Twitter Accounts Suggestion is concerned with recommending accounts for the users according to their tweet contents. Twitter contains a massive amount of data that can be useful for knowing each user’s preferences. This paper uses Named Entity Recognition (NER), one of the techniques used in Natural Language Processing (NLP). We propose a pipeline technique to analyze the textual content of tweets to recommend the proper user accounts (people or organizations) to facilitate mentioning other user accounts in the tweets.

The difference between minors and adults is an important legal distinction codified within multiple legal frameworks. Moreover, the distinction is important information to extract from unstructured text when applying natural language processing tasks such as anonymization, and abuse detection. Despite the value, identifying minors remains a manually performed task of which little research has been dedicated and no benchmarks are established. This paper seeks to address this need with two goals. First, the creation of labelled and publicly available dataset. Second, to demonstrate proof-of-concept with a BERT named entity recognition model, fine-tuned to make the subject distinction based on surrounding context. Toward this end, we created a custom dataset consisting of 2,534 sequences with 8,770 labelled instances of minors. This data was used to fine-tune a BERT named entity recognition model which demonstrated context aware tagging of minors versus adults. The model achieved an 89% f1 score in detecting minors and a 61% f1 score in detecting adults. While the performance of this model may not be suited for a production environment, we have established a starting point for future research in specific privacy protections for minors within a data processing stack.

In metropolitan cities, outdoor air pollution and ambient outdoor noise transmission are significant environmental hazards, degrading indoor environmental quality. Natural ventilation is often a viable option for diluting indoor air pollutants, but transportation noise transmission is a crucial ecological conflict. Therefore, personal control over the concentration of outdoor/indoor air pollutants and noise levels is a significant threshold for natural ventilation availability. This study proposes real-time data detection and notification solutions using sensors and artificial intelligence (AI) to improve indoor air quality and outdoor air quality and outdoor noise transmission. An intelligent real-time detection and notification system was implemented in a distributed computing framework with cloud and edge computing. The objective of this study is placed on three facets: (1) development of sensors and NVIDIA Jetson Nano prototype for air quality and noise level detection, (2) application of machine learning for air quality and noise level prediction and classification, and (3) web interface for real-time monitoring and prediction for air quality and noise level detection. The results showed that the proposed user interface provides building occupants with real-time data of outdoor/indoor Air Quality Index (AQI) and noise levels for the optimized occupant control over Indoor Air Quality (IAQ). The personal control over indoor environmental quality (IEQ) enables occupants to promote natural ventilation behaviors and integrate with the existing building system on optimized IEQ by interacting with AI-based real-time data.

Heart diseases are considered the leading cause of death globally. Early diagnosis of disease may help give appropriate prescribing medicines, which may help control and reduce conditions. The current clinical diagnosis methods such as Electrocardiograms, computed tomography, echocardiogram, Magnetic Resonance Imaging, etc. provide valuable information for diagnosis and treatment. However, these techniques are time-intensive, operator-dependent, and expensive. In this paper, we propose a low-cost solution real-time solution to diagnose heart diseases. We suggest an integrated automatic multimodal heart disease classification (AMHDC) system using the Phonocardiogram (PCG) signal. For this purpose, first, we have developed an advanced fusion method using pre-processing techniques such as Data Normalization and Data Augmentation. Secondly, we have extracted the spectrograms from heart sound and used them as features and images for signal and image processing. Finally, we created a real-time integrated Convolutional Neural Network (CNN) model for high-performance heart disease classification. The results show our model outperformed the state-of-art research, whose accuracy is 89.7%., while our model reported accuracy of 93% for audio and 96% for image-based approach.

In this paper, we propose a new Internet of things (IoT) solution, called the Urban Noise Monitoring (UNM) system, which can classify real-time environmental audio sound using an embedded system such as Raspberry pi 4 and log the data in the Google Cloud. The reported events will be available for future usage, i.e., selection of the safe area for living. The real-time audio classification has been a big challenge for deep learning in environmental sounds due to the high noisy nature of sound. We have implemented a real-time IoT system for urban sound classification and monitored the historical reports generated. We have developed an advanced fusion method using normalization techniques such as peak, RMS, and EBU and an efficient data augmentation method using various factors, including time stretch, pitch shifting, and dynamic range compression. Further, we have integrated the normalization and the augmentation methods into 2D Convolutional Neural Network (CNN) with the TensorFlow framework on Raspberry pi 4 for urban sound classification. Our classification model outperformed the state of the art performance: 95% accuracy with the Urban sound dataset. The outstanding performance confirmed the effectiveness of the proposed method on the IoT system for urban noise monitoring.

Human emotions are essential to recognize the behavior and state of mind of a person. Emotion detection through speech signals has started to receive more attention lately. This paper proposes the method for detecting human emotions using speech signals and its implementation in real-time using the Internet of Things (IoT) based deep learning for the care of older adults in nursing homes. The research has two main contributions. First, we have implemented a real-time system based on audio IoT, where we have recorded human voice and predicted emotions via deep learning. Secondly, for advance classification, we have designed a model using data normalization and data augmentation techniques. Finally, we have created an integrated deep learning model, called Speech Emotion Detection (SED), using a 2D convolutional neural networks (CNN). The best accuracy that was reported by our method was approximately 95%, which outperformed all state-of-the-art approaches. We have further extended to apply the SED model to a live audio sentiment analysis system with IoT technologies for the care of older adults in nursing homes.

The aim of the paper is to perform audio analytics based on the audio sensor data that is continuously monitoring the home environment automatically through an audio Internet of Things (IoT) system. Domestic violence is one of the major problems in many cities nowadays. We have proposed a home automation system where IoT sensors records the audio in home environment continuously and the audio is sent to machine learning server where the audio is split into small clips and classified into different categories. The need of an automatic detection system is urgent for enforcing home safety and safe neighborhood. If IoT system detects any suspicious sound, it generates an emergency notification to nearest emergency services for possible action to be taken. The classification of audio such as gunshots, explosion, glass breaking, screaming and siren is based on shallow learning (Support vector machine, Decision tree, Random forest and Naïve Bayes) and deep learning (Convolutional neural network and Long short-term memory). Our experiments validated that Convolutional Neural Network shows the best performance (89% accuracy) compared to other machine learning algorithms.