Enhancing multi-class web video categorization model using machine and deep learning approaches Article Swipe
Wael M. S. Yafooz
,
Abdullah Alsaeedi
,
Reyadh Alluhaibi
,
Abdel-Hamid M. Emara
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v12i3.pp3176-3191
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v12i3.pp3176-3191
With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.11591/ijece.v12i3.pp3176-3191
- https://ijece.iaescore.com/index.php/IJECE/article/download/26024/15731
- OA Status
- diamond
- Cited By
- 5
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220947955
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220947955Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijece.v12i3.pp3176-3191Digital Object Identifier
- Title
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Enhancing multi-class web video categorization model using machine and deep learning approachesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-14Full publication date if available
- Authors
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Wael M. S. Yafooz, Abdullah Alsaeedi, Reyadh Alluhaibi, Abdel-Hamid M. EmaraList of authors in order
- Landing page
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https://doi.org/10.11591/ijece.v12i3.pp3176-3191Publisher landing page
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https://ijece.iaescore.com/index.php/IJECE/article/download/26024/15731Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ijece.iaescore.com/index.php/IJECE/article/download/26024/15731Direct OA link when available
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Computer science, Categorization, Artificial intelligence, Entertainment, Machine learning, Deep learning, The Internet, Internet video, Class (philosophy), Multimedia, World Wide Web, Art, Visual artsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3131630641, https://openalex.org/W3156627980, https://openalex.org/W3008262264, https://openalex.org/W2936412133, https://openalex.org/W3034776505, https://openalex.org/W2619573303, https://openalex.org/W2782709738, https://openalex.org/W2883024665, https://openalex.org/W3024710233, https://openalex.org/W2954053534, https://openalex.org/W4231779508, https://openalex.org/W2413158896, https://openalex.org/W6785121960, https://openalex.org/W2733236492, https://openalex.org/W2526050071, https://openalex.org/W3004334530, https://openalex.org/W2245536265, https://openalex.org/W2883913871, https://openalex.org/W2962899219, https://openalex.org/W2967875212, https://openalex.org/W2402341335, https://openalex.org/W2100454127, https://openalex.org/W3020333167, https://openalex.org/W2966989105, https://openalex.org/W2585485261, https://openalex.org/W2971640505, https://openalex.org/W2948813376, https://openalex.org/W2772380162, https://openalex.org/W3167799566, https://openalex.org/W3100255860, https://openalex.org/W2963704562 |
| referenced_works_count | 31 |
| abstract_inverted_index.a | 27, 110 |
| abstract_inverted_index.DL | 185 |
| abstract_inverted_index.In | 33 |
| abstract_inverted_index.TV | 70 |
| abstract_inverted_index.as | 18, 64 |
| abstract_inverted_index.be | 75 |
| abstract_inverted_index.by | 181 |
| abstract_inverted_index.in | 9, 59 |
| abstract_inverted_index.is | 104, 117 |
| abstract_inverted_index.of | 24, 47, 157, 177 |
| abstract_inverted_index.on | 13, 30, 119, 149, 154, 163, 190 |
| abstract_inverted_index.or | 90, 95 |
| abstract_inverted_index.97% | 178 |
| abstract_inverted_index.99% | 180 |
| abstract_inverted_index.The | 171 |
| abstract_inverted_index.Two | 145 |
| abstract_inverted_index.WVC | 115 |
| abstract_inverted_index.all | 23 |
| abstract_inverted_index.and | 7, 21, 69, 122, 140, 169, 179, 184, 195 |
| abstract_inverted_index.are | 147, 188 |
| abstract_inverted_index.can | 74 |
| abstract_inverted_index.for | 55, 113 |
| abstract_inverted_index.has | 37 |
| abstract_inverted_index.the | 44, 60, 150, 155, 158 |
| abstract_inverted_index.two | 159 |
| abstract_inverted_index.web | 78 |
| abstract_inverted_index.(DL) | 143 |
| abstract_inverted_index.MLCs | 183 |
| abstract_inverted_index.This | 72 |
| abstract_inverted_index.deep | 141 |
| abstract_inverted_index.from | 125 |
| abstract_inverted_index.high | 102, 174 |
| abstract_inverted_index.long | 196 |
| abstract_inverted_index.many | 4, 48 |
| abstract_inverted_index.mode | 112 |
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| abstract_inverted_index.rely | 12 |
| abstract_inverted_index.same | 61 |
| abstract_inverted_index.such | 17, 63 |
| abstract_inverted_index.than | 41 |
| abstract_inverted_index.that | 83, 116, 187 |
| abstract_inverted_index.this | 107, 129 |
| abstract_inverted_index.user | 120 |
