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A robot is usually an electro-mechanical machine which is guided by computer or electronic programming. Conventional line tracer robots follow path in given map. In some kind of robots path is already store in its memory and it simply follows that path. When such types of robots are left to traverse through any random maze, these robots tend to traverse all possible paths every time having no facility to remember the right path. In our robot we are providing it with the capability to traverse through any random maze and remember the right path. Thus when robot travels through the same maze again it knows which is the right path to reach destination. Also it can share this information with its other counterparts in the swarm of robots. This project depends on local path-planning algorithm using a human's heuristic and a laser range finder which has an excellent resolution with respect to angular and distance measurements is presented for real-time navigation of a mobile robot. General TermsNavigation of Robot, Automation of Robot et. al.

Cervical cell classification is a clinical biomarker in cervical cancer screening at early stages. An accurate and early diagnosis plays a vital role in preventing the cervical cancer. Recently, transfer learning using deep convolutional neural networks; have been deployed in many biomedical applications. The proposed work aims at applying the cutting edge pre-trained networks: AlexNet, ImageNet and Places365, to cervix images to detect the cancer. These pre-trained networks are fine-tuned and retrained for cervical cancer augmented data with benchmark CERVIX93 dataset available publically. The models were evaluated on performance measures viz; accuracy, precision, sensitivity, specificity, F-Score, MCC and kappa score. The results reflect that the AlexNet model is best for cervical cancer prediction with 99.03% accuracy and 0.98 of kappa coefficient showing a perfect agreement. Finally, the significant success rate makes the AlexNet model a useful assistive tool for radiologist and clinicians to detect the cervical cancer from pap-smear cytology images.

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