Python Tool Research Articles

Long-term and short-term rainfall forecasting using deep neural network optimized with flamingo search optimization algorithm

Rainfall forecasting is essential because heavy and irregular rainfall creates many impacts like destruction of crops and farms. Here, the occurrence of rainfall is highly related to atmospheric parameters. Thus, a better forecasting model is essential for an early warning that can minimize risks and manage the agricultural farms in a better way. In this manuscript, Deep Neural Network (DNN) optimized with Flamingo Search Optimization Algorithm (FSOA) is proposed for Long-term and Short-term Rainfall forecasting. Here, the rainfall data is obtained from the standard dataset as Sudheerachary India Rainfall Analysis (IRA). Moreover, the Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) approach is utilized for pre-processing process. Also, the deep neural network is utilized for performing rainfall prediction and classification. Additionally, the parameters of the DNN model is optimizing by Flamingo Search Optimization Algorithm. Finally, the proposed MFEEWT-DNN- FSOA approach has effectively predict the rainfall in different locations around India. The proposed model is implemented in Python tool and the performance metrics are calculated. The proposed MFEEWT-DNN- FSOA approach has achieved 25%, 26%, 25.5% high accuracy and 35.8%, 24.7%, 15.9% lower error rate for forecasting rainfall in Cannur at Kerala than the existing Map-Reduce based Exponential Smoothing Technology for rainfall prediction (MR-EST-RP), modular artificial neural networks with support vector regression for rainfall prediction (MANN-SVR-RP), and biogeography-based extreme learning machine (BBO-ELM) (BBO-ELM-RP) methods respectively.

CAFA-evaluator: a Python tool for benchmarking ontological classification methods.

https://pypi.org/project/cafaeval.

An Open-Access Data Set of Active-Source and Passive-Wavefield DAS and Nodal Seismometer Measurements at the Newberry Florida Site

Abstract This article documents a comprehensive subsurface imaging experiment using seismic waves in a well-studied outdoor laboratory at Newberry, Florida, which is known for significant spatial variability, karstic voids, and underground anomalies. The experiment used approximately two kilometers of distributed acoustic sensing (DAS) fiber-optic cable, forming a dense 2D array of 1920 horizontal-component channels, and a 2D array of 144 SmartSolo three-component nodal seismometers, to sense active-source and passive-wavefield seismic waves. The active-source data were generated using a powerful, triaxial vibroseis shaker truck (T-Rex) and impact sources (accelerated weight drop and an eight-pound sledgehammer) that were simultaneously recorded by both the DAS and nodal seismometers. The vibroseis truck was used to excite the ground in three directions (two horizontal and one vertical) at 260 locations inside and outside the instrumented array, whereas the impact sources were used at 268 locations within the instrumented array. The passive-wavefield data recorded using the nodal seismometers comprised 48 hr of ambient noise collected over a period of four days in four 12-hour time blocks, whereas the passive wavefield data collected using DAS consisted of four hours of ambient noise recordings. This article aims to provide a comprehensive overview of the testing site, experiment layout, the DAS and nodal seismometer acquisition parameters, and implemented raw data processing steps. Although potential use cases, such as surface-wave testing, full-waveform inversion, and ambient noise tomography, are discussed relative to example data, the focus of this article is on documenting this unique data set and presenting its initial data quality rather than on generating subsurface imaging results. The raw and processed data, along with detailed documentation of the experiment and Python tools to aid in visualizing the DAS data set, have been made publicly available.

LyZNet with Control: Physics-Informed Neural Network Control of Nonlinear Systems with Formal Guarantees

Optimal control for high-dimensional nonlinear systems remains a fundamental challenge. One bottleneck is that classical approaches for solving the Hamilton-Jacobi-Bellman (HJB) equation suffer from the curse of dimensionality. Recently, physics-informed neural networks have demonstrated potential in overcoming the curse of dimensionality in solving certain classes of PDEs, including special cases of HJB equations. However, one perceived limitation of neural networks is their lack of formal guarantees in the solutions they provide. To address this issue, we have built LyZNet, a Python tool that combines physics-informed learning with formal verification. The previous version of the tool demonstrated the capability for stability analysis and region of attraction estimates. In this paper, we present the tool for solving optimal control problems. We expand the functionalities of the tool to support the formulation and solving of optimal control problems for control-affine systems via physics-informed neural network policy iteration (PINN-PI). We outline the methodology that enables the learning and verification of PINN for optimal stabilization tasks. We demonstrate with a classical control example that the learned optimal controller indeed has significantly improved performance and verifiable regions of attraction.

