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Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

The present work deals with the microwave-assisted compression moulding of high-density polyethylene (HDPE)-based composites. In the present work, 20 wt% of reinforcement in the form of kenaf and multi-walled carbon nanotube (MWCNT) was used to fabricate HDPE/kenaf and HDPE/MWCNT polymer composites. The mechanical characterizations of the microwave-processed composites were carried out in terms of uniaxial tensile test with different strain rate, multistep stress relaxation, flexural and impact test. The uniaxial tensile test revealed that the tensile modulus of microwave-processed four-layered HDPE/kenaf polymer composite was 35.2% higher than that of HDPE/MWCNT polymer composite. The HDPE/MWCNT polymer composite showed a minimum of 1.25 GPa and a maximum of 4.7 GPa of elastic modulus when tested at different strain rate. The impact energy absorbed by the HDPE/kenaf polymer composite (1.055 J) was 81.12% higher than the HDPE/MWCNT polymer composite (0.582 J).