Готові списки джерел за темами / Jupiter Notebook

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Добірка наукової літератури з теми "Jupiter Notebook"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Jupiter Notebook"

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Vishwakarma, Sagar, and Dr S. C. Solanki. "Predicting sales during COVID using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2481–89. http://dx.doi.org/10.22214/ijraset.2022.41822.

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Анотація:
Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.
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Kopoteva, A. V., A. A. Maksimov, and N. A. Sirotina. "Perm Region Natural Resource Potential Forecasting Using Machine Learning Models." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 21, no. 4 (November 2021): 126–36. http://dx.doi.org/10.14529/ctcr210411.

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Анотація:
In the article we consider a complex indicator of region natural resource potential modeling and forecasting quality improvement using different machine learning models. Problem under consideration importance is determined by the fact that the models traditionally used for these purposes demonstrate either low quality, or high configuration and parameters evaluation difficulty. The aim of the study is determination of machine learning models that provide the optimal values of various modeling quality metrics. Materials and methods. For this study purposes we considered the multiple linear regression, decision tree, random forest, gradient boosting and multilayer perceptron mo¬dels. We used the determination coefficient R2, the root mean square error of modeling RMSE, the average absolute error of modeling MAE, and the relative error of prediction for 1 and 2 time intervals as quality metrics. This study is based on data of the complex indicator of the Perm Region natural resource potential and the system of its determining factors in the time interval from 2001 to 2018. We evaluate models and calculate quality metrics using Pandas and Scikit-learn Python libraries in Jupiter Notebook environment. Results. According to our research the classical multiple linear regression model demonstrates the worst results for all quality metrics under consideration. The decision tree model demonstrates determination coefficient maximum value and minimum root mean square error and mean absolute error. Minimum relative forecasting error for 2017 is provided by the gradient boosting model, for 2018 – by the multilayer perceptron model. Conclusion. Our study allows us to affirm that nonlinear machine learning models for the task of region natural resource potential modeling and forecasting demonstrate better approximating and predictive properties compared to multiple linear regression and thus can be used to improve the quality of natural resource management.
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Wazery, Y. M., Marwa E. Saleh, Abdullah Alharbi, and Abdelmgeid A. Ali. "Abstractive Arabic Text Summarization Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (January 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/1566890.

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Анотація:
Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.
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Parial, Prithwish. "Python the game changer in the field of Machine Learning, Data Science and IoT: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1827–37. http://dx.doi.org/10.22214/ijraset.2021.37668.

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Анотація:
Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.
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Prasetyo, Eko Wahyu, Nambo Hidetaka, Dwi Arman Prasetya, Wahyu Dirgantara, and Hari Fitria Windi. "Spatial Based Deep Learning Autonomous Wheel Robot Using CNN." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11, no. 3 (December 22, 2020): 167. http://dx.doi.org/10.24843/lkjiti.2020.v11.i03.p05.

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Анотація:
The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.
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Vijayvergia, Varunika, Aruna Vyas, Nazneen Pathan, Rajni Sharma, Snigdha Purohit, Akriti Aggarwal, Neha Sharma, and Nitya Vyas. "A Study on Requirement of Cold Chain Maintenance for Reliable Testing of SARS-CoV-2 Samples." JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH, 2021. http://dx.doi.org/10.7860/jcdr/2021/50307.15298.

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Анотація:
Introduction: Coronavirus Disease 2019 (COVID-19) has been haunting the world since December 2019 and has grown to pandemic proportions from March 2020. Even after a full year of research and study, the most effective way to control the spread of this infection is early diagnosis and isolation of the cases. Real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is considered the standard test all over the world for the diagnosis of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) infection. All the sample collection guidelines have recommended stringent maintenance of the cold chain for the sample transport. However, it is not possible for the resource constrained developing countries with inadequate infrastructure to comply with these guidelines all the time. Aim: To determine necessity of stringent transport criteria and the effect of temperature on the clinical sensitivity of a RT-PCR assay for diagnosis of SARS-CoV-2 infection. Materials and Methods: In this prospective experimental study conducted in November 2020, 49 positive samples were kept at ambient room temperature and were tested everyday with RT- PCR for the detection of SARS-CoV-2 Ribonucleic Acid (RNA). The samples were also kept under refrigeration at 4°C and were also tested by RT-PCR and the results were compared with their respective counterparts kept at room temperature till nine days. Python Jupiter notebook SciPy and Anaconda software was used for statistical analysis. Results: It was observed that the positivity of the RT-PCR results were not deteriorated till five days and there was no significant deterioration even after nine days of samples being stored at room temperature suggesting that even if the viral RNA itself is not stable outside strict temperature control but small fragment or target genetic sequences are enough for detection of virus by RT-PCR. Conclusion: It is possible to keep samples at this ambient temperature for five days without any loss of positivity in RT-PCR.
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Дисертації з теми "Jupiter Notebook"

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Солодкий, Віталій Петрович. "Розробка системи, для візуалізації медичних показників за ресурсом PhysioNet". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/29095.

