New Advancements in Cybersecurity: A Comprehensive Survey

Hassan, Muhammad Abul; Ali, Sahibzada Muhammad; Imad, Muhammad; Bibi, Shaista

doi:10.1007/978-3-031-05752-6_1

Cited by 14 publications

(9 citation statements)

References 58 publications

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“…These developments require the formation of new models of travel behaviour, new models of product design [58] [59], and new models of research and evaluation which, in turn, establish a new paradigm of tourism management [60].…”

Section: Discussionmentioning

confidence: 99%

Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

Serra

Seabra

Caldeira

2022

EAI Endorsed Trans Smart Cities

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The smart tourism ecosystem concept, in addition to integrating various components, processes, and actions in the design of a place, advocates certain results through the convergence of technological resources, business environments, and value-inducing experiences. This conceptual paper should result in theoretical contributions regarding the specificity of the tourist experience within the framework of a smart tourism ecosystem, with a view to the competitiveness and sustainability of accommodation and destinations. From the perspective of a smart tourism ecosystem, the production and consumption of tourist value − which, hopefully, should be socially, culturally, environmentally, and economically sustainable −, is shared, and generate distinctive experiences, and the corresponding interactions are promoted by technology, through the collection, processing, and communication of data. The suggested approach has relevant implications at the management level, given the need to obtain differentiating factors, mediated by technology, with the incorporation of added value for the stakeholders.

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Section: Discussionmentioning

confidence: 99%

Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

Serra

Seabra

Caldeira

2022

EAI Endorsed Trans Smart Cities

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“…These classifiers are usually referred to as supervised classifiers for sentiment analysis and are a component of machine learning. These methods need supervised training data to make decisions [14]. Most mutual techniques that had been used in the past are Support Vector Machine (SVM) [15][16][17], Maximum Entropy [18], naive Bayes (NB) [19][20][21].…”

Section: Traditional Sentiment Analysis Methodsmentioning

confidence: 99%

Domain adaptive learning for multi realm sentiment classification on big data

Ijaz,

Anwar,

Safran

et al. 2024

PLoS ONE

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Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

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“…Artificial neural networks, fuzzy logic, evaluation computation, and hybrid methods are other subcategories of computational intelligence. Each branch of computational intelligence was examined in [ 44 ] from the cybersecurity perspective, along with their benefits and drawbacks.…”

Section: Related Workmentioning

confidence: 99%

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

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Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

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New Advancements in Cybersecurity: A Comprehensive Survey

Cited by 14 publications

References 58 publications

Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

Domain adaptive learning for multi realm sentiment classification on big data

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

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