Relevant bibliographies by topics / Python for Machine Language (ML)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Python for Machine Language (ML)'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Python for Machine Language (ML).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Python for Machine Language (ML)"

1

Patel, Prof Rahulkumar, Devendra Joshi, Aniket Patil, Prajakta Yeole, and Dhanashri Wani. "Visualization and Forecasting of Stocks Using Python and ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1814–20. http://dx.doi.org/10.22214/ijraset.2023.53954.

Full text
Abstract:
Abstract: Stock trading is one of the most important activities in the world of finance. Market forecasting is the act of trying to determine the future price of other financial instruments traded on the financial exchange . This document explains the forecasting of the market using machine learning. Most stockbrokers use technical and fundamental or time series analysis when making stock forecasts. The programming language used to predict stock markets using machine learning is Python. In this paper, we propose a machine learning (ML) approach that will learn from the data available at yfinance, and derive the intelligence and then use the information gained to make accurate predictions. In this case, this study uses a machine learning technique called LSTM to predict the closing price of stocks of five different stocks using the daily and price last minute frequency.
APA, Harvard, Vancouver, ISO, and other styles
2

Katiyar, Dr Alok, Sajid Ali, and Sameer Ray. "Multiple Disease Prediction Using ML." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 1 (2023): 15–18. http://dx.doi.org/10.35940/ijrte.a7568.0512123.

Full text
Abstract:
Accurate and on-time analysis of any health-related drawback is vital for the interference and treatment of the sickness. The normal method of diagnosing might not be sufficient within the case of a significant illness. Developing a medical diagnosing system supported machine learning (ML) algorithms for prediction of any unwellness will facilitate during a lot of correct diagnosis than the standard methodology. We've designed a disease prediction system using ML. Disease Prediction System using Machine Learning could be a system that predicts the sickness supported data or symptoms that he/she enter into the system and gives correct results supported that data. This predictive disease using Machine Learning is completed entirely with the assistance of Learning Machines and Python programing language with its Flask Interface and mistreatment antecedently offered databases with hospitals that use that we'll predict the unwellness.
APA, Harvard, Vancouver, ISO, and other styles
3

Prof, Mayur Tembhurney, and Pise Sakshi. "Stack Market Prediction Using Machine Learning (ML) Algorithms." International Journal for Indian Science and Research Volume-1, Issue -1 (2022): 08. https://doi.org/10.5281/zenodo.6787069.

Full text
Abstract:
In the world, stock marketing is one of the most important activities. The main objective of this paper is to predict the value of the stock market index Nifty 50 and compare the Algorithms which is best for Stock Market Prediction by comparing the graph of the four Algorithms. This Programing Language used is Python Programing Language. In this paper, we used a Machine Learning (ML) approach for training modules from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In this, we going to use four machine learning techniques called Support Vector Machine (SVM), and Random Forest.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
5

Dr., Alok Katiyar, Ali Sajid, and Ray Sameer. "Multiple Disease Prediction Using ML." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 1 (2023): 15–18. https://doi.org/10.35940/ijrte.A7568.0512123.

Full text
Abstract:
<strong>Abstract:</strong> Accurate and on-time analysis of any health-related drawback is vital for the interference and treatment of the sickness. The normal method of diagnosing might not be sufficient within the case of a significant illness. Developing a medical diagnosing system supported machine learning (ML) algorithms for prediction of any unwellness will facilitate during a lot of correct diagnosis than the standard methodology. We&#39;ve designed a disease prediction system using ML. Disease Prediction System using Machine Learning could be a system that predicts the sickness supported data or symptoms that he/she enter into the system and gives correct results supported that data. This predictive disease using Machine Learning is completed entirely with the assistance of LearningMachines and Python programing language with its Flask Interface and mistreatment antecedently offered databases with hospitals that use that we&#39;ll predict the unwellness.
APA, Harvard, Vancouver, ISO, and other styles
6

Manikanta, K.B, Sai M. Bhagavath, and Venkat I. "Text Summarization using Ml and Nlp." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1188–90. https://doi.org/10.35940/ijeat.D7278.049420.

