BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer.

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO2) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders and contribute to cancer development. The present study introduces a novel approach for monitoring and predicting Delhi's nitrogen dioxide concentrations by leveraging satellite data and ground data from the Sentinel 5P satellite and monitoring stations. The research gathers satellite and monitoring data over 3 years for evaluation. Exploratory data analysis (EDA) methods are employed to comprehensively understand the data and discern any discernible patterns and trends in nitrogen dioxide levels. The data subsequently undergoes pre-processing and scaling utilizing appropriate techniques, such as MinMaxScaler, to optimize the model's performance. The proposed forecasting model uses a hybrid architecture of the Transformer and BiLSTM models called BREATH-Net. BiLSTM models exhibit a strong aptitude for effectively managing sequential data by adeptly capturing dependencies in both the forward and backward directions. Conversely, transformers excel in capturing extensive relationships over extended distances in temporal data. The results of this study will illustrate the proposed model's efficacy in predicting the levels of NO2 in Delhi. If effectively executed, this model can significantly enhance strategies for controlling urban air quality. The findings of this research show a significant improvement of RMSE = 9.06 compared to other state-of-the-art models. This study's primary objective is to contribute to mitigating respiratory health issues resulting from air pollution through satellite data and deep learning methodologies.

A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST.

Air pollution is one of the main environmental issues in densely populated urban areas like Delhi. Predictions of the PM2.5 concentration must be accurate for pollution reduction strategies and policy actions to succeed. This research article presents a novel approach for forecasting PM2.5 pollution in Delhi by combining a pre-trained CNN model with a transformer-based model called CATALYST (Convolutional and Transformer model for Air Quality Forecasting). This proposed strategy uses a mixture of the two models. To derive attributes of the PM2.5 timeline of data, a pre-existing CNN model is utilized to transform the data into visual representations, which are analyzed subsequently. The CATALYST model is trained to predict future PM2.5 pollution levels using a sliding window training approach on extracted features. The model is utilized for analyzing temporal dependencies in PM2.5 time-series data. This model incorporates the advancements in the transformer-based architecture initially designed for natural language processing applications. CATALYST combines positional encoding with the Transformer architecture to capture intricate patterns and variations resulting from diverse meteorological, geographical, and anthropogenic factors. In addition, an innovative approach is suggested for building input-output couples, intending to address the problem of missing or partial data in environmental time-series datasets while ensuring that all training data blocks are comprehensive. On a PM2.5 dataset, we analyze the proposed CATALYST model and compare its performance with other standard time-series forecasting approaches, such as ARIMA and LSTM. The outcomes of the experiments demonstrate that the suggested model works better than conventional methods and is a potential strategy for accurately forecasting PM2.5 pollution. The applicability of CATALYST to real-world scenarios can be tested by running more experiments on real-world datasets. This can help develop efficient pollution mitigation measures, impacting public health and environmental sustainability.

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope.

Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

Automated human mind reading using EEG signals for seizure detection.

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is performed before surgery and also to predict seizure at the time operation which is useful in neuro stimulation device. But in most of cases visual examination is done by neurologist in order to detect and classify patterns of the disease but this requires a lot of pre-domain knowledge and experience. This all in turns put a pressure on neurosurgeons and leads to time wastage and also reduce their accuracy and efficiency. There is a need of some automated systems in arena of information technology like use of neural networks in deep learning which can assist neurologists. In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems. The developed system will significantly help neurologists in their performance.