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Анотація:
Обсяг роботи 109 сторінок, кількість ілюстрацій – 29, таблиць – 30, додатків – 1, джерел за переліком посилань – 50. Мета дослідження – розробка пакета програм візуалізації та напівавтоматичної розмітки електрокардіографічних даних, для підвищення ефективності систем автоматичного аналізу ЕКГ та серцевого ритму. Об’єкт дослідження – медичні показники ЕКГ та серцевого ритму за ресурсом PhysioNet. Предметом дослідження є розробка програми, що дозволяє взаємодіяти з фізіологічними сигналами формату PysioNet. Додаток дозволяє переглядати сигнали, анотації до них, а також скачувати потрібні записи при підключенні, до мережі Інтернет. Програма здійснює перетворення, сигналу з формату EDF та CSV формату PhysioNet. Для створення додатка використовується мова програмування Python. Як середовище програмування вибрана Jupiter Notebook. Для взаємодії з сигналами до проекту підключена бібліотека Waveform Database Library. У роботі є короткий огляд методів електрокардіографії, мови Python і платформи PysioNet, так як всі ці складові були використанні при розробці додатку. Публікація: 1. Солодкий В. П. Програмний додаток для візуалізації медичних показників за ресурсом PhysioNet. / Солодкий В.П. // Міжнародний науковий журнал "Інтернаука". — 2019. — №6. — 78 — 86 С. (стаття) Розроблена програма дозволяє виконувати всі поставлені функції. Але відомі шляхи поліпшення і модифікації програми. Наприклад, можливість шукати та скачувати медичні засоби за певними показниками з анотацій.
Volume of pages - 109, number of illustrations - 30, tables - 30, applications - 1 , sources - 50. The purpose of the study is to develop a package of visualization programs and semi-automatic marking of electrocardiographic data to improve the efficiency of ECG and heart rate systems. The object of the study is the medical parameters of ECG and cardiac rhythm with the PhysioNet resource. The subject of the study is the development of a program that allows you to interact with the physiological signals of the PysioNet format. The application allows you to view signals, annotations to them, and download the required entries when connected to the Internet. The program converts the signal from the EDF format and the CSV format to PhysioNet. The Python programming language was used to create the application. Jupiter Notebook was choose as the programming environment. The Waveform Database Library library was connected to the project to interact with the signals. There is a brief overview of electrocardiography, Python and PysioNet, as all of these components were used in the development of the application. Publication: 1. Solodky V.P. Software application for visualization of medical indicators with the resource PhysioNet. / Solodkyi V.P. // International scientific magazine "Internet Science". — 2019 — №6. — 78 — 86 P. (article) The developed program allows you to perform all the functions assigned. However, there are ways to improve and modify the program. For example, the ability to search and download medical devices for certain indicators of annotations.
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Книги з теми "Jupiter Notebook"

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Horn, Rebecca. Rebecca Horn: Trägerin des Alexej von Jawlensky-Preises 2007 der Landeshauptstadt Wiesbaden : Jupiter im Oktogon. Nürnberg: Verlag für moderne Kunst, 2007.

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Explorers, World. JUPITER Graph Composition Notebook: Minimalistic Notebook of Solar System. Perfect Astronomy Gift for Students. Explorer Stylish Journal with Space X Cover. Independently Published, 2020.

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Ordon, Stanley. Notebook: Jupiter a Gas Gigant / 100 Lined Pages. Interesting Facts on the Back of the Cover. Independently Published, 2020.

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4

Ordon, Stanley. Notebook: Jupiter / Slippery Cover / Dot Graph Paper / 100 Pages / / Interesting Facts on the Back of the Cover. Independently Published, 2020.

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5

Edition, Moon. Lunar Abundance: Moon Book Lined Notebook / Journal Gift, 100 Pages, 6x9, Jupiter Effect Cover, Matte Finish Inspirational Quotes Journal, Notebook, Diary, Composition Book. Independently Published, 2020.

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6

Edition, Moon. Moon Cycle Journal: Moon Book Lined Notebook / Journal Gift, 100 Pages, 6x9, Jupiter Effect Cover, Matte Finish Inspirational Quotes Journal, Notebook, Diary, Composition Book. Independently Published, 2020.

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Edition, Moon. Vintage Celestial Journal: Moon Book Lined Notebook / Journal Gift, 100 Pages, 6x9, Jupiter Effect Cover, Matte Finish Inspirational Quotes Journal, Notebook, Diary, Composition Book. Independently Published, 2020.

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8

tate, nicholas. Notebook Journal : Jupiter Hermit Crab Pocket Size Space Nerd Animal: Unique Appreciation Gift with Beautiful Design and a Premium Matte Softcover Gift Ideas for Your Son. Independently Published, 2020.

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