Relevant bibliographies by topics / Google Colab

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Google Colab'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Google Colab"

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Ray, Sujan, Khaldoon Alshouiliy, and Dharma P. Agrawal. "Dimensionality Reduction for Human Activity Recognition Using Google Colab." Information 12, no. 1 (December 23, 2020): 6. http://dx.doi.org/10.3390/info12010006.

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Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.
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Kuroki, Masanori. "Using Python and Google Colab to teach undergraduate microeconomic theory." International Review of Economics Education 38 (November 2021): 100225. http://dx.doi.org/10.1016/j.iree.2021.100225.

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Gunawan, Teddy Surya, Arselan Ashraf, Bob Subhan Riza, Edy Victor Haryanto, Rika Rosnelly, Mira Kartiwi, and Zuriati Janin. "Development of video-based emotion recognition using deep learning with Google Colab." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 5 (October 1, 2020): 2463. http://dx.doi.org/10.12928/telkomnika.v18i5.16717.

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Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, et al. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23 (November 25, 2020): 3860. http://dx.doi.org/10.3390/rs12233860.

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Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
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Elsayed, Eman, and Doaa Fathy. "Semantic Deep Learning to Translate Dynamic Sign Language." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 316–25. http://dx.doi.org/10.22266/ijies2021.0228.30.

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Dynamic Sign Language Recognition aims to recognize hand gestures of any person. Dynamic Sign Language Recognition systems have challenges in recognizing the semantic of hand gestures. These challenges come from the personal differences in hand signs from one person to another. Real-life video gesture frames couldn’t be treated as frame-level as a static sign. This research proposes a semantic translation system for dynamic hand gestures using deep learning and ontology. We used the proposed MSLO (Multi Sign Language Ontology) in the semantic translation step. Also, any user can retrain the system to be a personal one. We used Three-dimensional Convolutional Neural Networks followed by Convolutional long short-term memory to improve the recognition accuracy in Dynamic sign language recognition. We applied the proposed system on three dynamic gesture datasets from color videos. The recognition accuracy average was 97.4%. We did all the training and testing processes on the Graphics Processing Unit with the support of Google Colab. Using "Google Colab" in the training process decreases the average run time by about 87.9%. In addition to adding semantic in dynamic sign language translation, the proposed system achieves good results compared to some dynamic sign language recognition systems.
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Kharisudin, I., A. Hidayati, A. Agoestanto, and M. Mashuri. "Convolutional neural network for classification of skin cancer based on image data using google colab." Journal of Physics: Conference Series 1968, no. 1 (July 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/1968/1/012015.

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Mohialden, Yasmin Makki, Muhanad Tahrir Younis, and Nadia Mahmood Hussien. "A Novel Approach to Arabic Chabot, Utilizing Google Colab and the Internet of Things: A Case Study at a Computer Center." Webology 18, no. 2 (December 23, 2021): 946–54. http://dx.doi.org/10.14704/web/v18i2/web18365.

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A Chabot is a software program for humans to interact with natural-language computers. It has numerous applications in business, service, education, and healthcare, among others. Arabic Chabot’s, on the other hand, fight to generate and display Arabic characters correctly because of linguistic problems. In this paper, we propose a new method for the development of effective Arabic Chabot’s, which is improved by the use of the Internet of things (IOT). An experiment was performed utilizing Google Colab and the Python Chatterbot library to build and deploy an Arabic Chabot for a computer center based on IOT.
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Veeramsetty, Venkataramana, Bhavana Reddy Edudodla, and Surender Reddy Salkuti. "Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks." Algorithms 14, no. 11 (November 8, 2021): 329. http://dx.doi.org/10.3390/a14110329.

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Zero-crossing point detection is necessary to establish a consistent performance in various power system applications, such as grid synchronization, power conversion and switch-gear protection. In this paper, zero-crossing points of a sinusoidal signal are detected using deep neural networks. In order to train and evaluate the deep neural network model, new datasets for sinusoidal signals having noise levels from 5% to 50% and harmonic distortion from 10% to 50% are developed. This complete study is implemented in Google Colab using deep learning framework Keras. Results shows that the proposed deep learning model is able to detect zero-crossing points in a distorted sinusoidal signal with good accuracy.
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Zhang, Lisa, Pouria Fewzee, and Charbel Feghali. "AI education matters." AI Matters 7, no. 3 (September 2021): 18–20. http://dx.doi.org/10.1145/3511322.3511327.

