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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.
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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.
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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.
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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.
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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.
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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.
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Sahani, Sweety, and Sushmitha Mary. "Chatbot Using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3565–68. http://dx.doi.org/10.22214/ijraset.2022.43045.
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Abstract: A chatbot enables a user to simply ask questions in the same manner that they would respond to humans. The most well-known chatbots currently are voices chatbots: SIRI and Alexa. However, chatbots have been adopted and brought into the daily application at a high rate on the computer chat platform. NLP also allows computers and algorithms to understand human interactions through various languages. Recent advances in machine learning have greatly improved the accurate and effective of natural language processing, making chatbots a viable option for many organizations. This improvement in NLP is firing a great deal of additional research which should lead to continued improvement in the effective of chatbots in the years to come.A bot is trained on and according to the training, based on some rules on which it is trained, it answers questions. It is called ruled based approach. The language by which these bots can be created is Artificial Intelligence Markup Language (AIML). It is a language based on XML which allows the developer to write the rules which the bot will follow. In this research paper, We are trying to understand these chatbots and understanding their shortcomings. question or statement submitted by a user and allow the user to control over the content to be displayed Keywords: AI; ML; Wordnet; Chatbot; NLP
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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.
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<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'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'll predict the unwellness.
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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.
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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.
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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.
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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 & 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.
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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.
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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 & 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.
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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.
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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.
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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.
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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.
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Youssra, Zahidi, El Younoussi Yacine, and Al-Amrani Yassine. "A powerful comparison of deep learning frameworks for Arabic sentiment analysis." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 745–52. https://doi.org/10.11591/ijece.v11i1.pp745-752.
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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.
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Abdelrahman, Mahmoud M., and Ahmed Mohamed Yousef Toutou. "[ANT]: A Machine Learning Approach for Building Performance Simulation: Methods and Development." Academic Research Community publication 3, no. 1 (2019): 205. http://dx.doi.org/10.21625/archive.v3i1.442.
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In this paper, we represent an approach for combining machine learning (ML) techniques with building performance simulation by introducing four methods in which ML could be effectively involved in this field i.e. Classification, Regression, Clustering and Model selection . Rhino-3d-Grasshopper SDK was used to develop a new plugin for involving machine learning in design process using Python programming language and making use
 of scikit-learn module, that is, a python module which provides a general purpose high level language to nonspecialist user by integration of wide range supervised and unsupervised learning algorithms with high performance, ease of use and well documented features. ANT plugin provides a method to make use of these modules inside Rhino\Grasshopper to be handy to designers. This tool is open source and is released under BSD simplified license. This approach represents promising results regarding making use of data in automating building performance development and could be widely applied. Future studies include providing parallel computation facility using PyOpenCL module as well as computer vision integration using scikit-image.
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Rostom, Yousri A., Salah-Eldin Abd-El-Moneim, Nevine Makram Labib, Samia Gharib, Marwa Shaker, and Nayera Mahmoud. "Python-based preprocessing for applying machine learning in breast cancer metastasis prediction." Journal of Clinical Oncology 39, no. 15_suppl (2021): e13558-e13558. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e13558.
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e13558 Background: Artificial intelligence (AI) and machine learning (ML) have outstanding contributions in oncology. One of the applications is the early detection of breast cancer. Recently, several ML and data mining techniques have been used for both detection and classification of breast cancer cases. It is found that about 25% of breast cancer cases have an aggressive cancer at diagnosis time, with metastatic spread. The absence or presence of metastatic spread largely determines the patient’s survival. Hence, early detection is very important for reducing cancer mortality rates Methods: This study aims at applying ML and data mining, using AI techniques, for exploring and preprocessing breast cancer dataset, before building the ML classification Model for breast cancer metastasis prediction. The model will be implemented for mass screening, to prioritize patients who are more likely to develop metastases. A dataset of breast cancer cases was provided by the Oncology and Nuclear Medicine Department, Faculty of Medicine, Alexandria University. It contains clinical records of 5236 patients, diagnosed with breast cancer. ML libraries in Python programming language was used to explore the dataset and determine ratio of missing data, define data types, redundant data, and specify class label and predictors that to be used for the classification model. Results: In this work, the results showed that missing data ratio in some columns exceeds 90%, there are redundant features to be eliminated, data type conversion and feature reduction should be applied to prepare the data. Conclusions: Based on the previous findings, it is recommended to use ML preprocessing python libraries to prepare the dataset before building ML classification model of breast cancer metastasis prediction.
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Bulhakova, Olha, Yuliia Ulianovska, Victoria Kostenko, and Tatyana Rudyanova. "Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients." Technology audit and production reserves 4, no. 2(66) (2022): 14–18. http://dx.doi.org/10.15587/2706-5448.2022.262562.
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The object of the research is modern online services and machine learning libraries for predicting the probability of the bank client's consent to the provision of the proposed services. One of the most problematic areas is the high unpredictability of the result in the field of banking marketing using the most common technique of introducing new services for clients – the so-called cold calling. Therefore, the question of assessing the probability and predicting the behavior of a potential client when promoting new banking services and services using cold calling is particularly relevant. In the course of the study, libraries of machine learning methods and data analysis of the Python programming language were used. A program was developed to build a model for predicting the behavior of bank customers using data processing methods using gradient boosting, regularization of gradient boosting, random forest algorithm and recurrent neural networks. Analogous models were built using cloud machine learning services Azure ML, BigML and the Auto-sklearn library. Data analysis and prediction models built using Python language libraries have a fairly high quality – an average of 94.5 %. Using the Azure ML cloud service, a predictive model with an accuracy of 88.6 % was built. The BigML machine learning service made it possible to build a model with an accuracy of 88.8 %. Machine learning methods from the Auto-sklearn library made it possible to obtain a model with a higher quality – 94.9 %. This is due to the fact that the proposed libraries of the Python programming language allow better customization of data processing methods and machine learning to obtain more accurate models than free cloud services that do not provide such capabilities. Thanks to this, it is possible to obtain a predictive model of the behavior of bank customers with a fairly high degree of accuracy. It is worth noting that in order to make a prediction (forecast), it is necessary to study the context of the task, process the data, build various machine learning algorithms, evaluate the quality of the models and choose the best of them.
