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Cloud services make it easy to get infrastructure in a flexible and on-demand way. While there are many cloud services to choose from, Google Cloud Platform offers unique services that let you focus on building powerful applications. Google Cloud Services in Action teaches readers to build and launch web applications that scale while leveraging the Google Cloud Platform. Readers begin with the basics, learning how cloud services work, and the specifics of the Google Cloud Platform. The book includes hands-on step-by-step instruction on deploying applications, handling large amounts of data, and much more. By the end, readers will know how to build, leverage, and deploy cloud-based applications so web applications get started more quickly, suffer fewer disasters, and require less maintenance.

The international higher education system should be grounded in an educational approach in which teaching and learning methods aim to transform the student into an active agent in their learning process. The present study aims to learn how intention to use a personal learning environment based on Google applications for supporting collaborative learning is formed, in the context of university student learning. For this purpose, an expansion of the technology acceptance models was proposed including subjective norms and social image. The model was empirically evaluated using survey data collected from 267 students from a marketing management degree course, on which Google applications (apps) were used to design a learning environment to support project work and learning. The results show the suitability of the extended TAM to explain the intention to use Google apps as a personal learning environment in the university context. More specifically, subjective norms contributed to the indirect effect on the intention to use Google apps through social image and had a substantial positive influence on the social image. Meanwhile, social image had a significant positive direct effect on perceived usefulness. The results of the present study have a series of practical implications for the higher education sector.

Beneficial to scholars and students in the fields of media and communication, politics and technology, this book outlines the significant role of search engines in general and Google in particular in widening the digital divide between individuals, organisations and states. It uses innovative methods and research approaches to assess and illustrate the digital divide by comparing the popular search queries in Google and Yahoo in different countries as well as analysing the various biases in Google News and Google Earth. The different studies developed and presented in this book provide various indications of the increasing customisation and popularisation mechanisms employed by popular search engines, which together with “organising the world’s information” inevitably also intensify information inequalities and reinforce commercial and US-centric priorities and agendas.Develops an extensive historical investigation of information, power and the digital divideProvides new social and political perspectives to understand search engines in general and Google in particularSuggests original methods to study and assess the digital divide as well as the extent of commercialisation and Americanisation worldwide

This book will deep-dive into the concept of analytics on the cloud with the design and business considerations. You will build an end-to-end analytics engine to perform smart analytics using machine learning and deep learning concepts. From ingestion to processing your data, this book contains the best practices using Google Cloud Platform.

The future of user interfaces is moving away from touch based and mouse clicking web interfaces to voice and conversational based user interfaces. This book will take you on a journey from getting started with voice apps to building your very own smart assistants which not only understand voice commands but respond to them.

YouTube has become a dominant force in digital media, yet current video popularity analytics remain limited in capturing the emotional and cultural dimensions of viewer engagement, particularly in underrepresented regions like Pakistan. While existing research focuses predominantly on Western markets and quantitative metrics (views, likes, comments), these approaches overlook sentiment‐driven interactions critical to understanding regional audience behavior. This study bridges this gap by introducing a sentiment‐aware framework for YouTube video classification in Pakistan, combining traditional popularity metrics with advanced sentiment analysis of user comments. We curated the PAK VIDEOS (2021–2023) dataset using YouTube Data APIs, comprising metadata and user comments from Pakistan's top trending videos. Leveraging Natural Language Processing (NLP) techniques, we extracted sentiment scores from comments to classify videos into four categories: non‐popular, overwhelmingly positive, overwhelmingly negative, and neutral. This hybrid approach enabled a nuanced evaluation of content reception beyond quantitative metrics. Four machine learning models—random forest, stochastic gradient descent classifier (SGDC), gradient boosting, and XGBoost—were evaluated for classification. XGBoost achieved superior performance (84.3% accuracy), outperforming baseline models by up to 20%. Our framework demonstrates that integrating sentiment analysis significantly enhances popularity prediction, particularly in culturally distinct contexts. Leveraging NLP techniques, sentiment scores are assigned to user comments, which provide a comprehensive insight of audience reactions. Furthermore, different multiclass classifiers such as random forest, SGDC, gradient boosting, and extreme gradient boosting are used for modeling of PAK_VIDEOS dataset. Experimental results demonstrated that the proposed framework performed better with XG‐Boost than other experimented models, achieving an accuracy of 84.3%. Also, Performance comparison against existing state‐of‐the‐art approaches verified that the proposed framework gives better and more accurate results by a maximum margin of 20% with random forest.

Satellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure. In this study, we propose a cross-cloud system for ML-based satellite image detection, focusing on the financial and performance aspects of utilizing Amazon Web Service (AWS) Lambda and Amazon SageMaker for advanced machine learning tasks. Our system utilizes Google Apps Script (GAS) to create a web-based control panel, providing users with access to our AWS-hosted satellite detection models. Additionally, we utilize AWS to manage expenses through a strategic combination of Google Cloud and AWS, providing not only economic advantages, but also enhanced resilience. Furthermore, our approach capitalizes on the synergistic capabilities of AWS and Google Cloud to fortify our defenses against data loss and ensure operational resilience. Our goal is to demonstrate the effectiveness of a cloud environment in addressing complex and interdisciplinary challenges, particularly in the field of object analysis using spatial imagery.

Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user’s experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users’ reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results.

PurposeThis paper aims to explore the consumer insights and ethical concerns surrounding the online payday loan services available in the Google Play Store. This research was conducted to compare whether the presence or absence of debt collection protection acts in a country creates differences in consumer experiences regarding the ethics of payday loan collection. Specifically, the study compares customers’ experiences in both the Indonesian and US markets.Design/methodology/approachIndonesia and the USA were chosen because they have very different regulatory structures for the payday loan industry. The data was scraped using Python from 27 payday loan apps on the Indonesian Play Store, resulting in a total of 244,697 reviews extracted from the Indonesian market. For the US market, 446,010 reviews were extracted from 14 payday loan apps. The data was further analyzed using NVIVO.FindingsThe results suggest that consumers of payday loans in Indonesia and the USA hold positive views about the benefits of payday loan apps, as revealed by the word frequency and word cloud analysis. Notably, customers in both countries did not express any negative sentiments regarding the unethical interest rate charged by the payday loan, contradicting what is commonly reported in academic literature. However, a distinct pattern of unethical conduct was observed in both countries concerning marketing communication and debt collection practices. In the Indonesian market, payday loan companies were found to engage in unethical debt collection activities. In the US market, payday lenders exhibited unethical behavior in their marketing communication, particularly through deceptive advertising that makes promises to consumers that are not delivered.Originality/valueThe study aims to provide evidence on the various experiences of customers in the presence and absence of debt collection regulations using a novel methodology and a large sample, which strengthens the results and conclusions of the study. The study also intends to inform policymakers, particularly the Indonesian government, about the need for specific laws to regulate the debt collection process and prevent unethical practices. Ultimately, the study is expected to protect the rights of consumers from a deceptive marketing communication or unethical debt collection practices in both the Indonesian and US markets.