Intelligent Interactive Multimedia by Converging the Intention of Spectator and Multimedia Creator

In this research, we propose a new approach on how human and technology interact with each other. Here, by enhancing the current HCI framework, it will enable interaction between human and technology become more effective and ideally. The aim of this research is to create an Intelligent Interactive Multimedia by converging the intention of spectator and multimedia creator. Several methods are proposed to achieve the conception of Intelligent Interactive Multimedia. Digital Drawing Block is the interactive multimedia with the initial intention of multimedia creator and it forms an interaction with spectator. Spectator intention has been categorized into four common categories, additionally, five features of hand gesture recognition is proposed to deduce the spectator intention. All these five features will be captured by the web-cam during the spectator’s interaction with the Digital Drawing Block. Moreover, captured features will be sent to the machine learning for analyzing. Proposed user models are to assist the machine learning to evaluate the most appropriate category of human behaviour which matches the spectator actual intention. Lastly, graphic that represents spectator intention will be generated together with the initial intention of multimedia creator. The new creation from spectator and multimedia creator will be displayed through the Digital Drawing Block. The conception of Intelligent Interactive Multimedia can represent as 70%'s effort of Multimedia Creator + 30%'s effort of spectator

Parallel classification and optimization of telco trouble ticket dataset

In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machine-learning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data processing framework for the research activities. The dataset used in this research is related to the telco trouble ticket, identified as one of the large volume datasets. The study aims to solve the data classification problem in a single machine using traditional classifiers such as W-J48. The proposed solution is to enable a conventional classifier to execute the classification method using big data platforms such as Hadoop. This study’s significant contribution is the output matrix evaluation, such as accuracy and computational time taken from both ways resulting from hyper-parameter tuning and improvement of W-J48 classification accuracy for the telco trouble ticket dataset. Additional optimization and estimation techniques have been incorporated into the study, such as grid search and cross-validation method, which significantly improves classification accuracy by 22.62% and reduces the classification time by 21.1% in parallel execution inside the big data environment

Probabilistic Risk Assessment of COVID-19 Patients at COVID-19 Assessment Centre

COVID-19 started impacting Malaysia in early 2020, and the cases have reached 4.4 million as of April 27, 2022, with 35507 deaths. Since then, federal and state governments have set up COVID-19 Assessment Centres (CACs) to monitor, manage and assess the risk of COVID-19-positive patients. However, a large number of patients within a day has caused the CACs to experience a shortage in medical officers and subsequently resort to overwhelming administrative work. A misassignment of a patient to either home quarantine or COVID-19 Quarantine and Treatment Center or immediate hospital admission (PKRC) could potentially increase the Brought-In-Dead (BID) cases. Therefore, this study aimed to overcome the challenges by achieving the following two main objectives: (i) to identify the optimal feature sets for adult and child patients when they require hospital admission, (ii) to construct predictive models that perform preliminary assessment of a patient, which a medical officer later confirms. In this study, the predictive models developed were Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression and Decision Tree. The datasets were obtained from one of the CACs in Malaysia and were imbalanced in nature. The empirical findings showed that Logistic Regression outperformed the rest with a slight difference. The findings suggested that while there are shared symptoms among adult and child patients, such as runny nose and cough, the child patients exhibited extra symptoms such as vomiting, lung disease, and persistent fever.COVID-19 started impacting Malaysia in early 2020, and the cases have reached 4.4 million as of April 27, 2022, with 35507 deaths. Since then, federal and state governments have set up COVID-19 Assessment Centres (CACs) to monitor, manage and assess the risk of COVID-19-positive patients. However, a large number of patients within a day has caused the CACs to experience a shortage in medical officers and subsequently resort to overwhelming administrative work. A misassignment of a patient to either home quarantine or COVID-19 Quarantine and Treatment Center or immediate hospital admission (PKRC) could potentially increase the Brought-In-Dead (BID) cases. Therefore, this study aimed to overcome the challenges by achieving the following two main objectives: (i) to identify the optimal feature sets for adult and child patients when they require hospital admission, (ii) to construct predictive models that perform preliminary assessment of a patient, which a medical officer later confirms. In this study, the predictive models developed were Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression and Decision Tree. The datasets were obtained from one of the CACs in Malaysia and were imbalanced in nature. The empirical findings showed that Logistic Regression outperformed the rest with a slight difference. The findings suggested that while there are shared symptoms among adult and child patients, such as runny nose and cough, the child patients exhibited extra symptoms such as vomiting, lung disease, and persistent fever

