Tropical Cyclone Tracking and Forecasting Using BiGRU [TCTFB]

Khandelwal, Aryan; S, Ramya R; S, Ayushi; R, Bhumika; P, Adhoksh; Jhawar, Keshav; Shah, Ayush; Venugopal, K R

doi:10.21203/rs.3.rs-1955121/v1

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Many patterns in nature have been rooted in mathematic series (Khandelwal and Sahni, 2005). While some mathematical patterns are observed more frequently, all patterns existing in Mother Nature can be potentially effective.…”

Section: Ratios and Autismmentioning

confidence: 99%

Comparing desired spatial ratios among autistic and non-autistic children

Sheykhmaleki

Yazdanfar

Litkouhi

2023

ARCH

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PurposeAlthough some architects have found spatial order and proportion strategy effective in designing the environment for autistic children, it is not clear what spatial ratios are preferred for autistic people. Therefore, this study aims to find the desired ratio among autistic and non-autistic children.Design/methodology/approachThe scale model questionnaire was chosen to determine the difference in the ratio preferences for autistic and non-autistic children, whereas two access types between the space zones are also considered. The questionnaire was administered to 50 autistic children, with a half-and-half distribution of moderate and mild autism groups, while males are twofold. It was also administered to 50 non-autistic children, approximately equal in terms of gender division. The scale model was designed in the form of a dollhouse to be played with a doll in 6 varied rooms categorized by 3 different ratios (1:1, golden ratio, 1.6:1 and 5:2) and 2 access types (linear and radial access) to measure how many times each room was selected by each group to run numerical analysis.FindingsIt was shown that, although the golden ratio has been previously considered in space design for autistic children, they appealed to the ratio of 2:5 with high frequency. While there is a significant difference between the spatial ratios preferred by autistic and non-autistic children choosing the golden ratio, the same type of space access system is preferred by both groups in their results.Originality/valueDespite the prevalent use of the golden ratio in design for autism design, this research shaped an empirical study for autistic users concluding a different perspective in design for autism.

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Section: Ratios and Autismmentioning

confidence: 99%

Comparing desired spatial ratios among autistic and non-autistic children

Sheykhmaleki

Yazdanfar

Litkouhi

2023

ARCH

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“…A Bi-directional Gated Recurrent Unit, frequently called a Bi-GRU, is a sequential processing model composed of 2D-GRU. 20 It receives input from the forward and backward directions. A GRU is a type of RNN like an LSTM but it has only two gates they are updated and reset gates.…”

Section: Bigru Modelmentioning

confidence: 99%

“…A Bi‐directional Gated Recurrent Unit, frequently called a Bi‐GRU, is a sequential processing model composed of 2D‐GRU 20 . It receives input from the forward and backward directions.…”

Section: The Proposed Modelmentioning

confidence: 99%

Automated cyberattack detection using optimal ensemble deep learning model

Vaiyapuri,

Shankar,

Rajendran

et al. 2023

Trans Emerging Tel Tech

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In recent times, the Industrial Internet of Things (IIoT) has developed significantly. In the application of automation, and intelligence, industrial digitalization introduced cyber risks, and the varied and complex industrial IoT platform presented a novel attack surface for network invaders. Several Intrusion Detection Systems (IDS) were advanced recently as many computer networks exposure to privacy and security threats. Availability, Data confidentiality, and integrity, the damage will happen in case of IDS prevention failure. Traditional methods were ineffective in dealing with advanced attacks. Advanced deep learning (DL) methods were designed for automatic ID and abnormal behavior detection of networks. Therefore, this article focuses on the design of Improved Reptile Search Optimization with Ensemble Deep Learning based Cybersecurity (IRSO‐EDLCS) technique in the IIoT environment. The major aim of the IRSO‐EDLCS technique lies in the accurate identification of cyberattacks in the IIoT environment. To accomplish this, the presented IRSO‐EDLCS technique performs IRSO algorithm‐based feature selection (IRSO‐FS) technique. In addition, the IRSO‐EDLCS technique performs an ensemble of three DL models namely deep belief network (DBN), bidirectional gated recurrent unit (BiGRU), and autoencoder (AE). The hyperparameter tuning process is performed by a modified gray wolf optimizer (MGWO) to enhance detection process. To exhibit the improved performance of the IRSO‐EDLCS algorithm, a wide range of simulations were performed on the benchmark database. The experimental outcomes depict the betterment of the IRSO‐EDLCS technique over other existing models.

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“…Much research has been carried out on detection and segmentation with Detectron2. For instance, we can track and detect tropical cyclone, ship structure, and transportation detection with aerial images [2], [22]. Pham et al [23] experimented with various detecting models to predict road damage for better and more efficient road maintenance, and the results show that Detectorn2's Faster R-CNN outperformed all other models with 51% of the F1 score.…”

Section: Detectron2mentioning

confidence: 99%

“…detection and segmentation approaches have been successfully applied to multiple interdisciplinary domains ranging from medical imaging to autonomous vehicles[1],[2]. The following study details efforts to apply segmentation methods to the critical infrastructure domain.…”

mentioning

confidence: 99%