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Knowledge-Based Urban Development The Impact of Knowledge- Based Urban Development in the Growth of Contemporary Cities
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Urban Development refers to many topics such as: increased population density, city size, and individual’s production, distribution of technology and the growth of commercial, industrial and service professions. Such development is linked to the coordination of social and cultural trends in order to achieve social progress and economical prosperity. Knowledge as a topic now is known as intellectual capital wich led to upgrae the concept of urban development to be extended into many fields of knowledge, for example, cultural, social and human development to move the level of community culture into a new better standard.

The research adopted the urban transformation based on knowledge as an important factor in growth and development of contemporary cities. T he lack of studies regarding this was the research problem which led to a hypothesis set as (Knowledge-based urban development is an important tool in contemporary cities growth). Research aimed (to build a knowledge frame-work related to knowledge-based urban development impact on contemporary cities growth) through the following sequence:

  1. Creating a knowledge-based urban development literature review.
  2. Clarifying the relationship between the knowledge-based urban development and knowledge workers.
  3. Determining the theoretical framework to recognize level of impact of knowledge-based urban development on the city growth.
  4. Testing the hypothesis according to the theoretical framework in selecting designated cities such as Sydney & Melbourne were selected as a case study, since they represent good examples for knowledge cities.

The research concluded that: knowledge-based urban development in cities depend on technical economic and community directories as a mechanism to achieve knowledge-based economy and build a new spatial relationship (Knowledge City).

Keywords: knowledge-based urban development, knowledge, knowledge workers, knowledge-based economy, Knowledge City.

Urban Development refers to many topics such as: increased population density, city size, and individual’s production, distribution of technology and the growth of commercial, industrial and service professions. Such development is linked to the coordination of social and cultural trends in order to achieve social progress and economical prosperity. Knowledge as a topic now is known as intellectual capital wich led to upgrae the concept of urban development to be extended into many fields of knowledge, for example, cultural, social and human development to move the level of community culture into a new better standard.

The research adopted the urban transformation based on knowledge as an important factor in growth and development of contemporary cities. T he lack of studies regarding this was the research problem which led to a hypothesis set as (Knowledge-based urban development is an important tool in contemporary cities growth). Research aimed (to build a knowledge frame-work related to knowledge-based urban development impact on contemporary cities growth) through the following sequence:

  1. Creating a knowledge-based urban development literature review.
  2. Clarifying the relationship between the knowledge-based urban development and knowledge workers.
  3. Determining the theoretical framework to recognize level of impact of knowledge-based urban development on the city growth.
  4. Testing the hypothesis according to the theoretical framework in selecting designated cities such as Sydney & Melbourne were selected as a case study, since they represent good examples for knowledge cities.

The research concluded that: knowledge-based urban development in cities depend on technical economic and community directories as a mechanism to achieve knowledge-based economy and build a new spatial relationship (Knowledge City).

 

 

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Publication Date
Fri Nov 21 2025
Journal Name
Journal Of Advances In Information Technology
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

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