Multi-step forecasting of dissolved oxygen in River Ganga based on CEEMDAN-AdaBoost-BiLSTM-LSTM model.

Accurate prediction of Dissolved Oxygen (DO) is an integral part of water resource management. This study proposes a novel approach combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with AdaBoost and deep learning for multi-step forecasting of DO. CEEMDAN generates Intrinsic Mode Functions (IMFs) with different frequencies, capturing non-linear and non-stationary characteristics of the data.

RDD2020: An annotated image dataset for automatic road damage detection using deep learning.

This data article provides details for the RDD2020 dataset comprising 26,336 road images from India, Japan, and the Czech Republic with more than 31,000 instances of road damage. The dataset captures four types of road damage: longitudinal cracks, transverse cracks, alligator cracks, and potholes; and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images in RDD2020 were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions.

Impact of lockdown on air quality over major cities across the globe during COVID-19 pandemic.

In present study, the variation in concentration of key air pollutants such as , , , and during the pre-lockdown and post-lockdown phase has been investigated. In addition, the monthly concentration of air pollutants in March, April and May of 2020 is also compared with that of 2019 to unfold the effect of restricted emissions under similar meteorological conditions. To evaluate the global impact of COVID-19 on the air quality, ground-based data from 162 monitoring stations from 12 cities across the globe are analysed for the first time.

Impact of lockdown measures during COVID-19 on air quality- A case study of India.

A novel infectious coronavirus disease (COVID-19) identified in late 2019 has now been labelled as a global pandemic by World Health Organization (WHO). The COVID-19 outbreak has shown some positive impacts on the natural environment. In present work, India is taken as a case study to evaluate the effect of lockdown on air quality of three Indian cities.

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits.

With the availability of smart devices and affordable data plans, social media platforms have become the primary source of information dissemination across geographically dispersed users/locations. It has shown great potential across different application domains including event detection, opinion analysis, recommendation, and prediction. However, the process of extracting useful information from the collected voluminous social media data during natural hazards is a standing problem that needs significant attention from the research community.

Global Awareness Landscape for Ailments-A Twitter Based Microscopic View Into Thought Processes of People.

In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and better quality of life.

Shunt connection: An intelligent skipping of contiguous blocks for optimizing MobileNet-V2.

Enabling deep neural networks for tight resource constraint environments like mobile phones and cameras is the current need. The existing availability in the form of optimized architectures like Squeeze Net, MobileNet etc., are devised to serve the purpose by utilizing the parameter friendly operations and architectures, such as point-wise convolution, bottleneck layer etc.

Detection of anomalous nitrogen dioxide (NO) concentration in urban air of India using proximity and clustering methods.

Owing to accurate future air quality estimates, need for detecting the anomalously high increase in concentration of pollutants cannot be adjourned. Plentiful approaches were proposed in the past to substantially determine the abnormal conditions, but most of the statistical approaches were computationally expensive and ignored the false alarm ratios. Thus, a hybrid of proximity- and clustering-based anomaly detection approaches to identify anomalies in the air quality data is suggested in this work.

A new approach: role of data mining in prediction of survival of burn patients.

The prediction of burn patient survivability is a difficult problem to investigate till present times. In present study a prediction Model for patients with burns was built, and its capability to accurately predict the survivability was assessed. We have compared different data mining techniques to asses the performance of various algorithms based on the different measures used in the analysis of information pertaining to medical domain.