Data Mining Practical Machine Learning Tools and Techniques

Psychology: An Introduction

The introduction of Artificial Intelligence technology enables the integration of Chatbot systems into various aspects of education. This technology is increasingly being used for educational purposes. Chatbot technology has the potential to provide quick and personalised services to everyone in the sector, including institutional employees and students. This paper presents a systematic review of previous studies on the use of Chatbots in education. A systematic review approach was used to analyse 53 articles from recognised digital databases. The review results provide a comprehensive understanding of prior research related to the use of Chatbots in education, including information on existing studies, benefits, and challenges, as well as future research areas on the implementation of Chatbot technology in the field of education. The implications of the findings were discussed, and suggestions were made.

Chatbots applications in education: A systematic review

Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms These three elements (eg, BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

In this paper, a novel shape recognition method based on radial basis probabilistic neural network (RBPNN) is proposed. The orthogonal least square algorithm (OLSA) is used to train the RBPNN and the recursive OLSA is adopted to optimize the structure of the RBPNN. A leaf image database is used to test the proposed method. And a modified Fourier method is applied to descript the shape of the plant leaf. The experimental result shows that the RBPNN achieves higher recognition rate and better classification efficiency with respect to radial basis function neural network (RBFNN), BP neural network (BPNN) and multi-Layer perceptron network (MLPN) for the plant species identification.

Shape recognition based on radial basis probabilistic neural network and application to plant species identification

Information Visualization (InfoVis), as an analytics and visualization tool, had been argued to be befitting in attending to the experience
of information overload, and subsequent decision making constraint of higher education institutions’ (HEIs) decision makers.This experience is said to be as a result of increase in volume of students’ data and the limitations
of the available data management tools. However, due to the diversity of domains in which application of InfoVis are demanded, designing domain-specific structural components of the intending InfoVis is compulsory, so as to address its peculiar domain problem.Adapting the generic Design Research method, we employed critical review of documentations of selected InfoVis tools and mapped the findings with the outcome of our previous investigation of the HEIs’ decision makers’ explicit knowledge preferences.This work therefore highlights the structural components of the HEIs’ students’ data-focused InfoVis.

Structural components of students' data-focused information visualization

Amount of data generated and stored in relational databases has motivated numerous researchers to study and develop learning algorithms on learning relational data mining. One of the most important relational tasks is to discover knowledge from relational data for a better decision making. Despite that, various representations can be generated using the same data by applying the Self-Organizing Map (SOM) methods in clustering relational data. This can be achieved by tuning the parameters used in Self-Organizing Map (SOM), such as the number of clustering, weights, seeds, epoch and others. Thus, this paper proposes a summarization method that applies SOM as the main algorithm to cluster relational data and applies the concept of data fusion in order to get better results in learning relational data. Input data obtained from Dynamic Aggregation of Relational Attributes will be clustered using the SOM method by tuning the SOM parameters. Results generated will be fused and embedded into the target table to form a single representation. A few representations will be formed and fed into the classifiers (J48 Decision Tree and Naive Bayes classification model) as input data. Throughout the experiments conducted, representations that are extracted by tuning the number of cluster produced better results compared to the representations that are extracted by tuning the other parameters. Overall, the data summarization approach based on individual data fusion is found to perform better compared to the other types of data fusion. In addition to that, the clusters based data fusion with average number of clusters provided better accuracy performances compared to clusters based data fusion with small and large number of clusters.

Data Fusion Based on Self-Organizing Map Approach to Learning Medical Relational Data

In the Automatic Text Summarization domain, a Sentence Compression (SC) technique is applied to the summary sentence to remove unnecessary words or phrases. The purpose of SC is to preserve the important information in the sentence and to remove the unnecessary ones without sacrificing the sentence's grammar. The existing development of Malay Natural Language Processing (NLP) tools is still under study with limited open access. The issue is the lack of a benchmark dataset in the Malay language to evaluate the quality of the summaries and to validate the compressed sentence produced by the summarizer model. Hence, our paper outlines a Syntactic-based Sentence Validation technique for Malay sentences by referring to the Malay Grammar Pattern. In this work, we propose a new derivation set of Syntactic Rules based on the Malay main Word Class to validate a Malay sentence that undergoes the SC procedure. We experimented using the Malay dataset of 100 new articles covering the Natural Disaster and Events domain to find the optimal compression rate and its effect on the summary content. An automatic evaluation using ROUGE (Recall-Oriented Understudy for Gisting Evaluation) produced a result with an average F-measure of 0.5826 and an average Recall value of 0.5925 with an optimum compression rate of 0.5 Confidence Conf value. Furthermore, a manual summary evaluation by a group of Malay experts on the grammaticality of the compressed summary sentence produced a good result of 4.11 and a readability score of 4.12 out of 5. This depicts the reliability of the proposed technique to validate the Malay sentence with promising summary content and readability results.

A syntactic-based sentence validation technique for malay text summarizer

Human emotions rely upon the ability to correctly attribute beliefs, goals and percepts to other people or social respond. Many of the quests to model human emotions derived from diverse aspects of research themes.Most of the research works related to the work of researchers in artificial intelligence, psychology, cognitive sciences as well as robotics. This research works addresses on faces features through simulation of anthropomorphic agent.A salience feature of this definition of emotion is described in terms of goals and roles.Therefore, it provides a basic framework of goal-driven processing, which is to investigate computational models of emotion.The main goal of this research is to provide a natural interaction scheme where the affective interaction emotive state of the user can be identified.One aspect of developing such a capability is the ability of the system to recognize emotional state using intelligent supervised learning algorithms, personality of the user and respond appropriately.An anthropomorphic agent will serve to express the reaction through affective feedback given. Thus, it will give a new direction of human-computer interaction issues in designing the most suitable interface that can understand our behaviour and respond to it Scope of Presentation: Artificial Intelligence, Cognitive Science, Affective Computing, and Human-Computer Interaction.

The design for antrophomorphic sociable agent interaction through emotion detection

Community services are among the important business aspect to strengthen the profit and desired between industries and social responsibility alike.Therefore, there is a need for a competitive company to gain trust and loyalty from their potential consumer. This project utilized knowledge based technology to address the need of diabetes patient to improvise their quality of life. Based on the case study done at B Braun Medical (M) Industries Sdn. Bhd., the project offers an ample space of interaction between the company and diabetic community via World Wide Web.

Mohd Shamrie Sainin

Papers

Structural components of students' data-focused information visualization

Data Fusion Based on Self-Organizing Map Approach to Learning Medical Relational Data

A syntactic-based sentence validation technique for malay text summarizer

The design for antrophomorphic sociable agent interaction through emotion detection

The design of an intelligent community portal for diabetes patient