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Air quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution control measures to diminish the effect of air contamination. Air Quality Index (AQI) helps in understanding the level of pollution. This study briefs various state of art methods such as SVM, RF,ANN, RNN and FL in predicting air quality using machine learning. The results shows prominent outcomes which can be deployed in various cost effective hardware platforms for household and commercial purposes. The proposed model used eight parameters like NO2CO, O3, PM2.5, PM10, SO2, TEMP, PRES, DEWP, RAIN,WD, WSPM of Beijing dataset in order to predict AQI and it is tested with regional dataset. The Proposed model works based on process monitoring model. The efficient and flexible way to solve environmental issues is to combine machine learning algorithm with air quality prediction.

This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI. [ABSTRACT FROM AUTHOR]

This article uses cutting-edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time-consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision-based techniques for automatic damage detection have been developed. This paper's civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning-based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%. [ABSTRACT FROM AUTHOR]