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Journal articles on the topic 'Python tool'

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Published: 2 June 2025

Last updated: 31 July 2025

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1

Conway, Matthew J. "Python: a GUI development tool." Interactions 2, no. 2 (1995): 23–28. http://dx.doi.org/10.1145/205350.205354.

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J, LAKSHMI. "A Python - Powered Keylogger Detection Tool." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31982.

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Keyloggers are a prevalent threat that record users' keystrokes to steal credentials and sensitive data. This paper presents a Python-based keylogger detection tool that scans running processes and checks them against indicators of compromise (IOCs). The detector integrates process inspection, signature matching, and automated remediation functions. A graphical user interface allows easy operation and threat response. The tool was tested against known keyloggers and demonstrated effective discovery with minimal false positives. This demonstrates Python's capabilities for building specialized s

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Preeti, Tupsakhare. "Python for Automation and Scripting: Streamlining Operations and Increasing Efficiency." Journal of Scientific and Engineering Research 6, no. 9 (2019): 222–27. https://doi.org/10.5281/zenodo.13918609.

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In today's rapidly evolving technological landscape, Python has emerged as a pivotal tool for automation and scripting, streamlining operations and significantly increasing efficiency across various industries. This white paper delves into the numerous advantages of Python, highlighting its accessibility, simplicity, and the powerful ecosystem of libraries that support a wide range of automation tasks. From automating mundane data entry tasks to orchestrating complex network operations, Python's versatility makes it an indispensable tool for developers and non-developers alike. We will explore

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Murthy, P. V. S. N. "Automated Penetration Testing Tool Using Python." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1100–1105. https://doi.org/10.22214/ijraset.2025.67480.

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This title accurately reflects the core objectives of the project creating a tool that automates the penetration testing process and generates detailed reports [4]. It encapsulates the primary focus of the project, which is to enhance the efficiency of penetration testing through automation, while ensuring that the resulting reports provide valuable insights into system vulnerabilities and security weaknesses. The tool will streamline the testing procedure, making it accessible to both experienced security professionals and those new to penetration testing.

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Dronov, V. Y., and G. A. Dronova. "Python as an automation tool in IS. Protecting Database Access in Python." Journal of Physics: Conference Series 2182, no. 1 (2022): 012093. http://dx.doi.org/10.1088/1742-6596/2182/1/012093.

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Abstract The activity of modern organizations, regardless of the form of ownership, is highly risky. There are up to 600 risk factors in total. It is possible to reduce risk levels only after assessing them. The basis of risk assessment is analytical processing of information relating to the sphere of organization’s activity. The information to be analyzed is usually accumulated in databases. This information is usually accessed with SQL queries and analyzed with application software. The most rational variant is when the business unit employees are able to select the data from the database an

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Karunamurthy, Dr A., Vithanala Manoj Kumar, and Prasanth C. "AIRMATH GESTURE-DRIVEN VIRTUAL CALCULATOR." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–9. https://doi.org/10.55041/isjem02161.

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The Virtual Calculator project is a Python-based application that allows users to perform mathematical calculations through an intuitive graphical user interface (GUI). Designed to support basic operations such as addition, subtraction, multiplication, and division, the calculator offers a simple yet powerful tool for solving everyday mathematical problems. The application leverages Python's Tkinter library to create an easy-to-use interface, ensuring that users can interact with the tool effortlessly, regardless of their technical background. This project demonstrates the versatility of Pytho

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Dronov, V. Y., and G. A. Dronova. "PYTHON AS AN AUTOMATION TOOL IN INFORMATION SECURITY. PROTECTING DATABASE ACCESS IN PYTHON." Dynamics of Systems, Mechanisms and Machines 9, no. 4 (2021): 054–59. http://dx.doi.org/10.25206/2310-9793-9-4-54-59.

