Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

Al-Sit, Waleed T.

doi:10.30534/ijatcse/2020/235932020

Cited by 4 publications

(7 citation statements)

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“…FTS is a set of terms, which are observed following time points, of a random variable. Some basis concepts of FTS are recalled from papers [1] in the following. Definition 1.…”

Section: Fuzzy Time Seriesmentioning

confidence: 99%

“…Let AX = (X, G, C, H, ) be a ClinHA. An fm: X  [0,1] is said to be a fuzziness measure of terms in X if: (1). fm(c  )+fm(c + ) = 1 and ( )…”

Section: Hedge Algebrasmentioning

confidence: 99%

“…fm hx fm hy fm x fm y  Proposition 1. For each fuzziness measure fm on X the following statements hold: (1). fm(hx) = (h)fm(x), for every x  X; (2).…”

Section: Hedge Algebrasmentioning

confidence: 99%

“…Time series forecasting continues to be a hot topic [1][2][3]. There are many approaches to solving this problem, in which using the fuzzy time series model is a fairly effective way.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

2020

IJATCSE

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The aim of this paper is to introduce a new model of fuzzy time series (FTS) based on hedge algebras (HA) with three improvements compared with previous ones. Firstly, the new way of partitioning the universe of discourse is applied, secondly, the relationship groups following time points to determine relationships among fuzzy time series values are used, thirdly, weighs following time points are employed to build formula for computing forecasting values. The empirical results indicate that the proposed model outperform the existing models.

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“…FTS is a set of terms, which are observed following time points, of a random variable. Some basis concepts of FTS are recalled from papers [1] in the following. Definition 1.…”

Section: Fuzzy Time Seriesmentioning

confidence: 99%

“…Let AX = (X, G, C, H, ) be a ClinHA. An fm: X  [0,1] is said to be a fuzziness measure of terms in X if: (1). fm(c  )+fm(c + ) = 1 and ( )…”

Section: Hedge Algebrasmentioning

confidence: 99%

“…fm hx fm hy fm x fm y  Proposition 1. For each fuzziness measure fm on X the following statements hold: (1). fm(hx) = (h)fm(x), for every x  X; (2).…”

Section: Hedge Algebrasmentioning

confidence: 99%

“…Time series forecasting continues to be a hot topic [1][2][3]. There are many approaches to solving this problem, in which using the fuzzy time series model is a fairly effective way.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

2020

IJATCSE

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“…According to the latest research, almost half the world's population uses social networks [2]. Advertising headlines were found in newspapers and banners before the existence of the Internet [3]. With the emergence of different utilities on the Internet, clickbait has gained popularity [4].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection

Razaque

Alotaibi

et al. 2022

IEEE Access

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When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blacklisted/white-listed source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchainenabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker's successful content capturing rate.

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Predicting Highest Professional Qualification Applying the Usage of Strategic Management Tools and Techniques and Years of Professional Experience

Oletubo¹,

Iroham²,

Ajibola³

et al. 2022

ADAS

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Professional service firms employ strategic management (SM) in achieving their long term goals. Estate valuation and surveying firms, a subset of professional firms, are not an outlier in SM practices and strategic management tools and techniques (SMTT) usage. The aim of this paper is to predict the highest professional qualification of Estate Surveyors and Valuers (ESVs) in Southwest Nigeria, applying the degree of usage of strategic management tools and techniques (SMTTs) and years of professional experience. In this context, the highest professional qualification is being a Fellow of the Nigerian Institution of Estate Surveyors and Valuers (FNIESV), a step above being an Associate (ANIESV). The data is from a field survey and data mining models were used. The result showed that professional status (FNIESV or ANIESV) could be classified based on SMTT usage and years of professional experience. Adaptive boosting gave the best classification accuracy, specificity, and other evolution metrics.

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Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

Cited by 4 publications

References 0 publications

Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection

Predicting Highest Professional Qualification Applying the Usage of Strategic Management Tools and Techniques and Years of Professional Experience

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