Yue Wu, and Pavel Loskot. "Reconsidering Python Syntax to Enhance Programming Productivity." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 776–85. http://dx.doi.org/10.22214/ijraset.2024.58903.
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Abstract: Data analytics plays a crucial role in today's society across various domains, driven by technological advancements and exponential data growth. Handling large-scale data poses a challenge due to increased computational and storage requirements. The heterogeneity of tasks in data analytics programming languages complicates integration and interaction, necessitating effective cross-language integration for productivity and extended capabilities. This paper proposes a generalized interpreter accepting various language syntaxes, primarily based on Python and MATLAB, with comparisons to R and Julia. Findings reveal Python's beginner-friendly learning curve and rich resources, Julia's high-performance computing, MATLAB’s numerical prowess and specialized toolbox, and Python and R's focus on flexibility. Both Python and R boast active communities, while Python offers extensive portability, and Julia emphasizes interoperability. Despite syntactic differences, a common interpreter offers flexibility and efficiency, benefiting developers by enabling language selection based on project needs. Challenges can be mitigated through good design and technical solutions. Encouragement for research and innovation in universal interpreter development fosters collaboration, enhancing opportunities in data analysis and scientific computing. Active participation from developers and researchers is encouraged for continual improvement and advancement in the field.
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Bronshteyn, I. E. "Type inference for Python programming language." Proceedings of the Institute for System Programming of RAS 24 (2013): 161–90. http://dx.doi.org/10.15514/ispras-2013-24-9.
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Sibiya, Malusi. "Pattern Matching in Python: Expanding the Horizons of Engineering Applications." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 80–86. http://dx.doi.org/10.59200/icarti.2023.011.
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Pattern matching is a powerful programming construct that simplifies code, enhances readability, and enables efficient handling of complex data structures. This paper introduces the pattern matching feature newly introduced in Python 3.10 and explores its applications in various engineering domains. The aim of this research is to showcase how Python's pattern matching capability can be leveraged for parsing and analyzing data, structural matching in data analysis, model and system validation, and signal processing. Through illustrative examples and case studies, we demonstrate the versatility and effectiveness of Python pattern matching in solving real-world engineering problems. By introducing pattern matching in Python, this research opens new avenues for engineers and scientists to tackle complex data processing tasks, enhance system validation techniques, and streamline algorithmic implementations. With the integration of pattern matching into Python's ecosystem, the language becomes even more powerful and expressive, empowering practitioners to write cleaner, more concise, and efficient code. This research lays the foundation for the adoption and exploration of pattern matching techniques in Python, highlighting its potential impact on engineering applications and providing a roadmap for future research and development in this field.
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Tripathi, Ramesh Chandra. "Python: The future programming language." Asian Journal of Multidimensional Research 10, no. 11 (2021): 105–9. http://dx.doi.org/10.5958/2278-4853.2021.01067.3.
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Goto, Isao. "Python for Natural Language Processing." Journal of The Institute of Image Information and Television Engineers 72, no. 11 (2018): 909–12. http://dx.doi.org/10.3169/itej.72.909.
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Munawar, Kashif, and Muhammad Shumail Naveed. "The Impact of Language Syntax on the Complexity of Programs: A Case Study of Java and Python." Vol 4 Issue 3 4, no. 3 (2022): 683–95. http://dx.doi.org/10.33411/ijist/2022040310.
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Programming is the cornerstone of computer science, yet it is difficult to learn and program. The syntax of a programming language is particularly challenging to comprehend, which makes learning arduous and affects the program's testability. There is currently no literature that definitively gives quantitative evidence about the effect of programming language complex syntax. The main purpose of this article was to examine the effects of programming syntax on the complexity of their source programs. During the study, 298 algorithms were selected and their implementations in Java and Python were analyzed with the cyclomatic complexity matrix. The results of the study show that Python's syntax is less complex than Java's, and thus coding in Python is more comprehensive and less difficult than Java coding. The Mann-Whitney U test was performed on the results of a statistical analysis that showed a significant difference between Java and Python, indicating that the syntax of a programming language has a major impact on program complexity. The novelty of this article lies in the formulation of new knowledge and study patterns that can be used primarily to compare and analyze other programming languages.
