The Covid-19 pandemic has driven the educational sector to transform the traditional face to face... more The Covid-19 pandemic has driven the educational sector to transform the traditional face to face learning to an online education platform. Given the global impact of the pandemic on the educational sector, this study utilizes a quantitative research method that attempts to find out the effect of Covid-19 on undergraduate computing students' performance at higher education in Malaysia and Pakistan. This paper proposed a research model that focus on the accessibility, proficiency, financial circumstance, assessment, self-concern, and student-support in affecting students' performance during Covid-19 confinement. A pilot-test was conducted to test on the research model and to validate the instrument. The findings showed that all composite reliability values in Malaysia and Pakistan were found to meet the threshold value of 0.70, suggesting strong indicator reliability of the constructs.
The aim of this research is to analyse the performance of six different classifiers, which are κ-... more The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%
International journal of engineering & technology, Dec 13, 2018
Previous surveys proved that data mining is one of the methods that can be utilized for climate p... more Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.
Knowledge acquisition is one of important aspect of Knowledge Discovery in Databases to ensure th... more Knowledge acquisition is one of important aspect of Knowledge Discovery in Databases to ensure the correct and interesting knowledge is extracted and represented to the stakeholders and decision makers. The process can undertake using several techniques as such in this study, it is using data mining to extract the knowledge patterns and representing the knowledge described using ontology based representation. In this paper, a data set of Logistic Cargo Distribution is selected for the experiment. The dataset describes the shipment of logistic items for the Malaysian Army.
Crosswinds are one of the natural disasters that pose risks to high-profile vehicles especially o... more Crosswinds are one of the natural disasters that pose risks to high-profile vehicles especially on the highway. There are possibilities of fatal accidents when drivers and motorists failed to control their vehicles when this dangerous wind blows perpendicular to the vehicles. Even though, each area of the crosswinds is placed with crosswinds signs (i.e. windsocks) but these signs are not so obvious especially when there are obstacles such as rain, poor lighting and fog. In addition, the colour of the windsock itself is faded due to the exposure to the various changes of the weather and environments. Here, the Wind Speed Detection System is proposed in helping to monitor and manage this critical situation by providing crucial information to the vehicles and motorists regarding the crosswinds conditions, plan and preparation and many others. The system is able to measure the wind speed level and alert the motorists and vehicles with warning lamp/light according to the crosswinds conditions. Furthermore, the system can record the wind speed reading and save it into a file that can be further analysed.
The analysis of relation between student performance and other variables in education setting is ... more The analysis of relation between student performance and other variables in education setting is often useful in identifying influential factors on performance. Consequently, the need for adopting an effective tool to process these big data has risen. The analysis of big data will transform passive data into useful information. Data mining is referred to an analytic process designed that discovers data patterns and relationships between datasets. In this study, clustering is used to cluster student grade datasets to generate trend line clusters. The aim of the study is to assist lecturers and academic advisors to recognize the progress of their students.
Malware threat poses consequences, damage, and impact to businesses, resulting in financial losse... more Malware threat poses consequences, damage, and impact to businesses, resulting in financial losses, operation interruption and reputation damage. The cybercriminal takes advantage of organisations that are not protected by using malware to penetrate the organisation's system or services. This can lead to data breaches, intrusion attacks, confidential data losses or ransomware attacks that completely cease business operations. SMEs also need to be prepared against malware attack since the business is part of the supply chain ecosystem, and the operation is highly dependent on IT technology which can also be exposed to cyber threats. This paper aims to develop a new framework for addressing malware threats in strategic ways for the SME's organisation to immediately access the risk situation and plan proper contingency and remedial action.
International journal of engineering & technology, Dec 13, 2018
This preliminaries study aims to propose a good classification technique that capable of doing do... more This preliminaries study aims to propose a good classification technique that capable of doing document classification based on text mining technique and create an algorithm to automatically classify document according to its folder based on document's content while able to do sentiment analyses to data sets and summarize it. The objective of this paper to identify an efficient text mining classification technique which can resulted with highest accuracy of classifying document into document folder, capable of extracting valuable information from context-based term that can be used as an output for algorithm to do automatic classification and evaluate the classification technique. Methodology of this study comprises in 5 modules which is 1) Document collection, 2) Pre-Processing Stage, 3) Term Frequency-Inversed Document Frequency, 4) Classification Technique and Algorithm, and lastly 5) Evaluation and Visualization of the classification result. The proposed framework will have utilized Term Frequency-Inversed Document Frequency (TF-IDF) and Decision Tree technique which TF-IDF used as purposes to rank all the terms based on most frequent to least frequent terms so, while decision tree function as decision making in terms of deciding which folder the document belongs to.
