Time Series Forecasting (TSF) Using Various Deep Learning Models

Shi, Jimeng; Jain, Mahek; Narasimhan, Giri

doi:10.48550/arxiv.2204.11115

Cited by 13 publications

(11 citation statements)

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“…Deep learning (DL) has been successfully applied across a wide range of applications and data modalities, including text [85,86], images [87,88,89], graphs [90,91], and time series data [92,93,94,95]. DL has also been applied to various problems in the field of protein science.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Comprehensive Survey of Scoring Functions for Protein Docking Models

Shirali,

Stebliankin,

Shi

et al. 2023

Preprint

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The protein-protein docking problem is fundamental to our understanding of how proteins interact. It is also important because of its relevance to drug discovery and vaccine design. Docking methods typically involve two steps. The first step generates many possible candidate conformations, while the second step evaluates the candidates using some scoring function. Despite numerous experiments with many innovative scoring functions, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches for scoring protein-protein complexes, evaluating their strengths and weaknesses to aid researchers in understanding progress made in this field. Our survey compares the state-of-the-art in classical and deep learning-based scoring methods.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Comprehensive Survey of Scoring Functions for Protein Docking Models

Shirali,

Stebliankin,

Shi

et al. 2023

Preprint

Self Cite

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“…For the global scores, Equation (20) describes the weighting [ 29 , 30 ] . In this case, the same weight was assigned to each metric (25%), assuming AHH and AAFH as one because they describe similar processes.…”

Section: Proposed Modelmentioning

confidence: 99%

Decision-making analysis in cooperative environments for decentralized cognitive radio networks

Hernandez,

Giral,

Martínez

2024

Heliyon

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“…Since our goal was to predict object occurrence in a video surveillance service as a time series prediction problem, we evaluated commonly used deep learning models for time series prediction [28,29]. We decided to choose the RMSE metric [30] to measure the error of the deep learning model predictions as we could penalize larger errors [31], because we sometimes had larger prediction errors because of sudden object occurrence in the video surveillance services.…”

Section: Second Edge Nodementioning

confidence: 99%

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

Ugli

Kim²,

Mohammed

et al. 2023

Sensors

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Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.

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Time Series Forecasting (TSF) Using Various Deep Learning Models

Cited by 13 publications

References 0 publications

A Comprehensive Survey of Scoring Functions for Protein Docking Models

A Comprehensive Survey of Scoring Functions for Protein Docking Models

Decision-making analysis in cooperative environments for decentralized cognitive radio networks

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

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