Scalable Network Intrusion Detection in Cloud Environments through Parallelized Swarm-Optimized Neural Networks

Cloud computing (CC) offers on demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remains a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffics flow that targets a cloud environment.NetworkIntrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce theproblem. In this study, we gather a NSL-KDD network traffic dataset.Secondly, collected data is preprocessed using Z score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%)

Hybridized Clustering Algorithm and Ensemble Learning for Monitoring Paddy Crop Growth Analysis

In food security control and agricultural management, crop monitoring is important to improve the crop yield rate and ensure the paddy growth. The paddy crop is monitored via remote sensing satellite images that cover the entire cultivating region. The satellite images are affected due to satellite movement and human errors, which causes over fitting issues. In addition, the remote images are facing difficulties while managing system robustness. The research issue is overcome by applying the Hybridized Clustering Algorithm-based Ensemble Learning Hybridized Clustering Algorithm with Ensemble Learning (HCA-EL) approach. Initially, the satellite images are handled to remove geometric, radiometric, and atmospheric corrections using pre-processing techniques. This process uses the Ground Control Points (GCP) and histogram process to remove unwanted information. Then vegetation index value data points are obtained to progress the overall crop monitoring. The data points are analyzed using spectral clustering based on the eigenvector and eigenvalue, minimizing the dimensionality issues. Finally, the crop monitoring criteria, such as soil quality and water levels are computed to improve paddy growth. The monitoring process is done with the help of the Ensemble Learning Extreme Networks (ELEN), which uses the voting criteria to identify the quality parameters. The HCA-EL approach-based crop monitoring process was implemented using the Python tool with respective performance metrics like Accuracy, Error Rate, MCC and F1-Score.

Scalable Network Intrusion Detection in Cloud Environments through Parallelized Swarm-Optimized Neural Networks

Cloud computing (CC) offers on-demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remain a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffic flow that targets a cloud environment. Network Intrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce the problem. In this study, we gather an NSL-KDD network traffic dataset. Secondly, collected data is preprocessed using Z-Score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%).

АВТОМАТИЗАЦИЯ ПРОЦЕССА ЭКОНОМИЧЕСКОЙ ОТЧЕТНОСТИ ОРГАНИЗАЦИИ С ПРИМЕНЕНИЕМ ИНСТРУМЕНТОВ ЯЗЫКА PYTHON И MS EXCEL

The article is an overview of the use of Python and MS Excel programming language tools to automate the reporting process in an organization. The authors discuss methods and techniques for using Python to collect, process, and analyze data, as well as generate reports. Particular attention is paid to the integration of Python with MS Excel, which allows you to combine the powerful analytical capabilities of Python with the convenience and flexibility of Excel spreadsheets. The article provides Python code examples and explains steps to create automated reports using tools from both platforms. This approach can significantly increase the efficiency of the reporting process and reduce the likelihood of errors by automating routine tasks. In addition, the article highlights the benefits of using Python, such as the ability to work with large amounts of data, create custom functions and scripts for specialized analytical tasks. This approach allows organizations to significantly reduce the time spent on reporting preparation and improve the quality of its analysis.

Enhancing Inventory Management through Advanced Technologies and Mathematical Methods: Utilizing Neutrosophic Fuzzy Logic

Optimal inventory management is one of the most critical components for companies to thrive in the competitive market while meeting their customers’ demands, reducing costs, and developing their operations. In this paper, the utilization of different technologies and instruments ranging from the most modern ones to mathematical ones was analyzed to demonstrate how the system can function successfully. It is expected that Neutrosophic fuzzy logic is one of the most complicated approaches that allow for proper uncertainty management, forecasting, and inventory control improvements. Fundamentally, the process could be that much more insightful due to the availability of mathematical modelling and on-the-go support systems. Through the use of dynamic programming with the help of Python tools to process these models, Full optimization under fuzzy demand is possible to achieve. Therefore, one could conclude that companies have many opportunities to develop their operations, reduce costs, and keep their customers happy even in a highly dynamic and uncertain business environment.