| abstract_inverted_index.uses | 131 |
| abstract_inverted_index.when | 53 |
| abstract_inverted_index.with | 98, 101, 135 |
| abstract_inverted_index.(ANN) | 194 |
| abstract_inverted_index.(WVC) | 81 |
| abstract_inverted_index.Users | 11 |
| abstract_inverted_index.along | 134 |
| abstract_inverted_index.arise | 52 |
| abstract_inverted_index.audio | 91 |
| abstract_inverted_index.based | 118, 162, 189 |
| abstract_inverted_index.basis | 156 |
| abstract_inverted_index.exert | 26 |
| abstract_inverted_index.human | 31 |
| abstract_inverted_index.media | 15 |
| abstract_inverted_index.model | 130, 172 |
| abstract_inverted_index.novel | 111 |
| abstract_inverted_index.paper | 108 |
| abstract_inverted_index.rates | 176 |
| abstract_inverted_index.using | 182 |
| abstract_inverted_index.video | 68, 79, 85, 126 |
| abstract_inverted_index.which | 25 |
| abstract_inverted_index.(MLCs) | 139 |
| abstract_inverted_index.become | 38 |
| abstract_inverted_index.health | 168 |
| abstract_inverted_index.impact | 29 |
| abstract_inverted_index.lives. | 32 |
| abstract_inverted_index.memory | 198 |
| abstract_inverted_index.models | 186 |
| abstract_inverted_index.neural | 192 |
| abstract_inverted_index.people | 5 |
| abstract_inverted_index.shows. | 71 |
| abstract_inverted_index.simply | 42 |
| abstract_inverted_index.social | 14 |
| abstract_inverted_index.solved | 76 |
| abstract_inverted_index.videos | 36, 57, 97 |
| abstract_inverted_index.visual | 88 |
| abstract_inverted_index.(LSTM), | 199 |
| abstract_inverted_index.YouTube | 20 |
| abstract_inverted_index.because | 46 |
| abstract_inverted_index.content | 100 |
| abstract_inverted_index.dataset | 153 |
| abstract_inverted_index.digital | 2 |
| abstract_inverted_index.machine | 136 |
| abstract_inverted_index.models. | 144 |
| abstract_inverted_index.namely, | 165 |
| abstract_inverted_index.network | 193 |
| abstract_inverted_index.problem | 73 |
| abstract_inverted_index.similar | 99 |
| abstract_inverted_index.sports. | 170 |
| abstract_inverted_index.textual | 86 |
| abstract_inverted_index.through | 77 |
| abstract_inverted_index.utilize | 84 |
| abstract_inverted_index.However, | 50, 93 |
| abstract_inverted_index.Twitter, | 22 |
| abstract_inverted_index.accuracy | 103, 175 |
| abstract_inverted_index.achieves | 173 |
| abstract_inverted_index.balanced | 152 |
| abstract_inverted_index.browsing | 43 |
| abstract_inverted_index.comments | 121 |
| abstract_inverted_index.domains, | 62 |
| abstract_inverted_index.features | 124 |
| abstract_inverted_index.internet | 45 |
| abstract_inverted_index.learning | 137, 142 |
| abstract_inverted_index.proposed | 151, 160 |
| abstract_inverted_index.proposes | 109 |
| abstract_inverted_index.reasons. | 49 |
| abstract_inverted_index.specific | 56 |
| abstract_inverted_index.watching | 35 |
| abstract_inverted_index.weighted | 123 |
| abstract_inverted_index.Facebook, | 19 |
| abstract_inverted_index.conducted | 148 |
| abstract_inverted_index.enhancing | 114 |
| abstract_inverted_index.features, | 89 |
| abstract_inverted_index.learning, | 133 |
| abstract_inverted_index.obtaining | 96 |
| abstract_inverted_index.politics, | 66, 167 |
| abstract_inverted_index.searching | 54 |
| abstract_inverted_index.today’s | 1 |
| abstract_inverted_index.Therefore, | 106 |
| abstract_inverted_index.accurately | 58 |
| abstract_inverted_index.algorithms | 161 |
| abstract_inverted_index.approaches | 82 |
| abstract_inverted_index.artificial | 191 |
| abstract_inverted_index.education, | 67, 166 |
| abstract_inverted_index.platforms, | 16 |
| abstract_inverted_index.preferable | 40 |
| abstract_inverted_index.retrieving | 94 |
| abstract_inverted_index.short-term | 197 |
| abstract_inverted_index.supervised | 132 |
| abstract_inverted_index.approaches. | 92 |
| abstract_inverted_index.classifiers | 138 |
| abstract_inverted_index.collaborate | 8 |
| abstract_inverted_index.communicate | 6 |
| abstract_inverted_index.cyberspace. | 10 |
| abstract_inverted_index.experiments | 146 |
| abstract_inverted_index.particular, | 34 |
| abstract_inverted_index.revolution, | 3 |
| abstract_inverted_index.challenging. | 105 |
| abstract_inverted_index.considerable | 28 |
| abstract_inverted_index.difficulties | 51 |
| abstract_inverted_index.information, | 87 |
| abstract_inverted_index.Specifically, | 128 |
| abstract_inverted_index.descriptions. | 127 |
| abstract_inverted_index.categorization | 80 |
| abstract_inverted_index.entertainment, | 65 |
| abstract_inverted_index.multi-classes, | 164 |
| abstract_inverted_index.<p><span>With | 0 |
| abstract_inverted_index.respectively.</span></p> | 200 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.64560353 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |