An IoT-based water contamination analysis for aquaculture using lightweight multi-headed GRU model.

Maintaining the quality of water is essential for the health and productivity of aquatic organisms, including fish in aquaculture ponds. However, water contamination can severely impact fish health and survival, making it necessary to develop monitoring systems that can detect early signs of water contamination. Initial deep learning models had limitations in capturing the temporal and spatial dependencies of time-series data, which can lead to inaccurate predictions. In this paper, we propose a smart monitoring system that uses IoT devices to collect water quality data and segment it into contaminated and non-contaminated categories based on a water toxic index (WTI), a measure of water contamination levels. To address the limitations of early deep learning models for classification of toxic and non-toxic water quality, an enhanced light-weight multi-headed gated recurrent unit (MHGRU) model that captures the spatial and temporal dependencies of water quality parameters. Our study demonstrates that the proposed model outperforms existing models, achieving an impressive accuracy of 99.7% when evaluated on real-time data. Notably, our model also excels when tested on a public dataset, achieving an accuracy of 99.12%. In comparison, best performed existing ANN models achieve accuracies of 99.52% and 98.71% on the respective datasets.

Deep reinforcement learning for forecasting fish survival in open aquaculture ecosystem.

Ensuring the classification of water bodies suitable for fish habitat is essential for animal preservation and commercial fish farming. However, existing supervised machine learning models for predicting water quality lack specificity regarding fish survival. This study addresses this limitation and presents a novel model for forecasting fish viability in open aquaculture ecosystems. The proposed model combines reinforcement learning through Q-learning and deep feed-forward neural networks, enabling it to capture intricate patterns and relationships in complex aquatic environments. Moreover, the model's reinforcement learning capability reduces the reliance on labeled data and offers potential for continuous improvement over time. By accurately classifying water bodies based on fish suitability, the proposed model provides valuable insights for sustainable aquaculture management and environmental conservation. Experimental results show a significantly improved accuracy of 96% for the proposed DQN-based model, outperforming existing Gaussian Naive Bayes (78%), Random Forest (86%), and K-Nearest Neighbors (92%) classifiers on the same dataset. These findings highlight the effectiveness of the proposed approach in forecasting fish viability and its potential to address the limitations of existing models.

Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks.

Online social media networks have become a significant platform for persons with mental illnesses to discuss their struggles and obtain emotional and informational assistance in recent years. One such platform is Reddit, where sub-groups called 'subreddits' exist, based on a variety of topics including mental illnesses such as anxiety or depression. We analyse the user's interactions to calculate the mental health status by formulating and using a parameter called 'emotional tone' representing the user's emotional state. VADER sentiment analysis and TextBlob are used to categorise emotional tone and find distribution of emotional polarity and subjectivity of comments. For final tone prediction, RNN and State-Of-The-Art word embedding techniques are used to develop a predictive model. The resultant model provides end-to-end categorization and prediction of emotional tone. We obtain results with respect to Weighted L1 Loss that deals with extreme responses. The MODEL transcends all the baselines by at least 12.1% and the final emotional status of the authors is positive.

Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan-China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models.