Research Opportunities for Application of Gamification Models with VR for Crop Cultivation: A Systematic Literature Review

The learning delivery model in the increasingly developing information technology era and the era of teaching and learning between students and lecturers during the Covid-19 pandemic requires lecturers' creativity. The application of gamification using virtual reality is an alternative solution that can be applied to fill the saturation of online learning using Google Meeting or Zoom meeting. This review of the literature aims to see how many gamification applications using virtual reality are specifically applied to plant cultivation. The process of obtaining this literature review is based on the systematic literature review (SLR) method. The assessment process is carried out through four online databases based on the keywords gamification and virtual reality. The results obtained are 32 relevant literature on gamification using virtual reality although gamification using virtual reality for plant cultivation is not found and only one paper on forestry only discusses the concept. However, the results of this process become the basic literature for further research on the application of virtual reality gamification in plant cultivation. &nbsp

Research Opportunities for Application of Gamification Models with VR for Crop Cultivation: A Systematic Literature Review

The learning delivery model in the increasingly developing information technology era and the era of teaching and learning between students and lecturers during the Covid-19 pandemic requires lecturers' creativity. The application of gamification using virtual reality is an alternative solution that can be applied to fill the saturation of online learning using Google Meeting or Zoom meeting. This review of the literature aims to see how many gamification applications using virtual reality are specifically applied to plant cultivation. The process of obtaining this literature review is based on the systematic literature review (SLR) method. The assessment process is carried out through four online databases based on the keywords gamification and virtual reality. The results obtained are 32 relevant literature on gamification using virtual reality although gamification using virtual reality for plant cultivation is not found and only one paper on forestry only discusses the concept. However, the results of this process become the basic literature for further research on the application of virtual reality gamification in plant cultivation. &nbsp

Spatio-temporal fMRI data in the spiking neural network

Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment and others. This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture. In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture. The spatio-temporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label starplus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture

Development of a new computational model for mapping, learning and mining of 3D spatio-temporal fMRI data

The application of data mining techniques, particularly classification of spatio-temporal 3D functional magnetic resonance images has received growing attention in the literature. Spatio or spatial component as well as temporal component are factors of high importance in determining and recognizing brain state in response to external stimuli. Structural and functional brain data have been hugely collected, in an attempt to improve brain cognitive abilities and processing capabilities, as well as advancement in medicine, health, education, Brain Computer Interface and games. A particular spatio- and spectro-temporal brain data (STBD), functional Magnetic Resonance Imaging (fMRI), provides a comprehensive detail of brain activation when a certain stimulus is presented to the subject resulting from the changes of oxygen level in the blood vessel of the brain. This oxygen difference between active neurons and inactive neurons is captured in sequence, and the images generated from this (the fMRI) are composed of tens of thousands of individual voxels. These massive voxels are the features to this thesis, which became one of the challenges that had to be faced, in addition to the complex format of the data itself. To some extent, conventional machine learning techniques has successfully process and classify fMRI data. However, these techniques are only best at dealing spatial data, which completely neglect the temporal information that this data has. Thus, this study proposes and presents a novel computational model that specifically process spatial and temporal information of fMRI data, which make use of the newly proposed NeuCube model as its foundation. The derived model, denoted as NeuCube^B utilized the 3D evolving SNN architecture of NeuCube in mapping and learning the data. The model learns from the data; then creates and updates connections between the neurons based on their weights. These connections represent chains of neuronal activities which could be reproduced even when only part of the stimuli data is presented, therefore making the NeuCube connections as an associative memory. The model can be used not only to classify brain activation patterns, but also to determine functional trail from the data i.e. to identify brain areas that receive the most activation from the stimulus. There are two case studies presented in the thesis involving different set of fMRI data which are in different format. The dataset is used and experimented by many researchers, which utilized different types of conventional machine learning techniques. In NeuCube^B, the fMRI features (voxels) are modelled and studied as both spatial and temporal information involving phases of data reading, mapping, and encoding, before they are transferred to initialization and unsupervised learning stage. Connectivity of neurons in the network could be visualized and studied. The visualization can reveal crucial spatio-temporal relationship unseen from the data that are completely ignored by the standard classifiers. For both experiments involving two different sets of fMRI data, NeuCube^B model results in better classification accuracy as compared to the standard classifiers. From this result, it can be concluded that NeuCube^B model is not vulnerable to noise, that normally reside in fMRI data. In addition the result can be further interpreted to better understand the brain activation under which the brain data is collected. However, these results and interpretations could still be improved, and further exploration on the subject matter is indeed a huge research prospect

