The explosion in wireless telecommunication technologies has lead to a huge increase in the number of mobile users. The greater dependency on the mobile devices has raised the user's expectations to always remain best connected. In the process, the user is always desiringgood signal strength even at certain black spots and indoors. Moreover, the exponential growth of the number of mobile devices has overloaded macrocells. Femtocells have emerged out as a good promising solution for complete coverage indoors and for offloading macrocell. Therefore, a new handover strategy between femtocells and macrocell is proposed in this paper. The proposed handover algorithm is mainly based on calculating equivalent received signal strength along with dynamic margin for performing handover. The simulation results of proposed algorithm are compared with the traditional algorithm. The proposed strategy shows improvement in two major performance parameters namely reduction in unnecessary handovers and Packet Loss Ratio. The quantitative analysis further shows 55.27% and 23.03% reduction in packet loss ratio and 61.85% and 36.78% reduction in unnecessary handovers at a speed of 120kmph and 30kmph respectively. Moreover, the proposed algorithm proves to be an efficient solution for both slow and fast moving vehicles. Keyword:Femtocell Handover Macrocell Packet loss ratio Received signal strength

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Drug discovery involves identifying novel drug-target (DT) interactions. Most proposed computer models for predicting drug-target interactions have emphasized binary classification, but the aim is to determine whether two drug targets interact.However, it is more practical but more challenging to anticipate the binding affinity, which evaluates the strength of a DT pair's association. The drug may not work if the binding affinity is not strong enough. Due to this reason, we need an expert system for predicting the affinity score between the drug and target protein. Advanced deep learning techniques can predict binding affinities because there are more new public affinity data in databases related to DT. This paper uses a comparative analysis of different drug and protein-encoding techniques to predict DT binding affinities based on similarities between drugs and proteins. The validation results on the standard dataset show that the proposed model is an excellent way to predict how well DT binds and can be very helpful in the process of new drugs. Hence, the model on the DAVIS dataset achieved a higher concordance index, that is, 0.897, and the lowest mean square error, that is, 0.226; for the KIBA dataset, the concordance index score achieved is 0.867, and the mean square error is 0.191. The findings are compared to baseline methods using some evaluation parameters, including the mean squared error and the concordance index.

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