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We introduce a Model AI Assignment (Neller et al., 2021) where students combine various techniques from a deep learning course to build a denoising autoencoder (Shen, Mueller, Barzilay, & Jaakkola, 2020) for news headlines. Students then use this denoising autoencoder to query similar headlines, and interpolate between headlines. Building this denoising autoencoder requires students to apply many course concepts, including data augmentation, word and sentence embeddings, autoencoders, recurrent neural networks, sequence-to-sequence networks, and temperature. As such, this assignment can be ideal as a final assessment that synthesizes many topics. This assignment is written in PyTorch, uses the torchtext package, and is intended to be completed on the Google Colab platform.
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Brandolini, Filippo, Guillem Domingo-Ribas, Andrea Zerboni, and Sam Turner. "A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features." Open Research Europe 1 (September 3, 2021): 22. http://dx.doi.org/10.12688/openreseurope.13135.2.

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The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the “natural” and “cultural” landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. This research represents one of the first applications of the Google Earth Engine (GEE) Python application programming interface (API) in studies of historic landscapes. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab, which could be adapted and replicated in different areas of the world. A multi-temporal approach has been adopted to investigate the potential of Sentinel-2 satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. The protocol's effectiveness in identifying palaeo-riverscape features has been tested in the Po Plain (N Italy).
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Dissertations / Theses on the topic "Google Colab"

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Giambi, Nico. "Sperimentazione di tecniche di Deep Learning per l'Object Detection." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21557/.

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Il lavoro svolto in questa tesi ruota intorno alla sperimentazione di tecniche di Deep Learning per l'Object Detection, ovvero la costruzione di un Object Detector a la YOLO partendo da zero testando per ogni parte della costruzione più alternative possibili per verificarne la praticità e correttezza, estrapolando per le varie fasi le soluzioni migliori, sia dal punto di vista funzionale sia per quanto riguarda la semplicità. In questa tesi è stato creato un Object Detector sfruttando MobileNet (una Convolutional Neural Network molto veloce) associata ad un algoritmo in stile YOLO (principalmente YOLOv2) e allenata sul dataset COCO (Common Objects in COntext). Le prove effettuate spaziano in tutti i campi, dalla scelta di usare un modello pre-allenato su un altro dataset alla decisione di alcuni parametri da usare come threshold in fase di post-processing. All'interno della tesi verranno spiegati brevemente i temi principali toccati dall'argomento e tutte le prove svolte, spiegando quali di ognuna di queste sia risultata migliore.
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Gunn-Graffy, Colin. "When in Rome, Beijing or Brussels: Cultural Considerations of International Business Communication." Thesis, Boston College, 2007. http://hdl.handle.net/2345/565.

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Thesis advisor: Donald Fishman
This thesis examines the role of culture in international business communication through case studies of several multinational corporations. The first case looks at Coca-Cola's product recall crisis in Belgium in 1999, which exhibited an uncharacteristic deviation from the company's well-known brand marketing brilliance. The second case deals with problems that Disney encountered as it tried to establish its first theme park in Europe in the 1990s and found itself facing a culture as proud and protective as Disney itself. At the heart of these cases were outdated international strategies and an attitude of arrogance that assumed that an American business approach could easily be transferred to a different country and culture. The thesis concludes with an analysis of Google as a case study for the future followed by suggestions for successful international strategies and final thoughts on globalization's effect on culture and corporations
Thesis (BA) — Boston College, 2007
Submitted to: Boston College. College of Arts and Sciences
Discipline: Communication
Discipline: College Honors Program
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Book chapters on the topic "Google Colab"

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Paper, David. "Build Your First Neural Network with Google Colab." In TensorFlow 2.x in the Colaboratory Cloud, 25–45. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6649-6_2.

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Shariar, Sarjil, and K. M. Azharul Hasan. "Parallel Computation of Hadamard Product for Dynamic Dataset on Google Colab." In ICT with Intelligent Applications, 479–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4177-0_48.

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Nguyen, Son, Matthew Quinn, Alan Olinsky, and John Quinn. "Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, 58–79. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8455-2.ch003.

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In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this study is done using cloud computing services offered by Google Colab Pro.
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Muhammad, Wazir, Irfan Ullah, and Mohammad Ashfaq. "An Introduction to Deep Convolutional Neural Networks With Keras." In Machine Learning and Deep Learning in Real-Time Applications, 231–72. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch011.

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Deep learning (DL) is the new buzzword for researchers in the research area of computer vision that unlocked the doors to solving complex problems. With the assistance of Keras library, machine learning (ML)-based DL and various complicated or unresolved issues such as face recognition and voice recognition might be resolved easily. This chapter focuses on the basic concept of Keras-based framework DL library to handle the different real-life problems. The authors discuss the codes of previous libraries and same code run on Keras library and assess the performance on Google Colab Cloud Graphics Processing Units (GPUs). The goal of this chapter is to provide you with the newer concept, algorithm, and technology to solve the real-life problems with the help of Keras framework. Moreover, they discuss how to write the code of standard convolutional neural network (CNN) architectures using Keras libraries. Finally, the codes of validation and training data set to start the training procedure are explored.
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Conference papers on the topic "Google Colab"

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Shariar, Sarjil, and K. M. Azharul Hasan. "GPU Accelerated Indexing for High Order Tensors in Google Colab." In 2020 IEEE Region 10 Symposium (TENSYMP). IEEE, 2020. http://dx.doi.org/10.1109/tensymp50017.2020.9230789.

