Santosh Singh Rathore

Sanskar Soni, Satyendra Singh Chouhan, Santosh Singh Rathore

In recent years, deep learning-based models have significantly improved the Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural Network (CNN), initially used for computer vision, has shown remarkable performance for text data in various NLP problems. Most of the existing CNN-based models use 1-dimensional convolving filters n-gram detectors), where each filter specialises in extracting n-grams features of a particular input word embedding. The input word embeddings, also called sentence matrix, is treated as a matrix where each row is a word vector. Thus, it allows the model to apply one-dimensional convolution and only extract n-gram based features from a sentence matrix. These features can be termed as intra-sentence n-gram features. To the extent of our knowledge, all the existing CNN models are based on the aforementioned concept. In this paper, we present a CNN-based architecture TextConvoNet that not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. To evaluate the performance of TextConvoNet, we perform an experimental study on five text classification datasets. The results are evaluated by using various performance metrics. The experimental results show that the presented TextConvoNet outperforms state-of-the-art machine learning and deep learning models for text classification purposes.

Jitendra Parmar, Satyendra Singh Chouhan, Vaskar Raychoudhury et al.

Traditional machine learning mainly supervised learning, follows the assumptions of closed-world learning, i.e., for each testing class, a training class is available. However, such machine learning models fail to identify the classes which were not available during training time. These classes can be referred to as unseen classes. Whereas open-world machine learning (OWML) deals with unseen classes. In this paper, first, we present an overview of OWML with importance to the real-world context. Next, different dimensions of open-world machine learning are explored and discussed. The area of OWML gained the attention of the research community in the last decade only. We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for OWML. It also presents the research gaps, challenges, and future directions in open-world machine learning. This paper will help researchers understand the comprehensive developments of OWML and the likelihood of extending the research in suitable areas. It will also help to select applicable methodologies and datasets to explore this further.

Saurabh Tiwari, Santosh Singh Rathore

In this paper, we present an approach to select a subset of requirement elicitation technique for an optimum result in the requirement elicitation process. Our approach consists of three steps. First, we identify various attribute in three important dimensions namely project, people and the process of software development that can influence the outcome of an elicitation process. Second, we construct three p matrix (3PM) separately for each dimension, that shows a relation between the elicitation techniques and three dimensions of a software. Third, we provide a mapping criteria and use them in the selection of a subset of elicitation techniques. We demonstrate the applicability of the proposed approach using case studies to evaluate and provide the contextual knowledge of selecting requirement elicitation technique.