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Patil, Mr Vrushab, Mr Pradeep Parit, Miss Ruchita Yadav, Mr Aniruddha Yalgudre, Mr Prathamesh Gurav, and Prof P. R. Desai. "Deaf Helper Using Python." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 244–48. http://dx.doi.org/10.22214/ijraset.2023.56432.
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Abstract: The Deaf helper using machine learning project represents a pivotal endeavor aimed at bridging communication gaps and enhancing accessibility for the deaf and hard of hearing community. In a world where spoken language dominates, this project harnesses the power of machine learning to facilitate seamless communication for individuals who rely on sign language as their primary mode of expression. At its core, this project leverages state-of-the-art machine learning techniques, including computer vision and natural language processing, to recognize and translate sign language gestures into written or spoken language and vice versa. By fusing these technologies, the project endeavors to create an inclusive and accessible communication tool.
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Sahani, Sweety, and Sushmitha Mary. "Chatbot Using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3565–68. http://dx.doi.org/10.22214/ijraset.2022.43045.
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Abstract: A chatbot enables a user to simply ask questions in the same manner that they would respond to humans. The most well-known chatbots currently are voices chatbots: SIRI and Alexa. However, chatbots have been adopted and brought into the daily application at a high rate on the computer chat platform. NLP also allows computers and algorithms to understand human interactions through various languages. Recent advances in machine learning have greatly improved the accurate and effective of natural language processing, making chatbots a viable option for many organizations. This improvement in NLP is firing a great deal of additional research which should lead to continued improvement in the effective of chatbots in the years to come.A bot is trained on and according to the training, based on some rules on which it is trained, it answers questions. It is called ruled based approach. The language by which these bots can be created is Artificial Intelligence Markup Language (AIML). It is a language based on XML which allows the developer to write the rules which the bot will follow. In this research paper, We are trying to understand these chatbots and understanding their shortcomings. question or statement submitted by a user and allow the user to control over the content to be displayed Keywords: AI; ML; Wordnet; Chatbot; NLP
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Gagan, B. R., Shivaprakash T, Thirumalai Shaktivel C, Vaishak P, and Kushal Kumar B. N. "Design of a New Language Seeks Literature Survey." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1623–27. http://dx.doi.org/10.22214/ijraset.2022.40949.
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Abstract: In a scientific study, computing is a must-have tool. In general, scientists have various difficulties, requirements, and views when it comes to computation, which need to be addressed by the programming language that they use, this cannot be satisfied by general-purpose languages. Also, researchers need to concentrate on the issue they are working on rather than the optimizations for the calculations, so instead of using a general-purpose language, if there exists a language whose compiler would take care of those optimizations, it would make their work easier and faster. This is a survey of the work undertaken to design the programming language and its compiler. The primary goal of this research is to examine the function of work, implementation strategy, steps taken for improving the performance, the procedure of benchmarking, and finally, the outcome of the papers studied. The survey's main conclusions are that: the most common language mentioned among the papers was Python which appears to be more popular among developers due to its simple syntax and library support for computing. On the other hand, Python lacks performance, to compensate for this performance issue, the community has developed tools like Cython, Numba, Pythran, etc, which can be used to speed up Python. Domain-specific languages such as Wolfram, Seq, and ELI highlighted various methods for overcoming problems. Some languages like Wolfram and ELI moved from interpreter to compiler to get the performance boost. Most of the compilers use LLVM as the backend for optimizations and code generation. Keywords: scientific computation, compiler, programming language
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Boulle, A., and J. Kieffer. "High-performance Python for crystallographic computing." Journal of Applied Crystallography 52, no. 4 (2019): 882–97. http://dx.doi.org/10.1107/s1600576719008471.
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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.
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Wang, Meng, and Fanghui Hu. "The Application of NLTK Library for Python Natural Language Processing in Corpus Research." Theory and Practice in Language Studies 11, no. 9 (2021): 1041–49. http://dx.doi.org/10.17507/tpls.1109.09.
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Corpora play an important role in linguistics research and foreign language teaching. At present, the relevant research on the corpus in China mainly uses WordSmith, Antconc and other retrieval tools. NLTK library, which is based on Python language, can provide more flexible and rich research methods, and it can use unified data standards to avoid the trouble of various data type conversion. At the same time, with the help of Python’s numerous third-party libraries, it can make up for the shortcomings of other tools in syntax analysis, graphic rendering, regular expression retrieval and other aspects. In terms of the main links in corpus research, such as text cleaning, word form restoration, part of speech tagging and text retrieval statistics, this paper takes the US presidential inaugural speech in the corpus as an example to show how to use this tool to process the language data, and introduces the application of Python NLTK library in corpus research.