Kita tutup aurat! : aplikasi pembelajaran menutup aurat untuk kanak-kanak

Menutup aurat adalah salah satu kewajipan dalam agama Islam. Oleh itu, pemahaman konsep menutup aurat adalah asas yang perlu dipelajari pada usia muda. Namun begitu, kanak-kanak hanya belajar menutup aurat mengikut konsep teori pembelajaran sahaja di sekolah. Kanak-kanak sukar mengikuti dan memahami pembelajaran yang menggunakan konsep teks dan fakta. Justeru itu, aplikasi pembelajaran yang berkonsepkan pembelajaran mudah alih (m-pembelajaran) yang dinamakan “Kita Tutup Aurat!” mengenai aurat untuk kanak-kanak ini dibangunkan. Aplikasi ini dibangunkan dengan menggunakan model ADDIE kerana kesesuaiannya dalam pembangunan aplikasi pembelajaran. Pengujian aplikasi telah dijalankan oleh 30 responden dari kalangan kanak-kanak sekolah rendah sekitar Parit Raja serta beberapa orang guru. Hasil pengujian menunjukkan lebih 70% responden bersetuju aplikasi yang dibangunkan merupakan satu pendekatan menarik dalam mempelajari dan memahami konsep menutup aurat dalam Islam. Kesimpulannya, aplikasi telah berjaya mencapai objektif yang telah ditentukan kerana ia menepati kehendak pengguna dan mempunyai fungsi-fungsi yang terkandung dalam skop sistem. Secara keseluruhan, implementasi aplikasi pembelajaran ini berpotensi untuk dijadikan kaedah alternatif dalam memahami konsep aurat sekaligus mendidik kanak-kanak untuk menutup aurat dengan sempurna

M-Hafazan: aplikasi mudah alih untuk pengajian tahfiz

Pendidikan tahfiz secara formal telah berkembang pesat serta mendapat perhatian istimewa dari masyarakat Islam di Malaysia. Perkembangan ini disebabkan oleh kesedaran dan permintaan ibu bapa dalam menerapkan al-Quran kepada anak-anak melalui hafazan. Pada masa kini, terdapat banyak aplikasi yang dapat menyokong pembelajaran tahfiz. Namun begitu, tiada aktiviti pengukuhan disediakan bagi membolehkan penghafaz al-Quran menguji tahap ingatan mereka terhadap ayat-ayat yang telah dihafaz. Aplikasi m-Hafazan ini dibangunkan dengan tujuan menyokong pengajian tahfiz dengan lebih baik. Aplikasi ini dibangunkan dengan menggunakan metodologi Multimedia Mobile Content Development. Metodologi ini dipilih kerana ia dapat mempercepatkan proses pembangunan aplikasi serta mengurangkan penggunaan pemprosesan data peranti mudah alih. Pengujian aplikasi telah dijalankan terhadap pelajar-pelajar dan guru dari Sekolah Rendah Islam Tahfiz, Parit Raja. Hasil pengujian menunjukkan 78.3% pelajar sangat bersetuju aplikasi m-Hafazan sesuai digunakan sebagai medium alternatif dalam pengajian hafazan al-Quran. Selain itu, 85% pelajar sangat bersetuju bahawa aplikasi ini dapat membantu pengukuhan hafazan mereka. Kesimpulannya, walaupun kaedah talaqqi (bersemuka) merupakan pendekatan yang terbaik dalam pengajian hafazan, namun implementasi aplikasi m-Hafazan berpotensi untuk dijadikan kaedah alternatif dalam pembelajaran hafazan bagi pelajar tahfiz mahupun bukan