Full text
Abstract:
Quantity of data produced per day is around 2.5 quintillion bytes. Right now, no one has the time to pursue each and everything. With the growth of technology and digital media, people are becoming very lazy; they are looking for everything more smartly. If they want to read any article or newspaper, they cannot go through every line that has been given. To overcome this problem, an automatic text summarizer using Machine Learning (ML) and Natural Language Processing (NLP) with the python programming language has been introduced. This automatic text summarizer will generate a concise and meaningful summary of the text from resources like textbooks, articles, messages by using a text ranking algorithm. The input text that is given will be split into sentences; these sentences are again converted into vectors. These vectors are represented as a similarity matrix and based on these similarities; matrices sentence rankings will be given. The higher ranked sentences will be the final summary of the given input text.
APA, Harvard, Vancouver, ISO, and other styles
7

Mahmoudi, Omayma, Mouncef Filali Bouami, and Mustapha Badri. "Arabic Language Modeling Based on Supervised Machine Learning." Revue d'Intelligence Artificielle 36, no. 3 (2022): 467–73. http://dx.doi.org/10.18280/ria.360315.

Full text
Abstract:
Misinformation and misleading actions have appeared as soon as COVID-19 vaccinations campaigns were launched, no matter what the country’s alphabetization level or growing index is. In such a situation, supervised machine learning techniques for classification appears as a suitable solution to model the value &amp; veracity of data, especially in the Arabic language as a language used by millions of people around the world. To achieve this task, we had to collect data manually from SM platforms such as Facebook, Twitter and Arabic news websites. This paper aims to classify Arabic language news into fake news and real news, by creating a Machine Learning (ML) model that will detect Arabic fake news (DAFN) about COVID-19 vaccination. To achieve our goal, we will use Natural Language Processing (NLP) techniques, which is especially challenging since NLP libraries support for Arabic is not common. We will use NLTK package of python to preprocess the data, and then we will use a ML model for the classification.
APA, Harvard, Vancouver, ISO, and other styles
8

Kapinus, Mariia, Kateryna Liashenko, Nikolina Ljepava, Larysa Liashenko, and Valerii Danylov. "PREDICTING STOCK MARKET TRENDS WITH PYTHON." Grail of Science, no. 40 (June 18, 2024): 109–16. http://dx.doi.org/10.36074/grail-of-science.07.06.2024.012.

Full text
Abstract:
Artificial Intelligence (AI) and machine learning (ML) have revolutionized the realm of stock market prediction, offering sophisticated tools to analyze vast volumes of data and anticipate market trends. This article provides a comprehensive overview of AI techniques, focusing on Python as the preferred platform for implementation. Beginning with an exploration of AI fundamentals, including machine learning and deep learning, it delves into various techniques employed for stock market prediction. Traditional statistical models such as linear regression and ARIMA are under scientific discussion alongside advanced ML algorithms like random forests and support vector machines. Moreover, the article highlights the efficacy of deep learning methodologies, particularly recurrent neural networks (RNNs) and long &amp; short-term memory (LSTM) networks, in capturing temporal dependencies within stock market data. We also explored innovative developments such as Generative Adversarial Networks (GANs) for their potential in revealing hidden patterns influencing price movements. Throughout the discussion, we concluded that Python emerges as the preferred programming language due to its simplicity, extensive libraries, and versatility. Key libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow play a pivotal role in data manipulation, preprocessing, and model development. The article outlines a structured approach to building predictive models, encompassing data collection, preprocessing, feature engineering, model selection, training, evaluation, and prediction. Despite the advancements in AI, challenges persist in stock market prediction, including market volatility, data quality issues, complexity of influencing factors, and risks of overfitting. Ultimately, we may witness AI and Python synergy, which empowers analysts and investors with deeper insights, enabling informed decision-making amidst the complexities of financial markets.
APA, Harvard, Vancouver, ISO, and other styles
9

Bodele, Prof Harna, Manju Tagde, Samiksha Rangari, Yash Jadhao, Vishakha Bawankar, and Tushar Kumre. "Medicine Recommendation System Using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 836–44. https://doi.org/10.22214/ijraset.2024.65843.

Full text
Abstract:
Abstract: The growing demand for personalized healthcare solutions has led to the development of intelligent systems that assist in medical decision-making. This project focuses on creating a Medicine Recommendation System that utilizes machine learning techniques to recommend suitable medicines based on user inputs such as symptoms or medical conditions. The system leverages a well-structured medical dataset to train a machine learning model capable of accurately predicting medicine recommendations.By analyzing user-provided symptoms, the system identifies potential diagnoses and suggests relevant medicines, ensuring improved healthcare accessibility and support. The frontend of the system is designed to be interactive and user-friendly, utilizing HTML, CSS, and jQuery, while the backend integrates a robust Python-based framework, such as Flask or Django, to process user inputs and interact with the machine learning model.The implementation incorporates essential features like data preprocessing, symptom encoding, and model optimization to enhance the accuracy of predictions. Additionally, the system includes a feedback mechanism for continuous improvement and warns users about potential medicine interactions to ensure safety.This project has the potential to revolutionize patient care by offering real-time, data-driven medicine recommendations, thereby empowering users to make informed healthcare decisions. Future developments may include advanced personalization based on patient history and natural language processing to understand user inputs more effectively.
APA, Harvard, Vancouver, ISO, and other styles
10

