Bimal Bhattarai

Bimal Bhattarai
Universitetet i Agder | UIA · Department of Information- and Communication Technology (ICT)

PhD

About

14
Publications
3,339
Reads
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87
Citations
Introduction
Bimal Bhattarai currently works at the Department of Information and Communication Engineering, Chosun University. Bimal does research in Computer Engineering and Electronic Engineering. Their current project is 'Indoor Positioning'.

Publications

Publications (14)
Conference Paper
Full-text available
Due to the presence of large steel components inside building, the geomagnetic field is affected and distorted. Such changes in magnetic field are used for indoor localization. We present an indoor positioning system using magnetic sensor in a smartphone to detect the position of user. Unlike other positioning systems based on Wi-Fi, Beacon, and ul...
Article
Full-text available
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained anal...
Preprint
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, h...
Preprint
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word-level is crucial to provide a more fine-grained anal...
Preprint
Full-text available
Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they ar...
Conference Paper
Full-text available
Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification pur...
Preprint
Full-text available
Using logical clauses to represent patterns, Tsetlin machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. A team of Tsetlin automata (TAs) composes each clause, thus driving the entire learning process. These are rewarded/penalized according to three...
Article
Full-text available
Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ult...
Article
Full-text available
The unstable nature of radio frequency (RF) signals and the need for external infrastructure inside buildings have limited the use of positioning techniques, such as Wi-Fi and Bluetooth fingerprinting. Compared to these techniques, the geomagnetic field exhibits a stable signal strength in the time domain. However, existing magnetic positioning met...
Poster
Full-text available
The traditional indoor magnetic positioning system cannot give good accuracy in wide space because of the anomaly in geomagnetic data. We propose long short-term memory (LSTM) based deep recurrent neural network (DRNN) model for indoor position system (IPS), which is capable of capturing discriminative features in long-range input sequences. We col...
Conference Paper
Full-text available
Due to the presence of large steel components inside building, the geomagnetic field is affected and distorted. Such changes in magnetic field are used for indoor localization. We present an indoor positioning system using magnetic sensor in a smartphone to detect the position of user. Unlike other positioning systems based on Wi-Fi, Beacon, and ul...

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Projects

Projects (3)
Project
To find the interpretability of context-dependent NLP tasks using the Tsetlin Machine.
Project
* Recursive relational Tsetlin machines * Causal Tsetlin machines * Representation learning with Tsetlin machines * Natural language processing with Tsetlin Machines
Project
Develop an efficient indoor positioning system using geomagnetic signal.