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Thaker, Nimit, and Abhilash Shukla. "Python as Multi Paradigm Programming Language." International Journal of Computer Applications 177, no. 31 (2020): 38–42. http://dx.doi.org/10.5120/ijca2020919775.
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Borcherds, P. H. "Python: a language for computational physics." Computer Physics Communications 177, no. 1-2 (2007): 199–201. http://dx.doi.org/10.1016/j.cpc.2007.02.019.
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Chodarev, Sergej, and Sharoon Ilyas. "Metamodel-based Language Definition with Python." IPSI Transactions on Internet Research 19, no. 01 (2023): 32–38. http://dx.doi.org/10.58245/ipsi.tir.2301.06.
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Most of the parser tools are concentrated on concrete syntax and grammar definition. This paper describes a language definition tool that uses a metamodel specification instead of grammar as the basis of the language definition. Inspired by a similar Java tool known as YAJCo, the metamodel is defined using usual object-oriented techniques—as classes in the Python programming language, and the result of the parsing process is a graph of objects. The tool is demonstrated in a case study of a simple imperative programming language. We explain our design decisions and also demonstrate the suitability of a dynamic language such, as Python, for this task.
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Chen, Junqiao. "Model Algorithm Research based on Python Fast API." Frontiers in Science and Engineering 3, no. 9 (2023): 7–10. http://dx.doi.org/10.54691/fse.v3i9.5591.
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In recent years, the application of Python programming language in developing web services has gained significant attention, with FASTAPI emerging as a prominent framework for its rapid development and efficient performance. This paper delves into the realm of model algorithm research, leveraging the capabilities of Python's FASTAPI framework. Through this study, we explore the integration of advanced algorithms within the context of web-based applications. By focusing on the seamless amalgamation of algorithmic processes with FASTAPI's structure, we aim to demonstrate the feasibility and advantages of utilizing this combination in various research and practical scenarios. Coupled with illustrative examples, this paper highlights the potential of Python FASTAPI as a robust platform for driving model algorithm research across diverse domains.
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Sahu, Chiranjeev, and Kranti Kumar Dewangan. "Stock Market Prediction using Twitter." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26020.
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This study presents an assessment of monetary trade discussions on Twitter using Python. The fast improvement of online diversion has uncovered it a critical focal point for getting a handle on feeling towards stocks. We preprocess an enormous dataset of tweets connected with explicit stock images by using Python's strong elements. We use feeling assessment strategies to gauge the assessment (great, negative, or unprejudiced) imparted in these tweets. Additionally, we are able to identify potential correlations between changes in the stock market and patterns and trends in Twitter sentiment by employing tools for statistical analysis and visualization. This examination exhibits how to really utilize Python to investigate Twitter information and gives financial backers valuable data for going with informed securities exchange choices. In the present speedy monetary scene, information driven direction is fundamental for financial backers and merchants. This theoretical presents an extensive examination of Twitter's financial exchange execution utilizing Python, a flexible and strong programming language for information investigation and representation. The review starts by social event authentic stock cost information for Twitter (NYSE: TWTR) utilizing well known monetary APIs or web scratching strategies. Python libraries, for example, Pandas and NumPy are utilized to control and clean the information, guaranteeing its reasonableness for examination. Different information perception instruments like Matplotlib and Seaborn are saddled to make shrewd outlines and diagrams that give a visual portrayal of Twitter's stock presentation over the long haul. To acquire further experiences, the investigation integrates factual and monetary measurements, for example, moving midpoints, relative strength file (RSI), and beta coefficient. These measurements are International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 10 | October - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM26020 | Page 2 determined utilizing Python's numerical libraries and are critical in surveying the stock's unpredictability, energy, and market risk. Opinion examination likewise assumes a huge part in understanding what Twitter's stock is meant for by web-based entertainment. Regular Language Handling (NLP) libraries like NLTK or spaCy are used to dissect tweets and news stories connected with Twitter. Feeling scores are processed to measure the public's opinion towards the organization, and this information is connected with stock cost developments. Moreover, AI models can be carried out utilizing Python's Scikit-Learn or TensorFlow libraries to anticipate future stock cost patterns in view of authentic information and opinion examination results. Techniques for time series forecasting like ARIMA and LSTM can offer useful insights into potential price movements. All in all, this Twitter Securities exchange Examination utilizing Python exhibits the force of information driven dynamic in the monetary world. Investors and traders can use Python's data manipulation, visualization, and machine learning capabilities to make better decisions, reduce risks, and possibly take advantage of market opportunities in Twitter's stock. The study demonstrates how Python's adaptability and the stock market's dynamic nature complement one another. Key Words: Twitter, Stock Market
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Joshi, Vivek. "Virtual Assistant Using Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33777.
