Search Results - (( based optimization model algorithm ) OR ( data normalization learning algorithm ))

One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

“…In this project, an Artificial Neural Network (ANN) trained by the Invasive Weed Optimization (IWO) learning algorithm is proposed for short term load forecasting (STLF) model. …”
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Flock optimization algorithm-based deep learning model for diabetic disease detection improvement by Balasubramaniyan, Divager, Husin, Nor Azura, Mustapha, Norwati, Mohd Sharef, Nurfadhlina, Mohd Aris, Teh Noranis

“…Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…The best algorithm will not be the same for all the data sets. …”
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Performance evaluation of intrusion detection system using selected features and machine learning classifiers by Raja Mahmood, Raja Azlina, Abdi, AmirHossien, Hussin, Masnida

“…These evolutionary-based algorithms are known to be effective in solving optimization problems. …”
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Characterization of oil palm fruitlets using artificial neural network by Olukayode, Ojo Adedayo

“…The results also showed that contrary to the widely reported gap between the accuracy of the LM algorithm and other feed forward neural network training algorithms, the RP trained network performed as good as that of the LM algorithm for the range of data considered. …”
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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli by Ahmad Kamaruddin, Saadi, Md. Ghani, Nor Azura, Mohamed Ramli, Norazan

“…The backpropagation algorithm is one of the most famous algorithms to train neural network based on the mean square error (MSE) of ordinary least squares (OLS). …”
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IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management by Eugine Teh, Yin Jie, Chee Soon, Chong, Rozaimi, Ghazali, Hazriq Izzuan, Jaafar, Muhamad Fadli, Ghani, Howe Cheng, Teng, Nur Farhanah, Zulkipli, Siaw Hong, Liew

“…Advanced data preprocessing, such as augmentation and normalization, ensures robust model training, while optimized algorithms guide waste sorting based on classification results. …”
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The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

“…The architecture of artificial neural network (ANN) laid the foundation as a powerful technique in handling problems such as pattern recognition and data analysis. It’s data-driven, self-adaptive, and non-linear capabilities channel it for use in processing at high speed and ability to learn the solution to a problem from a set of examples. …”
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Deep learning-based item classification for retail automation by Ling, Ji Xiang

“…The CNN model was optimized for both accuracy and speed, incorporating regularization techniques such as dropout and batch normalization. …”
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Predicting Market Trends : A Stock Prices Forecasting with Artificial Neural Network for Apple Inc. and Microsoft Corp. by Nur Fazliana, Rahim, Farah Liyana, Azizan, Nur’azra Alia Nisa, Zulpakar

“…Consequently, numerous studies have explored the use of machine learning for stock price forecasting. Hence, this study employs an Artificial Neural Network model as a machine learning algorithm for forecasting stock prices. …”
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Hybrid neural network in medicolegal degree of injury determination based on Visum et Repertum by Wardhana, Mohammad Hadyan

“…Pre-processing phase overcomes the issue of incomplete data by performing data cleansing and data normalization. …”
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Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods by Chin, Ying Teh, Tay, Kai Meng, Chee, Peng Lim

“…The same observation can be made, empirically, using a system identification method, e.g., a derivative–based optimization method and the genetic algorithm. …”
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Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS by Zhang, Guanjin, Roslan, Siti Nur Aliaa, Mohd Shafri, Helmi Zulhaidi, Zhao, Yanxi, Wang, Ci, Quan, Ling

“…In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines by Aker, Elhadi Emhemed Alhaaj Ammar

“…The BayesNet provides the best integrated MLADR fault classifier model better at a 5 % significance level than other deployed algorithms in the intelligent supervised learning model realization. …”
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Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models. by Kwad, Ayad Mahmood

“…Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. …”
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Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

“…Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. …”
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Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo