News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Fagbola, Temitayo Matthew; Thakur, Surendra; Olugbara, Oludayo O.

doi:10.14716/ijtech.v10i4.2339

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…With the widespread of popular culture, many educational theorists popularize music education to cater to students' psychology. This, however, has restricted the diversified development of music education [ 13 – 15 ]. Due to the multiculturalism need and the pluralistic society, teachers should use sufficient excellent traditional music knowledge and OES on the Internet to supplement students outside the classroom.…”

Section: Methodsmentioning

confidence: 99%

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Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.

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Section: Methodsmentioning

confidence: 99%

“…is, however, has restricted the diversified development of music education [13][14][15]. Due to the multiculturalism need and the pluralistic society, teachers should use sufficient excellent traditional music knowledge and OES on the Internet to supplement students outside the classroom.…”

Section: Development and Utilization Of Music Oermentioning

confidence: 99%

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Xue

2022

Computational Intelligence and Neuroscience

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“…Generally, deep learning networks have a superior capability to symbolize high-level features, examine complex mappings from the input, and support multiple outputs (Fagbola et al, 2019;Berawi, 2020;Siswanto, 2022). In this section, different deep convolutional, recurrent neural networks, and data fusion techniques are applied for multimodality input data fusion to enhance the accuracy of falling prediction.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Fall Detection and Motion Analysis Using Visual Approaches

Lau¹,

Connie²,

Goh³

et al. 2022

IJTech

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Falls are considered one of the most ubiquitous problems leading to morbidity and disability in the elderly. This paper presents a vision-based approach toward the care and rehabilitation of the elderly by examining the important body symmetry features in falls and activities of daily living (ADL). The proposed method carries out human skeleton estimation and detection on image datasets for feature extraction to predict falls and to analyze gait motion. The extracted skeletal information is further evaluated and analyzed for the fall risk factors in order to predict a fall event. Four critical risk factors are found to be highly correlated to falls, including 2D motion (gait speed), gait pose, 3D trunk angle or body orientation, and body shape (width-to-height ratio). Different variants of deep architectures, including 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Gated Recurrent Units (GRU) model, and attentionbased mechanism, are investigated with several fusion techniques to predict the fall based on human body balance study. A given test gait sequence will be classified into one of the three phases: non-fall, pre-impact fall, and fall. With the attention-based GRU architecture, an accuracy of 96.2% can be achieved for predicting a falling event.

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“…Therefore, there is a need to complement visual-based recognition with audio-based input to improve the reliability of the proposed system. In this study, a pipeline approach is presented to process both the visual and audio input for the animals using AI and machine learning techniques (Berawi, 2020;Siswanto, 2022;Fagbola, 2019). Discriminative features are extracted from both types of signals and the extracted features are fused using deep learning approach.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Visual and Audio Signals for Wildlife Surveillance

Ng¹,

Connie²,

Choo³

et al. 2022

IJTech

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Wildlife-vehicle collision (WVC) has been a significant threat to endangered species in Malaysia. Due to excessive development, tropical rainforests and their inhabitants have been edged towards extinction. Road buildings and other linear infrastructures, for instance, have caused forest destruction and forced wild animals to come out from their natural habitats to compete for resources with the human-beings. In Malaysia, much precious wildlife have been lost due to road accidents. Road signs and warning lights have been set up near wildlife crossing, but these do not help much. In this paper, we aim to propose a wildlife surveillance mechanism to detect the existence of wildlife near roadways using visual and audio input. Machine learning classifiers, including Convolution Neural Network (CNN), Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naive Bayes, are adopted in the study. We focus on five types of the most frequently occurring wildlife on the roads: elephants, tapirs, Malayan bears, tigers, and wild boars. Experimental results demonstrate that a good accuracy as high as 99% can be achieved using the proposed approach. On the other hand, the Naïve Bayes classifier ranks the lowest in performance with an accuracy value only up to 86%.

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News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Cited by 6 publications

References 12 publications

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Fall Detection and Motion Analysis Using Visual Approaches

Fusion of Visual and Audio Signals for Wildlife Surveillance

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