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Деятельность современных организаций, не зависимо от форм собственности, является высокорискованной. Всего насчитывается до 600 риск факторов. Снижение уровней риском, можно только оценив их. Основой оценки рисков является аналитическая обработка информации, относящейся к сфере деятельности организации. Информация, подлежащая анализу, обычно аккумулируется в базах данных. Доступ к этой информации обычно осуществляется с помощью SQL-запросов, а анализ проводится с помощью прикладного программного обеспечения. Наиболее рациональный вариант - когда сотрудники бизнес-подразделения самостоятельно с

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Li, Yang, Yanchen Zou, and Yanda Qian. "Practice of Python in Programming and Optimization of Quantitative Analysis Model of Fixed Income." Mathematical Modeling and Algorithm Application 3, no. 1 (2024): 23–26. http://dx.doi.org/10.54097/z441c084.

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Python has become an indispensable tool in fixed income trading. This paper introduces the principles and general process of data analysis visualisation by analysing the basic operation and performance of personal investment and finance, combined with data analysis in the era of big data. The library of data analysis tools using Python has become an indispensable tool in fixed income trading. In fixed income trading, it supports fixed income traders to handle tasks such as bond pricing, interest rate modelling, and credit risk analysis Using Python's powerful data processing capabilities, trad

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Gely, Mario F., and Gary A. Steele. "QuCAT: quantum circuit analyzer tool in Python." New Journal of Physics 22, no. 1 (2020): 013025. http://dx.doi.org/10.1088/1367-2630/ab60f6.

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Yadav, Vinay, Subhankar Karmakar, Pradip P. Kalbar, and A. K. Dikshit. "PyTOPS: A Python based tool for TOPSIS." SoftwareX 9 (January 2019): 217–22. http://dx.doi.org/10.1016/j.softx.2019.02.004.

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Tahat, Amani, Jordi Marti, Kaher Tahat, and Ali Khwaldeh. "AJAC: Atomic data calculation tool in Python." Chinese Physics B 22, no. 4 (2013): 048901. http://dx.doi.org/10.1088/1674-1056/22/4/048901.

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Escobar-Cerezo, Jesus, Fernando Caudepón Moreno, Fabio Pérez Gómez, et al. "DOSE REFERENCE LEVEL CLASSIFICATION TOOL WITH PYTHON." Physica Medica 104 (December 2022): S185—S186. http://dx.doi.org/10.1016/s1120-1797(22)02568-6.

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LOBARYOV, D., and N. LOBARYOV. "SYNTHESIS OF NONDETERMINISTIC FINITE AUTOMATON FROM REGULAR EXPRESSIONS BY GLUSHKOV'S ALGORITHM IN JFF FORMAT." HERALD OF POLOTSK STATE UNIVERSITY. Series С FUNDAMENTAL SCIENCES, no. 1 (April 18, 2025): 9–13. https://doi.org/10.52928/2070-1624-2025-44-1-9-13.

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The paper presents the results of the implementation of a software tool for synthesizing nondeterministic finite automaton using regular expressions in JFF format. Glushkov's algorithm is used as a method for synthesizing automata. When developing the program, the integrated development environment for the Python – Visual Studio Code, the JFLAP software package for visualizing finite automaton, as well as the Python libraries xml.etree and pythonds were used.

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Oladipo, Francisca Onaolapo, and Memunat A. Ibrahim. "The CodeEazee Tool Support for Computational Thinking in Python." European Journal of Engineering Research and Science 3, no. 3 (2018): 12. http://dx.doi.org/10.24018/ejers.2018.3.3.637.

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This paper describes the development of CodeEazee, a problem solving, self- teaching tool for python programming which deploys templates and games. In this work, the authors conducted a survey to determine the factors responsible for the reduced interests of learners in programming, reviewed the various approaches used in teaching programming, and developed a python-for-python teaching system to teach programming skills, computational thinking, algorithms’ design, programming in general and Python programming specifically. The work would show how the third party environment had enabled users w

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Oladipo, Francisca Onaolapo, and Memunat A. Ibrahim. "The CodeEazee Tool Support for Computational Thinking in Python." European Journal of Engineering and Technology Research 3, no. 3 (2018): 12–20. http://dx.doi.org/10.24018/ejeng.2018.3.3.637.