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Today there is huge Advancement in the Technical field which is increasing day by day. In early days there were only computer systems where we were able to perform only few tasks, but today new technologies like machine learning, artificial intelligence, deep learning, and few some others have made computer systems so advance that we can perform any type of task with them. In recent years, Artificial Intelligence (AI) have done remarkable progress and its Capability is increasing day by day. One of the application Area of AI is Natural Language Processing (NLP). Natural Language Processing (NLP) helps Humans to communicate with the computer system in their own Language. For example, Voice Assistant. Various voice assistants were developed and they are still being improved more for better performance to overcome struggling of humans to interact with their machine. we are trying to develop a voice assistant using python which will help user to perform any type of task without interaction with keyboard. The aim of this paper is to study how voice assistants behaves smartly and can be used to get everyday work done and also be used for educational purpose also. Keywords: Virtual Assistant, UI, Artificial Intelligent, Python Library Key Features of the system are: ❖ Natural Language Understanding (NLU) ❖ Speech Recognition ❖ Text-to-Speech (TTS) Conversion ❖ User Interface ❖ Context Management
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shi, Dongzhe. "Simulating strong gravity-lensing effect using python with 10 source and 20 lensing galaxies." Theoretical and Natural Science 14, no. 1 (2023): 85–90. http://dx.doi.org/10.54254/2753-8818/14/20240883.
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This report explores using Python, a coding language, to create simulated images of a gravitational lens system, using the Hubble Space Telescope (HST) parameters. With Pythons helpful tools, like NumPy for math operations and Astropy for astronomy tasks, we build algorithms that recreate the interactions within our chosen group of galaxies and take into account HSTs unique imaging capabilities. Our method combines theory of gravitational lensing with practical coding strategies to make simulations show these complex light-bending interactions. The report walks through how the algorithms are developed with specific scientific simulation models like Sersic profile and point-spread function (PSF), showcasing the important role of computer simulations in deepening our understanding of space. In this report, I will introduce how we can use python code to create simulation images of a gravitational lens system. This system involves with 10 source galaxies ,20 lensing galaxies and with consideration of dark matter halo.
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Xiong, Taisong, and Yuanyuan Huang. "Research on Python Language Teaching Based on Case." Scholars Journal of Arts, Humanities and Social Sciences 9, no. 10 (2021): 513–15. http://dx.doi.org/10.36347/sjahss.2021.v09i10.005.
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Python language is a more and more widely used programming language. It becomes an inevitable choice to chose Python language as an undergraduate programming language teaching. Aiming at the current Python language teaching, focusing on basic grammar explanations, lack of case-based and comprehensive application of knowledge points. We propose a case-based python teaching plan, and these cases include comprehensively Python grammar knowledge to effectively enhance students' learning interest and learning effect.
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Kuznetsova, А. А. "APPLYING OF PYTHON TOOLS IN A STATISTICS COURSE." CURRENT PROBLEMS OF TEACHING MATHEMATICS AT TECHNICAL UNIVERSITY 10 (2023): 64–68. http://dx.doi.org/10.25206/2307-5430-2023-10-64-68.
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The methodological aspects of teaching mathematical statistics in a technical university using laboratory work are considered. Examples of tasks that can be solved using the SciPy and NumPy libraries of the Python language are given. The advantages of this programming language over other computer mathematical systems are substantiated. The problems of generating data from a given distribution, constructing confidence intervals, testing hypotheses, correlation analysis, and some others are considered.
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Pampana, Venkatesh, Daniel Lavin, Markus Duchon, and Ankit Srivastava. "Supercap-Python: An Open-Source Python Based Super Capacitor Modelling Package." International Journal of Electronics and Electrical Engineering 9, no. 4 (2021): 93–99. http://dx.doi.org/10.18178/ijeee.9.4.93-99.