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Canesche, Michael, Lucas Braganca, Omar Paranaiba Vilela Neto, Jose A. Nacif, and Ricardo Ferreira. "Google Colab CAD4U: Hands-On Cloud Laboratories for Digital Design." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401151.

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Silva, Martony Demes da. "Aplicação da Ferramenta Google Colaboratory no Ensino de Ciências de Dados." In Simpósio Brasileiro de Sistemas Colaborativos. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbsc.2021.16017.

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Esse trabalho apresenta uma avaliação do ambiente Google Colaboratory (ou colab) para o auxílio no ensino de estudante de um treinamento de Ciências de Dados. Essa ferramenta facilita a interação do aluno na prática de atividades de programação, aplicação de técnicas de Aprendizagem de Máquina e Inteligência Artificial. Para validação desta proposta, utilizou-se de uma análise de utilidade percebida e facilidade de uso percebida da ferramenta. Resultados mostram que em 10, das 12 questões, apresentaram índice acima de 90%.
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Da Silva, Martony Demes. "Aplicação da Ferramenta Google Colaboratory para o Ensino da Linguagem Python." In Escola Regional de Engenharia de Software. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/eres.2020.13717.

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Esse trabalho apresenta a aplicação da ferramenta Google Colaboratory (ou colab) no ensino de linguagem de programação Python. Por meio desse ambiente, é possível a interação do estudante na prática de atividades de programação de maneira fácil e dedutiva. Para validar essa facilidade de uso, fez-se uma avaliação por meio da Utilidade Percebida e Facilidade de Uso percebida nesta ferramenta. Os resultados apontaram que as duas análise tiveram índices superiores a 90%. Nesse contexto, a plataforma é de fácil utilização e sem necessidade de esforço
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Ali, Ihsan, Aftab Khan, and Muhammad Waleed. "A Google Colab Based Online Platform for Rapid Estimation of Real Blur in Single-Image Blind Deblurring." In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2020. http://dx.doi.org/10.1109/ecai50035.2020.9223244.

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de Morais, Rene Avalloni, and Baidya Nath Saha. "End-to-End Speech Recognition Using Recurrent Neural Network (RNN)." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.20.

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Deep learning algorithms have received dramatic progress in the area of natural language processing and automatic human speech recognition. However, the accuracy of the deep learning algorithms depends on the amount and quality of the data and training deep models requires high-performance computing resources. In this backdrop, this paper adresses an end-to-end speech recognition system where we finetune Mozilla DeepSpeech architecture using two different datasets: LibriSpeech clean dataset and Harvard speech dataset. We train Long Short Term Memory (LSTM) based deep Recurrent Neural Netowrk (RNN) models in Google Colab platform and use their GPU resources. Extensive experimental results demonstrate that Mozilla DeepSpeech model could be fine-tuned for different audio datasets to recognize speeches successfully.
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Fagundes-Junior, Leonardo, Michael Canesche, Ricardo Ferreira, and Alexandre Brandão. "A Nonlinear UAV Control Tuning Under Communication Delay using HPC Strategies in Parameters Space." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/wscad.2021.18526.

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In practical applications, the presence of delays can deteriorate the performance of the control system or even cause plant instability. However, by properly controlling these delays, it is possible to improve the performance of the mechanism. The present work is based on a proposal to analyze the asymptotic stability and convergence of a quadrotor robot, an unmanned aerial vehicle (UAV), on the performance of a given task, under time delay in the data flow. The effects of the communication delay problem, as well as the response-signal behavior of the quadrotors in the accomplishment of positioning mission are presented and analyzed from the insertion of fixed time delay intervals in the UAVs' data collected by its sensors system. Due to the large search space in the set of parameter combinations and the high computational cost required to perform such an analysis by sequentially executing thousands of simulations, this work proposes an open source GPU-based implementation to simulate the robot behavior. Experimental results show a speedup up to 4900x in comparison to MATLAB® implementation. The implement is available in Colab Google platform.
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Quiroz-Martinez, Miguel-Angel, Ronald-Dario Montoya-Guillen, Galo-Enrique Valverde-Landivar, and Maikel-Yelandi Leyva-Vazquez. "Benchmarking of activation functions for breast cancer detection." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001084.

