Kekangan pembelajaran ICT di sekolah luar bandar: Satu kajian

Kajian ini bertujuan mengenal pasti permasalahan yang dihadapi oleh pelajar-pelajar khususnya di luar bandar dalam pembelajaran matapelajaran ICT di sekolah, khususnya di Langkawi. Fokus diberi kepada para pelajar tentang permasalahan yang dihadapi semasa proses pembelajaran samada di sekolah mahupun di luar waktu persekolahan. Untuk mencapai objektif tersebut, satu kajian tindakan telah dijalankan selama satu bulan di Sekolah Menengah Kebangsaan Tunku Putra, Langkawi yang melibatkan pelajar tingkatan 1 di sekolah tersebut. Dapatan kajian menunjukan terdapat 60% pelajar menghadapi masalah dalam pembelajaran matapelajaran ICT Literacy dan memerlukan satu tindakan yang sewajarnya dalam membantu pelajar-pelajar tersebut. Beberapa cadangan telah dikemukakan untuk mengatasi masalah tersebut

Detection of Freezing of Gait using Unsupervised Convolutional Denoising Autoencoder

At the advanced stage of Parkinson’s disease, patients may suffer from ‘freezing of gait’ episodes: a debilitating condition wherein a patient’s “feet feel as though they are glued to the floor”. The objective, continuous monitoring of the gait of Parkinson’s disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when faced with the different walking styles of Parkinson’s disease patients. Inspired by state-of-art deep learning techniques, this research aims to improve the detection model by proposing a feature learning deep denoising autoencoder to learn the salient characteristics of Parkinsonian gait data that is applicable to different walking styles for the elimination of manually handcrafted features. Even with the elimination of manually handcrafted features, a reduction in half of the data window sizes to 2s, and a significant dimensionality reduction of learned features, the detection model still managed to achieve 90.94% sensitivity and 67.04% specificity, which is comparable to the original Daphnet dataset research

EfficientNet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi

Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the Softmax layer in the EfficientNet with the KNN. We train our model using the FER-2013 dataset and compare its performance with different architectures trained on the same dataset. We perform optimization on the Fully Connected layer, loss function, loss optimizer, optimizer learning rate, class weights, and KNN distance function with the k-value. Despite running on the Raspberry Pi hardware with very limited processing power, low memory capacity, and small storage capacity, our proposed model achieves a similar accuracy of 75.26% (with a slight improvement of 0.06%) to the state-of-the-art’s Ensemble of 8 CNN model

A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Glowworm Search Optimization (GSO) in two possible conditions.

<p><i>a</i>, <i>b</i>, <i>c</i>, <i>d</i>, <i>e</i>, <i>f</i>, <i>i</i>, <i>j</i>, and <i>k</i> are the glowworm agents. In Figure 6.1, figure illustrates three glowworm agents with different sensor range and local-decision range. It shows if agent within local-decision of other agent, the agent with lower <i>luciferin</i> values move towards agent with higher <i>luciferin</i> values. In Figure 6.2, glowworm agents are ranked based on their <i>luciferin</i> values with lower number represent higher <i>luciferin</i> values and higher number represent lower <i>luciferin</i> values [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122827#pone.0122827.ref058" target="_blank">58</a>].</p

Ant Colony Optimization Algorithm processes.

<p>N and S denote Nest and Source with <i>a</i> is ongoing direction and <i>b</i> is returning direction. Sub Figure 2.1 shows early process where ants start find a path between nest and source and lay pheromone. Figure 2.2 shows intermediate process where ants went through all possible paths. Figure 2.3 shows most of ants choose path with highest pheromone [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122827#pone.0122827.ref018" target="_blank">18</a>].</p

Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array

Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC. This research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures, i.e., SC multiplexer multiply-accumulate, multiply-accumulate function generator, and binary rectified linear unit, and successfully scaled and implemented a fully parallel CNN model on Kintex7 FPGA. The proposed SC hardware only compromises 0.14% accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72% energy saving per image feedforward and 31× more data throughput than modern hardware. Unique to SC, early decision termination pushed the performance baseline exponentially with minimum accuracy loss, making SC CNN extremely lucrative for AI edge computing but limited to classification tasks. The SC’s inherent noise heavily penalizes CNN regression performance, rendering SC unsuitable for regression tasks

Particle Swarm Optimization movement towards global optima over iteration numbers [33].

<p>Particle Swarm Optimization movement towards global optima over iteration numbers [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122827#pone.0122827.ref033" target="_blank">33</a>].</p

Experimental settings of the utilized methods.

<p>Experimental settings of the utilized methods.</p

PSO Basic Behaviors.

<p>Figure 3.1 shows separation behavior where particle avoiding other particles. Figure 3.2 shows alignment behavior where particle moving towards head of local flockmates and maintain the speed between them. Figure 3.2 shows cohesion behavior where particle moving towards the average position of local flockmates [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122827#pone.0122827.ref030" target="_blank">30</a>].</p