Many studies have been conducted to determine how data mining can be used in predicting climate c... more Many studies have been conducted to determine how data mining can be used in predicting climate change. Previous studies showed many data mining methods have been used in related to climate prediction, however classification and clustering methods are widely used to generate the climate prediction model. In this study, Association Rule Mining (ARM) is used to discover hidden rules in time series climate data from previous years and to analyze the relationship between the discovered rules. The dataset used in this study is a set of weather data from the Petaling Jaya observation station in Selangor for the year 2013 to 2015. This paper aims to utilize ARM for extracting behavioural patterns within the climate data that can be used to develop the prediction model for climate variability. The proposed framework is developed to provide a better approach in understanding how ARM can be used to find meaningful patterns in the climate data and generate rules that can be used to build a prediction model.
This paper describes a technique of text analytics on peacekeeping documents to discover signific... more This paper describes a technique of text analytics on peacekeeping documents to discover significant text patterns exist in the documents. These documents are considered as unstructured textual data.The paper proposes a framework that consists of 3 stages (i) data collection (ii) document preprocessing and (iii) text analytics and visualization.The technique is developed using R text mining package for text analytics experiments
Our planet is known as a digital earth, circulating around data. Growth in data is exponential, l... more Our planet is known as a digital earth, circulating around data. Growth in data is exponential, leading to an elevated interest in Big Data Analytics, to collect, store, process, analyze and visualize unparalleled amount of data. Modern information driven society will continue to be shaped by big data, where there will be potential to extract meaningful insights and hidden patterns impacting businesses in unforeseen measures. Most employers in Malaysia provide medical benefits which includes general medical costs to hospitalization benefits and insurance coverages; with these data and information stored by the HR (Human Resource), leading to a potential to analyze and identify patterns in historical claims - these insights would lead to improved decision making to better understand employee population health and the usage of the premium coverage. In predictive analysis, common techniques applied are Decision Tree and Regression. Therefore, the aim of this research is to propose a conceptual prediction model to better understand the patterns present in the employee healthcare data while predicting if an employee would be at any health risks to understand the population health and the usage of premium coverage provided by the employer. Additionally, to apply an ensemble method called Stacking, where multiple predictive models will be combined to perform a prediction. An ensemble model will present the opportunity to build a more robust and accurate model which could be applied across various industries instead of being industry specific.
The Covid-19 pandemic has driven the educational sector to transform the traditional face to face... more The Covid-19 pandemic has driven the educational sector to transform the traditional face to face learning to an online education platform. Given the global impact of the pandemic on the educational sector, this study utilizes a quantitative research method that attempts to find out the effect of Covid-19 on undergraduate computing students' performance at higher education in Malaysia and Pakistan. This paper proposed a research model that focus on the accessibility, proficiency, financial circumstance, assessment, self-concern, and student-support in affecting students' performance during Covid-19 confinement. A pilot-test was conducted to test on the research model and to validate the instrument. The findings showed that all composite reliability values in Malaysia and Pakistan were found to meet the threshold value of 0.70, suggesting strong indicator reliability of the constructs.
The aim of this research is to analyse the performance of six different classifiers, which are κ-... more The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%
International journal of engineering & technology, Dec 13, 2018
Previous surveys proved that data mining is one of the methods that can be utilized for climate p... more Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.
Knowledge acquisition is one of important aspect of Knowledge Discovery in Databases to ensure th... more Knowledge acquisition is one of important aspect of Knowledge Discovery in Databases to ensure the correct and interesting knowledge is extracted and represented to the stakeholders and decision makers. The process can undertake using several techniques as such in this study, it is using data mining to extract the knowledge patterns and representing the knowledge described using ontology based representation. In this paper, a data set of Logistic Cargo Distribution is selected for the experiment. The dataset describes the shipment of logistic items for the Malaysian Army.
Crosswinds are one of the natural disasters that pose risks to high-profile vehicles especially o... more Crosswinds are one of the natural disasters that pose risks to high-profile vehicles especially on the highway. There are possibilities of fatal accidents when drivers and motorists failed to control their vehicles when this dangerous wind blows perpendicular to the vehicles. Even though, each area of the crosswinds is placed with crosswinds signs (i.e. windsocks) but these signs are not so obvious especially when there are obstacles such as rain, poor lighting and fog. In addition, the colour of the windsock itself is faded due to the exposure to the various changes of the weather and environments. Here, the Wind Speed Detection System is proposed in helping to monitor and manage this critical situation by providing crucial information to the vehicles and motorists regarding the crosswinds conditions, plan and preparation and many others. The system is able to measure the wind speed level and alert the motorists and vehicles with warning lamp/light according to the crosswinds conditions. Furthermore, the system can record the wind speed reading and save it into a file that can be further analysed.