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This paper describes the development of CodeEazee, a problem solving, self- teaching tool for python programming which deploys templates and games. In this work, the authors conducted a survey to determine the factors responsible for the reduced interests of learners in programming, reviewed the various approaches used in teaching programming, and developed a python-for-python teaching system to teach programming skills, computational thinking, algorithms’ design, programming in general and Python programming specifically. The work would show how the third party environment had enabled users w

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Hendriks, D. D., and R. G. Izzard. "binary_c-python: A Python-based stellar population synthesis tool and interface to binary_c." Journal of Open Source Software 8, no. 85 (2023): 4642. http://dx.doi.org/10.21105/joss.04642.

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Taggart, Patrick L., Stephen Morris, and Charles G. B. Caraguel. "The impact of PIT tags on the growth and survival of pythons is insignificant in randomised controlled trial." PeerJ 9 (June 30, 2021): e11531. http://dx.doi.org/10.7717/peerj.11531.

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Individual identification is fundamental to the study of captive and wild animals but can have adverse impacts if the method of identification is inappropriate for the species or question of interest. We conducted a randomised controlled trial to test whether passive integrated transponder (PIT) tags reduced the growth or survival of pythons. We randomly allocated 200 captive-bred Burmese python (Python bivittatus) hatchlings into two groups, tagged versus untagged. Hatchlings were individually identified using a combination of PIT tags and unique colour patterns, and their mass, snout-vent le

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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

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Aravind Ayyagiri, Dr. Arpit Jain, and Om Goel. "Utilizing Python for Scalable Data Processing in Cloud Environments." Darpan International Research Analysis 12, no. 2 (2024): 183–98. http://dx.doi.org/10.36676/dira.v12.i2.78.

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In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The s

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Gellis, Jason, Camila Smith, and Robert Foley. "PyLithics: A Python package for stone tool analysis." Journal of Open Source Software 7, no. 69 (2022): 3738. http://dx.doi.org/10.21105/joss.03738.

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Cornelis, Hugo, Armando L. Rodriguez, Allan D. Coop, and James M. Bower. "Python as a Federation Tool for GENESIS 3.0." PLoS ONE 7, no. 1 (2012): e29018. http://dx.doi.org/10.1371/journal.pone.0029018.

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Wagemann, Julia, Sabrina Szeto, Simone Mantovani, and Federico Fierli. "LTPy - Learning tool for Python on Atmospheric Composition." Journal of Open Source Education 6, no. 60 (2023): 172. http://dx.doi.org/10.21105/jose.00172.

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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 suitabil

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ЮДИНА, О. В., and В. В. КИРИЛЛОВА. "CHOICE OF PYTHON AS A TOOL FOR SOLVING THE PROBLEMS OF THE DIGITAL ECONOMY." Экономика и предпринимательство, no. 2(151) (May 31, 2023): 1244–46. http://dx.doi.org/10.34925/eip.2023.151.2.249.

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В данной статье проводится сравнение языка программирования Python, с другими не менее популярными и функциональными языками программирования. Прежде всего сравнение проводилось с Java, так как он является главным конкурентом. Описаны главные отличия и сходства разных языков программирования. Выполнен сравнительный анализ как PHP и C++, были определены сферы применения Python в цифровой экономике. После чего были подведены краткие итоги. This article compares the Python programming language with other equally popular and functional programming languages. First of all, the comparison was made w

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Ricardo, Agostinho de Rezende Junior, Gustavo Goroso Daniel, Luis Puglisi José, and Rodrigues da Silva Robson. "CARDIO JUNCTION: FERRAMENTA COMPUTACIONAL IMPLEMENTADA A PARTIR DE UM MODELO DE COMPARTIMENTOS PARA O MIÓCITO VENTRICULAR DE RATO." Revistaft 27, no. 122 (2023): 09. https://doi.org/10.5281/zenodo.7934209.