Zahidi, Youssra, Yacine El Younoussi, and Yassine Al-Amrani. "A powerful comparison of deep learning frameworks for Arabic sentiment analysis." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 745. http://dx.doi.org/10.11591/ijece.v11i1.pp745-752.

Full text
Abstract:
Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Python for Machine Language (ML)"

1

Conti, Matteo. "Machine Learning Based Programming Language Identification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20875/.

Full text
Abstract:
L'avvento dell'era digitale ha contribuito allo sviluppo di nuovi settori tecnologici, i quali, per diretta conseguenza, hanno portato alla richiesta di nuove figure professionali capaci di assumere un ruolo chiave nel processo d'innovazione tecnologica. L'aumento di questa richiesta ha interessato particolarmente il settore dello sviluppo del software, a seguito della nascita di nuovi linguaggi di programmazione e nuovi campi a cui applicarli. La componente principale di cui è composto un software, infatti, è il codice sorgente, il quale può essere rappresentato come un archivio di uno o più file testuali contenti una serie d'istruzioni scritte in uno o più linguaggi di programmazione. Nonostante molti di questi vengano utilizzati in diversi settori tecnologici, spesso accade che due o più di questi condividano una struttura sintattica e semantica molto simile. Chiaramente questo aspetto può generare confusione nell'identificazione di questo all'interno di un frammento di codice, soprattutto se consideriamo l'eventualità che non sia specificata nemmeno l'estensione dello stesso file. Infatti, ad oggi, la maggior parte del codice disponibile online contiene informazioni relative al linguaggio di programmazione specificate manualmente. All'interno di questo elaborato ci concentreremo nel dimostrare che l'identificazione del linguaggio di programmazione di un file `generico' di codice sorgente può essere effettuata in modo automatico utilizzando algoritmi di Machine Learning e non usando nessun tipo di assunzione `a priori' sull'estensione o informazioni particolari che non riguardino il contenuto del file. Questo progetto segue la linea dettata da alcune ricerche precedenti basate sullo stesso approccio, confrontando tecniche di estrazione delle features differenti e algoritmi di classificazione con caratteristiche molto diverse, cercando di ottimizzare la fase di estrazione delle features in base al modello considerato.
APA, Harvard, Vancouver, ISO, and other styles
2

Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.

Full text
Abstract:
Pharmacovigilance, also referred to as drug safety, is an important science for identifying risks related to medicine intake. Side effects of medicine can be caused by for example interactions, high dosage and misuse. In order to find patterns in what causes the unwanted effects, information needs to be gathered and mapped to predefined terms. This mapping is today done manually by experts which can be a very difficult and time consuming task. In this thesis the aim is to automate the process of mapping side effects by using machine learning techniques. The model was developed using information from preexisting mappings of verbatim expressions of side effects. The final model that was constructed made use of the pre-trained language model BERT, which has received state-of-the-art results within the NLP field. When evaluating on the test set the final model performed an accuracy of 80.21%. It was found that some verbatims were very difficult for our model to classify mainly because of ambiguity or lack of information contained in the verbatim. As it is very important for the mappings to be done correctly, a threshold was introduced which left for manual mapping the verbatims that were most difficult to classify. This process could however still be improved as suggested terms were generated from the model, which could be used as support for the specialist responsible for the manual mapping.
APA, Harvard, Vancouver, ISO, and other styles
3

Björkman, Desireé. "Machine Learning Evaluation of Natural Language to Computational Thinking : On the possibilities of coding without syntax." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424269.

