A novel approach to data mining using simplified swarm optimization

Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. However, existing traditional data classification and feature selection techniques used in data management are no longer enough for such massive data. This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organising ability to emerge in highly distributed control problem space, and is flexible, robust and cost effective to solve complex computing environments. The proposed SSO classifier has been implemented to classify audio data. To the author’s knowledge, this is the first time that SSO and PSO have been applied for audio classification. Furthermore, two local search strategies, named Exchange Local Search (ELS) and Weighted Local Search (WLS), have been proposed to improve SSO performance. SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from the UCI repository database. Meanwhile, SSO-WLS has been implemented in Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed. The empirical analysis showed promising results with high classification accuracy rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99 datasets. Therefore, the proposed SSO rule-based classifier with local search strategies has offered a new paradigm shift in solving complex problems in data mining which may not be able to be solved by other benchmark classifiers

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

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

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

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

2D platform-based game of arithmetic game-based learning

Mobile games have attracted attention among game developers and users. Throughout the years, mobile games have developed to become one of the domination of the digital world. They have evolved from just a non-coloured with few animated dots and lines to a coloured and more realistic three-dimensional gameplay. Besides, mobile games do not only act as an entertainment but also as a medium for improving mental arithmetic skill if the content is designed accordingly. However, most of mobile games nowadays have been developed for leisure purpose only. There is less game on IQ test and education while more on skills and action alternatively. Hence, arithmetic game-based learning application is developed as an alternative to be included in the variety of gameplay or content of the game. The game created based on platform genre with endless running style. Arithmetic questions are provided in the gameplay as one of the obstacles to both provide challenges and IQ test for the players. This game is implemented in mobile platform. Technology Acceptance Model (TAM) is adopted to measure the game quality based on the evaluation of user acceptance level towards the gameplay, functionality and playability, and overall performance of the game. Overall, 50% of respondents agreed that the gameplay is satisfying. In addition, 40% of respondents strongly agreed that the functionality and playability of the game is stable and the overall performance of the game functions perfectly

Augmented Reality Technology Implementation in Local Automobile Advertising

Advertising plays a vital role in businesses. Businesses use advertising to achieve various goals and spread their brand as well as to directly sell products and services to the public. As technology advances, mobile advertising is becoming more and more critical for brands and services. Therefore, Augmented Reality (AR) technology has entered the mobile advertising industry to enable advertisers to increase consumer engagement and revenue. Hence, in this project, an implementation of AR technology for local automobile advertising on Android platform is proposed as an alternative to advertising new cars. Perodua Myvi is chosen as the case study in this implementation. By using a marker, users will have an unprecedented way to interact and view the car’s exterior, features and specification regardless of time and location. This project has been developed successfully to provide an alternate method of advertising to the automobile consumer

Effective audio classification algorithm using Swarm-based optimization

The effectiveness and usefulness of large audio databases is greatly dependent on the ability to classify and retrieve audio files based on their properties or content. Automatic classification using machine learning is much more practical than manual classification. In this paper, a new audio classification algorithm using Simplified Swarm Optimization (SSO) based on Particle Swarm Optimization (PSO) is presented. The performance of the new algorithm is compared with two existing state-of-the-art classifiers, PSO and Support Vector Machine (SVM), for an audio dataset being classified into five classes of musical instruments. The experimental results show that the proposed SSO-based classifier has improved classification accuracy (91.7%) when compared with PSO (87.2%) and SVM (88.5%). Additionally, the algorithm is shown to have simpler particle update calculations than PSO, and also requires fewer particles for classification training

PARAMETRIC FLATTEN-T SWISH: AN ADAPTIVE NONLINEAR ACTIVATION FUNCTION FOR DEEP LEARNING

QActivation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN-5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks