ABSTRACT. Masked face recognition is now an essential part of health safety, security and surveillance systems which offers incredible advantages in our daily lives, especially in the era of the pandemic ushered in by the outbreak of coronavirus disease in the year 2019 (COVID-19). Applications such as; face mask compliance checks, facial security checks, facial attendance records and facial authentication for access control now requires an effective masked face recognition system. The existing systems of masked face recognition were developed to automatically detect and understand faces occluded with masks using computer vision and deep learning techniques, the systems are yet to work effectively in real-time. This study gives an analysis of some techniques used for the implementation of masked face recognition system, with emphasis on Convolutional Neural Network (CNN). The strengths and enhancement areas of the highlighted techniques towards real-time implementation were discussed.

ABSTRACT. Cervical cancer is one of the most prevalent diseases in women ranking fourth in worldwide, mostly occurring in less-developed countries. This is perceived when certain vagaries occur in a woman's cervix. These cancer cells can also spread to other vital organs like lungs, liver and bladder which complicates the problem. Previous discoveries, tests and careful monitoring showed high levels of recovery rates at early detection of cancerous cells. But distinguishing cervical cells in Pap smear is demanding piece of work due to some constraints. Some of the constraint includes entanglement of the morphological changes in the structural parts of the cells. Although there are two methods to obtain the cells which are Colposcopy and pap-test but in reality, Pap-smear test are most favoured due to low cost and pain free diagnosis. This paper presents deep learning classification methods applied on the SIPAKMED pap-smear image dataset in order to establish a reference point for the assessment of forthcoming classification techniques.

ABSTRACT. Wireless sensor networks has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.