BACKGROUND: One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist’s efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI’s pattern recognizing ability can expedite the diagnostic process. OBJECTIVE: In this study, we propose and implement an image classification technique to identify breast cancer. METHODS: We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC). RESULT: The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (-) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive). CONCLUSION: The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.

Application of humanoid robots has been common in the field of healthcare and education. It has been recurrently used to improve social behavior and mollify distress level among children with autism, cancer and cerebral palsy. This article discusses the same from a human factors' perspective. It shows how people of different age and gender have a different opinion towards the application and acceptance of humanoid robots. Additionally, this article highlights the influence of cerebral condition and social interaction on a user's behavior and attitude towards humanoid robots. Our study performed a literature review and found that: (a) children and elderly individuals prefer humanoid robots due to inactive social interaction;(b) The deterministic behavior of humanoid robots can be acknowledged to improve social behavior of autistic children; (c) Trust on humanoid robots is highly driven by its application and a user's age, gender, and social life.

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