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The objective of this study is to analyse the performance of IPO during the pandemic era. The current study covers 10 IPO’s issued during the period from 1st March2020 to 31st March 2021. The following are the IPO’s studied, SBI Cards and Payment Ltd., Rossari Biotech, Yes Bank Ltd, Mindspace Business Parks, Happiest Minds Technology Ltd., Route Mobile Ltd, Computer Age Management Services Ltd, Chemcon Specialty Chemicals Ltd, Angel Broking and UTI Asset Management Company. For the study of IPO’s, the following parameters were considered, Offer Price, listing day Close Price, Age of the Firm, Time Subscribed and Nifty 50 Index- Closing Price. The following were the observations made that the short-term performance of IPO is positive as the average MAER is 38 % and there is a high chance for Indian investor to earn high profits. IPO provide positive abnormal returns by the end of first trading day. The medium-term performance of the IPO sample is positive as the average MAER is 33% which is less when compared with the short term. The long-term performance of IPO sample is positive as the average MAER IS 59%, as the two IPO Happiest Mind Technologies Ltd. and Route Mobile Ltd. received good returns. This study, shows that investment in IPO gives good returns to an Indian investor.

A study was conducted to develop optimal irrigation schedules and crop water production function for cassava in the three major growing areas of India, viz., Salem (Tamil Nadu), Thiruvananthapuram (Kerala), and West Godavari (Andhra Pradesh). The irrigation schedules and water requirements were simulated using CROPWAT, and the model result was verified with field values in Thiruvananthapuram, one of the study areas. The results indicated the wider applicability of CROPWAT in calculating water requirement as well as developing irrigation schedules for cassava irrespective of the agro-climatic conditions. The optimal gross irrigation requirement simulated by CROPWAT was 362, 610, and 703 mm in Thiruvananthapuram, West Godavari, and Salem, respectively, based on the soil, crop and climatic parameters. The optimal schedules were also developed in the study locations using CROPWAT, and it can be used for further irrigation practices and plans to maximize water productivity. Also, the field experimental data in Thiruvananthapuram showed a quadratic relation between crop water requirement and yield (R2 = 0.86). The derived crop water production function (CWPF), $${\text{yield}} = - \,0.0013{\text{ET}}_{\text{c}}^{ 2} \, + \,1.138{\text{ET}}_{\text{c}} \, - \,203.03$$ provided yield of 39 × 103 kg ha−1 of cassava corresponding to the simulated gross irrigation requirement of 362 mm in Thiruvananthapuram. Thus the CWPF together with the optimal schedules is a useful tool for water managers and farmers to develop appropriate irrigation plans in advance to mitigate the water scarcity as the result of climate change.

The purpose of speech emotion recognition system is to differentiate the speaker's utterances into four emotional states namely happy, sad, anger and neutral. Automatic speech emotion recognition is an active research area in the field of human computer interaction (HCI) with wide range of applications. Extracted features of our project work are mainly related to statistics of pitch and energy as well as spectral features. Selected features are fed as input to Support Vector Machine (SVM) classifier. Two kernels linear and Gaussian radial basis function are tested with binary tree, one against one and one versus the rest classification strategies. The proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately and combining both, SAVEE database as well as with a self made database in Malayalam language containing samples of female only. Finally results for different combination of the features and on different databases are compared and explained. The highest accuracy is obtained with the feature combination of MFCC +Pitch+ Energy on both Malayalam emotional database (95.83%) and Berlin emotional database (75%) tested with binary tree using linear kernel.