Full text
Abstract:
Voice commands are used in today's society to offer services like putting events into a calendar, tell you about the weather and to control the lights at home. This project tries to extend the possibilities of voice commands by improving an earlier proof of concept system that interprets intention given in natural language to program code. This improvement was made by mixing linguistic methods and neural networks to increase accuracy and flexibility of the interpretation of input. A user testing phase was made to conclude if the improvement would attract users to the interface. The results showed possibilities of educational purposes for computational thinking and the issues to overcome to become a general programming tool.
APA, Harvard, Vancouver, ISO, and other styles
4

Scaglione, Emanuel. "BlenderBot 2.0: Studio e Modellazione di un Chatbot basato su Transformers." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

Find full text
Abstract:
I Transformers hanno raggiunto lo stato dell’arte in qualsiasi ambito del Natural Language Processing (NLP) e del Natural Language Understanding (NLU), in questo lavoro di tesi è stata studiata l’architettura originale e alcune delle migliorie apportate a questa nel corso degli ultimi anni. In una seconda fase è stato studiato un Chat Bot rivoluzionario reso pubblico nel Luglio del 2021, chiamato Blender Bot 2.0. Questo bot di Facebook è sia capace di sfruttare una memoria a lungo termine facilmente estendibile e sostituibile per immagazzinare informazioni sui propri interlocutori e sul mondo esterno, sia di effettuare ricerche online quando posto di fronte a quesiti di cui non è sicuro di conoscere la risposta. Il tutto è stato osservato non solo in termini di qualità dei risultati generati dai modelli, ma anche da un punto di vista di risorse impiegate. L’obiettivo è stato quello di minimizzare il consumo di memoria e il tempo necessario per addestrare i modelli, in modo da poter rendere accessibili le loro abilità su larga scala anche in presenza di hardware economici, diminuendo conseguentemente i costi per chiunque voglia farci affidamento; un grande passo per singoli individui appassionati, ma soprattutto per aziende interessate ad impiegarli in un contesto produttivo.
APA, Harvard, Vancouver, ISO, and other styles
5

Gennari, Riccardo. "End-to-end Deep Metric Learning con Vision-Language Model per il Fashion Image Captioning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25772/.

Full text
Abstract:
L'image captioning è un task di machine learning che consiste nella generazione di una didascalia, o caption, che descriva le caratteristiche di un'immagine data in input. Questo può essere applicato, ad esempio, per descrivere in dettaglio i prodotti in vendita su un sito di e-commerce, migliorando l'accessibilità del sito web e permettendo un acquisto più consapevole ai clienti con difficoltà visive. La generazione di descrizioni accurate per gli articoli di moda online è importante non solo per migliorare le esperienze di acquisto dei clienti, ma anche per aumentare le vendite online. Oltre alla necessità di presentare correttamente gli attributi degli articoli, infatti, descrivere i propri prodotti con il giusto linguaggio può contribuire a catturare l'attenzione dei clienti. In questa tesi, ci poniamo l'obiettivo di sviluppare un sistema in grado di generare una caption che descriva in modo dettagliato l'immagine di un prodotto dell'industria della moda dato in input, sia esso un capo di vestiario o un qualche tipo di accessorio. A questo proposito, negli ultimi anni molti studi hanno proposto soluzioni basate su reti convoluzionali e LSTM. In questo progetto proponiamo invece un'architettura encoder-decoder, che utilizza il modello Vision Transformer per la codifica delle immagini e GPT-2 per la generazione dei testi. Studiamo inoltre come tecniche di deep metric learning applicate in end-to-end durante l'addestramento influenzino le metriche e la qualità delle caption generate dal nostro modello.
APA, Harvard, Vancouver, ISO, and other styles
6

Rasocha, David. "Návrh řídicího systému pro malý zkušební stroj." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417777.

Full text
Abstract:
This thesis focuses on design of small testing machine for measuring tensile strength of materials. Appropriate hardware for driving the motor with serial communication will be used. Main drive is a stepper motor with microstepping. Instructions for motor is provided by microcontroler which will be comunicating with aplication in computer. This aplication will have all user functions nessesary for using this device.
APA, Harvard, Vancouver, ISO, and other styles
7

Montalti, Giacomo. "Identificazione di farmaci e dispositivi medici equivalenti con tecniche di natural language processing e deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16828/.

Full text
Abstract:
Il deep learning è un campo relativamente giovane le cui potenzialità sono ancora tutte da esplorare, in grado di elaborare in maniera ancora più approfondita i dati, e sarà affrontato nel dettaglio all'interno di questo lavoro di tesi. Questa tecnologia ha permesso di migliorare drasticamente i risultati raggiunti in passato in tantissimi settori, consentendo ad esempio lo sviluppo di auto a guida autonoma, assistenti virtuali in grado di comprendere una conversazione e di fornire risposte alle nostre domande o macchinari medicali capaci di identificare masse tumorali con una precisione maggiore rispetto a quella umana. All'interno di questo elaborato verranno analizzati e sperimentati diversi approcci recenti in ambito natural language processing (NLP) e deep learning (DL), allo scopo di identificare prodotti medicali equivalenti dalla loro breve descrizione testuale destrutturata.
APA, Harvard, Vancouver, ISO, and other styles
8

Lat, Radek. "Nástroj pro automatické kategorizování webových stránek." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236054.

