A Noble Double-Dictionary-Based ECG Compression Technique for IoTH

Qian, Jia; Tiwari, Prayag; Gochhayat, Sarada Prasad; Pandey, Hari Mohan

doi:10.1109/jiot.2020.2974678

Cited by 40 publications

(16 citation statements)

References 25 publications

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“…It can be used in IoT healthcare system and will be transmitted data securely according to the authors of Rani et al ( 2019 ). Furthermore, the authors of Qian et al ( 2020 ) conducted an unsupervised dictionary learning in an internet-based healthcare system for patient monitoring. They offered an ECG compression method where they measured EEG.…”

Section: Literature Reviewsmentioning

confidence: 99%

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Khamparia

Bharati

Podder

et al. 2021

Multidim Syst Sign Process

135

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Section: Literature Reviewsmentioning

confidence: 99%

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Khamparia

Bharati

Podder

et al. 2021

Multidim Syst Sign Process

135

View full text Add to dashboard Cite

“…The main aim of this review is to thoroughly examine published works of literature on prolonging the lifetime of IoT sensor networks using data compression approaches. There are many techniques and concepts devoted to save energy and extend the lifetime of IoT sensor networks, mainly focused to reduce data transmissions, like predictive monitoring, clustering, aggregation, routing scheduling, data compression, radio optimization, and battery repletion (11,12,13,14,15,16,17). Please observe that several algorithms of data compression have been used in WSNs.…”

Section: Related Workmentioning

confidence: 99%

Compression-based Data Reduction Technique for IoT Sensor Networks

2021

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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.

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“…The animal information is also helpful for the study of the welfare of the animals, and the concept of animal rights (Andersen and Winter, 2017). The technologies for the Internet of Things (Gochhayat et al, 2019;Kumar et al, 2019;Bebortta et al, 2020;Qian et al, 2020) are also widely used in different domains for understanding the functionality and structure of the brain and address some problems in human daily life. De Albuquerque et al (2017) investigate the applications of brain computer interface systems.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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A Noble Double-Dictionary-Based ECG Compression Technique for IoTH

Cited by 40 publications

References 25 publications

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Compression-based Data Reduction Technique for IoT Sensor Networks

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

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