Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Faculty's used books e-platform

The study was carried out in order to construct a Web-based application of Faculty's Used Books E-platform. This is done due to communication problems that occur among sellers and buyers of used university textbooks. This study also recognizes the e�commerce capabilities and marketing strategies that could assist in developing the applications. Moreover, this study also embarks on the search of technopreneurship initiatives in Malaysia to develop a guide to technopreneurship as encouragement for students to become a technopreneur. The System Development Research Methodology is the methodology used in the study. The prototype of Faculty's Used Books E�platform is built using the MySQL relational database, together with PHP and Apache Web server. The benefits of these technologies are discussed in this paper. The development of Faculty's Used Books E-platform will improved the communication between sellers and buyers of used books community and could be a reference to students of Faculty of Computer Science and Information Technology, University of Malaya

Designing Prototype Model of an Online Collaborative Learning System for Introductory Computer Programming Course

AbstractThis paper discusses the design of the prototype model of the online collaborative learning system for introductory computer programming course. The methodology used involves three phases which are the data collection, analysis and design and the implementation phase. Initially, fifty respondents from the first year students of the Diploma in Computer Science in Universiti Teknologi MARA (UiTM) Perlis, Malaysia have been randomly selected to participate in the data collection phase in order to investigate the students’ interests, learning styles as well as their learning preferences. The results have shown the need for the development of online small group discussions that could facilitate online communication and collaboration from dispersed location, hence encouraging distance learning education. A design of a structure model for an online collaborative learning system has been constructed in order to support the online collaborative learning activities in a virtual environment. The logical designs of the Online Collaborative Learning System or OCLS are being designed using the object-oriented models which are the use-case model and class diagram in order to show the concise processes of virtual “Think-Pair-Share” collaborative activities. The “Think-Pair-Share” collaborative learning technique that is being used in the design structures has been chosen because of its simplicity and relatively low-risk. Later, the physical design of the prototype model is being constructed using the Web-based technologies which are the MySQL database, PHP and Apache web server. This paper also discusses the impact of the online collaborative learning system towards the students’ performance where analysis has shown that the t-test result had a significant value of 0.01, which is less than 0.05 (sig. 2-tailed)

Patterns of Counselling-related Problems in a Malaysian Corporate Setting

Categories of counselling related problems in a corporation were identified. Subsequently, a comparative analysis was initiated differentiating the problems according to age, gender and job position. The sample size in the study n =288 was determined with a power set at .8 and oc =.05, thus ensuring a reduction of Type II Error. Through frequency analysis, the result pattern showed that problems related with retirementwas highest (24.5%); career problems (16.1 %); personal problems (13%); family and marital problems (11.4%). Also, the results indicated that retirement problems showed a significant difference among age groups. The comparison between gender indicated a significant difference only on personal problems. There was no significant difference in all categories of problems between the executives and the non-executives

Android based application for monitoring patients health and medicine intake

The number of individuals who suffer from chronic disease continues to increase worldwide (WHO, 2015). Health awareness together with the improvement in living conditions and treatment has increased the life expectancy of people suffers from chronic disease; nevertheless without efficient health management and monitoring, the quality of life is decreased (Whitehead, Seaton, 2016). Progressive growth in computer-mediated technologies such as social networking, smartphones and medical applications provide a useful platform for self-health management and awareness. Towards empowering people in practicing self-health management, individuals who suffers from chronic disease need to have access to timely information, advice, assessment and treatment from medical practitioners in order for them to manage their long-term illnesses conditions systematically (Zoffmann et al. 2016). Medical practitioners play an important role in empowering self-health management by giving guidance, monitoring adverse events and identify areas for improvement while giving patients independently self-management their health (Whitehead, Seaton, 2016, Smith et. al, 2016). Through computer-mediated technologies, systematic intervention from medical practitioners and community is feasible thus improving quality of life for individuals (Lorig et. al., 2016)

Predicting Customer Loyalty Using Machine Learning for Hotel Industry

The popularity of machine learning is growing and the demand for it is increasing in various fields including tourism and hospitality industry specifically hotels industry. The purpose of this research is to apply machine learning classification techniques to predict customers’ loyalty in hotel company so that hotel company can use the result to create possible solutions for customer relationship management. The experiment will be performed by implementing CRISP-DM methodology and three proposed algorithms such as decision tree, random forest and logistic regression and the result will be compared with each other to obtain the best algorithm among them by using confusion matrix. The dataset that will be used is obtained from Findbulous technology company. From the analysis result, logistic regression, decision tree and random forest algorithms generate 57.83%, 71.44% and 69.91% accuracy score respectively. For further improvement, this research approach can be used with other dataset or implement a new algorithm to identify each algorithm strengths and limitations