Full text
Abstract:
Tato diplomová práce popisuje návrh a implementaci nástroje pro automatickou kategorizaci webových stránek. Cílem nástroje je aby byl schopen se z ukázkových webových stránek naučit, jak každá kategorie vypadá. Poté by měl nástroj zvládnout přiřadit naučené kategorie k dříve nespatřeným webovým stránkám. Nástroj by měl podporovat více kategorií a jazyků. Pro vývoj nástroje byly použity pokročilé techniky strojového učení, detekce jazyků a dolování dat. Nástroj je založen na open source knihovnách a je napsán v jazyce Python 3.3.
APA, Harvard, Vancouver, ISO, and other styles
9

Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

Full text
Abstract:
Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character-based language models an alternative model based on TensorFlow im2txt has been created. The model changes the token-generation architecture into handling character-sized tokens instead of word-sized tokens. The results suggest that a character-based language model could outperform the current token-based language models, although due to time and computing power constraints this study fails to draw a clear conclusion. A problem with one of the methods, subsampling, is discussed. When using the original method on character-sized tokens this method removes characters (including special characters) instead of full words. To solve this issue, a two-phase approach is suggested, where training data first is separated into word-sized tokens where subsampling is performed. The remaining tokens are then separated into character-sized tokens. Future work where the modified subsampling and fine-tuning of the hyperparameters are performed is suggested to gain a clearer conclusion of the performance of character-based language models.
APA, Harvard, Vancouver, ISO, and other styles
10

Borghesi, Andrea. "Topic Analysis della letteratura scientifica sul tema Computer Chess con Metodi di Text Mining Non Supervisionati." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24252/.

Full text
Abstract:
Progettazione e implementazione di modelli di text mining non supervisionati su un dataset di dati non strutturati: articoli sulla storia del computer chess. Si sono affrontati per cui argomenti legati al Natural Language Processing (NLP). Inoltre, sono state affrontate tecniche di text augmentation per provvedere al bilanciamento delle classi del dataset. Tra i modelli utilizzati sono presenti: LDA, Word Embeddings, algoritmi di Clustering e Transformers.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Python for Machine Language (ML)"

1

Loukides, Mike, and Shannon Cutt, eds. Thoughtful Machine Learning with Python: A Test-Driven Approach. O’Reilly Media, 2017.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Osinga, Douwe. Deep Learning Cookbook: Practical Recipes to Get Started Quickly. Edited by Rachel Roumeliotis and Jeff Bleiel. O’Reilly Media, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tache, Nicole, ed. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. O’Reilly Media, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Amunategui, Manuel. Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud. Apress, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lee, Wei-Meng. Python Machine Learning. Wiley & Sons, Limited, John, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lee, Wei-Meng. Python Machine Learning. Wiley & Sons, Incorporated, John, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Python Machine Learning. Wiley & Sons, Incorporated, John, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Wei-Meng. Python Machine Learning. Wiley & Sons, Incorporated, John, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Advanced Machine Learning with Python. Packt Publishing - ebooks Account, 2016.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Python Machine Learning Cookbook. Packt Publishing - ebooks Account, 2016.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Python for Machine Language (ML)"

1

Gupta, Pramod, and Anupam Bagchi. "Python Language Basics." In Essentials of Python for Artificial Intelligence and Machine Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43725-0_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Mehare, Hussam Bin, Jishnu Pillai Anilkumar, and Naushad Ahmad Usmani. "The Python Programming Language." In A Guide to Applied Machine Learning for Biologists. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22206-1_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gupta, Pramod, and Naresh K. Sehgal. "Trends in Hardware-Based AL and ML." In Introduction to Machine Learning in the Cloud with Python. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71270-9_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Beysolow II, Taweh. "Text Generation, Machine Translation, and Other Recurrent Language Modeling Tasks." In Applied Natural Language Processing with Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3733-5_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Guerraoui, Rachid, and Rafael Pinot. "Adversarial Evasion on LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_20.