PyngoST: fast, simultaneous and accurate multiple sequence typing of Neisseria gonorrhoeae genome collections.

Extensive gonococcal surveillance has been performed using molecular typing at global, regional, national and local levels. The three main genotyping schemes for this pathogen, multi-locus sequence typing (MLST), Neisseria gonorrhoeae multi-antigen sequence typing (NG-MAST) and N. gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR), allow inter-laboratory and inter-study comparability and reproducibility and provide an approximation to the gonococcal population structure. With whole-genome sequencing (WGS), we obtain a substantially higher and more accurate discrimination between strains compared to previous molecular typing schemes. However, WGS remains unavailable or not affordable in many laboratories, and thus bioinformatic tools that allow the integration of data among laboratories with and without access to WGS are imperative for a joint effort to increase our understanding of global pathogen threats. Here, we present pyngoST, a command-line Python tool for fast, simultaneous and accurate sequence typing of N. gonorrhoeae from WGS assemblies. pyngoST integrates MLST, NG-MAST and NG-STAR, and can also designate NG-STAR clonal complexes, NG-MAST genogroups and penA mosaicism, facilitating multiple sequence typing from large WGS assembly collections. Exact and closest matches for existing alleles and sequence types are reported. The implementation of a fast multi-pattern searching algorithm allows pyngoST to be rapid and report results on 500 WGS assemblies in under 1 min. The mapping of typing results on a core genome tree of 2375 gonococcal genomes revealed that NG-STAR is the scheme that best represents the population structure of this pathogen, emphasizing the role of antimicrobial use and antimicrobial resistance as a driver of gonococcal evolution. This article contains data hosted by Microreact.

Open-source Python module to automate GC-MS data analysis developed in the context of bio-oil analyses

Open-source Python tool automates GC-MS biofuel analysis, promoting sustainable energy research.

Stock Portfolio Optimization with Support of Python

: Securities business activities are understood as the activities of organizations, individuals, securities companies, stock brokers and other intermediary financial institutions in the stock market to perform operations. Securities trading aims to make a profit. A common task in portfolio management is to find the optimal investment portfolio according to the return and risk approach, that is, for the same level of risk, portfolio whose highest return gives is the optimal investment portfolio. Similarly, corresponding to the same predetermined rate of return, the portfolio with the lowest level of risk is the optimal investment portfolio. There are several popular data analysis tools today, including Python. This article will demonstrate the application of Python tools in data analysis to determine optimal stock investment portfolios.

Uncovering Malware Families Using Convolutional Neural Networks (CNN)

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently classifies malware into families by analyzing visual representations of malicious software as greyscale images using a Convolutional Neural Network (CNN). The Malwizard is a flexible Python tool suitable for both organizations and end-users enabling automated and rapid malware analysis within email system. Malwizard could be use as an Outlook Email’s add-in and an API service for SOAR platforms. The study evaluates this novel approach using the Microsoft Classification Challenge dataset, where image representations are encrypted to address privacy concerns. Experimental results show that the proposed approach performs comparably to the best existing model on plain text data, accomplishing the task in one-third of the time. For the encrypted dataset, adjustments to classical techniques are necessary for improved efficiency.

The crystal and electronic structure of RE23Co6.7In20.3 (RE = Gd–Tm, Lu): A new structure type based on intergrowth of AlB2- and CsCl-type related slabs

New ternary rare-earth indides RE23Co6.7In20.3 (RE = Gd–Tm, Lu) have been synthesized by arc-melting the elements under argon and subsequent annealing at 870 K for 1200 h. Single-crystal X-ray diffraction revealed Er23Co6.7In20.3 to crystallize in a new structure type in oP100, space group Pbam and Wyckoff sequence h11g13da with a = 23.203(5), b = 28.399(5), c = 3.5306(6) Å. The crystal structures of RE23Co6.7In20.3 (RE = Tb, Ho, Er and Tm) were determined from single crystal and powder X-ray diffraction data and further investigated by DFT methods. The compounds belong to a large family of ternary rare-earth indides with intergrowth of the AlB2- and CsCl-type related slabs. In the Er23Co6.7In20.3 structure, four types of fragments REIn and RET of CsCl-type, as well as RET2 and REIn2 of AlB2-type, are present simultaneously. A simple Python tool was developed to determine the coordination number for each crystallographic site with various methods and tested on the complex structure of RE23Co6.7In20.3.