Comparative analysis of spatio/spectro-temporal data modelling techniques

A fundamental challenge in spatio/spectro-temporal data (SSTD) is to learn the pattern and extract meaningful information that lies within the data. The close interrelationship between the space and temporal components of SSTD directly increases the complexity and challenges in modelling the data [1]. Other challenges include the dynamic pattern of spatial components features and inconsistency in the number of samples and feature-length used in the training and sampling datasets [2]. Data pre-processing method such as removal of irregular-feature data structure, however, may cause data loss which will lead to the final result become error prone. Despite the difficulties to process information from SSTD, several works on predictive modelling have been published, including applications on brain data processing [3], stroke data [4-5], forecasting of weather-driven damage in electrical distribution system [6], and ecological or environmental event prediction [7]. According to [8], environmental events often occur in a predictable temporal structure. Hence, the ability to exploit spiking neural network (SNN) by incorporating SSTD modelling techniques may be able to aid the process of discovering the hidden pattern and relationship between the two components of STTD; time and space. Recent work in [5], stated that most events occurring in nature form SSTD which requires measuring spatial or/and spectral components over time. Therefore, this paper presents the comparative analysis between various techniques used to process information from SSTD. Section 2 overviews two different inference-based techniques for SSTD modelling which includes global modelling, local modelling, and personalized modelling; and data modelling for SSTD classifier including, support vector machines (SVM), Evolving Classification Function (ECF), k-Nearest Neighbor (kNN), weighted k-Nearest Neighbor (wkNN), and weighted-weighted k-Nearest Neighbor (wwkNN). Section 3 presents the results of the assessment both SSTD inference-based modelling techniques and data training algorithms, while Section 4 concludes the analysis and ideas for future works

Pengaruh motivasi dan kesannya terhadap prestasi akademik: tinjauan terhadap pelajar Sarjana Muda Kejuruteraan Mekanikal Sesi 1999/2000 KUiTTHO

Laporan Projek Sarjana ini mempersembahkan hasil kajian yang bertajuk 'PENGARUH MOTIVASI DAN KESANNYA TERHADAP PRESTASI AKADEMIK'. Kajian ini bertujuan untuk mengenalpasti hubungan faktor-faktor yang signifikan dalam penentuan prestasi akademik pelajar (faktor dalaman, luaran dan persekitaran) dengan prestasi akademik yang diukur melalui Purata Markah Keseluruhan atau CGPA. Sampel kajian adalah seramai 60 orang pelajar Saijana Muda Kejuruteraan Mekanikal sesi 1999/2000 KUiTTHO. Kajian adalah berbentuk tinjauan yang menggunakan sejenis instrumen kajian dalam mendapatkan data iaitu borang soal selidik. Kesemua data dianalisis dan dikemukakan dalam bentuk analisis statistik secara deskriptif dan secara inferensi. Korelasi Pearson digunakan untuk melihat hubungan antara setiap pembolehubah. Terdapat tiga faktor utama yang dikaji iaitu faktor dalaman(min=3.6), faktor luaran(min=3.7) dan faktor persekitaran (min=2.9). Hasil kajian menunjukkan bahawa ketiga-tiga tiga faktor tersebut mempunyai hubungan yang positif dengan prestasi akademik. Faktor dalaman yang paling memberi hubungan yang signifikan dalam prestasi akademik dengan 0.795, faktor luaran 0.650 dan faktor persekitaran 0. 339. Di akhir kajian ini, pengkaji mencadangkan agar (i) Mengadakan banyak Kem Motivasi, (ii) Peningkatan cara pengajaran pensyarah, (iii) Penyediaan peralatan pembelajaran yang mencukupi, (iv) Sumber rujukan seperti buku dan majalah di Perpustakaan mesti mencukupi

Sistem pengurusan maklumat saudara baharu di Batu Pahat

Saban tahun, Pejabat Agama Daerah Batu Pahat terus membuat penambahan rekod ke fail saudara baharu. Sehingga kini, rekod masih disimpan di dalam fail secara manual yang mana proses ini sering menyebabkan kesukaran dalam capaian data. Secara umumnya, setiap saudara baharu perlu mengisi maklumat diri di dalam borang dan borang tersebut akan dimasukkan ke dalam fail mengikut tahun memeluk Islam. Pejabat agama juga masih menggunakan kertas untuk merekod kehadiran saudara baharu ke setiap kelas pengajian dan laporan akan dianalisis berdasarkan senarai nama saudara baharu di dalam buku yang akan menyulitkan lagi proses analisis. Justeru, suatu sistem berasaskan web dan pangkalan data bagi Sistem Pengurusan Maklumat Saudara Baharu di Batu Pahat dibangunkan bagi memudahkan urusan saudara baharu terutamanya apabila memeluk Islam, maklumat saudara baharu terus didaftarkan ke dalam sistem supaya data lebih tersusun dan mudah dicapai. Model prototaip digunakan sebagai metodologi kerana prototaip perlu dibangunkan untuk mengetahui sama ada sistem yang dibangunkan memenuhi kehendak pengguna sasaran. Selain itu, perisian Bracket sebagai tempat mengaplikasikan sumber kod dengan bantuan pangkalan data, PHPMyAdmin untuk menyimpan segala data bagi membangunkan sistem ini. Sistem ini menjadikan pengurusan maklumat berkaitan saudara baharu lebih bersistematik bagi mengurangkan kadar pengulangan data