Impact of Deep Learning Models for Technology Sustainability in Tourism Using Big Data Analytics

Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks. Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colony-based Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.

Big data analytics and sustainable tourism: A comprehensive review and network based analysis for potential future research

In recent years, ride-hailing platforms such as Uber have significantly transformed the transportation industry by offering convenient, flexible, and technology-driven services. These platforms have made travel more accessible by enabling users to book rides instantly through mobile applications. However, customer satisfaction is not always consistent, as users frequently report issues related to pricing, driver behavior, service reliability, and application performance. Understanding these concerns is essential for improving service quality and enhancing user experience.This study focuses on analyzing customer reviews collected from Uber using sentiment analysis techniques. Since customer feedback is typically unstructured and large in volume,manual analysis is inefficient. Therefore, Natural Language Processing (NLP) techniques are used to process and interpret the textual data. The research applies both machine learning and deep learning models, including Logistic Regression, Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM), to classify reviews into positive, negative, and neutral sentiments. The dataset used in this study consists of approximately 12,000 customer reviews, which provide a diverse representation of user experiences. Data preprocessing techniques such as cleaning, tokenization, stopword removal, and stemming are applied to improve data quality. The experimental results demonstrate that deep learning models, particularly LSTM,outperform traditional machine learning approaches in terms of accuracy and contextual understanding. The proposed model achieved an overall accuracy of 90%, indicating its effectiveness in correctly classifying customer sentiments. The analysis also reveals key factors influencing customer satisfaction, including frequent ride cancellations, surge pricing, driver-related issues, and technical problems within the application. Overall, this research highlights the importance of sentiment analysis as a powerful tool for understanding customer opinions in ride-hailing platforms. The insights obtained from this study can help companies make data-driven decisions, improve service quality, and enhance customer satisfaction.

8 - Deep learning techniques for optimizing medical big data

Using big marine data to train deep learning models is not efficient, or sometimes even possible, on local computers. In this paper, we show how distributed learning in the cloud can help more efficiently process big data and train more accurate deep learning models. In addition, marine big data is usually communicated over wired networks, which if possible to deploy in the first place, are costly to maintain. Therefore, wireless communications dominantly conducted by acoustic waves in underwater sensor networks, may be considered. However, wireless communication is not feasible for big marine data due to the narrow frequency bandwidth of acoustic waves and the ambient noise. To address this problem, we propose an optimized deep learning design for low-energy and real-time image processing at the underwater edge. This leads to trading the need to transmit the large image data, for transmitting only the low-volume results that can be sent over wireless sensor networks. To demonstrate the benefits of our approaches in a real-world application, we perform fish segmentation in underwater videos and draw comparisons against conventional techniques. We show that, when underwater captured images are processed at the collection edge, 4 times speedup can be achieved compared to using a landside server. Furthermore, we demonstrate that deploying a compressed DNN at the edge can save 60% of power compared to a full DNN model. These results promise improved applications of affordable deep learning in underwater exploration, monitoring, navigation, tracking, disaster prevention, and scientific data collection projects.

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.

Big data streams are now widespread because real-time applications that generate enormous amounts of data quickly have proliferated during the past 10 years. Big data is a treasure trove of vast quantities of data. Big data is commonly used in conjunction with other current buzzwords like data science, artificial intelligence, deep learning, and machine learning. Big data has a very promising future because of the demand for data across many technical industries. A massive amount of data has been produced as a result of the COVID-19 global epidemic. Big data analytics is helpful for evaluating, maintaining, and archiving information about COVID-19 patients for later use during the pandemic phase. Big Data Analytics (BDA), a growingly important technology, can significantly enhance sustainable tourism in terms of destination selection, traveller satisfaction, and the quality of travel experiences. Streaming Big Data is the term used to describe data that needs to be saved, processed, and analysed and that is continuously generated from various data sources, including Internet of Things (IoT) devices. The article provides information on - Big data analytics techniques in social media, COVID-19 Pandemic in the New Era of Big Data Analytics, Big data analytics and sustainable tourism, Predictive big data analytics for supply chain demand forecasting, Streaming Big Data Analytics, Big data stream analysis and Big Data Analytics in Crypto Currency. Keywords: Big Data, Streaming Big Data, Predictive Big Data, Sustainable Tourism

This article emphasizes the importance of artificial intelligence in the tourism sector. Various technologies are being integrated to enhance customer services. The world is making significant efforts to strengthen industrial competitiveness using big data and artificial intelligence technologies, and tourism is facing increasing demands for new strategic solutions through the digital transformation of the existing tourism industry using cutting-edge technologies. Artificial intelligence quickly addresses customer requirements by providing timely information on critical factors such as hotels, airplanes, cathedrals, general facilities, and natural resources. This information can include interactive messages, virtual tours, interactive booking processes, language translations, and global positioning system technology. Artificial intelligence algorithms in the tourism industry help predict demand, revenue, and business trends. The tourism industry incorporates new technologies like virtual reality, chatbots, and language translation. Adopting artificial intelligence apps requires significant investments, including initial capital costs, maintenance, software updates, and staff training. Understanding the tourism and artificial intelligence industry will help build an artificial intelligence that will significantly transform the tourism industry in the future. The rise of artificial intelligence simplifies the process of making travel plans. Tourists used to decide their destinations and activities using pictures in a catalog or on the Internet. They have recently begun using artificial intelligence to find and tailor their specific requirements. Artificial intelligence is a valuable supplementary aspect to the future of the tourism industry. However, it cannot surpass the human touch, an essential determinant of experiential tourism.