From feature selection to building of bayesian classifiers: A network intrusion detection perspective

Abstract: Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC), Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms

Advancement on sustained antiviral ocular drug delivery for herpes simplex virus keratitis: recent update on potential investigation

The eyes are the window to the world and the key to communication, but they are vulnerable to multitudes of ailments. More serious than is thought, corneal infection by herpes simplex viruses (HSVs) is a prevalent yet silent cause of blindness in both the paediatric and adult population, especially if immunodeficient. Globally, there are 1.5 million new cases and forty thousand visual impairment cases reported yearly. The Herpetic Eye Disease Study recommends topical antiviral as the front-line therapy for HSV keratitis. Ironically, topical eye solutions undergo rapid nasolacrimal clearance, which necessitates oral drugs but there is a catch of systemic toxicity. The hurdle of antiviral penetration to reach an effective concentration is further complicated by drugs’ poor permeability and complex layers of ocular barriers. In this current review, novel delivery approaches for ocular herpetic infection, including nanocarriers, prodrugs, and peptides are widely investigated, with special focus on advantages, challenges, and recent updates on in situ gelling systems of ocular HSV infections. In general congruence, the novel drug delivery systems play a vital role in prolonging the ocular drug residence time to achieve controlled release of therapeutic agents at the application site, thus allowing superior ocular bioavailability yet fewer systemic side effects. Moreover, in situ gel functions synergistically with nanocarriers, prodrugs, and peptides. The findings support that novel drug delivery systems have potential in ophthalmic drug delivery of antiviral agents, and improve patient convenience when prolonged and chronic topical ocular deliveries are intended

Penggunaan simulasi komputer bagi merealisasikan fenomena tidak sahih : satu alternatif mewujudkan konflik kognitif dalam pembelajaran sains

Dalam mempelajari sains, konsep alternatif yang dibina oleh pelajar bukan sahaja seringkali bercanggah dengan teori sains tetapi keadaan ini turut menghambatkan proses pengajaran dan pembelajaran sains. Dalam usaha memperbaiki konsep alternatif, artikel ini membincangkan penggunaan simulasi komputer bagi merealisasikan fenomena tidak sahib bagi mewujudkan konflik kognitif yang berupaya memperbaiki kefahaman pelajar dalam mempelajari sains. Untuk itu, satu perisian berasaskan simulasi komputer telah dibina bagi merealisasikan fenomena tidak sahib dalam mengkaji hubungan antara konsep jisim(m), kekenyalan(k) dan tempoh ayunan(1) dalam Sistem Ayunan Spring Berbeban. Bagi menguji keberkesanan perisian, lima belas orang pelajar sekolah menengah telah dipilib untuk berinteraksi dengan simulasi komputer. Data diperoleh secara data logging komputer dan proses pengesahan konflik kognitif dan perubahan konsep dilakukan secara temubual. Hasil kajian menunjukkan penggunaan simulasi komputer yang merealisasikan fenomena tidak sahib berupaya mewujudkan konflik kognitif dan membantu pelajar dalam memahami konsep sains yang abstrak. Dengan itu, kajian ini membuka satu lambaran barn mengenai penggunaan simulasi komputer fenomena tidak sahib bagi meningkatkan kefahaman pelajar memahami konsep sains yang abstrak yang tidak dapat direalisasikan secara eksperimen sebenar

Decision-Theoretic Approach To Designing Scientific Inquiry Based Learning Environment

The thesis focuses on developing a learner model for INQUIRY PROCESS(INQPRO), a scientific inquiry learning environment developed within this research work

Digital Click Stream Data for Airline Seat Sale Prediction using GBT

Revenue Management is important for every airline business and the seat is the main product of an airline. The purpose of the revenue management is to maximize the revenue of each airline routes based on demand. This demand, however, depends on factors such as historical demand, seasonality, seat pricing based on purchase lead days, competitors pricing and customer behaviour. Prediction of passenger demand helps to forecast revenue on future flights and thus allow the airline to generate optimal prices for the corresponding flights. Therefore, minimizing the prediction error constitute the most crucial goal of good revenue management. In this paper, A GBT based model has been proposed for airline seat sale prediction to optimize the revenue. To optimize the prediction accuracy, an analytic dataset has been developed by combining digital attributes and traditional operational and transactional attributes. This paper will also highlight an efficient data extraction and processing pipeline have been proposed to aggregate a large volume of unstructured data from various data sources. The empirical findings suggested applying GBT on transformed dataset can predi