Feng Shui Garden adviser System (FengShuiGAS)

This paper explores an approach to building an adaptive expert system prototype in an environment of human-computer collaboration. Components of an adaptive system are identified, with an emphasis on the mechanisms that enable adaptive behavior to occur.An adaptive expert system is necessary in order to communicate with the user and also adapts to user’sneeds. The adaptive expert system in this particular project is implemented on a Feng Shui garden design domain.A frame-based data representation and rule-based approach is applied to this project. In this research, the Feng Shui aspiration is adapted to users’ assessment and choice based on their preferences. This experimental expert system prototype displays low level adaptive capabilities that show sufficient promise to warrant further research

Designing a Chatbot-Enabled Laptop Diagnostic Assistant

This paper proposes a chatbot developed with deep learning techniques to help people troubleshoot operating system errors in laptops. In today's world, people can't wait for anything and expect an immediate response when they have a question because they want their problems solved quickly and completely. The system addresses the software aspect of technical laptop issues concerning a laptop's operating system. Deep learning is used to create the chatbot because it has been shown to be more accurate in selecting its response when conversing with users. The chatbot will be integrated into Telegram, an instant messaging service, and users will be able to communicate about laptop issues via Telegram

Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks

Comparative study of apriori-variant algorithms

Big Data era is currently generating tremendous amount of data in various fields such as finance, social media, transportation and medicine. Handling and processing this “big data” demand powerful data mining methods and analysis tools that can turn data into useful knowledge. One of data mining methods is frequent itemset mining that has been implemented in real world applications, such as identifying buying patterns in grocery and online customers’ behavior.Apriori is a classical algorithm in frequent itemset mining, that able to discover large number or itemset with a certain threshold value. However, the algorithm suffers from scanning time problem while generating candidates of frequent itemsets.This study presents a comparative study between several Apriori-variant algorithms and examines their scanning time.We performed experiments using several sets of different transactional data.The result shows that the improved Apriori algorithm manage to produce itemsets faster than the original Apriori algorithm

CLASSIFICATION OF PADDY WEED LEAF USING NEURO-FUZZY METHODS

Paddy weed appears to be one of the many visible threats to paddy crop production and subsequently farmers’ income. It is for this reason that the growth of paddy weeds in paddy fields should be controlled as it results in a significant decrease of paddy yields. However, farmers might have limited knowledge on weed types, and are thus unable to identify and determine the right prevention methods. This paper presents classification methods for paddy weeds through the leaf shape extraction and applies neuro-fuzzy methods for recognizing the types of weeds. The types being focussed are the Sphenoclea zeylanica, Ludwigia hyssopifolia and Echinochloa crus-galli. The developed e-prototype methods would be able to classify paddy weeds with 83.78% accuracy. Hopefully, the findings in this study would assist farmers and researchers in increasing their paddy yields and eliminating weed growth respectively. The production of paddy in Malaysia would eventually be improved with the proposed methods, which can be considered as a technology advancement in the field of paddy production

Courier delivery services visualisor (CDSV) with an integration of genetic algorithm and A* engine

Online shopping has become one of the popular mediums for people to use online transactions due to its economical and easiness.It is more convenient to those who simply do not have time to shop physically and prefer delivery service. However, the courier services nowadays are unable to keep up with the increasing consumer demand. The problem is caused by the delivery process that is not synchronized due to the problem of finding the best route of distribution. Distributors are unable to plan their distribution path with the minimal distance.Furthermore distributors are only able to reach each district distribution centre once a day and revisit the distribution centre will increase the time spent and operation cost. This study developed Courier Delivery Services Visualisor (CDSV) that is able to visualize the best route to be taken by distributor, so that the courier service can arrive on time.CDSV employed Genetic Algorithm (GA) and Astar Algorithm (A*) that integrates with Geographical Information System (GIS) data.A graphical user interface in the form of simulation map that suggests the best route and the optimal distance are displayed for easier courier service distribution references

Sentiment classification from reviews for tourism analytics

User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain

Classification prediction of PM10 concentration using a tree-based machine learning approach

The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution