Search Results - (( data transformational model algorithm ) OR ( java implication based algorithm ))

Data Transformation Model For Addressing Incomplete And Inconsistent Quality Issues Of Big Data by Onyeabor, Grace Amina

“…The practical contribution is the provision of enhanced data transformation models for DQ leading to better data analysis and strategic planning.…”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

“…The proposed algorithm named as Box-Whisker Data Transformation considered all samples contain in a MLCC dataset in order to generate artificial samples. …”
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Data mining techniques for transformer failure prediction model: A systematic literature review by Ravi N.N., Mohd Drus S., Krishnan P.S.

Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data by Terence Chia Yi Kai, Agus Saptoro, Zulfan Adi Putra, King Hann Lim, Wan Sieng Yeo, Jaka Sunarso

“…Industrial process time series data could be processed with ease by deep learning algorithms, particularly transformer-based models because of their multi-head attention mechanism. …”
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CSI-based human activity recognition via lightweight compact convolutional transformers by Wong, Yan Chiew, Abuhoureyah, Fahd Saad Amed, Al-Taweel, Malik Hasan, Abdullah, Nihad Ibrahim

“…By leveraging the strengths of both CNNs and transformer models, the CCT algorithm achieves state-of-the-art accuracy on various benchmarks, emphasizing its excellence over traditional algorithms. …”
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Volatility forecasting with the wavelet transformation algorithm GARCH model: Evidence from African stock markets by Ismail, Mohd Tahir, Audu, Buba, Tumala, Mohammed Musa

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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques by Salami, Momoh Jimoh Eyiomika, Ismail, Z.

“…Direct deconvolution approach often leads to poor resolution of ihe estimated decay rates since the fast Fourier transform (FFT) algorithm is used to analyze the resulting deconvolved data. …”
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Assisted History Matching by Using Genetic Algorithm and Discrete Cosine Transform by Abdul Rashid, Abdul Hadi

“…Next, fluid flow equations were derived to obtain the forward model and eventually, the objective function. Later, an algorithm combining both Genetic Algorithm and Discrete Cosine Transform was proposed, which shows the step-by-step sequence of both methods. …”
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Penggunaan algoritma genetik bagi menentukur model anjakan mod pengangkutan by Siti Hajar, Riza Atiq, Amiruddin Ismail, Hassan Basri

“…Logarithmic transformation together with linear regression are usually used to calibrate the model. …”
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Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

“…Good planning and controlling procedure would lead to successful business.There are two categories of forecasting techniques; namely qualitative and quantitative.Qualitative technique is more towards judgmental forecasting and usually used when data is limited. While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid by Khalid, Nurul Syafiqah

“…Lastly, to compare the accuracy assessment of watershed transformation segmentation and local maxima algorithm. …”
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Development of machine learning-based algorithm to determine the condition in transformer oil by Mohsen Al-Katheri, Hussein Hasan

“…The interpretation of dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This paper aims to develop a model for taking into consideration the results obtained from DGA to investigate the condition of transformer oil fault. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows by Zhang, Shasha, Dong, Qiming, Yasin, Megat Al Imran, Fang, Ng Chwee

“…The Multimodal Transformer Fusion (MMTF) model uses the cross-modal attention mechanisms to combine these streams of data to produce unified emotional representations. …”
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Transformer T-joint optimization using particle swarm optimization and hemisphere-shape design of the core by Yehya, Omar Sharaf Al-Deen

“…The transformer was simulated on the basis of real dimensions obtained from the transformer manufacture’s data. …”
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Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-... by Abdullah, Nor Azliana, Rahim, Nasrudin Abd, Gan, Chin Kim, Nor Adzman, Noriah

“…In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. …”
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Computationally efficient single layer transformer convolutional encoder for accurate price prediction of agriculture commodities by Bundak, Caceja Elyca, Abd Rahman, Mohd Amiruddin, Mohd Haniff, Nurin Syazwina, Afrizal, Nur Syaiful, Yusof, Khairul Adib, Abdul Karim, Muhammad Khalis, Mamat, Md Shuhazlly, Rahmat, Romi Fadillah

“…To obtain accurate predictions, the process usually involves large and complex datasets, which would add to computational costs for developing a model with good performance. Therefore, this study introduces the single-layer Transformer Convolutional Encoder algorithm (STCE), an improved version of the traditional transformer encoder. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques by Salami, Momoh Jimoh Emiyoka, Sidek, Shahrul Na'im

“…In this method of analysis the exponential signal is converted to a convolution model whose input is a train of weighted delta function that contains the signal parameters to be determined.The resolution of the estimated decay rates is poor if the conventional fast Fourier transform (FFT) algorithm is used to analyse the resulting deconvolved data. …”
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