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PREDICTING THE PRICE OF COTTON USING RNN AND LSTM by MOHAMAD, AHMAD LUKMAN

“…Prediction of the cotton price will enable companies to hedge their position in contract for difference market to minimize the effect of volatile price fluctuation and help to stabilize the supply chain of cotton. …”
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Enhanced foreign exchange volatility forecasting using CEEMDAN with optuna-optimized ensemble deep learning model by Kausar, Rehan, Iqbal, Farhat, Raziq, Abdul, Sheikh, Naveed, Rehman, Abdul

“…Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. …”
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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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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach by Pourdaryaei, Alireza, Mokhlis, Hazlie, Illias, Hazlee Azil, Kaboli, S. Hr. Aghay, Ahmad, Shameem

“…Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. …”
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A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar by Noorsal, Emilia, Osman, Muhammad Khusairi, Mokhtar, Norfadzilah

“…This project focuses only on the development of an Artificial Neural Network (ANN) for the classification of Volatile Organic Compounds (VOCs). …”
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Inter-cell and intra-cell facility layout models under different demand environments in cellular manufacturing systems by Ariafar, Shahram

“…Moreover, the computation time (CPU Time) of the developed SA algorithm is significantly less than the benchmarked algorithm. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Performance monitoring algorithm for optimizing electrical power generated by using photovoltaic system by Pradeep, M.V.K., Balbir, S.M.S., Norani, M.M.

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Development of intelligent portable electronic nose by Abu Hassan, Abdullah, Abdul Hamid, Adom, Ali Yeon, Md Shakaff, Mohd Noor, Ahmad, A., Zakaria, Marni Azira, Markom, E.S., Tan, N.A., Fikri, Rakhmad Arief, Siregar, Mahamad Akmal, Saad

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Optimization of food waste to sewage sludge ratio for anaerobic co-digestion process using Artificial Neural Network (ANN) and Genetic Algorithm (GA) by Mansor, Mariatul Fadzillah, Jamaludin, Nurul Syazwana, Tajuddin, Husna Ahmad

“…The performance of the ANN model was verified to be effective in predicting the methane production accurately with a desirable R2-value of 0.9838 and 0.9976. …”
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Short term electricity price forecasting with multistage optimization technique of LSSVM-GA by Razak I.A.W.A., Abidin I.Z., Siah Y.K., Abidin A.A.Z., Rahman T.K.A.

“…Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. …”

Short Term Electricity Price Forecasting With Multistage Optimization Technique Of LSSVM-GA by Wan Abdul Razak, Intan Azmira, Zainal Abidin, Izham, Keem Siah, Yap, Zainul Abidin, Aidil Azwin, Abdul Rahman, Titik Khawa

“…Price prediction has now become an important task in the operation of electrical power system.In short term forecast,electricity price can be predicted for an hour-ahead or day-ahead.An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour.It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour.However,only a few studies have been conducted in the field of hour-ahead forecasting.This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than singlesettlement system (real time).Therefore,a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features.So far,no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction.All the models are examined on the Ontario power market;which is reported as among the most volatile market worldwide.A huge number of features are selected by three stages of optimization to avoid from missing any important features.The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.…”
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Corrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique by Adi Hafizamri Ariffin, Wan Mohd Norsani Wan Nik, Samsuri Abdullah, Mohd Ikmar Nizam Mohamad Isa, Vincent Izionworu, Mohammad Fakhratul Ridwan Zulkifli