This article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, or the integration of complementary components to enhance and improve innovative technology in tackling this significant social issue. A sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results reveal that deep learning techniques are usually more effective. A reasonably high level of accuracy is achieved by KNN and SVM among classical algorithms, whereas Naïve Bayes performs the poorest. While deep learning approaches provide better results, tree-based models such as Random Forest and Decision Trees offer more consistent accuracy. Models based on neural networks, such as LSTM, CNN, and BI-LSTM, perform well, with LSTM-based methods excelling in particular. The most successful strategy for classification problems is the model presented, which obtains the greatest accuracy, precision, recall, F1-score of 95%. The research aids in the development of advanced tools and methodologies to mitigate hate speech on social media and foster positive online interactions. Future research may investigate alternative deep learning architectures, such as transformers, to enhance hate speech detection efficacy. The advancement of interpretable AI methodologies for identifying hate speech and delivering transparent forecasts might enhance user confidence and facilitate better content moderation decisions.

In this paper, we introduce a distributed deep learning platform, BAIPAS, Big Data and AI based Predication and Analysis System. In the case of deep learning using big data, it takes much time to train with data. To reduce training time, there is a method that uses distributed deep learning. When big data exists in external storage, training takes a long time because it takes a lot of network I/O time when data is loaded during deep learning operations. We propose data locality management as a way to reduce training time with big data. BAIPAS is a distributed deep learning platform that aims to provide quick learning from big data, easy installation and monitoring of the platform, and convenience for developers of deep learning models. In order to provide fast training using big data, data is distributed and stored in worker-server storage using a data locality and shuffling, and then training is performed. The data locality manager analyzes the training data and the state information of the worker servers. This distributes the data scheduling according to the available storage space of the worker server and the learning performance of the worker server. However, if each worker server conducts deep learning using the distributed training data, model overfitting may occur as compared with the method of learning with the full training data set. To solve this problem, we applied a shuffling method that moves already learned data to another worker server when training is performed. Thereby, each worker server can contain the full training data set. BAIPAS uses Kubernetes and Docker to provide easy installation and monitoring of the platform. It also provides pre-processing modules, management tools, automation of cluster creation, resource monitoring, and other resources; so developers can easily develop deep learning models.

This paper identifies a number of new technologies and applications in Information Communication Technologies that gradually revolutionize the tourism industry. Tourism industry is the most competitive business in the world and one of the most complicated industries. The tourism industry primarily consists of businesses that provide accommodation, transformation, flight trips, attractions, food and beverage, and integrated combination of these activities. This research is focused on using the new technologies to develop sustainable tourism in Egypt by increasing the tourist awareness of sustainability and sustainable tourism. This aim is achieved by using new technologies such as Big Data Analytic Technology and knowledge-based information system in order to forecast tourist's needs and demands in tourism industry. Any information concerning the future evolution of tourism flows is of great importance to hoteliers, tour operators and other industries concerned with tourism. Accurate forecasting of tourist services needs will be integrated for the industry's operations and allocation of resources such as manpower, financial management, marketing management… etc. to achieve effectiveness of advance planning and cost reduction. Showing that the Internet and sharp technological development have encouraged the increasing demand for tourism and using new technologies in forecasting the tourist demands and needs will naturally promote or potentiate an increase of tourism demand. Keywords: Information and Communication Technologies, ANNs, big data analytics, Tourist's Demands and needs.

Research in the field of sustainable tourism is increasingly important due to significant growth in tourism industries and the unsustainable impacts incurred. Innovation in sustainable tourism studies is required to meet a number of challenges including socio-ecological impacts; the critical turn in tourism research; and the growth of ICTs, mobile technologies and big data analytics. These shifts in particular are transforming the field and creating new research opportunities. This article seeks to identify potentially new methodological areas of application to sustainable tourism studies for both quantitative and qualitative methods. A range of methods are reviewed, focusing on big data (e.g. mobile device signaling, GPS, social media and search engine data) that elucidates wider patterns of tourist movement, as applied to forecasting travel demands and sustainable management of a destination. Three novel “small data” methods are also discussed, comprising visual methods, autoethnography and qualitative GIS, that provide deeper, contextual insights into the drivers, dynamics and impacts of sustainable tourism. We consider how expansive qualitative methodologies might yield potentially important insights concealed by existing methodologies. Furthermore, we argue that combined big data and small data approaches can address methodological imbalance and generate mutually reinforcing insights at a number of levels.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

In an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.