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Milakovic, Adrian, Drazen Draskovic, and Bosko Nikolic. "Visual Simulator for Mastering Fundamental Concepts of Machine Learning." Applied Sciences 12, no. 24 (2022): 12974. http://dx.doi.org/10.3390/app122412974.
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Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems.
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Rajanand Tawale, Shubham, Gaurav Subhash Jawalkar, and Sanket Sadashiv pathare. "STOCK PRICE PREDICTION IN PYTHON USING STREAMLIT." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (2022): 170–74. http://dx.doi.org/10.33564/ijeast.2022.v06i11.032.
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One of the most important practices in the financial world is stock trading. The act of attempting to forecast the future value of a stock or other financial instrument listed on a stock exchange is known as stock market prediction. This paper discusses how Machine Learning can be used to predict a stock's price. When it comes to stock forecasts, most stockbrokers use technical and fundamental analysis, as well as time series analysis. Python is the programming language used to forecast the stock market. In this paper, we propose a Machine Learning (ML) method that will be trained using publicly accessible stock data to obtain intelligence, and then use that intelligence to make an accurate prediction. In this context, this research builds a connection between facebook prophet and streatmlit which helps in predicting stock market, which is basically a Python scraper that extracts finance data from the Yahoo Finance platform; more precisely, a Recurrent Neural Network with LSTM cells was constructed, which is the current state-of-the-art in time series forecasting.
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Hairani, Hairani. "Pelatihan Implementasi Machine Learning pada Bidang Pendidikan." ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat 2, no. 2 (2022): 305–10. https://doi.org/10.30812/adma.v2i2.3046.
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Machine learning is a machine that can learn like humans. Machine learning (ML) technology was developed so that machines can learn by themselves without direction from the user. Machine learning consists of various disciplines such as statistics, mathematics and data mining so that machines can learn by analyzing data patterns without the need to be explicitly reprogrammed. Making machine learning applications is not easy because you have to have good understanding of methods and programming skills. Therefore, this service uses a solution to improve the abilities of the participants, namely a training approach by presenting material and demonstrating the use of machine learning in midwifery education. The activity was carried out on April 21 2021 online via the Zoom Meeting application with student participants. Based on the results of the material presentation session and hands-on practice using the Python programming language at Google Colab, it showed that the participants looked enthusiastic in following the material. Not only that, the participants know various machine learning methods and can apply them in completing a case study and building web applications with Flask tools.
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Lemenkova, Polina. "Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python." Examples and Counterexamples 7 (February 3, 2025): 100180. https://doi.org/10.5281/zenodo.14802292.
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Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. The data are obtained from the United States Geological Survey (USGS) and include the Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) multispectral satellite images. The images were collectedon 2013 and 2023 to evaluate land cover categories in each of the year. The study area covers the region of Nile Delta and the Faiyum Oasis, Egypt. A series of modules for raster image processing was applied using scripting language of GRASS GIS to process the remote sensing data. The satellite images were classified into raster maps presenting the land cover types. These include ‘i.cluster’ and ‘i.maxlik’ for non-supervised classification used as training dataset of random pixel seeds, ‘r.random’, ‘r.learn.train’, ‘r.learn.predict’ and ‘r.category’ for ML part of image processing. The consequences of various ML parameters on the cartographic outputs are analysed, such as speed and accuracy, randomness of nodes, analytical determination of the output weights, and dependence distribution of pixels for each algorithm. Supervised learning models of GRASS GIS were tested and compared including the Gaussian Naive Bayes (GaussianNB), Multi-layer Perceptron classifier (MLPClassifier), Support Vector Machines (SVM) Classifier, and Random Forest Classifier (RF). Though each algorithms was developed to serve different objectives of ML applications in RS data processing, their technical implementation and practical purposes present valuable approaches to cartographic data processing and image analysis. The results shown that the most time-consuming algorithms was noted as SVM classification, while the fastest results were achieved by the GaussianNB approach to image processing and the best results are achieved by RF Classifier.
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Colange, Maximilien, Guillaume Appé, Léa Meunier, Solène Weill, Akpéli Nordor, and Abdelkader Behdenna. "Abstract 6287: Bulk transcriptomic analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python." Cancer Research 85, no. 8_Supplement_1 (2025): 6287. https://doi.org/10.1158/1538-7445.am2025-6287.
