Pedestrian Counting Based on Piezoelectric Vibration Sensor

Yu, Yang; Qin, Xiangju; Hussain, Shabir; Hou, Weiyan; Weis, Torben

doi:10.3390/app12041920

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…The three categories into which sentiment analysis can be divided are the machine learning technique, the lexicon-based approach, and the hybrid strategy that combines the previous two approaches [10]. Nowadays, computational technologies are being used in various domains of life, including healthcare [14], security [15] [21] [25] and also in safety purposes [16], disaster [17], and situational awareness [19] [26] [27] in the educational domain [18] as well. Sentiment analysis is a prominent research topic in demand under the category of NLP [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

Sahar

Ayoub

Hussain

et al. 2022

PakJET

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The exponential growth in online reviews and recommendations availability drives sentiment classification, an interesting topic in industrial research. There is a vital requirement for organizations to explore client behaviour to assess the competitive business environment. This study aspires to examine and predict customer reviews using Transfer learning (TL) approaches. Reviews can span so many domains that it is challenging to gather annotated training data for all of them. Hence, this paper proposed an annotation algorithm to label a large unlabeled dataset. These reviews must be pulled and examined to predict the sentiment polarity, whether the review is positive, neutral, or negative. We propose a deep learning-based approach that learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation outperform state-of-the-art methods on a benchmark of reviews of cosmetics brands on Amazon or other platforms. Using the BERT for sentiment analysis, we achieved the highest accuracy of 93.21% compared to previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

Sahar

Ayoub

Hussain

et al. 2022

PakJET

View full text Add to dashboard Cite

show abstract

“…Vibration sensors can be tasked in indoor environments with detecting the presence and movement features of walkers [132], e.g., for indoor occupancy sensing and measurement [133]. The technique is also deployed in health care settings, where it can additionally be put to use in monitoring falls (using seismic sensing) [134].…”

Section: Sensorsmentioning

confidence: 99%

“…These insights are available chiefly from accelerometer (which measures acceleration as the derivative of velocity) and gyroscope (which measures angular velocity as the derivative of orientation) data. Vibration sensors can be tasked in indoor environments with detecting the presence and movement features of walkers [132], e.g., for indoor occupancy sensing and measurement [133]. The technique is also deployed in health care settings, where it can additionally be put to use in monitoring falls (using seismic sensing) [134].…”

Section: Sensorsmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

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Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.

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“…The methodology uses machine learning algorithms like Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), and k-nearest neighbor (k-NN). Nowadays, computational technologies are being used in various domains of life, including healthcare [25], security and also in safety purposes, disaster and situational awareness [26] in the educational domain [27] as well.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Social Communication Applications using Aspect Based Sentiment Analysis

Irfan

Hussain

Ayoub

et al. 2022

PakJET

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Google Play Store is a popular distribution channel with millions of applications. WhatsApp is the most downloaded communication application on Play Store. A few months ago, WhatsApp changed its privacy policy, triggering a wave of user reviews outrage. Privacy is essential in the application; users are worried about their data security and privacy. A computational system must be required to analyze the user’s reviews for WhatsApp authority to make better policies. This study aims to develop a deep learning-based model for automatically assessing reviews that can be adapted for future data analysis. We proposed a deep learning methodology by using Aspect-based sentiment analysis (ABSA) utilizing the communication app reviews scraped from the Google play store using the Google Play scrapper application. This study uses the text mining technique for ABSA on the user’s reviews. For Topic extraction, we have used Latent Dirichlet Allocation (LDA) and the deep learning method Long Short-Term Memory (LSTM) for topic classification. The results show that our proposed model gives us a promising outcome with 90% accuracy by using the LSTM model. WhatsApp authority can use the results to optimize communication applications by adding more efficient features and updating them.

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Pedestrian Counting Based on Piezoelectric Vibration Sensor

Cited by 15 publications

References 41 publications

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

Transfer Learning-Based Framework for Sentiment Classification of Cosmetics Products Reviews

Smart and Sentient Retail High Streets

A Comparative Analysis of Social Communication Applications using Aspect Based Sentiment Analysis

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