Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

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Python Implementation of HSGAdviser Chatbot: AI model for Sustainable Education by Suha Assayed (22454038)

“…<p dir="ltr">This repository contains the Python source code and model implementation for HSGAdviser, an AI speech assistant designed to provide personalized college and career guidance for high school students through conversational AI. …”

Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. by Samuel Cure (22250922)

“…<p>We simulate SIR epidemic processes on Watts-Strogatz networks with parameters <i>k</i>₀ = 5, <i>p</i> = 0.1 <b>(a)</b> and Barabási-Albert networks with parameter <i>m</i> = 5 <b>(b)</b> using the Python wrapper of our C++ implementation and compare its performance with the Python library from Ref. …”

Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

“…</li><li><b>Commercial Distribution</b>: Safely share Python code with clients or customers, implementing advanced <b>Python code protection tools</b> for sales or rentals.…”

Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool by Pavel Izosimov (20096259)

“…By implementing advanced security measures such as <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>end-to-end encryption</b></a>, <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>asymmetric encryption</b></a>, and <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>symmetric encryption</b></a>, the platform strives to keep code better protected during transmission and execution.Explore other tools offered by Alpha Beta Network:</p><ul><li><a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>Secure Python Code Manager</b></a>: A command-line tool for secure Python code sharing and protection using the Alpha Beta Network cloud platform.…”

System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

“…</li><li><b>Technical Support and Troubleshooting</b>: Support teams can quickly identify devices during support requests, track support history, and automate support processes using the HWID, improving response times and customer satisfaction.</li></ul><h2>Integration with Other Tools</h2><p dir="ltr">The System Hardware ID Generator Script is part of the broader suite of tools offered by the <a href="https://xn--mxac.net/" target="_blank">Alpha Beta Network</a>, dedicated to enhancing security and performance in <a href="https://xn--mxac.net/" target="_blank">Python programming</a>.…”

Tracking module training details. by Khelina Fedorchuk (19130089)

Segmentation module training details. by Khelina Fedorchuk (19130089)

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by Pau Fonseca i Casas (9507338)

Local Python Code Protector Script: A Tool for Source Code Protection and Secure Code Sharing by Pavel Izosimov (20096259)

“…<p dir="ltr">Local Python Code Protector Script: Advanced Tool for Python Code Protection and Secure Sharing</p><h2>Introduction</h2><p dir="ltr">The <b>Local Python Code Protector Script</b> is a powerful command-line tool designed to provide <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank"><b>source code protection</b></a> and <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank"><b>secure code sharing</b></a> for Python scripts. …”

Python scripts and notebooks by Ismail Belgacem (20845811)

Comparison of BlueRecording with existing tools by Joseph James Tharayil (21416715)

“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”

Fitting a feast/famine cycle to a glycolysis model. by Paul van Lent (16876977)

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Jaxkineticmodel allows for hybridizing kinetic models with neural networks. by Paul van Lent (16876977)

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Analysis of convergence properties for a collection of SBML models. by Paul van Lent (16876977)

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Overview of the SBML models used in this study. The collection of models was retrieved from Biomodels [43] and a previously reported collection of SBML benchmark models [11]. The n... by Paul van Lent (16876977)

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Prediction accuracy analysis over time steps. by Mohanad Faeq Ali (21354273)

“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”

FPR and FNR analysis based on different time steps. by Mohanad Faeq Ali (21354273)

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. …”

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

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