Dive in Deep Learning

Devi, V. Ajantha; Naved, Mohd

doi:10.1201/9781003107026-5

Cited by 33 publications

(17 citation statements)

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“…The LSTM model 16 is a type of recurrent neural network (RNN) that is particularly suited for time series forecasting tasks. It has been shown to be particularly effective in dealing with the vanishing gradient problem that arises in standard RNNs, allowing it to identify and retain relationships between distant elements in the data over an extended period of time.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of cloud resources forecasting using deep learning techniques based on VM workload traces

Kollu,

Janjanam,

Siram

2024

Trans Emerging Tel Tech

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The major cost of running a cloud is power consumption. Under‐utilization of resources that are kept idly on, over‐allocation of resources, and so on, are a few reasons for excessive power utilization by the data centers. So, to optimize the power consumption, future resource usage of virtual machines (VM) can be forecasted using their trace logs. Based on this prediction, excessively allocated VM resources can be freed thereby reducing the number of physical machines as well as the carbon footprint. In this work, we present a comparative study of some deep learning techniques such as multilayer perceptron (MLP), autoregressive neural network (ARNN), convolutional neural network (CNN), long short‐term memory (LSTM) network in forecasting the CPU, memory usage of many VMs. The GWA‐T‐12 Bitbrains data center dataset consisting of 1250 VMs' workload traces is used in this work. The main goal is to avoid underload/overload in VMs which occurs sometimes while trying to optimize resource allocation. We achieved a maximum of 98% accuracy in future resource requirement forecasting. Among all models, MLP attained the highest accuracy.

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Section: Methodsmentioning

confidence: 99%

Comparative analysis of cloud resources forecasting using deep learning techniques based on VM workload traces

Kollu,

Janjanam,

Siram

2024

Trans Emerging Tel Tech

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“…The first Level‐0 model measures the NS between the target PP and the SPs using a word embedding model. Word embedding is a technique that maps words to vector representations (Zhang et al., 2020). This study adopts global vector (GloVe; Pennington et al., 2014), one of the state‐of‐the‐art static word embeddings, to encode the meaning of the property name by observing the ratios of word co‐occurrence probabilities in the corpus.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

Yin

Tang

Webster

et al. 2023

Computer aided Civil Eng

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Element property is a crucial aspect of building information modeling (BIM) for almost all BIM‐based engineering tasks. Since there are limited properties predefined in Industry Foundation Classes (IFC) specifications, a vast number of property concepts were customized and stored in BIM models, which lack labor‐intensive data modeling and alignment for effective information management and reuse. To tackle the challenge, this study presents a natural language understanding (NLU)‐based method for the automatic ontological knowledge modeling of project‐specific property concepts from BIM models. A soft pattern matching model was used to acquire contextual definitions of concepts from a domain corpus before applying deep NLU models to transform the concept names and definitions into dense vector representations. These outputs were then fed into two stacking ensemble learning models to carry out two tasks: (a) classifying whether an unseen concept overlaps with the IFC ontology, and (b) aligning the repetitive concepts with the most relevant concepts in the ontology. Finally, all fresh properties were appended to an IFC ontology, either as new objects or new synonyms. The performance was evaluated based on 327 property concepts from real‐life BIM models. The results show that the proposed approach incorporating reading comprehension of definitions outperforms the existing name similarity‐based methods. Finally, a case study on a renovation project demonstrates the effectiveness of this study in automatic ontology modeling of property concepts.

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“…The output gate is needed to read out the entries from the cell. These inputs are processed by a fully connected layer with a sigmoid activation function to compute the values of input, forget and output gates [25]. The output values of all three gates are in the range [0, 1].…”

Section: The Selection Of Encoding and Decoding Layermentioning

confidence: 99%

NMTHC: a hybrid error correction method using a machine translation model based on the sequence-to-sequence framework

Wang

Chen

2023

Preprint

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Background With the development of sequencing technology, the reads produced by third-generation sequencing technologies have ultra-long lengths and cost-effectiveness and have drawn the favor of many application scenes. However, compared with the shorter reads obtained by next-generation sequencing, the high error rates of these long reads affect the accuracy in downstream analysis. Results In this work, a novel hybrid error correction algorithm (NMTHC) based on the sequence-to-sequence framework is proposed, which converts the long-read error correction problem into a machine translation problem in natural language processing. First, the high-precision short reads are aligned to the long reads to generate alignment information, and then the long reads and alignment information are tokenized to generate “long sentences” and “target sentences”. The generated sequences are padded to the same length and then divided into batches for training. Both the encoding and the decoding layer of the model use the bidirectional Long Short-term Memory (Bi-LSTM) network to encode and decode hidden state information, and the generated token and the updated state at the current time step are used to predict the token of the next time step. Conclusions We have tested our algorithm on subsets of long reads, and the results show that NMTHC is not only excellent in the number of aligned bases with the reference genome but also improves the alignment identity without any loss of the read length. In addition, it has been proved that NMTHC can be distributed in multiple GPUs to improve the correction speed. In summary, NMTHC can correct long reads more accurately while keeping read length and continuity. It can be applied to sequencing data from both mainstream platforms and provides a new perspective for hybrid error correction algorithms based on deep learning.

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Dive in Deep Learning

Cited by 33 publications

References 0 publications

Comparative analysis of cloud resources forecasting using deep learning techniques based on VM workload traces

Comparative analysis of cloud resources forecasting using deep learning techniques based on VM workload traces

A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

NMTHC: a hybrid error correction method using a machine translation model based on the sequence-to-sequence framework

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