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All Journal Knowledge Engineering and Data Science International Journal of Robotics and Control Systems
Mohammad Yasser Chuttur
University of Mauritius
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study A. Zaynah Dhunny; Reena Hansa Seebocus; Zaheer Allam; Mohammad Yasser Chuttur; Muhammed Eltahan; Harsh Mehta
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p1-10

Abstract

Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features are minimum temperature, maximum temperature, rainfall and humidity and our output decision is „flood‟ or „no flood‟. Using ANN, we achieved an accuracy of 98% for flood prediction and hence, we conclude that ANN is indeed a good predictor for flood occurrence even for regions with predominantly microclimatic conditions.
A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence Mohammad Yasser Chuttur; Yashinee Parianen
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p41-52

Abstract

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.
Optimizing Random Forest Algorithm to Classify Player's Memorisation via In-game Data Akmal Vrisna Alzuhdi; Harits Ar Rosyid; Mohammad Yasser Chuttur; Shah Nazir
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p103-113

Abstract

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. Then, we trained and optimized three Random Forest (RF) classifiers to predict the player's Memorization. We chose RF because it can generalize well given the high-dimensional dataset. We used RF as the classifier, subject to optimization using its hyperparameter: n_estimators. We implemented a Grid Search Cross Validation (GSCV) method to identify the best value of  n_estimators. We utilized the statistics of GSCV results to reduce the weight of  n_estimators by observing the region of interest shown by the graphs of performances of the classifiers. Overall, the classifiers fitted using the BEST n_estimators (i.e., 89, 31, 89, and 196 trees) from GSCV performed well with around 80% accuracy. Moreover, we successfully identified the smaller number of n_estimators (OPTIMAL), at least halved the BEST  n_estimators. All classifiers were retrained using the OPTIMAL  n_estimators (37, 12, 37, and 41 trees). We found out that the performances of the classifiers were relatively steady at ~80%. This means that we successfully optimized the Random Forest in predicting a player's Memorization when playing the Chem Fight game. An automated technique presented in this paper can monitor student interactions and evaluate their abilities based on in-game data. As such, it can offer objective data about the skills used.
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Agung Bella Putra Utama; Aji Prasetya Wibawa; Anik Nur Handayani; Mohammad Yasser Chuttur
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

Abstract

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
Co-Authors A. Zaynah Dhunny Agung Bella Putra Utama Aji Prasetya Wibawa Akmal Vrisna Alzuhdi Anik Nur Handayani Harits Ar Rosyid Harsh Mehta Muhammed Eltahan Reena Hansa Seebocus Shah Nazir Yashinee Parianen Zaheer Allam