Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health ...of a patient's heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes time-based features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.
In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the ...credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.
Online reviews significantly impact consumers who are purchasing or seeking services via the Internet. Businesses and review platforms need to manage these online reviews to avoid misleading ...customers through fake ones. This necessitates developing intelligent solutions to detect these fake reviews and prevent their negative impact on businesses and customers. Therefore, many fake review detection models have been proposed to help distinguish fake reviews from genuine ones. However, these techniques depend on a limited perspective of features, mainly review content, to detect fake reviews, leading to poor performance in discovering the new patterns of fake review content and the dynamic behaviour of spammers. Therefore, there is still a need to develop new solutions to detect the new patterns of fake reviews. Hence, this paper proposes an explainable multi-view deep learning model to identify fake reviews based on different feature perspectives and classifiers. The proposed model can extract essential features from different perspectives, including review content, reviewer data, and product description. Moreover, we employ an ensemble approach that combines three popular deep learning algorithms: Bi-LSTM, CNN, and DNN, to enhance the performance of the fake review detection model. The results of two real-life datasets presented demonstrated the efficiency of our proposed model, where it outperformed the state-of-the-art methods with improvements ranging from 1% to 7% in terms of the AUC metric. To provide visibility into the outcomes of our proposed model and demonstrate the trust and transparency in the obtained results, we also offer a comprehensive explanation for our model results using Shapely Additive Explanations (SHAP) method and attention techniques. The experimental results prove that our proposed model can provide reasonable explanations that help users understand why specific reviews are classified as fake.
•Comprehensive examination of various methodologies in the identification of false reviews.•A novel and practical approach that leverages transformer architecture to identify fake reviews.•A ...comprehensive assessment conducted on benchmark datasets with highly favourable outcomes.
Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake.
Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing ...studies on detecting fake news are written in English, restricting its application outside the English-speaking population. The lack of annotated corpora and technologies makes it difficult to identify false news in the scenario of low-resource languages, despite the growth in multilingual web content. Moreover, existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge up these challenges and deal with the multilingual fake news detection challenge, we develop a new semantic graph attention-based representation learning framework to extract structural and semantic representations of texts. Our experiments on TALLIP fake news datasets showed that the classification performance had been significantly enhanced, ranging from 1% to 7% in terms of accuracy metric, and our proposed framework outperformed the state-of-the-art techniques for the multilingual fake news detection task.
•Study the concept drift in fake reviews data.•We evaluate four different concept drift detectors.•Tests compared in terms of accuracy, evaluation time and a number of drifts.•Using real world ...reviews to evaluate the classifiers.•Concept drift negatively affects the prediction model performance in fake reviews detection.
Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. As such, the truthfulness of internet reviews is critical for both consumers and vendors. Fake reviews not only mislead innocent clients and influence customers' choice, leading to inaccurate descriptions and sales. This raises the need for efficient fake review detection models and tools that can address these issues. Analysing a text data stream of fake reviews in concept drift appears to reduce the effectiveness of the detection models. Despite several efforts to develop algorithms for detecting fake reviews, one crucial aspect that has not been addressed is finding a real correlation between the concept drift score and the classification of performance over-time in the real-world data stream. Consequently, we have introduced a comprehensive analysis to investigate the concept drift problem within fake review detection. There are two methods to achieve this goal: benchmarking concept drift detection method and content-based classification methods. We conducted our experiment using four real-world datasets from Yelp.com. The results demonstrated that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models.
Fake news detection is an essential task; however, the complexity of several languages makes fake news detection challenging. It requires drawing many conclusions about the numerous people involved ...to comprehend the logic behind some fake stories. Existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge these challenges and deal with multilingual fake news detection, we present a semantic approach to the identification of fake news based on relational variables like sentiment, entities, or facts that may be directly derived from the text. Our model outperformed the state-of-the-art methods by approximately 3.97% for English to English, 1.41% for English to Hindi, 5.47% for English to Indonesian, 2.18% for English to Swahili, and 2.88% for English to Vietnamese language reviews on TALLIP fake news dataset. To the best of our knowledge, our paper is the first study that uses a capsule neural network for multilingual fake news detection.