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O cálcio (Ca<sup>2+</sup>), um dos íons envolvidos no complexo funcionamento do coração, é também considerado o mais importante pelo fato de ser o ativador direto dos miofilamentos que causam a contração. O seu fluxo irregular é uma das principais causas de arritmias cardíacas e disfunção contráctil. Nesse sentido, a formulação de novos modelos matemáticos e computacionais de miócito ventricular de mamífero aporta ferramentas importantes para entender os mecanismos de l

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Satvoldiev, Abrorjon. "THE COLLABORATION OF PYTHON AND DATABASES: A MODERN APPROACH TO DATA-DRIVEN APPLICATIONS." INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 2, no. 06 (2025): 88–90. https://doi.org/10.70728/tech.v2.i06.032.

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In the era of data-driven decision-making, the synergy between programming languages and database systems has become paramount. This study explores the integration of Python—a versatile and widely-used programming language—with relational databases such as SQLite, MySQL, and PostgreSQL. Employing libraries like sqlite3, psycopg2, and Object-Relational Mapping (ORM) tools such as SQLAlchemy and Django ORM, the research demonstrates how Python facilitates efficient database interactions. Through practical implementations and performance benchmarks, the study highlights Python's strengths in simp

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Soujanya, Reddy Annapareddy. "Real-World Applications of Python in Firmware and Software Automation." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 2 (2024): 1–6. https://doi.org/10.5281/zenodo.14540900.

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Python has become a versatile tool in firmware and software automation, enabling efficient development and testing workflows across various industries. Its simplicity, extensive libraries, and cross-platform support make it ideal for automating tasks like firmware flashing, version verification, and hardware communication. This paper explores real-world applications of Python in firmware and software automation, including integrating serial communication protocols, streamlining build and deployment processes, and implementing test frameworks for embedded systems. It highlights how Python's eco

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Poonam, Gunaji Bansode, Sandeep Muley Smita, and Govind Pardeshi Mohini. "Matrix Operations Using Python: An Efficient Approach for Computational Mathematics." International Journal of Advance and Applied Research S6, no. 22 (2025): 1098–102. https://doi.org/10.5281/zenodo.15542494.

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<em>This paper explores the implementation of various matrix operations using Python, with a particular focus on leveraging Python's capabilities for performing computationally intensive tasks in linear algebra. Matrix operations such as addition, multiplication, inversion, and eigenvalue decomposition are demonstrated with practical code examples using the powerful Sympy&Numpy library. The paper also discusses the relevance of matrix operations in real-world applications, such as machine learning, computer graphics, and scientific computing. This study demonstrates the efficiency and ease

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Samodra, Bayu, Vebby Amelya Nora, Fitra Arifiansyah, Gusti Ayu Putri Saptawati Soekidjo, and Muhamad Koyimatu. "DEVELOPMENT OF GRAPH GENERATION TOOLS FOR PYTHON FUNCTION CODE ANALYSIS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 3 (2025): 690–97. https://doi.org/10.33480/jitk.v10i3.6177.

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The increasing complexity of programs in software development requires understanding and analysis of code structure, especially in Python, which dominates machine learning and data science applications. Manual static analysis is often time-consuming and prone to errors. Meanwhile, static analysis tools for Python, like PyCG and Code2graph, are still limited to generating call graphs without including dependency and control flow analysis. This research addresses these shortcomings by proposing the development of a web-based tool that integrates the generation of function call graphs, function d

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Gupta, N., M. Nath, S. Chakraborty, and A. Bandyopadhyay. "Python – a Tool for Percolation Analysis in Triangular Lattice." Journal of Nano- and Electronic Physics 13, no. 2 (2021): 02009–1. http://dx.doi.org/10.21272/jnep.13(2).02009.

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Sharma, Rishav, and Javed Dhillon. "PyPSA: Open Source Python Tool for Load Flow Study." Journal of Physics: Conference Series 1854, no. 1 (2021): 012036. http://dx.doi.org/10.1088/1742-6596/1854/1/012036.

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Gerzsenyi, Dávid. "FRMOD, a Python tool for statistical landslide susceptibility assessment." Advances in Cartography and GIScience of the ICA 3 (November 29, 2021): 1–6. http://dx.doi.org/10.5194/ica-adv-3-4-2021.