Full text
Abstract:
AbstractWhile Machine Learning (ML) applications have shown impressive achievements in tasks such as computer vision, NLP, and control problems, such achievements were possible, first and foremost, in the best-case-scenario setting. Unfortunately, settings where ML applications fail unexpectedly, abound, and malicious ML application users or data contributors can trigger such failures. This problem became known as adversarial example robustness. While this field is in rapid development, some fundamental results have been uncovered, allowing some insight into how to make ML methods resilient to input and data poisoning. Such ML applications are termed adversarially robust. While the current generation of LLMs is not adversarially robust, results obtained in other branches of ML can provide insight into how to make them adversarially robust. Such insight would complement and augment ongoing empirical efforts in the same direction (red-teaming).
APA, Harvard, Vancouver, ISO, and other styles
6

Hirschi, Kevin, and Okim Kang. "Machine Learning (ML) tools for measuring second language (L2) intelligibility." In Routledge Handbook of Technological Advances in Researching Language Learning. Routledge, 2024. http://dx.doi.org/10.4324/9781003459088-42.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Nandini, Chinthakindi, Patil Ambika, Rithika Pagadala, Ravi Boda, and B. Mohan Rao. "Sign language detection and recognition using machine learning (ML) architectures." In Security Issues in Communication Devices, Networks and Computing Models. CRC Press, 2025. https://doi.org/10.1201/9781003591788-22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hahn, Sage, Nicholas Allgaier, and Hugh Garavan. "Brain Predictability Toolbox." In Neuromethods. Springer US, 2024. https://doi.org/10.1007/978-1-0716-4260-3_12.

Full text
Abstract:
AbstractThe Brain Predictability toolbox (BPt) is a Python-based library with a unified framework of machine learning (ML) tools designed to work with both tabulated data (e.g., brain-derived, psychiatric, behavioral, and physiological variables) and neuroimaging specific data (e.g., brain volumes and surfaces). The toolbox is designed primarily for ‘population’-based predictive neuroimaging; that is to say, machine learning performed across data from multiple participants rather than many data points from a single or small set of participants. The BPt package is suitable for investigating a wide range of neuroimaging-based ML questions. This chapter is a brief introduction to general principles of the toolbox, followed by a specific example of usage.
APA, Harvard, Vancouver, ISO, and other styles
9

Mathew, Sujith Samuel, Mohammad Amin Kuhail, Maha Hadid, and Shahbano Farooq. "Natural Language Processing and Text Mining with Python." In The Object-Oriented Approach to Problem Solving and Machine Learning with Python. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781032668321-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bonthu, Sridevi, S. Rama Sree, and M. H. M. Krishna Prasad. "Text2PyCode: Machine Translation of Natural Language Intent to Python Source Code." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84060-0_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Python for Machine Language (ML)"

1

Gowda, Thamme, Roman Grundkiewicz, Elijah Rippeth, Matt Post, and Marcin Junczys-Dowmunt. "PyMarian: Fast Neural Machine Translation and Evaluation in Python." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-demo.34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Romedenne, Marie, Praneeth Bachu, James Smialek, Govindarajan Muralidharan, and Rishi Pillai. "Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00554.

Full text
Abstract:
Abstract High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can enable a cost-effective acceleration of the development of new materials. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated.
APA, Harvard, Vancouver, ISO, and other styles
3

Hu, Brian, Evan Gunnell, and Yu Sun. "Smart Tab Predictor: A Chrome Extension to Assist Browser Task Management using Machine Learning and Data Analysis." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112318.

Full text
Abstract:
The outbreak of the Covid 19 pandemic has forced most schools and businesses to use digital learning and working. Many people have repetitive web browsing activities or encounter too many open tabs causing slowness in surfing the websites. This paper presents a tab predictor application, a Chrome browser extension that uses Machine Learning (ML) to predict the next URL to open based on the time and frequency of current and previous tabs. Nowadays, AI technology has expanded in people’s daily lives like self-driving cars and assistive-type robots. The AI ML module in our application is more basic and is built using Python and Scikit-Learn (Sklearn) machine learning libraries. We use JavaScript and Chrome API to collect the browser tab data and store it in a Firebase Cloud Firestore. The ML module then loads data from the Firebase, trains datasets to adapt to a user’s patterns, and predicts URLs to recommend opening new URLs. For Machine Learning, we compare three ML models and select the Random Forest Classifier. We also apply SMOTE (Synthetic Minority Oversampling Technique) to make the data-set more balanced, thus improving the prediction accuracy. Both manual tests and Cross Validation are performed to verify the predicted URLs. As a result, using the Smart Tab Predictor application will help students and business workers manage the web browser tabs more efficiently in their daily routine for online classes, online meetings, and other websites.
APA, Harvard, Vancouver, ISO, and other styles
4

Bao, Forrest Sheng, Mike Qi, Ruixuan Tu, and Erana Wan. "Funix - The laziest way to build GUI apps in Python." In Python in Science Conference. SciPy, 2024. http://dx.doi.org/10.25080/jfyn3740.

