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In optical lithography, decline in the profundity of centre has become an intense issue, alongside the trouble in improving goal. In memory chip creation, the advancement of stage move cover (PSM) innovation is promising to conquer these issues. Notwithstanding PSM innovation, some super-goal lithographic applications have likewise been proposed. In the later methodology, no PSMs are required, in this manner a portion of the challenges concerning cover innovation can be maintained a strategic distance from. Be that as it may, it isn't in every case simple to apply PSM or super-goal advancements to muddled cover designs, for example, for ASIC, chip rationale IC's. For fine occasional examples, a high spatial frequency upgrading channel is utilized with annular enlightenment. With sideways occurrence enlightenments, light shafts which are significantly diffracted by veil examples can go through the viewpoint student, in this way, the band-width of the spatial frequency transmission qualities of the focal point framework is expanded. The student channel smothers the transmission of direct bars concerning the diffracted pillars, improving the picture differentiates in the high frequency locale. Thorough re-enactments run rapidly on a workstation for complex 20 customary and stage moving covers, substrate bleaching, and optical metrology and arrangement issues.

The data which is in time stamped format is called as time series data. The time series data is everywhere for example Weather data, Stock market data, health care data, Sensor data, network data, sales data and many more. Time series have various components due to which the time series data became complex. Trend, Seasonality, Cyclical, and irregularities, these are different components. As everyone interested to know about future. That’s why Forecasting using time series data is important point of consideration. This research paper focuses on components of time series data simultaneously study of different time series modelling and forecasting techniques which are based on stochastic processes. Mainly all the models discussed here focus on use of past time series data for forecasting future values. The Research paper covers AR, MA, Random Walk, ARMA, ARIMA, SARIMA, and Exponential Smoothing processes (single, double and triple) which are used for forecasting time series data.

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity.  It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges.  Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%.