Teeth implant suffeers from stress sheildin and mismatch of bio-mechanical properties, such as weight and stifness. Multi-objective optimization. In order to design better implant, topology optimization of multi-objective function is used in this work. Weighted sum functions are offer a single function to be addressed and optimized which ease finding extremum and feasible design for the various functions that make this single multi-objective function. In this paper, a genetic algorithm is used to determine optimal weights of the three-objective function that used, which are minimizing compliance, minimizing local stress, and minimizing norm function of local stress. This multi-objective function is used to design the optimal shape of dental implant for minimal stress shielding in the jaw. Stress shielding problem is solved by designing the implant's compliance to match the compliance of the original missing tooth. two stress minimization function has been examined, which is local stress and norm function. Conditions of the design was matching the stiffness of implant which made from titanium with original replaced tooth and setting volume fraction of topology optimization to be 40% of original design problem to offer weight matching. Results shows feasible design. Strain based fatigue simulation shows considerably low difference between non-optimized implant and optimized implant.

aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN).  QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers.  Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.

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