Full text
Abstract:
The rise of machine learning (ML) and artificial intelligence (AI), especially the generative AI (GenAI), has increased the need for wrapping models or algorithms into GUI apps. For example, a large language model (LLM) can be accessed through a string-to-string GUI app with a textbox as the primary input. Most of existing solutions require developers to manually create widgets and link them to arguments/returns of a function individually. This low-level process is laborious and usually intrusive. Funix automatically selects widgets based on the types of the arguments and returns of a function according to the type-to-widget mapping defined in a theme, e.g., bool to a checkbox. Consequently, an existing Python function can be turned into a GUI app without any code changes. As a transcompiler, Funix allows type-to-widget mappings to be defined between any Python type and any React component and its props, liberating Python developers to the frontend world without needing to know JavaScript/TypeScript. Funix further leverages features in Python or its ecosystem for building apps in a more Pythonic, intuitive, and effortless manner. With Funix, a developer can make it (a functional app) before they (competitors) fake it (in Figma or on a napkin).
APA, Harvard, Vancouver, ISO, and other styles
5

Karnik, Saniya, Supriya Gupta, and Jason Baihly. "Machine Intelligence for Integrated Workover Operations." In SPE/ICoTA Well Intervention Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204423-ms.

Full text
Abstract:
Abstract Because of recent advancements in the field of natural language processing (NLP) and machine learning, there is potential to ingest decades of field history and heterogeneous production records. This paper proposes an analytics workflow that leverages artificial intelligence to process thousands of historical workover reports (handwritten and electronic), extract important information, learn patterns in production activity, and train machines to quantify workover impact and derive best practices for field operations. Natural language processing libraries were developed to ingest and catalog gigabytes of field data, identify rich sources of workover information, and extract workover and cost information from unstructured reports. A machine learning (ML) model was developed and trained to predict well intervention categories based on free text describing workovers found in reports. This ML model learnt pattern and context of repeating words pertaining to a workover type (e.g. Artificial Lift, Well Integrity, etc.) and to classify reports accordingly. Statistical models were built to determine return on investment from workovers and rank them based on production improvement and payout time. Today, 80% of an oilfield expert's time can be spent manually organizing data. When processing decades of historical oilfield production data spread across both structured (production timeseries) and unstructured records (e.g., workover reports), experts often face two major challenges: 1) How to rapidly analyze field data with thousands of historical records. 2) How to use the rich historical information to generate effective insights to optimize production. In this paper, we analyzed multiple field datasets in a heterogeneous file environment with 20 different file formats (PDF, Excel, and other formats), 2,000+ files and production history spanning 50+ years across and 2000+ producing wells. Libraries were developed to extract workover files from complex folder hierarchies through an intelligent automated search. Information from reports was extracted through Python libraries and optical character recognition technology to build master data source with production history, workover, and cost information. A neural network model was trained to predict workover class for each report with &amp;gt;85% accuracy. The rich dataset was then used to analyze episodic workover activity by well and compute key performance indicators (KPIs) to identify well candidates for production enhancement. The building blocks included quantifying production upside and calculating return of investment for various workover classes. O&amp;G companies have vast volumes of unstructured data and use less than 1% of it to uncover meaningful insights about field operations. Our workflow describes methodology to ingest both structured and unstructured documents, capture knowledge, quantify production upside, understand capital spending, and learn best practices in workover operations through an automated process. This process helps optimize forward operating expense (OPEX) plan with focus on cost reduction and shortens turnaround time for decision making.
APA, Harvard, Vancouver, ISO, and other styles
6

Peters, Heinrich, and Michael Parrott. "Model Share AI." In Python in Science Conference. SciPy, 2024. http://dx.doi.org/10.25080/mdce8355.

