A Study of Interrelation Between Ratings and User Reviews in Light of Classification

Mondal, Pritam; Ghosh, Anupam; Sinha, Abhirup; Goswami, Saptarsi

doi:10.1007/978-981-13-7403-6_60

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Except for ANNs and DNNs, numerous algorithms have been applied to predicted models. For example: Levenberg-Marquardt has been employed to estimate the state-of-charge of lithium-ion batteries [23]; Conjugate gradient with Powell/Beale restarts have been applied to plan the path of catering robots [24]; Polak-Ribiere conjugate gradient has been utilized to accumulate the global convergence of nonconvex functions [25]; Fletcher-Powell conjugate gradient has been used to predict component self-alignment [26]; One step secant has been applied to train a cascade ANN [27]; Resilient Backpropagation has been employed to improve the optical coherent transmission [28]; Bayesian regularization has been utilized to solve the global optimization problems [29]; Variable learning rate gradient descent has been employed to regulate the weight and threshold values of layers [30]; Support vector machine regression (Gaussian) has been utilized for heating and cooling load predictions [31]; Linear programming boosting has been employed to non-intrusive load monitoring systems [32]; Adaptive boosting has been improved to automatic wireless signal classification [33]; Extra trees classifier has been applied into natural language processing [34]; Broyden-Fletcber-Goldfarb-Shanno Quasi-Newton has been applied for brain image segmentation [35]; Moving average method has been employed to predict the solar power outputs [36]; Decision tree has been utilized to predict high-risk kidney transplantation [37]; Random subspace binary and multi-class has been applied for disease diagnosis [38]; Support vector machine regression (Linear) has been utilized to predict multi-parameter manufacturing quality [39]; Multiple proportion smoothing method has been applied to the STLF [40]; Random under sampling boosting has been employed to detect non-technical losses of electric distribution systems [41].…”

Section: Introductionmentioning

confidence: 99%

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

et al. 2020

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Deep neural networks of deep learning algorithms can be applied into regressions and classifications. While the regression performances and classification performances of the deep neural networks are depending on the hyper-parameters of the deep neural networks. To mitigate the adverse effect of the hyper-parameters for the deep learning algorithms, this paper proposes deep forest regression for the short-term load forecasting of power systems. Deep forest regression includes two procedures, i.e., multi-grained scanning procedure and cascade forest procedure. These two procedures can be effectively trained by two completely random forests and two random forests with the default configuration. Then, the deep forest regression is applied into the short-term load forecasting of power systems. The forecasting performances of deep forest regression are compared with that of numerous intelligent algorithms and conventional regression algorithms under the model with the data of previous 7-day, 21-day, and 40-day. Besides, the forecasting performances of deep forest regression with different parameters are compared. The numerical results show that the deep forest regression with default configured parameters can increase the accuracy of the short-term forecasting and mitigate the influences of the experiences for the configuration of the hyper-parameters of deep learning model. INDEX TERMS Deep forest regression, short-term load forecasting, multi-grained scanning procedure, cascade forest procedure.

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Section: Introductionmentioning

confidence: 99%

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

et al. 2020

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“…Interelasi yang dimaksud dalam penelitian tersebut adalah memetakan ayat-ayat Al-Qur'an sesuai dengan temanya. Dalam penelitian Mondal et al [10] juga dilakukan interelasi antara teks review pengguna dengan rating bintang yang diberikan oleh pengguna di profil bisnis apapun. Penelitian tersebut menggunakan algoritma klasifikasi KNN untuk menemukan interelasi atau hubungan antara teks review pengguna dengan rating bintang tersebut.…”

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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