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Abstract We introduce InMoose, an open-source Python environment for omic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or AI. As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. Our intent is to provide a drop-in replacement for R tools, so our approach focuses on the faithfulness to the original tools outcomes. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis. InMoose offers a Python implementation of several state-of-the-art tools originally written in R:• ComBat and ComBat-Seq (batch effect correction)• edgeR, DESeq2, limma (differential gene expression analysis)• splatter (RNA-Seq data simulation) To our knowledge, InMoose is the sole Python implementation of ComBat-Seq, edgeR and limma. InMoose also offers original features:• a quality control report for cohorts built through the batch effect correction features;• a differential gene expression meta-analysis module. We present the range of capabilities of InMoose, illustrating them with an example workflow. We also compare InMoose with the R original implementations and alternative Python implementations when available. Our experiments show that the results of InMoose are very similar, if not identical, to those of the original R tools. This positions InMoose as a key tool to bridge R and Python ecosystem and to ensure reproducibility and comparability between R-based and Python-based bioinformatics pipelines. We put the emphasis on making InMoose easy-to-use and open source to reach as many bioinformaticians as possible. Since the first version of InMoose has been made public, multiple users shared their experience with us, and even contributed code enhancements, which led us to update our software and plan to release more bioinformatics tools on Python in the future. Python has grown as the language of choice for machine learning and AI, and streamlining ML and AI-powered pipelines for biomolecular data has become an essential step to unlock the full potential of biomolecular analysis. Single-cell omic data is well connected to AI/ML tools through scanpy and the scverse ecosystem, but no similar initiative exists for bulk transcriptomic data. Omicverse aggregates existing Python tools into a single-entrypoint, but has not endeavored to port R capacities to the Python world. InMoose addresses this gap, which hinders the smooth integration of bulk transcriptomic data with ML and AI tools. Citation Format: Maximilien Colange, Guillaume Appé, Léa Meunier, Solène Weill, Akpéli Nordor, Abdelkader Behdenna. Bulk transcriptomic analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6287.
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Olha, Bulhakova, Ulianovska Yuliia, Kostenko Victoria, and Rudyanova Tatyana. "Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients." Technology audit and production reserves 4, no. 2(66) (2022): 14–18. https://doi.org/10.15587/2706-5448.2022.262562.
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<em>The object of the research is modern online services and machine learning libraries for predicting the probability of the bank client's consent to the provision of the proposed services.</em> <em>One of the most problematic areas is the high unpredictability of the result in the field of banking marketing using the most common technique of introducing new services for clients </em><em>–</em><em> the so-called cold calling. Therefore, the question of assessing the probability and predicting the behavior of a potential client when promoting new banking services and services using cold calling is particularly relevant.</em> <em>In the course of the study, libraries of machine learning methods and data analysis of the Python programming language were used. A program was developed to build a model for predicting the behavior of bank customers using data processing methods using gradient boosting, regularization of gradient boosting, random forest algorithm and recurrent neural networks. Analogous models were built using cloud machine learning services Azure ML, BigML and the Auto-sklearn library.</em> <em>Data analysis and prediction models built using Python language libraries have a fairly high quality </em><em>–</em><em> an average of 94.5</em><em> </em><em>%. Using the Azure ML cloud service, a predictive model with an accuracy of 88.6</em><em> </em><em>% was built. The BigML machine learning service made it possible to build a model with an accuracy of 88.8</em><em> </em><em>%. Machine learning methods from the Auto-sklearn library made it possible to obtain a model with a higher quality </em><em>–</em><em> 94.9</em><em> </em><em>%.</em> <em>This is due to the fact that the proposed libraries of the Python programming language allow better customization of data processing methods and machine learning to obtain more accurate models than free cloud services that do not provide such capabilities.</em> <em>Thanks to this, it is possible to obtain a predictive model of the behavior of bank customers with a fairly high degree of accuracy. It is worth noting that in order to make a prediction (forecast), it is necessary to study the context of the task, process the data, build various machine learning algorithms, evaluate the quality of the models and choose the best of them.</em>
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Bhushanamu, Mr M. Bala Naga. "Weather Forecasting Using Random Forest Regression in Django-Based Application." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45573.
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Abstract— Weather forecasting is a critical aspect of daily life, influencing various sectors such as agriculture, transportation, and emergency management. The traditional methods of weather prediction often rely on complex mathematical models and vast amounts of data, which require advanced computational tools and algorithm. In recent years, machine learning (ML) has emerged as a powerful tool for improving the accuracy and efficiency of weather forecasting systems. This project aims to develop a weather forecasting system using machine learning techniques, with Python as the primary programming language and Django as the web framework for building a user friendly interface.
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Purnita, Krishna Saha, and M. Rubaiyat Hossain Mondal. "Machine learning for DCO-OFDM based LiFi." PLOS ONE 16, no. 11 (2021): e0259955. http://dx.doi.org/10.1371/journal.pone.0259955.
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Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.
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Couto, Renato Camargos, Tania Moreira Grillo Pedrosa, Luciana Moreira Seara, et al. "Covid-19 vaccination priorities defined on machine learning." Revista de Saúde Pública 56 (March 11, 2022): 11. http://dx.doi.org/10.11606/s1518-8787.2022056004045.
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OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers.
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Lafta, Noor Abdalkareem, and Zainab Ali Abbood Abbood. "Comprehensive Review and Comparative Analysis of Keras for Deep Learning Applications: A Survey on Face Detection Using Convolutional Neural Networks." International Journal of Religion 5, no. 11 (2024): 1203–13. http://dx.doi.org/10.61707/gkh1m822.
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This paper aims to provide a literature review and comparative analysis focused on the importance of using Python as the primary language for machine learning (ML), deep learning (DL): The incorporation of libraries adapted to different areas is highlighted. One of the most depressing things about using Python in the programming sector is its easy and extensive libraries. Of these few, Keras shines with the majority of its design decisions being made based on the core concepts. Keras allows a number of possibilities for model deployment in a production mode, it supports multiple GPUs properly, and it also supports distributed model training. The design of Keras is very simple to use and can be easily learned due to its usage of Python programming language, making it a good open-source tool to be used in building and testing deep learning (DL) models. This paper focuses on Keras, an open-source deep learning application program interface that runs on Python and is based on TensorFlow but compatible with others such as PyTorch, TensorFlow, CODEEPNEATM, and Pygame. The review goes deeper into the details regarding Keras, including its goals, issues it sought to address, achievements it has made and some lessons drawn from its use.
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Mora-Cross, Maria, Adriana Morales-Carmiol, Te Chen-Huang, and María Barquero-Pérez. "Essential Biodiversity Variables: extracting plant phenological data from specimen labels using machine learning." Research Ideas and Outcomes 8 (August 23, 2022): e86012. https://doi.org/10.3897/rio.8.e86012.