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Abstract. Locating landslide-prone slopes is important, as landslides often threaten life or property where they occur. There is an abundance of statistical methods in the literature for estimating susceptibility to landslides, i.e., the likelihood of landslide occurrence based on the analyzed conditions. Still, there is a lack of readily available GIS tools for landslide susceptibility analysis, making it hard to reproduce or compare the results of different susceptibility assessments. The FRMOD is a Python-based tool for conducting landslide susceptibility analysis with the frequency ratio m

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Perkel, Jeffrey M. "Python power-up: new image tool visualizes complex data." Nature 600, no. 7888 (2021): 347–48. http://dx.doi.org/10.1038/d41586-021-03628-7.

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Chang, Sun Woo, Sama S. Memari, and T. Prabhakar Clement. "PyTheis—A Python Tool for Analyzing Pump Test Data." Water 13, no. 16 (2021): 2180. http://dx.doi.org/10.3390/w13162180.

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The Theis equation is an important mathematical model used for analyzing drawdown data obtained from pumping tests to estimate aquifer parameters. Since the Theis model is a nonlinear equation, a complex graphical procedure is employed for fitting this equation to pump test data. This graphical method was originally proposed by Theis in the late 1930s, and since then, all the groundwater textbooks have included this fitting method. Over the past 90 years, every groundwater hydrologist has been trained to use this tedious procedure for estimating the values of aquifer transmissivity (T) and sto

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Smith, Stephen A., and Joseph F. Walker. "Py PHLAWD : A python tool for phylogenetic dataset construction." Methods in Ecology and Evolution 10, no. 1 (2018): 104–8. http://dx.doi.org/10.1111/2041-210x.13096.

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Dejanović, I., R. Vaderna, G. Milosavljević, and Ž. Vuković. "TextX: A Python tool for Domain-Specific Languages implementation." Knowledge-Based Systems 115 (January 2017): 1–4. http://dx.doi.org/10.1016/j.knosys.2016.10.023.

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Peterson, Pearu. "F2PY: a tool for connecting Fortran and Python programs." International Journal of Computational Science and Engineering 4, no. 4 (2009): 296. http://dx.doi.org/10.1504/ijcse.2009.029165.

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de’ Michieli Vitturi, Mattia, Andrea Bevilacqua, Alessandro Tadini, and Augusto Neri. "ELICIPY 1.0: A Python online tool for expert elicitation." SoftwareX 25 (February 2024): 101641. http://dx.doi.org/10.1016/j.softx.2024.101641.

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Osamah, Alrouwab1 2. *. Ismail Mallian1 Buthaynah Ramadhan2 Kareemah Abdullah2 Omiema Aznad2 Abd Almajid Abu khashim3 Mahmoud Gargotti4. "Almespar: An Open Reading Frames Detection Tool Using Python." Alq J Med App Sci 6, no. 1 (2023): 172–81. https://doi.org/10.5281/zenodo.7818561.

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<strong>Background and aims. </strong>Open reading frames (ORFs) are sections of a reading frame that do not include any stop codons. A reading frame is a sequence of nucleotide triplets read as codons indicating amino acids; a single strand of DNA has three potential reading frames. Long ORFs in a DNA sequence may represent possible protein-coding areas. In addition to extended ORFs, which assist in gene locus prediction, there is yet another type of ORFS known as small open reading frames (smORFs), which have 100 codons or fewer. <strong>Methods</strong>. We develop an offline, cross-platfor

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Kumar, Deepak, Achin Srivastav, Manoj Kumar Sain, and Vikash Gautam. "Exploring Regression Analysis in Predictive Modeling with Python tool." SKIT Research Journal 14, no. 2 (2024): 52–55. https://doi.org/10.47904/ijskit.14.2.2024.52-55.

