• Title/Summary/Keyword: hybrid architecture

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An Extended Work Architecture for Online Threat Prediction in Tweeter Dataset

  • Sheoran, Savita Kumari;Yadav, Partibha
    • International Journal of Computer Science & Network Security
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    • v.21 no.1
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    • pp.97-106
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    • 2021
  • Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

Exploiting Neural Network for Temporal Multi-variate Air Quality and Pollutant Prediction

  • Khan, Muneeb A.;Kim, Hyun-chul;Park, Heemin
    • Journal of Korea Multimedia Society
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    • v.25 no.2
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    • pp.440-449
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    • 2022
  • In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

System Identification of Nonlinear System using Local Time Delayed Recurrent Neural Network (지역시간지연 순환형 신경회로망을 이용한 비선형 시스템 규명)

  • Chong, K.T.;Hong, D.P.
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.6
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    • pp.120-127
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    • 1995
  • A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

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ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete

  • Wu, Dizi;LI, Shuhua;Moayedi, Hossein;CIFCI, Mehmet Akif;Le, Binh Nguyen
    • Steel and Composite Structures
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    • v.45 no.2
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    • pp.281-291
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    • 2022
  • Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

A SE Approach for Machine Learning Prediction of the Response of an NPP Undergoing CEA Ejection Accident

  • Ditsietsi Malale;Aya Diab
    • Journal of the Korean Society of Systems Engineering
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    • v.19 no.2
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    • pp.18-31
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    • 2023
  • Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

Vascular Plants Distributed in Three Wetlands around Geumho River, Daegu Metropolitan City - Ganam Reservoir, Anshim Wetland and Jeomsae Swamp - (대구광역시 금호강 주변의 3개 습지에 분포하는 관속식물상 - 가남지, 안심습지, 점새늪을 중심으로 -)

  • You, Ju-Han
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.27 no.2
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    • pp.67-90
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    • 2024
  • The purpose of this study is to present the ecological data for conservation and management of three wetlands by surveying the vascular plants in Ganam reservoir, Ahnshim wetland and Jeomsae swamp. The whole taxa of vascular plants were 376 taxa including 90 families, 252 genera, 341 species, 7 subspecies, 24 varieties, 2 forms, 1 hybrid and 1 cultivar, and the planted species were 66 taxa including Ginkgo biloba and so on. The rare plants were 7 taxa including Euryale ferox(VU), Aristolochia contorta(LC), Koelreuteria paniculata(VU), Sagittaria trifolia(DD), Hydrocharis dubia(LC), Ottelia alismoides(LC) and Sparganium stoloniferum(VU). The Korean endemic plant was 1 taxon of Lespedeza maritima. In total, there were 21 taxa of floristic target species including 1 taxon of garde V, 2 taxa of grade IV, 6 taxa of grade III, 5 taxa of grade II and 7 taxa of grade I . The hydrophytes were 51 taxa including 36 taxa of emergent species, each 6 taxa of floating-leaved and submerged species and 3 taxa of free-floating species. The invasive alien plants were 79 taxa including 75 taxa of naturalized plants and 4 taxa of casual alien plant. The ecosystem disturbing species 6 taxa including Sicyos angulatus, Ambrosia artemisiifolia, Lactuca seriola, Symphyotrichum pilosum, Paspalum distichum and Humulus scandens.

New VLSI Architecture of Parallel Multiplier-Accumulator Based on Radix-2 Modified Booth Algorithm (Radix-2 MBA 기반 병렬 MAC의 VLSI 구조)