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Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML).A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).
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Salazar-Díaz, Ricardo, Boris Glavic, and Tilmann Rabl. "InferDB: In-Database Machine Learning Inference Using Indexes." Proceedings of the VLDB Endowment 17, no. 8 (2024): 1830–42. http://dx.doi.org/10.14778/3659437.3659441.
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The performance of inference with machine learning (ML) models and its integration with analytical query processing have become critical bottlenecks for data analysis in many organizations. An ML inference pipeline typically consists of a preprocessing workflow followed by prediction with an ML model. Current approaches for in-database inference implement preprocessing operators and ML algorithms in the database either natively, by transpiling code to SQL, or by executing user-defined functions in guest languages such as Python. In this work, we present a radically different approach that approximates an end-to-end inference pipeline (preprocessing plus prediction) using a light-weight embedding that discretizes a carefully selected subset of the input features and an index that maps data points in the embedding space to aggregated predictions of an ML model. We replace a complex preprocessing workflow and model-based inference with a simple feature transformation and an index lookup. Our framework improves inference latency by several orders of magnitude while maintaining similar prediction accuracy compared to the pipeline it approximates.
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Justo, David, Shaoqing Yi, Lukas Stadler, Nadia Polikarpova, and Arun Kumar. "Towards a polyglot framework for factorized ML." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2918–31. http://dx.doi.org/10.14778/3476311.3476372.
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Optimizing machine learning (ML) workloads on structured data is a key concern for data platforms. One class of optimizations called "factorized ML" helps reduce ML runtimes over multi-table datasets by pushing ML computations down through joins, avoiding the need to materialize such joins. The recent Morpheus system automated factorized ML to any ML algorithm expressible in linear algebra (LA). But all such prior factorized ML/LA stacks are restricted by their chosen programming language (PL) and runtime environment, limiting their reach in emerging industrial data science environments with many PLs (R, Python, etc.) and even cross-PL analytics workflows. Re-implementing Morpheus from scratch in each PL/environment is a massive developability overhead for implementation, testing, and maintenance. We tackle this challenge by proposing a new system architecture, Trinity , to enable factorized LA logic to be written only once and easily reused across many PLs/LA tools in one go . To do this in an extensible and efficient manner without costly data copies, Trinity leverages and extends an emerging industrial polyglot compiler and runtime, Oracle's GraalVM. Trinity enables factorized LA in multiple PLs and even cross-PL workflows. Experiments with real datasets show that Trinity is significantly faster than materialized execution (> 8x speedups in some cases), while being largely competitive to a prior single PL-focused Morpheus stack.
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Mansouri, Ehsan, Maeve Manfredi, and Jong-Wan Hu. "Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning." Sustainability 14, no. 20 (2022): 12990. http://dx.doi.org/10.3390/su142012990.
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In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.
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Adiba, Noor, Chopra Krish, Minj Ayush, Kumar Priyesh, Prasad Kauleshwar, and Kumar Bhawnani Dinesh. "Voice & Text Translator." Research and Applications of Web Development and Design 5, no. 1 (2022): 1–5. https://doi.org/10.5281/zenodo.6622274.
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<em>The Voice and Text Translator is about translating one form of speech to another. This is a multi-tasking project which can perform various tasks at the same time. The definition of voice recognition should be taken into account, as it is often correlated with the process of identifying a person from his or her voice, i.e., the recognition of a speaker. The main aim of this app is to provide a mechanism for Speech to Speech. It further provides the mechanism for Speech to Text, Text to Speech, and also Text to Text in various languages. This software which deals with speech recognition has to get adapted to the unpredictable and highly variable nature of the human race. Every algorithm that is involved in the speech recognition process is tested and trained on different speaking styles, languages, accents, phrasings, or speaking patterns. Moreover, all these softwares also have to separate the actually spoken speech(audio) from the unwanted background noise that often accompanies these signals.</em> <em>This also contains some advanced features like voice recognition, understanding, and conversion, which are difficult for a machine to perform. But nowadays AI and ML are dominating the Tech. At its core, this project is built in the python programming language. Python is known for its rich and vast library and its usage in almost all kinds of projects. So, this Translator also contains some very precious modules of Python like Google-Trans, gTTs, etc. A broad array and series of research in the fields of computer science and linguistics are used in the speech recognition process. To have an easier life or to take part in the trending technologies that include hands-free use of any device, almost all the modern devices are adapting and shifting towards integrating speech recognition functions into their device and making up with the new trending technologies.</em> <em>After the implementation of this project, there would be no need for any intermediate person who conveys a message from source to target, like in earlier days.</em>
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Bagheri, Maryam, Mohsen Bagheritabar, Sohila Alizadeh, Mohammad (Sam) Salemizadeh Parizi, Parisa Matoufinia, and Yang Luo. "Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management." Applied Sciences 15, no. 1 (2024): 296. https://doi.org/10.3390/app15010296.
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The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective healthcare management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding of how these models are divided and analyzed, leaving gaps in normalization and benchmarking. The present research usually overlooks holistic models for comparing ML-enabled ISs, significantly considering pivotal function criteria like accuracy, precision, sensitivity, and specificity. To address these gaps, we conducted a broad exploration of 306 state-of-the-art papers to present a novel taxonomy of ML-enabled IS for multi-objective healthcare management. We categorized these studies into six key areas, namely diagnostic systems, treatment-planning systems, patient monitoring systems, resource allocation systems, preventive healthcare systems, and hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is the most effective parameter throughout all models. In addition, the majority of papers were published in 2022 and 2023, with MDPI as the leading publisher and Python as the most prevalent programming language. This extensive synthesis not only bridges the present gaps but also proposes actionable insights for improving ML-powered IS in healthcare management.