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Supercapacitors have attained high power density and exceptional durability with the recent advancement in terms of their materials and chemistries. The potential scientific and industrial applications of supercapacitors are being explored continually. This instigates the need for model-based analysis and synthesis tools, which can describe dynamic phenomena, support multiphysics problems, and allow for immediate use in design and advanced control analysis. For these aspects, modelling of supercapacitors would be beneficial. However, there are no open-source simulation tools on supercapacitors available for the scientific community to work with. This paper presents the development of an open-source supercapacitor modelling package in python language. The proposed package is evaluated by comparing the results with a standard MATLAB/Simulink supercapacitor model. The simulation results have shown that both models yielded similar envelope.
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Georgieva, Rositsa. "GAME-BASED PROGRAMMING TEACHING FOR BEGINNERS IN PYGAME ZERO MODE – SAMPLE PYTHON TASKS. PART II – PYGAME LIBRARY." Mathematics and Informatics 66, no. 3 (2023): 244–55. http://dx.doi.org/10.53656/math2023-3-3-gam.
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This article is a natural continuation of its first part, published in the journal “Mathematics and Informatics”, where the terminology related to game-based learning and the role of tasks in learning the learning content, as well as example tasks and methodical implementation for non-profiled training in programming for high school students through the creation of games, are presented using Python's turtle graph. This article follows the structure of its first part and focuses on example tasks implemented with the Python programming language, the Pygame library, and the Mu programming environment. The sample tasks are supported by methodical instructions. Guidelines are also given for their use in training. In the conclusion, the possibilities for game-based programming learning in profiled and non-profiled high schools are discussed using the Pygame Python library.
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Roy, Indrajit. "AI Based Computer Assistant using Python." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 839–46. http://dx.doi.org/10.22214/ijraset.2023.57228.
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Abstract: Our proposed system AVATAR (AI Virtual Assistant Technology for Automatic Response) is an innovative voice assistant system, combining Artificial Intelligence (AI) and Python for human-like interactions. It seamlessly executes a range of functions, from dispatching emails to conducting searches on Wikipedia. The system's design incorporates essential Python packages and ultrasonic sensors for object detection and face recognition. Python's extensive libraries and clean syntax make it the optimal language for this project. AVATAR's security measures include biometric authentication and password protection. While internet connectivity is crucial for optimal performance, the system reliably operates within its designated input range. This research represents a significant leap in AI-driven applications, enhancing efficiency and user experience. Additionally, individuals who are blind or have amputations can utilize AVATAR, as the system is entirely voice-controlled.
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V C, Dr Mahavishnu, Roopakumar R, Vikhas S G, and Abivishvas A. "Standalone Chatbot Application in Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1244–50. http://dx.doi.org/10.22214/ijraset.2022.45445.
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Abstract: A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. This software is used to do duties such as replying swiftly to users, informing them, assisting with product purchases, and delivering better customer support. Business groups are increasingly using chatbots because they may minimize customer support costs and handle several consumers at once. However, in order to complete various jobs, chatbots must be as efficient as possible. In this project, we provide the architecture of a chatbot, which provides a human like and accurate response for any questions raised by users using Natural Language ToolKit(NLTK) and PyTorch with python language.
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Kornykhin,, E., and A. Khoroshilov. "Python-based constraint language for architecture models." Proceedings of the Institute for System Programming of RAS 27, no. 5 (2015): 143–56. http://dx.doi.org/10.15514/ispras-2015-27(5)-8.
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Jenkins, Tony. "The First Language - A Case for Python?" Innovation in Teaching and Learning in Information and Computer Sciences 3, no. 2 (2004): 1–9. http://dx.doi.org/10.11120/ital.2004.03020004.
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Pejovic, Predrag. "Application of python programming language in measurements." Facta universitatis - series: Electronics and Energetics 32, no. 1 (2019): 1–23. http://dx.doi.org/10.2298/fuee1901001p.
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Application of Python programming language in automation of measurement systems and creating virtual instruments is discussed in this paper. Requirements imposed to the software in order to perform these tasks are listed, and Python modules that support them are presented. Application of proposed techniques are illustrated in seven examples in different application areas. Analysis of software evolution, as well as the evolution of professional education yields conclusion that application of Python in automating measurement systems is promising.