Integrasi realiti terimbuh (AR) dalam aktiviti mewarna

Mewarna merupakan salah satu kaedah pembelajaran yang digunakan untuk meningkatkan kemahiaran psikomotor dan kreativiti kanak-kanak. Namun begitu, kandungan yang disediakan di dalam buku mewarna adalah bersifat statik dan tidak menyediakan elemen-elemen dinamik seperti interaktiviti. Kanak-kanak mudah merasa bosan kerana tiada interaksi dua hala yang berlaku antara mereka dan karakter ketika proses mewarna dilakukan. Sebagai penambahbaikan terhadap permasalahan tersebut, satu aplikasi mewarna yang dinamakan Dr Bubble Coloring AR dibangunkan. Aplikasi ini menggunakan teknik realiti terimbuh (AR) yang diintegrasikan ke dalam aplikasi mewarna. Imej yang diwarnakan menjadi penanda untuk diimbas oleh peranti mudah alih lalu dipaparkan secara maya dalam bentuk tiga dimensi (3D). Aplikasi ini menyediakan bebutang interaksi bagi membolehkan pengguna berinteraksi dengan karakter serta mengesan objek yang diwarnakan di dalam buku mewarna. Secara keseluruhan, 75% responden sangat bersetuju aplikasi ini menarik dan menyeronokkan, manakala 84% responden sangat bersetuju keseluruhan aplikasi ini berfungsi dengan baik dan sempurna

Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network

Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields.  It has been applied in many application areas include health, engineering, finances, environment, and others.  This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture.   In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture.  The spatiotemporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label star plus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture.

Pendekatan permainan mudah alih dalam pembelajaran congak

Kaedah congak merupakan salah satu kaedah yang sangat efektif untuk mengira dan menyelesaikan masalah matematik. Kaedah ini diajar kepada murid-murid terutamanya di sekolah rendah bagi meningkatkan keupayaan mental murid-murid sekaligus berupaya mengira dengan tepat dan pantas. Namun begitu kaedah ini boleh menimbulkan kebosanan kepada murid-murid terutamanya yang lemah dalam matematik. Pendekatan pembelajaran berasaskan permainan melalui aplikasi mudah alih dijangka akan dapat meningkatkan minat murid-murid mempelajari kaedah congak. Aplikasi ini dibangunkan dengan menggunakan model Game Development Life Cycle kerana kesesuaian model tersebut untuk pembangunan aplikasi permainan. Aplikasi ini telah diuji oleh kanak-kanak berusia 4 hingga 6 tahun. Secara keseluruhan, hasil pengujian menunjukkan aplikasi ini telah mencapai objektif-objektif yang telah ditentukan. Kesimpulannya, aplikasi ini telah berjaya dibangunkan dan berpotensi untuk dijadikan alat bantuan pembelajaran bagi kanak-kanak dalam memupuk minat dan kemahiran mereka dalam mencongak nombor

Voice converter on android

Voice Converter is an application developed for Android mobile platform that can help user to change his voice the way he likes. Existing voice changer mobile application provides the functions for users to change their voice into the voice of limited preset templates, this cannot see to the needs of film or animation producers, because the templates are limited and the uniqueness of the production cannot be secured. With the Voice Converter mobile application, film or animation producers can easily have access to a number of sound models that can be used to convert audio files which can be used in production later. Besides that, film makers can custom the sound of the conversion by inputting desired values of playback rate to change the audio sound in the way they would like to. The application uses speaker and microphone of the phones to play and record audio. For the recording process, the ‘MediaRecorder’ APIs is used to allow the device to capture audio, save the audio and play it back. The recordings captured can be converted into various sound models and also a model with custom parameters. The SoundPool Library is adopted to alter the playback rate of the audio. A mobile application named Voice Converter which is compatible for Android platforms is successfully developed. The functions of the application include recording and saving audio, converting input audio into existing template sound and converting recorded audio with custom paramete