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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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River segmentation using satellite image contextual information and Bayesian classifier by Yousefi, Paria, Jalab, Hamid Abdullah, Ibrahim, R.W., Mohd Noor, Nurul Fazmidar, Ayub, M.N., Gani, Abdullah

“…This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms by Essam Y., Huang Y.F., Ng J.L., Birima A.H., Ahmed A.N., El-Shafie A.

Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

“…As a result, 60% training with five hidden nodes demonstrated the best performance with R- value of 0.827 and MSE value of 52.283. The ANN-based models could serve as reliable and useful tools in estimating the WQI of the river.…”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms by Chia, See Leng

“…Least Square Support Vector Machine (LSSVM) base models with linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel and its hybrid models with integration of Hybrid of Particle Swarm Optimisation and Genetic Algorithm (HPSOGA), Whale Optimisation Algorithm based on Self-adapting Parameter Adjustment and Mix Mutation Strategy (SMWOA) and Ameliorative Moth Flame Optimisation (AMFO) were developed and used to predict the WQI at stations 1K06, 1K07 and 1K08 of the Klang River in Selangor, Malaysia. …”
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Time series modeling of water level at Sulaiman Station, Klang River, Malaysia by Galavi, Hadi

“…Using the cross validation method the best training subset is selected to train the ANFIS model based on that dataset. …”
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Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm by Mohd Sharif, Sharifah, Mohd Kusin, Faradiella, Asha’ari, Zulfa Hanan, Aris, Ahmad Zaharin

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Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow by Osman A., Afan H.A., Allawi M.F., Jaafar O., Noureldin A., Hamzah F.M., Ahmed A.N., El-shafie A.

Water Pollution Source Identification Based on Real-Time Monitoring System in Siak River by Kadir, E.A., Listia Rosa, S., Othman, M.

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Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition by Fong, Wai Mei

“…In this project, several neural networks will be developed and their performance are compared to yield the most suitable network that will be used to model the classification system for determination of river water quality based on algae composition. …”
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Java based expert system for selection of natural fibre composite materials by Ahmed Ali, Basheer A., Salit, Mohd Sapuan, Zainudin, Edi Syams, Othman, Mohamed

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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

“…This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. …”

Implementation of (AES) Advanced Encryption Standard algorithm in communication application by Moh, Heng Huong

“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning by Costache R., Pal S.C., Pande C.B., Islam A.R.M.T., Alshehri F., Abdo H.G.

“…The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance � 20%), distance from river (importance � 17.5%), land use (importance � 12%) and TPI (importance � 10%). …”

Application of artificial intelligence algorithms for hourly river level forecast: a case study of Muda River, Malaysia by Zakaria, Muhamad Nur Adli, Abdul Malek, Marlinda, Zolkepli, Maslina, Ahmed, Ali Najah

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Evaluation of land use land cover changes impacts on water quality at Nerus River using geospatial techniques / Noor Azzatul Najwa Azman by Najwa Azman, Noor Azzatul

“…This sample will be used in WQI algorithm to determine the WQI of the Nerus River whether in excellent, good or poor condition. …”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…Seven performance indexes were examined to evaluate the performance of the proposed Muskingum model integrated with IBA, with other models that were also based on the Muskingum Model with three-parameters but utilized different optimization algorithms. …”
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Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster by Afan H.A., Yafouz A., Birima A.H., Ahmed A.N., Kisi O., Chaplot B., El-Shafie A.

Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia by Ahmed A.N., Yafouz A., Birima A.H., Kisi O., Huang Y.F., Sherif M., Sefelnasr A., El-Shafie A.

“…In this study, six different Machine Learning (ML) algorithms were developed to predict the river�s water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. …”