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GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose. • Making a generation expansion planning • Making a sustainable environment • Presented a good solution for power generation • Analysed the total environmental emissions • Analysed the energy demands. [ABSTRACT FROM AUTHOR]

Earlier, crop cultivation was undertaken on the basis of farmers' hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others. [ABSTRACT FROM AUTHOR]

Focused crawler traverses the Web to only collect pages that are relevant to a particular topic, and is increasingly considered as a way to get around the scalability issues with current general-purpose search engines. But the data diversity in the Web forces these crawlers to face three significant problems: (i) inconsistency, (ii) ubiquity, and (iii) ambiguity, which causes misguidance in crawling. To handle these issues, this paper proposes a weakly supervised Gated Recurrent Unit (GRU) mechanism for an adaptive focused web crawler framework that matches semantically relevant t o p i c s and w e b p a g e c o n t e n t. This weakly supervised Gated Recurrent Unit model accepts the vector form of the t o p i c and the fetched w e b p a g e as input to produce meaningful s e m a n t i c vectors and incorporates the Manhattan distance rule to compute the topical relevance of the w e b p a g e. The proposed mechanism guides the focused crawler in downloading more relevant web pages by finding the relevant hyperlinks and omitting the irrelevant hyperlinks concerning the topic. The proposed method helps the focused crawler to semantically find, arrange, and index the web pages in a relatively narrow segment of the web to solve the inconsistency, ubiquity, and ambiguity problems of the focused crawlers. The experimental results indicate that the proposed technique outperforms the s t a t e − o f − t h e − a r t approaches in terms of h a r v e s t r a t e , p r e c i s i o n , r e c a l l , h a r m o n i c m e a n , and i r r e l e v a n c e r a t i o. In summary, the strategy described here works well and is important for focused crawlers. [ABSTRACT FROM AUTHOR]

A hybrid BAT with Grasshopper (GH) algorithm and BAT-MMVO (Modified Multiverse Optimization) are exhibited for harvesting maximum power from photovoltaics (PV) using the Xilinx System Generator (XSG) implanted controller. Using a hybrid BAT-GH and BAT-MMVO algorithm, the proposed implanted controller finds the best switching pulse for the boost converter. The implanted controller, switching schemes, and the Photovoltaic (PV) supported boost converter were built using the XSG domain. The hardware implementation of the best two cases were done using a microcontroller in a smaller scale. This aims to gather the maximum amount of power by a PV array for solar irradiation and cell temperature under varied environmental situations. The PV structure in the XSG domain is used to construct the system model for prediction. The major emphasis of this work is to keep the difference of actual power and reference power as minimum. Finally, the implanted controller's performance is compared to that of other existing hybrid controllers. The performance of the proposed algorithm is found to yield good results in terms of power extraction. The theoretical and experimental results are presented. The computational efforts for the implementation of the algorithm are found to be less complex when compared to other existing methods. [ABSTRACT FROM AUTHOR]