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Breast cancer is a common disease and one of the leading causes of death global-ly; there are several methods, technologies, algorithms, or functions to detect their presence. The objective is to develop a benchmarking of activation functions in the detection of breast cancer for its selection with the purpose of increasing the effectiveness in the diagnosis of this disease. The research methodology used in this work is observation in scientific articles, experimental in the implementation of the algorithm, quantitative analysis of the results, and a descriptive approach on the activation functions and the results of the algorithm. The results of this work are an implementation of the Activation Functions Sigmoid, ReLu, Swish, Tanh, and Softmax on the Keras framework; and the realization of benchmarking in Google Colab. It was concluded that this work is an opening towards new knowledge to favor the cooperation and cohesion of different actors; it is a way of betting on knowledge, innovation, and achieving dynamism with planning, analysis, and action of the idea to be implemented for an improvement in the field of health; ReLu has higher accuracy with 98.20% and is the first choice for pre-paring and training neural networks.
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Liu, Jinge, and Shuyu Wang. "Piano4Play: An Automated Piano Transcription and Keyboard Visualization System using AI and Deep Learning Techniques." In 8th International Conference on Control, Modeling and Computing (CMC 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120502.

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Piano keyboard visualization was very popular right now, but there are very few virtual piano keyboard visualizations right now [1]. I was using unity to show the virtual piano keyboard and then they can play piano pieces by themselves or play a recording online [2]. After that you can listen and see how the recording pieces play it on the visual keyboard to give them a clear idea about how the songs played on a keyboard2 [3]. For those who played by themselves it can let them heard and know also when the visual piano play for them, they can tell if they have offbeat playing or they missing not. Piano4Play is an automated piano transcription and keyboard visualization system using AI and deep learning techniques. The user could upload a recorded piece of music, and our app would visualize the music on a digital piano keyboard. The user could see how the music is played visually in order to help piano beginners to see how the music will be played on piano in order to help them learn more quickly and easier, and advanced players could use the app to see whether they made any mistake when they are playing so they can get some improvement. Our app uses wav and MIDI files, repl, real-time database, google Collab and Unity.
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Chaves, Iara, Antônio Gutemberg Mesquita Neto, Jéssica Vasconcelos Arrais, José Wellington Moraes Damasceno, and Antônia Márcia Macêdo de Sousa. "A ATUAÇÃO DO RESIDENTE EM SAÚDE NO MONITORAMENTO DE CASOS DO CORONAVÍRUS." In I Congresso Brasileiro de Saúde Pública On-line: Uma abordagem Multiprofissional. Revista Multidisciplinar em Saúde, 2021. http://dx.doi.org/10.51161/rems/2947.

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Introdução: A Atenção Básica é a principal porta de entrada tendo como articulador de promoção da saúde dos usuários ao Sistema Único de Saúde (SUS) e às Redes, orientada pelos princípios da acessibilidade, cuidado, continuidade e integralidade. Com o advento da pandemia, a Atenção Básica por meio de ações da saúde, ao qual promove o cuidado longitudinal e integral as pessoas com Coronavírus (COVID-19), proporcionando o acompanhamento necessário para uma adequada reabilitação e possibilitando a identificação de novos casos, considerando a reorganização dos serviços, desempenhando um papel crucial diante do enfrentamento ao período pandêmico. Objetivo: Relatar a experiência de residentes multiprofissionais em saúde no monitoramento dos casos de COVID-19. Material e Métodos: Trata-se de um relato de experiência dos residentes em saúde no período de março a agosto de 2021, nos Centro de Saúde da Família (CSF) Junco e COHAB III em Sobral - Ceará. Participaram do monitoramento os cirurgiões dentistas do serviço com o apoio de residentes de diversas categorias. O monitoramento era realizado através de ligações telefônicas, as informações coletadas registrados em uma planilha contendo dados sociodemográficos e clínicos de pacientes diagnosticados com COVID-19 por um período de 14 dias. Caso o paciente estivesse sob suspeita e não apresentando sintomas, poderia encerar-se no prazo de 48 horas, com uso de planilhas eletrônicas . Resultados: Através das estatísticas apresentadas nas planilhas do Google Drive estima-se uma média de que há 11 pacientes em monitoramento, 620 tiveram alta por cura, 953 descartados (negativos) e 29 óbitos, levando em consideração apenas um dos CSF. É importante ressaltar que independente da porta de entrada que o paciente tenha sido notificado para COVID-19, há um encaminhamento para o CSF mais próximo, possibilitando o monitoramento dos casos confirmados, suspeitos e seus contatos. Conclusão: Foi possível perceber o monitoramento como uma estratégia que possibilitou o cuidado integral a pacientes acometidos pelo Covid-19. Tendo em vista que o uso das tecnologias digitais como fundamentais nas intervenções como suporte de uma escuta qualificada e articulada aos princípios do SUS, em especial ao de promoção de saúde, da integralidade, universalidade e equidade.
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