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In order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.

The proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.

The output of these models is classified into non-drowsiness or drowsiness categories.

In this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.

This paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.

Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

Medical textile is one aspect of technical textiles and it is classified according to performance and functional properties for hygienic and healthcare products. Seaweeds have curative powers for curing most degenerative diseases. The paper aims to discuss these issues.

The present study focusses on the extraction of dyes from five seaweeds such as Ulva reticulata, Ulva lactuca, Sargassum wightii, Padina tetrastomatica and Acanthophora spicefera. The presence of bioactive compounds, antioxidant and antimicrobial properties of dye extracted from seaweeds was analysed. The dye extracted from green seaweed was applied on cotton fabric to obtain antimicrobial and other properties used to make non- implantable materials.

A maximum antioxidant inhibition percentage of 86.48+2.84 and a maximum antibacterial activity of 27 mm inhibition zone were obtained on the fabric treated with the dye extract from the Ulva lactuca seaweed. The physical properties such as tensile strength and tearing strength did not show much significant difference in untreated and treated fabric. The air permeability, water absorbency and wicking behaviour of treated fabric were reduced compared with untreated fabric. The washing and rubbing properties of treated fabric were very good after repeated washing.

This bioactive fabric has been used for non-implantable materials such as wound healing, face mask, surgical gowns and hygienic textiles in recent years.

The purpose of this study is to develop a lower bainite structure consists of a dispersion of fine carbide inside plates of bainitic ferrite from chemical composition unmodified conventional pearlitic steel under bainitic transformation and to investigate its effect on tensile properties and wear resistance.

A commercial hypereutectoid pearlitic rail steel was subjected to three different bainitic transformation treatments followed by tempering to develop a desirable microstructure with a DIL805 BÄHR dilatometer. A comprehensive microstructural study was performed by scanning electron microscopy and energy dispersive x-ray spectroscopy. Finally, the mechanical properties and wear resistance were evaluated by tensile, microhardness, and pin-on-disc tests.

The results showed that the best combination of mechanical properties and sliding wear resistance was obtained in the sample subjected to bainitic transformation at 300°C for 600 s followed by tempering at 400°C for 300 s. This sample, which contained a bainitic ferrite structure, exhibited approximately 20% higher hardness and approximately 53% less mass loss than the as-received pearlitic sample due to the mechanically induced transformation in the contact surface.

Although pearlitic steel is widely used in the construction of railways, recent studies have revealed that bainitic transformation at the same rail steels exhibited higher wear resistance and fatigue strengths than conventional pearlitic rail at the same hardness values. Such a bainitic microstructure can improve the mechanical properties and wear resistance, which is a great interest in the railway industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2019-0282/