In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network (DNN) model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

Electrical discharge method (EDM), based on the thermo electrical energy of the workpiece and the mechanical electrical machining, are non-traditional machining methods. Current development of the nano mixed dielectric discharge device has the latest trends in tough and soft materials finishing. This research assesses the impact on nano-particle mixed electric discharge extraction of the inconel 625 product by means of a copper electrode of the material removal frequency (MRR), device wear level (TWR), and superficial surface roughness. In contrast to dielectric fluid are the output parameters such as current, period and frequency, and nano-substances of titanium carbide. During this phase, a sequential, separate discharges between the electrode and the workpiece contribute to the product removal electro thermally. The method quality largely depends on the Electrode content, the production of the electrode workpiece product. Appropriate electrode choice may reduce machining costs. Die-Sinker EDM with the copper electrode is therefore able to provide for improvement of output parameters and reduction of production costs. In case of surface roughness, a copper electrode has improved efficiency and is better suited for nano powdered dielectric medium.

This paper provides an improved parallel data processing in Big Data mining using ClowdFlows platform. The big data processing involves an improvement in Proportional Integral Derivative (PID) controller using Reinforcement Adaptive Learning (RAL). The Reinforcement Adaptive Learning involves the use of Actor-critic State–action–reward–state–action (SARSA) learning that suits well the stream mining module of ClowdFlows platform. The study concentrates on batch mode processing in Big Data mining model with the use of proposed PID-SARSA-RAL. The experimental evaluation with the conventional ClowdFlows platform proved the effectiveness of the proposed method over continuous parallel workflow execution.