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Shivam Tiwari

Bio: Shivam Tiwari is an academic researcher from Indian Institutes of Information Technology. The author has contributed to research in topics: Cluster analysis & Autoencoder. The author has co-authored 3 publications.

Papers
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Proceedings ArticleDOI
01 Jan 2020
TL;DR: This work proposes a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines and efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations.
Abstract: Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of features for modeling user-specific characteristics. These characteristics, in turn, can be used for a number of tasks including resource management, power efficiency, and smart home applications. In recent years, the employment of topic models for BA has been found to successfully extract the dynamics of the sensed data. Topic modeling is popularly performed on text data for mining inherent topics. The task of finding the latent topics in textual data is done in an unsupervised manner. In this work we propose a novel clustering technique for BA which can find hidden routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently works on high dimensional data for BA without performing any computationally expensive reduction operations. We evaluate three different techniques namely LDA, the Non-negative Matrix Factorization (NMF) and the Probabilistic Latent Semantic Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets namely, the Intel Lab Data, Kyoto Data, and MERL data. Through rigorous experiments, we achieve silhouette scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset and 0.8312 over the MERL dataset for clustering.

1 citations

Journal ArticleDOI
15 Apr 2021
TL;DR: In this paper, the authors proposed a semi-supervised framework for subject recognition on low-modal ubiquitous and visual sensors by using a clustering-based pseudo label generation algorithm.
Abstract: Subject Recognition (SR) refers to the task of identifying persons performing activities in a smart environment using the data captured by the sensors installed in it. The existing literature mainly concentrates on supervised SR using the sensor data captured through multiple modalities. However, majority of the real-life sensor datasets are not annotated with the subjects performing the activities, which creates a scarcity of labeled data samples for this task. Issues of privacy and high manual annotation costs further complicate the problem of less labeled data. In addition to this problem, most of the datasets are of low modalities. Hence, the challenge lies in developing semi-supervised frameworks that are suitable for low-modal sensor data with sparse or no labels. Towards this, we initially perform benchmark experiments to analyze the factors of modality and amount of labeled data in the context of SR. Then, we propose semi-supervised frameworks for SR on the data collected by low-modal ubiquitous and visual sensors. In particular, we propose a clustering-based pseudo label generation algorithm to facilitate the training process in a semi-supervised domain for ubiquitous data. On the other hand, we propose Transfer Learning and Data Augmentation (TLDA) framework to perform SR on visual data in semi-supervised domain. To validate our proposed frameworks, we perform experiments on three real-world datasets, namely Smartphone, OPPORTUNITY, and UTD-MHAD dataset to achieve an accuracy of around 77%, 98%, and 91% respectively. Finally, we also provide an analysis on the aspect of merging modalities to propose a new research dimension for SR.
Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, various K-means-based clustering techniques are explored to generate pseudo-labels to facilitate the training of deep networks, and an auto-encoder (AE)-based dimensionality reduction method is employed.
Abstract: Recent development in deep learning (DL) methodologies has shown promising results on various computer vision tasks including the classification of hyperspectral data. However, these methodologies are expected to suffer in the presence of lack of training data, due to complex network architecture and a large number of parameters. In this paper, various K-means-based clustering techniques are explored to generate pseudo-labels to facilitate the training of deep networks. To tackle the curse of dimensionality, an auto-encoder (AE)-based dimensionality reduction method is employed. Finally, the classification is done using convolutional long short-term memory cells (ConvLSTM) which outperforms the rest of the deep neural networks used. In addition, an analysis of the effect of the proposed dimensionality reduction method on classification accuracy is presented. The efficacy of the proposed approach is demonstrated on two real-world hyperspectral image datasets namely the “University of Pavia” (UP) and “Salinas”.

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Journal ArticleDOI
TL;DR: This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing and presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing.
Abstract: In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field.

5 citations