•Artificial intelligence-based blood pressure estimation research using photoplethysmography (PPG) with their outcomes and significant findings.•A concise comparison of studies related to ECG and ...PPG.•Traditional features extraction techniques from PPG signals.•Experimental analysis for comparison of traditional features extraction techniques using PPG signals.•Future prospective and critical Implications.
Over the past two decades, machine learning systems have been proliferating in the healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and disease diagnostics. In addition to this, with technological advancement, physiological sensors paired with artificial intelligence have acquired people’s attention because of their multifarious advantages. Such sensors are predominantly inexpensive, portable, easy to use and can help measure health parameters continuously and non-invasively using artificial intelligence. Technologies, such as PPG (Photoplethysmography) and ECG (Electrocardiography), are two promising techniques with immense potential that can track cardiovascular health with significant impact. In this survey paper, we aim to analyse, summarise, and compare the state-of-the-art methods for machine learning-based blood pressure estimation in a continuous, cuffless, and non-invasive manner by PPG biosignals. This survey divides the research work into two machine learning categories: shallow learning and deep learning. PPG feature extraction techniques and datasets are also presented in this paper. Additionally, a concise comparative analysis of PPG and ECG has been provided from the literature. Moreover, to compare different state-of-the-art traditional feature extraction techniques using PPG biosignals, we applied several machine learning algorithms to predict hypertension and heart rate estimation. Finally, we conclude by summarising critical implications and propose some promising future perspectives that will lead to advancements in this domain.
User reviews can play a big part in deciding a company's income in the e-commerce industry. Before making selections regarding any product or service, online users rely on reviews. As a result, the ...trustworthiness of online evaluations is vital for organisations and can directly impact their reputation and revenue. Because of this, some firms pay spammers to publish false reviews. Most recent studies to detect fake reviews utilise supervised learning. However, neural network techniques, a recent form of advanced technology, have been utilised extensively to detect fake reviews and have demonstrated their ability to do so. Thus, this paper first provides a benchmark study to analyse the performance of various machine learning algorithms with different feature extraction methods on five fake review datasets to present our results. Second, we propose three advanced language models for embedding reviews into the classifiers. Third, we conduct an exhaustive feature set evaluation study to find the best features in detecting fake reviews. Fourth, we analyse the performance of traditional machine learning, deep learning, and advanced deep learning models using different feature extraction methods on five fake review datasets. Finally, we integrate the ELECTRA model with CNN which can identify real or fake reviews. Our proposed technique utilises accuracy, precision, recall, and F1 score as assessment criteria to determine the leniency of the proposed model. For deep contextualised representation and neural classification, we integrate Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN) following the embedding layer of unique pre-trained models like ELMo, ELECTRA, and GPT2. The experimental results indicate that our proposed model outperforms state-of-the-art methods with improvements ranging from 1 to 7% in terms of the accuracy, F1 score. To the best of our knowledge, no prior work has evaluated such advanced pre-trained models' efficiency in detecting fake reviews. Further, this research comprehensively evaluates several machine-learning approaches and feature extraction strategies for fake online review detection.
Foundation and Large Language Models (FLLMs) are models that are trained using a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs are very promising drivers for ...different domains, such as Natural Language Processing (NLP) and other AI-related applications. These models emerged as a result of the AI paradigm shift, involving the use of pre-trained language models (PLMs) and extensive data to train transformer models. FLLMs have also demonstrated impressive proficiency in addressing a wide range of NLP applications, including language generation, summarization, comprehension, complex reasoning, and question answering, among others. In recent years, there has been unprecedented interest in FLLMs-related research, driven by contributions from both academic institutions and industry players. Notably, the development of ChatGPT, a highly capable AI chatbot built around FLLMs concepts, has garnered considerable interest from various segments of society. The technological advancement of large language models (LLMs) has had a significant influence on the broader artificial intelligence (AI) community, potentially transforming the processes involved in the development and use of AI systems. Our study provides a comprehensive survey of existing resources related to the development of FLLMs and addresses current concerns, challenges and social impacts. Moreover, we emphasize on the current research gaps and potential future directions in this emerging and promising field.
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