Protein-Protein Interaction (PPI) is a network of protein interconnections which regulates most of the biological methods. A sound state of biota largely depends on synchronized interactions between protein molecules, and any aberrant interactions between protein molecules may lead to complications, including cervical leukemia, tuberculosis, and other neural disorders. In PPI investigation, a plethora of computational methods have been developed over the years to analyze and predict PPI conclusively; however, a majority of these techniques proved to be strenuous and expensive. Therefore, the need for faster, accurate, and critical analysis of PPI warrants the adoption of Machine Learning (ML) methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Model (RFM). These classifiers are useful in PPI unfolding in terms of amino acid sequence data. The SVM classifier, in particular, is serviceable in solving a majority of complex classification problems producing robust results in a reasonable time frame. This publication summarizes some state-of-art SVM based PPI investigations and challenges incurred in the application of the SVM method.INDEX TERMS Artificial neural network, machine learning, protein-protein interaction, support vector machine.• Detection of protein complexes.• Identification of domain interactions.

Hilsa, Tenualosa ilisha has received much attention for culture due to decline of the natural population. Lack of knowledge on larval rearing is the bottleneck for its culture.This study was aimed at developing larval rearing protocols for hilsa shad. Hilsa larvae (4 days old, 4.76 ± 0.06 mm/0.49 ± 0.01 mg) were stocked in fibreglass-reinforced plastic tanks (1.7 m 3 water volume) at 300, 600 and 1,200 nos/m 3 in triplicates in three experimental systems viz., E-I (circular, 0.567 m water depth), E-II (circular, 0.962 m water depth) and E-III (rectangular, 0.567 m water depth) and reared for 46 days. The larvae were supplied with Chlorella vulgaris, Brachionus calyciflorus, mixed phytoplankton and mixed zooplankton during 4-50, 6-25, 8-50 and 26-50 days of their age respectively. In each system, higher (p < 0.05) fry survival at 300 nos/m 3 than in higher densities indicates density dependent stress. Circular tanks showed higher survival (13.3%-61.31%) than in rectangular tanks (6.88%-27.26%) in each stocking density, indicating the importance of tank shape for rearing. Water depth affected fry survival in circular tanks (E-I and E-II) at 300 nos/m 3 ; at 0.962 m depth, survival was higher (61.31%, p < 0.05) than that of 0.567 m depth (49.93%). Good fry survival was achieved through feeding the larvae initially with Chlorella followed by co-feeding with Brachionus, mixed phytoplankton and zooplankton and rearing in circular tanks at 300 nos/m 3 densities at 1 m depth. This first-ever larval rearing protocol is useful for mass production of fry to support hilsa aquaculture in future. K E Y W O R D Sfry survival, stocking density, tank design, zooplankton culture

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The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days’ intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.