COVID-19 lateral flow test image classification using deep CNN and StyleGAN2

Introduction

Artificial intelligence (AI) in healthcare can enhance clinical workflows and diagnoses, particularly in large-scale operations like COVID-19 mass testing. This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification.

Methods

To address the absence of a RATD image dataset, we crowdsourced 900 real-world images focusing on positive and negative cases. Rigorous data augmentation and StyleGAN2-ADA generated simulated images to overcome dataset limitations and class imbalances.

Results

The best CNN model achieved a 93% validation accuracy. Test accuracies were 88% for simulated datasets and 82% for real datasets. Augmenting simulated images during training did not significantly improve real-world test image performance but enhanced simulated test image performance.

Discussion

The findings of this study highlight the potential of the developed model in expediting COVID-19 testing processes and facilitating large-scale testing and tracking systems. The study also underscores the challenges in designing and developing such models, emphasizing the importance of addressing dataset limitations and class imbalances.

Conclusion

This research contributes to the deployment of large-scale testing and tracking systems, offering insights into the potential applications of AI in mitigating outbreaks similar to COVID-19. Future work could focus on refining the model and exploring its adaptability to other healthcare scenarios.

Speech Emotion Recognition Using Attention Model

Speech emotion recognition is an important research topic that can help to maintain and improve public health and contribute towards the ongoing progress of healthcare technology. There have been several advancements in the field of speech emotion recognition systems including the use of deep learning models and new acoustic and temporal features. This paper proposes a self-attention-based deep learning model that was created by combining a two-dimensional Convolutional Neural Network (CNN) and a long short-term memory (LSTM) network. This research builds on the existing literature to identify the best-performing features for this task with extensive experiments on different combinations of spectral and rhythmic information. Mel Frequency Cepstral Coefficients (MFCCs) emerged as the best performing features for this task. The experiments were performed on a customised dataset that was developed as a combination of RAVDESS, SAVEE, and TESS datasets. Eight states of emotions (happy, sad, angry, surprise, disgust, calm, fearful, and neutral) were detected. The proposed attention-based deep learning model achieved an average test accuracy rate of 90%, which is a substantial improvement over established models. Hence, this emotion detection model has the potential to improve automated mental health monitoring.

Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas

Monitoring, predicting, and controlling the air quality in urban areas is one of the effective solutions for tackling the climate change problem. Leveraging the availability of big data in different domains like pollutant concentration, urban traffic, aerial imagery of terrains and vegetation, and weather conditions can aid in understanding the interactions between these factors and building a reliable air quality prediction model. This research proposes a novel cost-effective and efficient air quality modeling framework including all these factors employing state-of-the-art artificial intelligence techniques. The framework also includes a novel deep learning-based vegetation detection system using aerial images. The pilot study conducted in the UK city of Cambridge using the proposed framework investigates various predictive models ranging from statistical to machine learning and deep recurrent neural network models. This framework opens up possibilities of broadening air quality modeling and prediction to other domains like vegetation or green space planning or green traffic routing for sustainable urban cities. The research is mainly focused on extracting strong pieces of evidence which could be useful in proposing better policies around climate change.

Blockchain for decentralization of internet: prospects, trends, and challenges

Blockchain has made an impact on today's technology by revolutionizing the financial industry through utilization of cryptocurrencies using decentralized control. This has been followed by extending Blockchain to span several other industries and applications for its capabilities in verification. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols to services and applications. This paper investigates Blockchain's capacities to provide a robust and secure decentralized model for Internet. The paper conducts a critical review on recent Blockchain-based methods capable for the decentralization of the future Internet. We identify and investigate two research aspects of Blockchain that provides high impact in realizing the decentralized Internet with respect to current Internet and Blockchain challenges while keeping various design in considerations. The first aspect is the consensus algorithms that are vital components for decentralization of the Blockchain. We identify three key consensus algorithms including PoP, Paxos, and PoAH that are more adequate for reaching consensus for such tremendous scale Blockchain-enabled architecture for Internet. The second aspect that we investigated is the compliance of Blockchain with various emerging Internet technologies and the impact of Blockchain on those technologies. Such emerging Internet technologies in combinations with Blockchain would help to overcome Blockchain's established flaws in a way to be more optimized, efficient and applicable for Internet decentralization.

Mining and Analysis of Air Quality Data to Aid Climate Change

The data science and AI community has gathered around the world to support tackling the climate change problem in different domains. This research aims to work on the air quality through emissions and pollutant concentration data along with vegetation information. Authorities especially in urban cities like London have been very vigilant in monitoring these different aspects of air quality and reliable sources of big data are available in this domain. This study aims to mine and collate this information spread all over the place in different formats into usable knowledge base on which further data analysis and powerful Machine Learning approaches can be built to extract strong evidences useful in building better policies around climate change.