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Narkhede, Nirbhay. "Stock Market Prediction using Machine Learning and Cloud Computing." International Journal of Engineering and Computer Science 8, no. 09 (2019): 24847–50. http://dx.doi.org/10.18535/ijecs/v8i09.4361.
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In the world with increasing globalization , where money places a crucial role in determining the expansion and earnings of a company trading places a very crucial role. Multiple companies invest millions and billions of dollars in other countries with an expectation to make profits. In such a risky business Predicting the movement of the market can help companies or individual in making good decisions and can prevent severe loses. In this research paper we will discuss how we can use the computational power of the computer on cloud along with the machine learning algorithms to predict the closing values of the stocks which is a big challenge otherwise. For this purpose we will use Python as our programming language which supports a lot of ML based Libraries. The models we will be using are SVM(Support Vector Machine) , Linear Regression , Random Forest, XGBoost ,LSTM for deep learning
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Gaur, Varun, Sharad Bhardwaj, Utsav Gaur, and Sushant Gupta. "Stock Market Prediction & Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4404–8. http://dx.doi.org/10.22214/ijraset.2022.43403.
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Abstract: Stock trading is one of the most essential activities in the financial sector. The act of attempting to anticipate the future value of a stock or other financial instrument is known as stock market prediction. A financial exchange-traded instrument. This document illustrates how Machine Learning is used to predict a stock. The time series analysis or technical and fundamental analysis is used most stockbrokers use when deciding on a stock predictions. To forecast the outcome, the computer language is employed. Python is a stock market that uses machine learning. This paper is about We suggest a Machine Learning (ML) strategy that will be cost-effective. taught from publicly available stock data and intelligence and then applies what they've learned to make an accurate prediction. This work use machine learning in this setting. Support Vector Machine (SVM) is a technology for predicting Stock prices for large and small cap companies, as well as in the three different markets, using daily and weekly pricing Frequencies that are up to date. Keywords: Support Vector Machine, Stock Market, Machine Learning, Predictions
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Chacko, Akshay. "PD175 Using Machine Learning To Optimize Systematic Literature Reviews." International Journal of Technology Assessment in Health Care 40, S1 (2024): S160. https://doi.org/10.1017/s026646232400401x.
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IntroductionScreening and selecting publications are very time consuming when conducting systematic literature reviews. Currently, in the field of robotic-assisted surgery (RAS) there is an average of 12 to 15 studies published daily, making manual data management unsustainable. We aimed to investigate how machine learning (ML) can be used to optimize the manual processes of literature reviews.MethodsNew RAS publications in PubMed, Scopus, and Embase are routinely screened for relevancy and then tagged with metadata to aid future analysis. A curated library of approximately 40,000 tagged RAS publications served as our training dataset. To support manual screening and tagging efforts, multiple ML models were benchmarked, including logistic regression, decision trees, and gradient boosting. All model implementations came from the Python scikit-learn package. The evaluation metric for this study was the F1 score, and the fields of interest tagged were procedure type and surgical approach. Models were trained on publication abstracts and compared with a baseline keyword search to measure changes in performance.ResultsThe findings demonstrated that ML models can classify key metadata with high levels of accuracy. The decision tree model correctly labeled the five most common procedures in the dataset, with an average F1 score of approximately 0.90. This same model predicted surgical approach with an average F1 score of 0.84. It is important to note that different models performed best in different scenarios. To compensate for this variability, all models were fed into a stacking classifier—an ensemble model that takes the output of other models as input training data.ConclusionsIt is evident that ML models can reduce the cognitive burden of clinical librarians and shift their role from hand-screening papers to validating ML predictions. Future work may involve comparing the performance of traditional ML models with large language models (LLMs) to further improve F1 scores and reduce class imbalances.
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Alvarez, Lauren, Isabella Gransbury, Veronica Cateté, Tiffany Barnes, Ákos Ledéczi, and Shuchi Grover. "A Socially Relevant Focused AI Curriculum Designed for Female High School Students." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12698–705. http://dx.doi.org/10.1609/aaai.v36i11.21546.
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Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.
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Han, Mengmeng, Tennessee Leeuwenburg, and Brad Murphy. "Site-Specific Deterministic Temperature and Dew Point Forecasts with Explainable and Reliable Machine Learning." Applied Sciences 14, no. 14 (2024): 6314. http://dx.doi.org/10.3390/app14146314.
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Site-specific weather forecasts are essential for accurate prediction of power demand and are consequently of great interest to energy operators. However, weather forecasts from current numerical weather prediction (NWP) models lack the fine-scale detail to capture all important characteristics of localised real-world sites. Instead, they provide weather information representing a rectangular gridbox (usually kilometres in size). Even after post-processing and bias correction, area-averaged information is usually not optimal for specific sites. Prior work on site-optimised forecasts has focused on linear methods, weighted consensus averaging, and time-series methods, among others. Recent developments in machine learning (ML) have prompted increasing interest in applying ML as a novel approach towards this problem. In this study, we investigate the feasibility of optimising forecasts at sites by adopting the popular machine learning model “gradient boosted decision tree”, supported by the XGBoost package (v.1.7.3) in the Python language. Regression trees have been trained with historical NWP and site observations as training data, aimed at predicting temperature and dew point at multiple site locations across Australia. We developed a working ML framework, named “Multi-SiteBoost”, and initial test results show a significant improvement compared with gridded values from bias-corrected NWP models. The improvement from XGBoost (0.1–0.6 °C, 4–27% improvement in temperature) is found to be comparable with non-ML methods reported in the literature. With the insights provided by SHapley Additive exPlanations (SHAP), this study also tests various approaches to understand the ML predictions and increase the reliability of the forecasts generated by ML.