The analysis of relation between student performance and other variables in education setting is ... more The analysis of relation between student performance and other variables in education setting is often useful in identifying influential factors on performance. Consequently, the need for adopting an effective tool to process these big data has risen. The analysis of big data will transform passive data into useful information. Data mining is referred to an analytic process designed that discovers data patterns and relationships between datasets. In this study, clustering is used to cluster student grade datasets to generate trend line clusters. The aim of the study is to assist lecturers and academic advisors to recognize the progress of their students.
Malware threat poses consequences, damage, and impact to businesses, resulting in financial losse... more Malware threat poses consequences, damage, and impact to businesses, resulting in financial losses, operation interruption and reputation damage. The cybercriminal takes advantage of organisations that are not protected by using malware to penetrate the organisation's system or services. This can lead to data breaches, intrusion attacks, confidential data losses or ransomware attacks that completely cease business operations. SMEs also need to be prepared against malware attack since the business is part of the supply chain ecosystem, and the operation is highly dependent on IT technology which can also be exposed to cyber threats. This paper aims to develop a new framework for addressing malware threats in strategic ways for the SME's organisation to immediately access the risk situation and plan proper contingency and remedial action.
International journal of engineering & technology, Dec 13, 2018
This preliminaries study aims to propose a good classification technique that capable of doing do... more This preliminaries study aims to propose a good classification technique that capable of doing document classification based on text mining technique and create an algorithm to automatically classify document according to its folder based on document's content while able to do sentiment analyses to data sets and summarize it. The objective of this paper to identify an efficient text mining classification technique which can resulted with highest accuracy of classifying document into document folder, capable of extracting valuable information from context-based term that can be used as an output for algorithm to do automatic classification and evaluate the classification technique. Methodology of this study comprises in 5 modules which is 1) Document collection, 2) Pre-Processing Stage, 3) Term Frequency-Inversed Document Frequency, 4) Classification Technique and Algorithm, and lastly 5) Evaluation and Visualization of the classification result. The proposed framework will have utilized Term Frequency-Inversed Document Frequency (TF-IDF) and Decision Tree technique which TF-IDF used as purposes to rank all the terms based on most frequent to least frequent terms so, while decision tree function as decision making in terms of deciding which folder the document belongs to.
Many studies have been conducted to determine how data mining can be used in predicting climate c... more Many studies have been conducted to determine how data mining can be used in predicting climate change. Previous studies showed many data mining methods have been used in related to climate prediction, however classification and clustering methods are widely used to generate the climate prediction model. In this study, Association Rule Mining (ARM) is used to discover hidden rules in time series climate data from previous years and to analyze the relationship between the discovered rules. The dataset used in this study is a set of weather data from the Petaling Jaya observation station in Selangor for the year 2013 to 2015. This paper aims to utilize ARM for extracting behavioural patterns within the climate data that can be used to develop the prediction model for climate variability. The proposed framework is developed to provide a better approach in understanding how ARM can be used to find meaningful patterns in the climate data and generate rules that can be used to build a prediction model.
This paper describes a technique of text analytics on peacekeeping documents to discover signific... more This paper describes a technique of text analytics on peacekeeping documents to discover significant text patterns exist in the documents. These documents are considered as unstructured textual data.The paper proposes a framework that consists of 3 stages (i) data collection (ii) document preprocessing and (iii) text analytics and visualization.The technique is developed using R text mining package for text analytics experiments
Our planet is known as a digital earth, circulating around data. Growth in data is exponential, l... more Our planet is known as a digital earth, circulating around data. Growth in data is exponential, leading to an elevated interest in Big Data Analytics, to collect, store, process, analyze and visualize unparalleled amount of data. Modern information driven society will continue to be shaped by big data, where there will be potential to extract meaningful insights and hidden patterns impacting businesses in unforeseen measures. Most employers in Malaysia provide medical benefits which includes general medical costs to hospitalization benefits and insurance coverages; with these data and information stored by the HR (Human Resource), leading to a potential to analyze and identify patterns in historical claims - these insights would lead to improved decision making to better understand employee population health and the usage of the premium coverage. In predictive analysis, common techniques applied are Decision Tree and Regression. Therefore, the aim of this research is to propose a conceptual prediction model to better understand the patterns present in the employee healthcare data while predicting if an employee would be at any health risks to understand the population health and the usage of premium coverage provided by the employer. Additionally, to apply an ensemble method called Stacking, where multiple predictive models will be combined to perform a prediction. An ensemble model will present the opportunity to build a more robust and accurate model which could be applied across various industries instead of being industry specific.
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Papers by Zuraini Zainol