Full text
Abstract:
Machine learning (ML) is revolutionizing a wide range of research areas and industries, but many ML projects never progress past the proof-of-concept stage. To address this problem, we introduce Model Share AI (AIMS), a platform designed to streamline collaborative model development, model provenance tracking, and model deployment, as well as a host of other functions aiming to maximize the real-world impact of ML research. AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on holdout evaluation data, enabling users to run experiments and competitions. In addition, various model metadata are automatically captured to facilitate provenance tracking and allow users to learn from and build on previous submissions. Furthermore, AIMS allows users to deploy ML models built in Scikit-Learn, TensorFlow Keras, or PyTorch into live REST APIs and automatically generated web apps with minimal code. The ability to collaboratively develop and rapidly deploy models, making them accessible to non-technical end-users through automatically generated web apps, ensures that ML projects can transition smoothly from concept to real-world application.
APA, Harvard, Vancouver, ISO, and other styles
7

Tong, Michael T. "Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-102024.

Full text
Abstract:
Abstract Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
APA, Harvard, Vancouver, ISO, and other styles
8

Narayanan, Srikanth, N. M. Balamurugan, Maithili K, and P. Bini Palas. "Leveraging Machine Learning Methods for Multiple Disease Prediction using Python ML Libraries and Flask API." In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9792807.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

CERBU, Olga, Tatiana SESTACOVA та Nichita MIRONOV. "Введение в машинное обучение на Python: основы и практика". У Inter/transdisciplinary approaches in the teaching of the real sciences, (STEAM concept) = Abordări inter/transdisciplinare în predarea ştiinţelor reale, (concept STEAM). Ion Creangă Pedagogical State University, 2023. http://dx.doi.org/10.46727/c.steam-2023.p260-271.

Full text
Abstract:
This article presents a way to work with machine learning. Machine Learning (ML) is a subfield of artificial intelligence (AI) that develops algorithms and models that can extract knowledge and make predictions based on data. The article shows a way to work in the field of machine learning using Google Colab and Jupyter Notebook tools with implementation in Python.
APA, Harvard, Vancouver, ISO, and other styles
10

Carter, H., Jason Rupert, Alexander Chan, and Chris Vinegar. "Concerns with using Python in Machine Learning Flight Critical Applications." In Vertical Flight Society 79th Annual Forum & Technology Display. The Vertical Flight Society, 2023. http://dx.doi.org/10.4050/f-0079-2023-18015.

Full text
Abstract:
Python is a programming language that is proving irresistible to the Machine Learning development community. The characteristics that make it irresistible may not be well suited for flight critical applications, e.g., Python is an interpreted programming language reliant upon a virtual machine to execute bytecode. Programming languages used in flight critical applications have higher assurance expectations than non-flight critical applications. For aviation applications, Python may be appropriate for the development of Machine Learning models, but Python does not appear to be appropriate for the implementation of those Machine Learning models for deployment on flight critical applications. This paper explores concerns for using Python in those applications and offers potential courses of action to alleviate these concerns: (1) certify/qualify/mature Python, (2) transition from development in Python to implementation in a certified/qualified/maturity programming language, or (3) development and implementation in an environment with certification/qualification/maturity pedigree.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Python for Machine Language (ML)"

1

Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

Full text
Abstract:
Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.
APA, Harvard, Vancouver, ISO, and other styles
2

Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

Full text
Abstract:
Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.
APA, Harvard, Vancouver, ISO, and other styles
3

Kulhandjian, Hovannes. Smart Robot Design and Implementation to Assist Pedestrian Road Crossing. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2353.

Full text
Abstract:
This research focuses on designing and developing a smart robot to assist pedestrians with road crossings. Pedestrian safety is a major concern, as highlighted by the high annual rates of fatalities and injuries. In 2020, the United States recorded 6,516 pedestrian fatalities and approximately 55,000 injuries, with children under 16 being especially vulnerable. This project aims to address this need by offering an innovative solution that prioritizes real-time detection and intelligent decision-making at intersections. Unlike existing studies that rely on traffic light infrastructure, our approach accurately identifies both vehicles and pedestrians at intersections, creating a comprehensive safety system. Our strategy involves implementing advanced Machine Learning (ML) algorithms for real-time detection of vehicles, pedestrians, and cyclists. These algorithms, executed in Python, leverage data from LiDAR and video cameras to assess road conditions and guide pedestrians and cyclists safely through intersections. The smart robot, powered by ML insights, will make intelligent decisions to ensure a safer and more secure road crossing experience for pedestrians and cyclists. This project is a pioneering effort in holistic pedestrian safety, ensuring robust detection capabilities and intelligent decision-making.
APA, Harvard, Vancouver, ISO, and other styles
4

Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

Full text
Abstract:
The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Python for Machine Language (ML)' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography