Search Results - (( spatial visualization learning algorithm ) OR ( evolution optimization bat algorithm ))*

Multi-Swarm bat algorithm by Taha A.M., Chen S.-D., Mustapha A.

“…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”

Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

“…Traditional visual odometry algorithms require the careful fabrication of state-of-the-art building blocks based on geometry. …”
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Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

“…Traditional visual odometry algorithms require the careful fabrication of state-of-the-art building blocks based on geometry. …”
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

“…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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The comparison of interactive 3D visualization between static and animated approaches for learning binary tree topic / Mohd Zulhisam Yaakub by Yaakub, Mohd Zulhisam

“…This shows that both 3D visualization methods implemented in this study can increase the student learning achievements and spatial abilities. …”
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A hybrid spiking neural network model for multivariate data classification and visualization. by Ming, Leong Yii, Teh, Chee Siong, Chen, Chwen Jen

“…SOM is one of the most prominent unsupervised learning algorithms. Recently, many extensions for SOM have been proposed for temporal processing. …”
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A review of machine learning in hyperspectral imaging for food safety by Mainak Das, Yeo, Wan Sieng, Agus Saptoro

“…To address these limitations, advances in non-destructive monitoring techniques with the implementation of machine learning (ML) algorithms can be alternative solutions. …”
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Kernerlized Correlation Filters Parameters Optimization For Enhanced Visual Tracking by Ong, Chor Keat

“…A lot of researches have been conducted and many types of state-of-the-art methods and modifications such as sparse representation, online similarity learning, self-expressive, spatial kernel phase correlation filter and others are proposed in order to increase the robustness of the tracking. …”
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Coherent crowd analysis with visual attributes / Nurul Japar by Nurul , Japar

“…Therefore, contextual information from visual attributes is essential in learning semantic relations among individuals. …”
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Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

“…Focusing on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Mean Shift Clustering to understand customer characteristics and behaviors. …”
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Predicting saliency existence using reduced salient features based on compactness and boundary cues by Nadzri, Nur Zulaikhah

“…The selected salient features were trained, tested and compared on 3 learning algorithms which included generalised linear regression, Naïve Bayes, and Support Vector Machine. …”
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Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins by Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.

“…To forecast unexpected flood occurrences, faster flood prediction necessitates computational prediction models such as Machine Learning (ML) algorithms, which are extensively utilized around the world. …”

Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…By decreasing the quantity of interest regions, the time efficiency of feature encoding can thus be improved without sacrificing classification accuracy. The ERS algorithm is performed using unsupervised learning to determine the spatial information of the interest regions detected, indicating whether they are from the image background, and can thus be removed as noise. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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Content-based indexing of low resolution documents by Md Nor, Danial

“…K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. …”
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Unsupervised monocular depth estimation with multi-scale structural similarity powered loss function / Ali Kohan by Ali, Kohan

“…Depth Estimation refers to a set of techniques and algorithms that aim to obtain a representation of spatial information of a scene. …”
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Development of river water level estimation from surveillance cameras for flood monitoring system using deep learning techniques by Muhadi, Nur 'Atirah

“…Hence, deep learning technique was chosen for water segmentation procedure in this work. …”
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Wifi-based location-independent human activity recognition and localization using deep learning by Abuhoureyah, Fahd Saad Amed

“…First, we employ the advanced Seq2Seq Recurrent Neural Network (RNN) technique to achieve high accuracy in HAR with few layers of the Long Short-Term Memory (LSTM) algorithm. Precise activity recognition and incorporation of through-wall sensing capabilities are achieved within the deep learning framework. …”
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