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Shen, Yuanhao. "An Effective Sentimental Analysis Model Based on spaCy." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1065–72. http://dx.doi.org/10.54097/91axqs95.
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As machine learning and the Internet continue to advance, the role of sentiment analysis in discerning positive and negative emotions within text has become increasingly pervasive. Its applications extend to enhancing human-computer interactions, monitoring mental health, and conducting business analyses. Numerous efforts have been dedicated to sentiment analysis and prediction. This article utilizes a dataset of 50,000 movie reviews, sourced from natural language processing (NLP) and included in the spaCy library, to contribute to this ongoing body of work. Two feature extractions, Count Vectorizer and TF-IDF are used, and three Machine Learning (ML) algorithms, Logistic Regression (LR), Decision Tree (DT) and the Multilayer Perceptron (MLP) are used to make predictions on the IMDB review data set with Python programming language. Comparing the experimental results, it can be found that different models have significant differences under different feature extractions, and using TF-IDF feature extraction combined with logistic regression model achieves the best accuracy (88.53%). It proves that LR, among three tested models, performs best in sentimental analysis.
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Niño-Rondón, Carlos Vicente, Diego Andrés Castellano-Carvajal, Byron Medina-Delgado, Sergio Alexander Castro-Casadiego, and Dinael Guevara-Ibarra. "Preliminary Identification of Skin Lesions using Efficient Computational Learning Techniques." Eco Matemático 13, no. 1 (2022): 34–45. http://dx.doi.org/10.22463/17948231.3286.
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Machine learning (ML) is one of the fields of artificial intelligence that offers algorithms topredict from samples the effective detection of skin lesions caused by skin cancer. This paper presents thepreliminary identification of skin lesions using optimized algorithms for texture feature extraction byGLCM and feature-based learning (LightGBM, SVM and HAAR Cascade) as an initial stage for adiagnostic tool. The HAM10000 skin lesion image set, Python programming language and open sourcebased libraries are used to process the images, extract the features and train the learning models, determinethe performance and hit rate of the models. Based on the results obtained, the LightGBM classifier requiredthe shortest learning time, reduced CPU usage and 90 % accuracy rate
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Ellam, Ikenna Victor, Kingsley M. Okorie, and Uchenna Franklin Okebanama. "Fake News Detection System Using Natural Language Processing: An Optimized Approach." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 162–84. https://doi.org/10.59324/ejaset.2025.3(2).15.
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The aim of this research is to create a fake news detection system using natural language processing with an optimized approach. In this work, we recommend a machine learning-based technique for identifying online fake news. Our framework leverages natural language processing techniques and various machine learning algorithms to examine textual records, metadata, and user engagement patterns to distinguish between genuine and fabricated news articles, the vibrant segment of the system takes keyword/text from user and searches for truth probability of the news source and lastly delivers validity of news inputted by user. Python and its libraries were utilized; Django was utilized for the web based organization of the model which offers client side execution using HTML, CSS and Javascript. The design methodology embraced in the design and development of the suggested system is Rapid Application Development (RAD) model. The project was planned in a manner that prospect modifications can be achieved. The conclusion and result from development of the project include the computerization of the whole system increases efficiency, It offers pleasant graphical user interface that proves to be operable while matched to the current system, It successfully overwhelms the interruption in Fake news detection. The System has suitable room for modification in future when needed and prompts authenticity of News and Information. The system can also be used in various areas like social media websites, news companies, radio/television stations, media houses, government establishments, shopping malls, spars, hotels and other public sectors.
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Kadam, Mr Yash, Mr Sujay Kulkarni, Mr Suyog Lonsane, and Prof Anjali S. Khandagale. "A Survey on Stock Market Price Prediction System using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 322–30. http://dx.doi.org/10.22214/ijraset.2022.40635.
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Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.
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Jang, Jisu, and Jiyun Kang. "CnSR: Exploring Consumer Social Responsibility Using Machine Learning-Based Topic Modeling with Natural Language Processing." Sustainability 16, no. 1 (2023): 197. http://dx.doi.org/10.3390/su16010197.
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This study delves into Consumer Social Responsibility (CnSR) within the fashion industry, with the goal of understanding consumers’ sustainable and responsible behavior across three major consumption stages: acquisition, utilization, and disposal. While “corporate” social responsibility (CSR) has been extensively studied in the literature, CnSR that sheds light on “individual consumers” has received less attention and is understudied. Using topic modeling, an unsupervised machine learning (ML) technique that uses natural language processing (NLP) in Python, this study analyzed textual data consisting of open-ended responses from 703 U.S. consumers. The analysis unveiled key aspects of CnSR in each of the consumption processes. The acquisition stage highlighted various ethical and sustainable considerations in purchasing and decision making. During the utilization phase, topics concerning sustainable and responsible product usage, environmentally conscious practices, and emotional sentiments emerged. The disposal stage identified a range of environmentally and socially responsible disposal practices. This study provides a solid and rich definition of CnSR from the perspective of individual consumers, paving the avenue for future research on sustainable consumption behaviors and inspiring the fashion industry to create goods and services that are in line with CnSR.
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Ramos-Varela, Jose Manuel, Juan C. Cuevas-Tello, and Daniel E. Noyola. "A Machine Learning-Based Computational Methodology for Predicting Acute Respiratory Infections Using Social Media Data." Computation 13, no. 4 (2025): 86. https://doi.org/10.3390/computation13040086.
