Biology of the Bombayduck Harpadon nehereus (Hamilton, 1822) from the north-eastern Arabian Sea, India

The Bombayduck Harpadon nehereus (Hamilton, 1822) is a common species and major contributor to the fishery in the northern Arabian Sea. The biology of H. nehereus, exploited by set bagnets (SBN, dol netters) and trawlers from the northern Arabian Sea coast of India (Gujarat and Maharashtra) was investigated during 2014 to 2019. Fishes with a size range of 30-408 mm mainly contributed to the fish landing. The sex ratio (male: female) was 1:1.5. Length at maturity (Lm) for females was estimated at 207 mm TL. Mature ovaries contained ova of all maturity stages indicating the species to be a continuous spawner. The absolute fecundity ranged from 23,444 to 1,34,432 eggs per fish and relative fecundity ranged from 235-430 eggs g-1. The gonadosomatic index and monthly maturity stages suggested that H. nehereus is a continuous spawner with peaks occurring from February to April. The ‘b’ value in the length-weight relationship was 3.31 showing a positive allometric growth. The diet analysis showed H. nehereus to be a highly carnivorous predator which fed mainly on crustaceans (Index of Relative Importance, IRI = 82.7%) followed by teleosts (17.3%). The present article discusses the biology of H. nehereus in the north-eastern Arabian Sea

A Simple and Effective Scheme for Data Pre-processing in Extreme Classification

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. It has been shown to be an effective framework for addressing crucial tasks such as recommendation, ranking and web-advertising. In this paper, we propose a method for effective and well-motivated data pre-processing scheme in XMC. We show that our proposed algorithm, PrunEX, can remove upto 90% data in the input which is redundant from a classification view-point. Our scheme is universal in the sense it is applicable to all known public datasets in the domain of XMC.Peer reviewe

Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousands or even millions of labels.In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.We show three concrete realizations of this label representation space including: (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees.By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds—fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, Bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at https://github.com/xmc-aalto/bonsai.Peer reviewe

Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification

Extreme Multi-label Text Classification (XMTC) refers to supervised learning of a classifier which can predict a small subset of relevant labels for a document from an extremely large set. Even though deep learning algorithms have surpassed linear and kernel methods for most natural language processing tasks over the last decade; recent works show that state-of-the-art deep learning methods can only barely manage to work as well as a linear classifier for the XMTC task. The goal of this work is twofold : (i) to investigate the reasons for the comparable performance of these two strands of methods for XMTC, and (ii) to document this observation explicitly, as the efficacy of linear classifiers in this regime, has been ignored in many relevant recent works.Peer reviewe

Proceedings of National Conference on Relevance of Engineering and Science for Environment and Society

This conference proceedings contains articles on the various research ideas of the academic community and practitioners presented at the National Conference on Relevance of Engineering and Science for Environment and Society (R{ES}2 2021). R{ES}2 2021 was organized by Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India on July 25th, 2021. Conference Title: National Conference on Relevance of Engineering and Science for Environment and SocietyConference Acronym: R{ES}2 2021Conference Date: 25 July 2021Conference Location: Online (Virtual Mode)Conference Organizers: Shri Pandurang Pratishthan’s, Karmayogi Engineering College, Shelve, Pandharpur, India