77 research outputs found

    Non invasive skin hydration level detection using machine learning

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    Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions

    Robust Real-time Audio-Visual Speech Enhancement based on DNN and GAN

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    The human auditory cortex contextually integrates audio-visual (AV) cues to better understand speech in a cocktail party situation. Recent studies have shown that AV speech enhancement (SE) models can significantly improve speech quality and intelligibility in low signal-to-noise ratios ( SNR < −5dB ) environments compared to audio-only (A-only) SE models. However, despite substantial research in the area of AV SE, development of real-time processing models that can generalise across various types of visual and acoustic noises remains a formidable technical challenge. This paper introduces a novel framework for low-latency, speaker-independent AV SE. The proposed framework is designed to generalise to visual and acoustic noises encountered in real world settings. In particular, a generative adversarial network (GAN) is proposed to address the issue of visual speech noise including poor lighting in real noisy environments. In addition, a novel real-time AV SE based on a deep neural network is proposed. The model leverages the enhanced visual speech from the GAN to deliver robust SE. The effectiveness of the proposed framework is evaluated on synthetic AV datasets using objective speech quality and intelligibility metrics. Furthermore, subjective listening tests are conducted using real noisy AV corpora. The results demonstrate that the proposed real-time AV SE framework improves the mean opinion score by 20% as compared to state-of-the-art SE approaches including recent DNN based AV SE models

    Detection of atrial fibrillation using a machine learning approach

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    The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate

    Adopting Transition Point Technique for Persian Sentiment Analysis

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    Sentiment analysis is used to analyses people’s opinions, views and emotions towards different entities such as products, organizations, companies and events. People’s opinions are important for most others during their decision-making process. For example, if someone wants to buy a product, they might want to know more about that product and the experiences of others with that product. Sentiment analysis is able to classify the reviews based on their polarity; even if reviews are expressed in a sentence or document, sentiment analysis is used to classify it into positive, negative or neutral reviews. In this paper, we proposed a framework using TF-IDF and transition point to detect polarity in Persian movie reviews. The proposed approach has been evaluated using different classifiers such as SVM, Naive Bayes, MLP and CNN. The experimental results show the transition point is more effective in comparison with traditional feature such as TF-IDF.Output Status: Forthcomin

    Towards Arabic multi-modal sentiment analysis

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    In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information sharing platforms. Apart from these, a collection of product reviews, facts, poll information, etc., is a need for every company or organization ranging from start-ups to big firms and governments. Clearly, it is very challenging to analyse such big data to improve products, services, and satisfy customer requirements. Therefore, it is necessary to automate the evaluation process using advanced sentiment analysis techniques. Most of previous works focused on uni-modal sentiment analysis mainly textual model. In this paper, a novel Arabic multimodal dataset is presented and validated using state-of-the-art support vector machine (SVM) based classification method

    Persian Sentence-level Sentiment Polarity Classification

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    Assigning positive and negative polarity into Persian sentences is difficult task, there are different approaches has been proposed in various languages such as English. However, there is not any approach available to identify the final polarity of the Persian sentences. In this paper, the novel approach has been proposed to detect polarity for Persian sentences using PerSent lexicon (Persian lexicon). For this, we have proposed two different algorithms to detect polarity in the sentence and finally SVM, MLP and Na¨ıve Bayes classifier has been used to evaluate the performance of the proposed method. The SVM received better results in comparison with Na¨ıve Bayes and MLP.Output Status: Forthcomin

    Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

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    Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can exploit customer opinions to improve their products and services. However, the Quintilian bytes of the opinions generated every day cannot be manually read and summarized. Sentiment analysis and opinion mining techniques offer a solution to automatically classify and summarize user opinions. However, current sentiment analysis research is mostly focused on English, with much fewer resources available for other languages like Persian. In our previous work, we developed PerSent, a publicly available sentiment lexicon to facilitate lexicon-based sentiment analysis of texts in the Persian language. However, PerSent-based sentiment analysis approach fails to classify the real-world sentences consisting of idiomatic expressions. Therefore, in this paper, we describe an extension of the PerSent lexicon with more than 1000 idiomatic expressions, along with their polarity, and propose an algorithm to accurately classify Persian text. Comparative experimental results reveal the usefulness of the extended lexicon for sentiment analysis as compared to PerSent lexicon-based sentiment analysis as well as Persian-to-English translation-based approaches. The extended version of the lexicon will be made publicly available

    Persian Sentence-level Sentiment Polarity Classification

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    Assigning positive and negative polarity into Persian sentences is difficult task, there are different approaches has been proposed in various languages such as English. However, there is not any approach available to identify the final polarity of the Persian sentences. In this paper, the novel approach has been proposed to detect polarity for Persian sentences using PerSent lexicon (Persian lexicon). For this, we have proposed two different algorithms to detect polarity in the sentence and finally SVM, MLP and Na¨ıve Bayes classifier has been used to evaluate the performance of the proposed method. The SVM received better results in comparison with Na¨ıve Bayes and MLP

    An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

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    Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities
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