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S.Nandagopal, S.Priyanga, R.Vidhyasri, N.Sharmila, and M.Suvetha. "SIGN LANGUAGE DETECTION USING PYTHON AND OPENCV." international journal of engineering technology and management sciences 7, no. 2 (2023): 477–84. http://dx.doi.org/10.46647/ijetms.2023.v07i02.055.
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This work focuses on detection of (sign) hand gesture techniques and introduces the merits and limitations in various circumstances. The hand segmentation theories and hand detection system is used to construct hand gesture recognition using Python with OpenCV. The hand gestures use as a natural interface motivates research in gesture representations, taxonomies and recognition methods/algorithms, and software platforms/ frameworks, all of which are briefly covered in this work. All the processes have been done using webcam by keras and tensorflow. The ever-increasing public acceptance and funding for multinational projects emphasizes the need for sign language. The desire for computer-based solution is significant in recent age of technology for deaf people. Still, researchers are attacking the problem for quite sometimes and the results are showing promises. This work represents a comprehensive review of vision-based sign recognition methodologies, emphasizing importance of taking the things into consideration in addition with algorithm's recognition accuracy during predicting their success in real world applications. This project matches the sign language action with dataset images with various categories of sign (gestures) that already been trained using webcam. This project applies neural network to compare the actions with data set images. The coding language used is Python 3.10.
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Garkavenko, G. V., Yu S. Bakulina, and I. E. Silvestrov. "Modeling a text interface using Python language." Informatics in school, no. 5 (November 25, 2023): 57–61. http://dx.doi.org/10.32517/2221-1993-2023-22-5-57-61.
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The article discusses issues related to studying the Python programming language in an informatics course and improving students' understanding of such a programming language structure as an array or list. To achieve this goal, one meaningful example is used, that is modeling the operation of an electronic clock display in a text interface. First, patterns of numbers are developed to represent them on the scoreboard, and topics related to the study of matrices or lists of strings can be better developed. Next, the task is implemented using the Python programming language, while functions are used to create and process the output of numbers. Ideas for similar problems are also given. The article may be useful to informatics teachers, students of pedagogical universities and all readers who are interested in programming in Python.
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Moaiad, Yazeed Al, Mohammad Alobed, and Mahmoud Alsakhnini. "Python Solutions to Address Natural Language Challenges." International Journal of Membrane Science and Technology 10, no. 3 (2024): 3594–603. http://dx.doi.org/10.15379/ijmst.v10i3.3405.
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Arabic is one of six official languages, according to UNESCO. It's spoken by more than 422 million Arabs, and 1.5 billion Muslims around the world use it when they pray five times a day. Arabs spoke classical Arabic more than 1400 years ago. On the other hand, dialectal Arabic is the everyday language that is used informally and varies from region to region. Modern Standard Arabic borrows from and adds to other languages to fit the needs of its speakers. Arabic is harder to learn because there are three different ways to speak it: the classical way, the modern way, and the casual way. Arabic is hard to work with on computers for more than one reason. Because Arabic has a lot of inflection and derivation, one lemma can turn into many different words with different meanings.
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Zhu, Zhiwen. "Research on Interactive Online Teaching of Python Language Foundation Course." Review of Educational Theory 4, no. 1 (2021): 36. http://dx.doi.org/10.30564/ret.v4i1.2776.
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Python language, as one of the most popular programming languages, has become the preferred programming course in Colleges and universities. However, in traditional teaching, the dull and monotonous teaching of Python course leads to the low teaching efficiency of Python course and the unsatisfactory learning effect of students. Therefore, there is an urgent need for new teaching methods to improve classroom efficiency. Adopting Python interactive online teaching can not only improve the teaching efficiency of Python course, but also promote the reform of information technology course.
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Adawiyah Ritonga and Yahfizham Yahfizham. "Studi Literatur Perbandingan Bahasa Pemrograman C++ dan Bahasa Pemrograman Python pada Algoritma Pemrograman." Jurnal Teknik Informatika dan Teknologi Informasi 3, no. 3 (2023): 56–63. http://dx.doi.org/10.55606/jutiti.v3i3.2863.
