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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Exploration of COVID‑19 data in Malaysia through mapper graph by Carey Ling, Yu Fan, Piau, Phang, Liew, Siaw Hong, Vivek Jason, Jayaraj, Benchawan, Wiwatanapataphee

“…Still, the hierarchical clustering does not further subdivide Selangor data into two to three separate clusters as the Mapper algorithm does. …”
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Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Event feedback analytic and visualization through Twitter social platform / Muhammad Faiz Sharan by Sharan, Muhammad Faiz

“…The sentiment of the feedback will be classify using machine learning algorithm in two categories which are positive and negative feedback. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Predicting the onset of acute coronary syndrome events and in-hospital mortality using machine learning approaches / Song Cheen by Song , Cheen

“…This study used a comprehensive methodology to investigate the relationship between air pollution and ACS patient outcomes utilizing machine learning (ML) algorithms, including: 1) Linear Regression, 2) Logistic Regression, 3) Support Vector Machine (SVM), 4) Random Forest (RF), 5) XGBoost, 6) Naïve Bayes (NB), and 7) Stacked Ensemble ML utilizing data from the National Cardiovascular Disease Database (NCVD) Malaysia registry and air quality data from the Department of Environment (DOE) Malaysia. …”
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AI powered asthma prediction towards treatment formulation : An android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Muzahid, Abu Jafar Md, Sarker, Md. Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md.

“…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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Analyzing UiTMCTKKT vehicle utilization and travel pattern using predictive analytics by Syed Mohamad, Sharifah Masyitah

“…The primary objective of this project is to identify vehicle usage trends, monitor driver workload, and predict day types associated with peak or low demand periods. The project does not involve scheduling automation but instead emphasizes visualizing mileage and trip frequency to assist VMU in planning maintenance schedules, rotating vehicle use, and supporting budget allocation. …”
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Predicting saliency existence using reduced salient features based on compactness and boundary cues by Nadzri, Nur Zulaikhah

“…The selected salient features were trained, tested and compared on 3 learning algorithms which included generalised linear regression, Naïve Bayes, and Support Vector Machine. …”
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Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification by Sobhan Sheykhivand, Tohid Yousefi Rezaii, Zohreh Mousavi, Azra Delpak, Ali Farzamnia

“…Furthermore, this work does not require additional preprocessing and extraction of features, which is common in the existing methods. …”
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