  • Seo, Young-Ho;Kim, Dong-Wook
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.45 no.4
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    • pp.94-104
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    • 2008
  • In this paper, we propose a new architecture of multiplier-and-accumulator (MAC) for high speed multiplication and accumulation arithmetic. By combining multiplication with accumulation and devising a hybrid type of carry save adder (CSA), the performance was improved. Since the accumulator which has the largest delay in MAC was removed and its function was included into CSA, the overall performance becomes to be elevated. The proposed CSA tree uses 1's complement-based radix-2 modified booth algorithm (MBA) and has the modified array for the sign extension in order to increase the bit density of operands. The CSA propagates the carries by the least significant bits of the partial products and generates the least significant bits in advance for decreasing the number of the input bits of the final adder. Also, the proposed MAC accumulates the intermediate results in the type of sum and carry bits not the output of the final adder for improving the performance by optimizing the efficiency of pipeline scheme. The proposed architecture was synthesized with $250{\mu}m,\;180{\mu}m,\;130{\mu}m$ and 90nm standard CMOS library after designing it. We analyzed the results such as hardware resource, delay, and pipeline which are based on the theoretical and experimental estimation. We used Sakurai's alpha power low for the delay modeling. The proposed MAC has the superior properties to the standard design in many ways and its performance is twice as much than the previous research in the similar clock frequency.

The Analysis and Design of Advanced Neurofuzzy Polynomial Networks (고급 뉴로퍼지 다항식 네트워크의 해석과 설계)

  • Park, Byeong-Jun;O, Seong-Gwon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.39 no.3
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    • pp.18-31
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    • 2002
  • In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

Flow Noise Analysis of Ship Pipes using Lattice Boltzmann Method (격자볼츠만기법을 이용한 선박 파이프내 유동소음해석)

  • Beom-Jin Joe;Suk-Yoon Hong;Jee-Hun Song
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.5
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    • pp.512-519
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    • 2023
  • Noise pollution poses significant challenges to human well-being and marine ecosystems. It is primarily caused by the flow around ships and marine installations, emphasizing the need for accurate noise evaluation of flow noise to ensure environmental safety. Existing flow noise analysis methods for underwater environments typically use a hybrid method combining computational fluid dynamics and Ffowcs Williams-Hawkings acoustic analogy. However, this approach has limitations, neglecting near-field effects such as reflection, scattering, and diffraction of sound waves. In this study, an alternative using direct method flow noise analysis via the lattice Boltzmann method (LBM) is incorporated. The LBM provides a more accurate representation of the underwater structural boundaries and acoustic wave effects. Despite challenges in underwater environments due to numerical instabilities, a novel DM-TS LBM collision operator has been developed for stable implementations for hydroacoustic applications. This expands the LBM's applicability to underwater structures. Validation through flow noise analysis in pipe orifice demonstrates the feasibility of near-field analysis, with experimental comparisons confirming the method's reliability in identifying main pressure peaks from flow noise. This supports the viability of near-field flow noise analysis using the LBM.

A Study on Hybrid Characteristics of Public Space in Contemporary Cities Reinterpreted by the Idea of Liminal Space (역공간(Liminal Space) 개념으로 해석한 현대도시 공공공간의 혼성적 특성에 관한 연구)

  • Zoh, Kyung-Jin;Han, So-Young
    • Journal of the Korean Institute of Landscape Architecture
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    • v.39 no.4
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    • pp.49-59
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    • 2011
  • This study is a reinterpretation of characteristics of public space in contemporary cities with a view to liminal space. The conditions of pubic space now cannot be captured through the existing discourses of publicness, and public space. The basic premise of the study is that the idea of liminal space or liminality is useful to grasp the fluid and hybrid attribute of public space in contemporary cities. Liminal space, originally from anthropological studies, is the intermingled stage between two realms and the sustained period of the ritual. The idea has been widely used for various cultural phenomenon and spatial experiences. A literature review on public space and liminal space was carried out. Cases pertaining to public space with a view to liminal space were examined and discussed in detail. Through the careful reading of several public spaces with an angle toward liminal space, the new perspective toward public space will be drawn out. First, we need to emphasize the fluid spectrum of public space rather than the serial stage such as the public, the semi-public, the semi-private, and the private. Second, the idea will contribute to understanding the flexible state depending upon time. What we can learn from case studies is the volatile characteristics in public space as a common phenomenon support its vitality. This interpretation will contribute to the perception of a new horizon of public space. The nature of public space is unpredictable and free. In reality, the spectrum of public space will expand and fluctuate. Ironically, public space can be vitalized through enhancing and activating the private space. The intimate and complicated interface between the two realms is a key issue. The boundary of public space might be redefined to embrace the flexible the fragile nature of changing public space. These research implications will guide the thoughtful design and management of pubic space.