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Programming Language is a language used to write computer program codes. Programming languages allow programmers to instruct computers to perform certain tasks. There are many different programming languages such as Python, Java, C++, PHP, JavaScript and so on. This article only focuses on explaining the C++ and Python programming languages. The aim of this article is to find out the differences between the C++ and Python programming languages and to find out the advantages and functions of each programming language. From the results obtained, the two C++ programming languages are more focused on Windows development, while Python is more often used in software development and the C++ programming language is more difficult to understand than the Python programming language.
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Ильченко, О. Ю., В. Н. Сырицына, and О. Е. Кадеева. "Solving USE problems in computer science using the Python language." Higher education today, no. 11-12 (December 29, 2021): 44–56. http://dx.doi.org/10.18137/rnu.het.21.11-12.p.042.
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Рассматриваются предметные и методические аспекты единого государственного экзамена по информатике. Анализируются особенности решения сложной части заданий единого государственного экзамена по программированию на языке Python, приводятся примеры решения таких заданий. Subject and methodological aspects of the unifi ed state examination in informatics are considered. The peculiarities of solving a complex part of the exam tasks in Python programming are analyzed, examples of solving such tasks are given.
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Gubasheva, Khava A., Malika A. Biysultanova, and Rimma S. Zaripova. "EFFECTIVENESS OF IMPLEMENTING SWARM OPTIMIZATION IN THE PYTHON PROGRAMMING LANGUAGE." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 5/6, no. 146 (2024): 264–68. http://dx.doi.org/10.36871/ek.up.p.r.2024.05.06.034.
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In this article, we first looked at the essence and basic definitions of swarm intelligence. We examined the meaning and application in modern technologies, the role of swarm intelligence in modern technologies. We examined the basic principles of self-organization and coordination of swarm intelligence, which allow us to create flexible and effective systems inspired by the natural behavior of teams. These principles have applications in a variety of problems, from optimization to robotics, and are key to the success of the swarm approach in modern technology. Finally, a swarm optimization algorithm in the Python programming language is described. Swarm intelligence allows you to solve a wide range of optimization problems in real applications and in various fields of activity.
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Sailaja Kumar, K., D. Evangelin Geetha, and Pratap Rudra Sahoo. "A Methodology to Handle Heterogeneous Data Generated in Online Social Networks." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4098–102. http://dx.doi.org/10.1166/jctn.2020.9025.
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Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing the Twitter text data. Using this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.
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Viresh, Kasheenath Babaleshwar, Karade Sinchana, N. Sakshi, and Naidu Anush. "Audio encryption and decryption using AES algorithm technique." i-manager’s Journal on Electronics Engineering 14, no. 2 (2024): 8. http://dx.doi.org/10.26634/jele.14.2.20486.
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Audio cryptography is the practice of encrypting audio data to prevent illegal access to and listening to it. This paper presents an innovative technique of audio cryptography based on the Python computer language. To ensure secrecy and integrity, the suggested system encrypts and decrypts audio signals using advanced cryptographic techniques. A crucial component of AES, the cryptographic key is dynamically created to improve security. Python's broad library support and ease of use make it an ideal platform for implementing the AES algorithm, which ensures dependable and effective audio data encryption. The system utilizes Python's cryptography library for seamless integration and ease of implementation. Simulation results demonstrate the efficacy of the AES algorithm in securely encrypting and decrypting audio data with reduced noise compared to traditional methods.
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Alyoshin, S., E. Borodina, A. Kikot, and I. Zabran. "PYTHON PROGRAMMING FEATURES AND NEW POSSIBILITIES." Системи управління, навігації та зв’язку. Збірник наукових праць 4, no. 50 (2018): 95–98. http://dx.doi.org/10.26906/sunz.2018.4.095.
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language, and also to demonstrate the volumes of this language together with new software. The content of the article is an overview that allows you to understand the features and new features of the Python language. Results. It was found that to create programs for different purposes, use a powerful tool Python. Originality. Programs written in Python work in exactly the same way, regardless of which operating system they are running on, and also Python adds features that make it widely used. Practical value. The cost-effectiveness of new versions of Python and the volumes of this language together with the new provision provides a detailed analysis of the areas where Python is used. The conclusion is that Python has some noteworthy features that make it widely used. Conclusions. The arguments we have presented prove that the program written in Python will function exactly the same regardless of which operating system it is running in. Differences arise only in rare cases, and they are easy to anticipate due to the availability of detailed documentation.