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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

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ISIMOV, A. M., G. B. ADMANOVA, A. T. SARZHIGITOVA, N. K. KEMALOVA та B. BAKYTZHANKYZY. "PYTHON КАК ИНСТРУМЕНТ ДЛЯ РЕКОНСТРУКЦИИ И АНАЛИЗА МЕТАБОЛИЧЕСКОЙ МОДЕЛИ В МАСШТАБЕ ГЕНОМА". МИКРОБИОЛОГИЯ ЖӘНЕ ВИРУСОЛОГИЯ, № 4(43) (19 грудня 2023): 32–62. http://dx.doi.org/10.53729/mv-as.2023.04.03.

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The cost reduction of high-throughput technologies has led to the development of data analysis methods that deal with the increasing availability of omics datasets. Constraint-based genome-scale metabolic modeling is an example of such a method. It uses omics data and biochemical networks, a mechanistic framework to combine omics data, to describe steady-state metabolic flux distributions. Therefore, a system of mass balance equations is created that can be solved via linear programming. To date, most software for constraint-based genome-scale modeling has relied on MATLAB, a popular proprieta

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Yu, Yide, Huijie Li, Yue Liu, and Yan Ma. "Integrating Multi-Model Simulations to Address Partial Observability in Population Dynamics: A Python-Based Ecological Tool." Applied Sciences 15, no. 1 (2024): 89. https://doi.org/10.3390/app15010089.

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Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: insufficient multi-view assessment, a high learning curve, and a lack of seamless secondary development with Python. To address these issues, we developed a novel software tool named WAPET (Wildlife Analysis and Population Ecology Tool) (Python 3.10.12). WAPET integrates Monte Carlo simulation with e

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Tummod, Shiva. "Python Expense Tracker Application." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32043.

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It's a Python Expense Tracker that helps you keep track of your savings and spending. Plus, it alerts you if you go over your set limit. An expense tracker is like a digital notebook that helps you keep track of all your money transactions. It also gives you reports about your financial activities. Nowadays, life is very busy and people want to make more money, so they rush around a lot. But sometimes, they forget to keep track of their money properly. That's where an expense tracker comes in handy. It's a great tool that lets you manage and keep track of your expenses easily. Daily based expe

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Zhou, Chenliang, Dominic Kuang, Jingru Liu, et al. "AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13436–43. http://dx.doi.org/10.1609/aaai.v34i09.7068.

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AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level al

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Kanna, M. R. Rajesh, R. Pradeep Kumar, Sudharsan Vijayaraghavan, Ananda Kumar M.R, and D. Soner Nandappa. "MATHGRAPH : A PYTHON PACKAGE TO COMPUTE ENERGY AND TOPOLOGICAL INDICES OF GRAPHS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 01 (2019): 2990–3002. https://doi.org/10.5281/zenodo.14811254.

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In this paper, we introduce MathGraph, an open-source and cross-platform Python package. As a Python package, MathGraph is easily integrable with graph visualization softwares. This helps researchers in graph theory to either create a graph programmatically using Python program or draw the graph using Graphical User Interface (GUI) tool such as ‘Tulip’ to compute distinct sets, energies and topological indices of graphs. 

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Bokharaie, Vahid. "MiTfAT: A Python-based Analysis Tool for Molecular fMRI Experiments." Journal of Open Source Software 6, no. 58 (2021): 2827. http://dx.doi.org/10.21105/joss.02827.

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HT Ling, Maurice. "SeqProperties: A Python Command-Line Tool for Basic Sequence Analysis." Acta Scientific Microbiology 3, no. 6 (2020): 103–6. http://dx.doi.org/10.31080/asmi.2020.03.0615.

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Lou, X., S. van der Lee, and S. Lloyd. "AIMBAT: A Python/Matplotlib Tool for Measuring Teleseismic Arrival Times." Seismological Research Letters 84, no. 1 (2013): 85–93. http://dx.doi.org/10.1785/0220120033.

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Peere, Wouter, and Tobias Blanke. "GHEtool: An open-source tool for borefield sizing in Python." Journal of Open Source Software 7, no. 76 (2022): 4406. http://dx.doi.org/10.21105/joss.04406.

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