SEDEG: An automatic method for preprocessing and selection of seed genes from gene expression data

Select Essential Differential Expressed Genes (SEDEG) is a software pipeline designed to simplify the time-consuming and error-prone task of preparing Differential Expressed Genes (DEGs) for Pathway Enrichment Analysis (PEA). It automatically preprocesses, filters, and selects DEGs, making interpreting results of gene expression microarrays and Genome-Wide Association Studies easier.SEDEG is a Python tool that automates multiple different actions simultaneously, saving researchers significant time and effort. It identifies crucial DEGs and enriched pathways related to the condition being investigated.The SEDEG pipeline is a tool that enhances the consolidation process of DEGs, which is essential for computing PEA. It achieves this by automating several manual steps, resulting in more accurate lists of DEGs for PEA analysis. This automation also improves the relevance and significance of enriched pathways. To download SEDEG, please visit https://gitlab.com/giuseppeagapito/sedeg.

An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

PyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

BackgroundVariability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data.ResultsIn this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose.ConclusionsWe present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost.

Design and Implementation of Chatbot using Python

Chatbots have become increasingly popular in recent years, with applications spanning customer support, virtual assistants, and more. This research paper explores the design and implementation of a chatbot using Python. We delve into various aspects of chatbot development, including natural language processing (NLP), dialogue management, and user interface integration. The paper discusses key Python libraries and tools for building a chatbot and presents a case study demonstrating the creation of a simple chatbot.

Radwaste analysis and package optimization with the F4E-radwaste tool and application to the ITER IVVS

A methodology to evaluate the neutron activation, categorize as radwaste, generate radiological checklists, and optimize the packaging for disposal of activated components according to French regulations is presented. The methodology obtains results like specific activity, dose rates and radwaste relevant parameters in a fine 3D mesh superimposed over the geometry of interest and can handle the typically complex models common in the nuclear fusion analyses like those of the ITER project. State-of-the-art radiation transport and activation codes are used along with a purposefully developed Python tool (F4E-radwaste), which performs the post-processing of the large amount of data generated and includes a graphical user interface. The 3D nature of the results allows the organization of packages of activated materials in a way that minimizes the amount of mass classified with higher radwaste levels and is subject to more stringent regulations and expensive disposals. An application case study of the methodology is presented for the In-Vessel Viewing System (IVVS) ITER system. The IVVS is especially suited as an example for this type of analysis as its elongated shape pointing towards the plasma results in a significant gradient of neutron flux and activation levels, and therefore radwaste categories. It is demonstrated how the methodology enables the reduction of the total higher radwaste level mass by optimizing the packaging of the IVVS radwaste.

LPATH: A Semiautomated Python Tool for Clustering Molecular Pathways.

The pathways by which a molecular process transitions to a target state are highly sought-after as direct views of a transition mechanism. While great strides have been made in the physics-based simulation of such pathways, the analysis of these pathways can be a major challenge due to their diversity and variable lengths. Here, we present the LPATH Python tool, which implements a semiautomated method for linguistics-assisted clustering of pathways into distinct classes (or routes). This method involves three steps: 1) discretizing the configurational space into key states, 2) extracting a text-string sequence of key visited states for each pathway, and 3) pairwise matching of pathways based on a text-string similarity score. To circumvent the prohibitive memory requirements of the first step, we have implemented a general two-stage method for clustering conformational states that exploits machine learning. LPATH is primarily designed for use with the WESTPA software for weighted ensemble simulations; however, the tool can also be applied to conventional simulations. As demonstrated for the C7eq to C7ax conformational transition of the alanine dipeptide, LPATH provides physically reasonable classes of pathways and corresponding probabilities.