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This study aims to examine the role of economic political uncertainty (EPU) on various corporate policies, namely, cash reserves, investment, capital structure and operating activities of Indian listed firms.

To assess the influence of policy-related uncertainties, the author uses the India-specific EPU news-based index constructed by Baker et al. (2016) as a proxy for policy uncertainties. This study uses data from listed Indian firms spanning the period 2003 to 2019. The author uses panel regression models with firm-fixed effects to analyze the impact of EPU on corporate policies, including cash reserves, leverage and CAPEX, while considering key control variables.

In response to heightened EPU, firms tend to increase their cash reserves, curtail their investment activities and favour secured financing options. Notably, this study aligns with the “real options” framework, demonstrating that firms with substantial investment irreversibility significantly reduce their capital expenditures during periods of elevated EPU. Additionally, the results reveal that rising EPU corresponds to heightened borrowing costs and increased operating expenses for firms.

In contrast to prior research that predominantly investigated the impact of EPU on the decisions of listed firms in developed markets, this study delves into the role of EPU on corporate policies among listed firms in India. This focus is particularly relevant, given the significant policy changes that have transpired in the Indian business landscape in recent years.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.