RoMLP-AttNet modelling framework. by Jinbao Song (18601705)

“…Then, the correlation between topic tags is considered comprehensively by combining the temporal information and the similarity calculation method of topic tags. Finally, a timeline-based topic merging algorithm is proposed to construct a clear and orderly event story line. …”

Change in loss ratio with rounds. by Jinbao Song (18601705)

“…Then, the correlation between topic tags is considered comprehensively by combining the temporal information and the similarity calculation method of topic tags. Finally, a timeline-based topic merging algorithm is proposed to construct a clear and orderly event story line. …”

Hashtag post number over time. by Jinbao Song (18601705)

“…Then, the correlation between topic tags is considered comprehensively by combining the temporal information and the similarity calculation method of topic tags. Finally, a timeline-based topic merging algorithm is proposed to construct a clear and orderly event story line. …”

Hashtag number varies with density thresholds. by Jinbao Song (18601705)

“…Then, the correlation between topic tags is considered comprehensively by combining the temporal information and the similarity calculation method of topic tags. Finally, a timeline-based topic merging algorithm is proposed to construct a clear and orderly event story line. …”

The proposed framework. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Number of recognised words. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Word2Vec models [3]. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Clustering of named entities AraVec model. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Named entities table. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Set of positive and negative words. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Deep learning model parameters of LSTM. by Abdelrahman Hamdy (22146220)

“…Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms. In this work, we introduce a new model, <i>Arab2Vec</i>, that can be used in Twitter-based natural language processing (NLP) applications. …”

Outline of OSCNN. by Reyhane Ahmadi (14857363)

“…However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. …”

OSCNN accuracy with different feature extractors. by Reyhane Ahmadi (14857363)

“…However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. …”

<b>Real-Time Classroom Emotion Analysis Using Machine and Deep Learning for Enhanced Student Learning</b> by Prem Kumar Sholapurapu (21638282)

<b>Rapid Lithological Mapping Using Multi-Source Remote Sensing Data Fusion and Automatic Sample Generation Strategy</b> by Tao Zhang (21914624)

“…Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. …”

Data Sheet 1_Testing the applicability of a governance checklist for high-risk AI-based learning outcome assessment in Italian universities under the EU AI act annex III.pdf by Flavio Manganello (21449075)

“…Background<p>The EU AI Act classifies AI-based learning outcome assessment as high-risk (Annex III, point 3b), yet sector-specific frameworks for institutional self-assessment remain underdeveloped. …”

Dataset. by Jinshan Qi (5180714)

“…Finally, we conduct comprehensive experiments on diverse benchmark databases drawn from different areas to evaluate the proposed theories and algorithms. The results well demonstrate the effectiveness and superiority of our methods.…”

The proportion of different customer categories. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”

The <i>Q</i>-learning update process. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”

Description of the new features after transform. by Guanqun Wang (705958)

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with <i>K</i>-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”