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We study the relationship between tweets referencing Acute Respiratory Infections (ARI) or COVID-19 symptoms and confirmed cases of these diseases. Additionally, we propose a computational methodology for selecting and applying Machine Learning (ML) algorithms to predict public health indicators using social media data. To achieve this, a novel pipeline was developed, integrating three distinct models to predict confirmed cases of ARI and COVID-19. The dataset contains tweets related to respiratory diseases, published between 2020 and 2022 in the state of San Luis Potosí, Mexico, obtained via the Twitter API (now X). The methodology is composed of three stages, and it involves tools such as Dataiku and Python with ML libraries. The first two stages focuses on identifying the best-performing predictive models, while the third stage includes Natural Language Processing (NLP) algorithms for tweet selection. One of our key findings is that tweets contributed to improved predictions of ARI confirmed cases but did not enhance COVID-19 time series predictions. The best-performing NLP approach is the combination of Word2Vec algorithm with the KMeans model for tweet selection. Furthermore, predictions for both time series improved by 3% in the second half of 2020 when tweets were included as a feature, where the best prediction algorithm is DeepAR.
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Rauf, Aras Maruf, Trefa Mohammed Ali Mahmood, Miran Hikmat Mohammed, Zana Qadir Omer, and Fadil Abdullah Kareem. "Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study." Medicina 59, no. 11 (2023): 1973. http://dx.doi.org/10.3390/medicina59111973.
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Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.
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Zarifa Imanova, Ali Mikayilzadeh, Zarifa Imanova, Ali Mikayilzadeh, and Jala Dzhamalova Jala Dzhamalova. "4TH INDUSTRIAL REVOLUTION AND ARTIFICIAL INTELLIGENCE." ETM - Equipment, Technologies, Materials 16, no. 04 (2023): 29–33. http://dx.doi.org/10.36962/etm16042023-29.
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An overview of industrial revolutions is made. Then the analysis of the fourth industrial revolution. The main major components of Industry 4.0 are identified, and their essence is revealed. The tasks of artificial intelligence within Industry 4.0 are also considered. Developed software in Python language about data and machine learning (ML) to visualize objects and table data. In order to use the capabilities of AI to the maximum benefit for business, it is necessary to hire data scientists. Data science combines statistics, computer science and business knowledge to extract value from various data sources. Developers use artificial intelligence to more effectively perform tasks that would otherwise have to be done manually, interact with customers, identify patterns and solve problems. To start working with AI, developers will need mathematical knowledge and the ability to use algorithms. There are several stages in the development and deployment of machine learning models, including training and inference. AI learning and inference refers to the process of experimenting with machine learning models to solve a problem. For example, a machine learning engineer can experiment with various candidate models to solve a computer vision problem, such as detecting bone fractures in X-ray images. Keywords: fourth industrial revolution, artificial intelligence.
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Saxena, Anshul, Peter McGranaghan, Muni Rubens, et al. "Natural language processing (NLP) and machine learning (ML) model for predicting CMS OP-35 categories among patients receiving chemotherapy." Journal of Clinical Oncology 39, no. 15_suppl (2021): e13591-e13591. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e13591.
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e13591 Background: The Hospital Outpatient Quality Reporting Program is a pay-for-quality data reporting program implemented by the Centers for Medicare & Medicaid Services (CMS). Hospitals collect data on various measures of the quality of care provided in outpatient settings for the CMS. One such measure is OP-35, where data about patients who received chemotherapy in outpatient settings are collected. Such quality measures help hospitals assess their performance and allow patients to compare the quality of care among different hospitals in that region. Currently, the process to label data for OP-35 categories is manual. This study aims to develop a model using NLP and ML to predict the ten OP-35 complication categories and automate the process. Methods: Data from 1000 adult cancer patients who received chemotherapy at a comprehensive cancer center in the South Florida region between Sept and Oct 2019 were extracted to train the ML models. Text from the Chief Complaint field was manually labeled into ten binary categories: anemia, nausea, dehydration, neutropenia, diarrhea, emesis, pneumonia, fever, sepsis, and pain. The data were divided into a training set (80%) and a test set (20%). After initial pre-processing of the text, term frequency–inverse document frequency (TF-IDF) feature extraction method with a vocabulary size of 10,000 was applied. Various models (stochastic gradient descent, support vector classification [SVC], and binary relevance, etc.) were trained to predict multiple labels. These models were evaluated using Jaccard score, accuracy, F1 score, and Hamming loss. Additionally, two deep learning approaches: a single dense output layer and multiple dense output layer models, were also used for comparison. Python version 3.8 was utilized for the analysis. Results: The best performing model was SVC, with a Jaccard score of 85.13 and 90% accuracy. In the first deep learning approach, a single dense output layer was used with multiple neurons where each neuron represented only one label. In the second approach, a separate dense layer for each label was created with one neuron. The model with a single output layer produced an accuracy score of 32%, and the model with multiple output layer had an accuracy score of 31%. Both deep learning models with single and multiple output layers did not perform well compared to SVC. Conclusions: Our study shows an early indication regarding the feasibility of modern ML techniques in predicting multiple label categories or outcomes. As a potential clinical decision support system, this model could replace manual data entry, minimize human error, and decrease resources for data collection. In the next stage, healthcare providers will validate this model by manually checking the predicted labels. In the final stage, model will be deployed in real-time to predict OP-35 categories automatically.
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Plăcintă, Dimitrie-Daniel. "A Machine Learning Approach to Identify the Feature Importance for Admission in the National Military High Schools." Journal of Social and Economic Statistics 11, no. 1-2 (2022): 118–31. http://dx.doi.org/10.2478/jses-2022-0007.
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Abstract The article provides the impact of different averages (feature importance) within the admission exam for the national military high schools using and testing three supervised machine learning algorithms: logistic regression, K-Nearest Neighbors, and random forest. For this purpose, I have used the list with the results of candidates compounded by 430 rows, an unclassified document posted on the national military high school website, with details about: the final admission grade, the general grade for graduating of the secondary school, the general grade obtained at the national assessment, the mark obtained at admission test from Romanian language and mathematics items, etc. From the machine learning perspective, I have built a Jupyter notebook, a Python code using the specialized ML libraries (numpy, pandas, matplotlib, sklearn). In conclusion, the logistic regression algorithm identified the ‘feature importance’ (how each variable contributes to the predicted model) for admission in the national military high school: the mark obtained at admission test from Romanian language and Mathematics items - 4.821834, the general average obtained at the national assessment - 0.584434, the general average for graduating of the secondary school - 0.285446, etc. These are the expected results based on the admission methodology for the national military high schools.
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Mahendar, M. "A Survey on AI Powered Personalized Learning Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49460.
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ABSTRACT: AI Study Planner is a smart learning tool that creates personalized study schedules by understanding users’ progress, learning speed, and weak areas. It employs Reinforcement Learning (RL), Natural Language Processing (NLP), Machine Learning (ML), and a Generative Adversarial Network (GAN) to generate accurate study plans, provide resources, and identify weak areas. The system predicts how well users will remember topics, suggests revisions, and organizes subjects based on difficulty. It also offers summaries, explanations, and practice exercises to make learning easier and more effective. By tracking performance, it adapts to each user’s needs, ensuring better study habits and improved retention. Implemented using Python libraries like TensorFlow, it functions as a website for accessibility. This AI-driven planner enhances retention, reduces overload, and supports test preparation and skill-based learning, making it a valuable tool in modern education.
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Vashisht, Dr Vasudha, Ankit Rai, Dikshant Sharma, Ansh Sharma, and Ajinu Eapen Mathew. "Perceptive Personal Voice Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2567–71. http://dx.doi.org/10.22214/ijraset.2022.41852.
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Abstract: In recent years, Artificial Intelligence (AI) and Machine Learning (ML) has shown progress in the field of technology. We know that the future of technology can be full of robotics. One of the most useful application of AI is Natural Language Processing (NLP). Voice Assistants is one of the greatest innovation of all time. It changes the way of livingof the people in many aspects. Mainly it was first introduced in smart-phones and then it gets its own popularity in the market. Voice assistants are using cloud computing for communication with the users. It is mainly used in households to control a lot of technology related devices. Most common devices that are using voice assistants are smart speakers and they are being used in colleges, homes, schools etc. There are many voice assistants present in the market such as ’SIRI’ by Apple, ’Alexa’ by Amazon, ’Bixby’ by Samsung. We are also trying to create abasic Voice Assistant by using Python. There are so many useful technology such as speech recognition that will be used in this project. We are creating a VA names Arsenal that is capableof executing a lots of commands. The purpose of this paper is to study how voice assistants and smart speakers are usedin everyday life and they way in which we can create a voice assistant. Index Terms: Natural Language Processing, Voice Assistant, Speech Recognition, Python, Artificial Intelligence
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Eshwarappa, Sunil Mugalihalli, and Vinay Shivasubramanyan. "A novel dataset and part-of-speech tagging approach for enhancing sentiment analysis in Kannada." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1661. https://doi.org/10.11591/ijeecs.v37.i3.pp1661-1671.
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The problem addressed in this research is the limited availability of labelled datasets and effective sentiment analysis tools for the Kannada language. Existing challenges include linguistic variations, cultural diversities, and the absence of comprehensive datasets designed specifically for sentiment analysis in Kannada. This research aims to enhance sentiment analysis capabilities for the Kannada language, addressing challenges posed by linguistic variations and limited labelled datasets. A novel Kannada dataset derived from SemEval 2014 task 4 was created using a conversion process. The dataset was processed using part-of-speech tagging, and a specialized model called K-BERT (Kannada bidirectional encoder representations from transformers) was introduced and implemented using Python within the Anaconda environment. Performance evaluation results showcased K-BERT's superiority over traditional machine learning (ML) algorithms and the BERT model, achieving an accuracy of 0.98, precision of 0.97, recall of 0.97, and F-score of 0.98 in sentiment classification for Kannada text data. This work contributes a unique Kannada dataset, introduces the K-BERT model specifically designed for Kannada sentiment analysis, and emphasizes the importance of collaborative efforts in advancing natural language processing (NLP) research for multilingual environments.
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Sunil, Mugalihalli Eshwarappa Vinay Shivasubramanyan. "A novel dataset and part-of-speech tagging approach for enhancing sentiment analysis in Kannada." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1661–71. https://doi.org/10.11591/ijeecs.v37.i3.pp1661-1671.
Full textAbstract:
The problem addressed in this research is the limited availability of labelled datasets and effective sentiment analysis tools for the Kannada language. Existing challenges include linguistic variations, cultural diversities, and the absence of comprehensive datasets designed specifically for sentiment analysis in Kannada. This research aims to enhance sentiment analysis capabilities for the Kannada language, addressing challenges posed by linguistic variations and limited labelled datasets. A novel Kannada dataset derived from SemEval 2014 task 4 was created using a conversion process. The dataset was processed using part-of-speech tagging, and a specialized model called K-BERT (Kannada bidirectional encoder representations from transformers) was introduced and implemented using Python within the Anaconda environment. Performance evaluation results showcased K-BERT's superiority over traditional machine learning (ML) algorithms and the BERT model, achieving an accuracy of 0.98, precision of 0.97, recall of 0.97, and F-score of 0.98 in sentiment classification for Kannada text data. This work contributes a unique Kannada dataset, introduces the K-BERT model specifically designed for Kannada sentiment analysis, and emphasizes the importance of collaborative efforts in advancing natural language processing (NLP) research for multilingual environments.
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Biró, Attila, Antonio Ignacio Cuesta-Vargas, Jaime Martín-Martín, László Szilágyi, and Sándor Miklós Szilágyi. "Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools." Applied Sciences 13, no. 7 (2023): 4419. http://dx.doi.org/10.3390/app13074419.
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Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy.