As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in web search that covers web search engines, ranking algorithms, citation analysis. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
1.2 PROBLEM STATEMENT
1.3 AIM/OBJECTIVE OF THE PROJECT
1.4 SCOPE OF THE PROJECT
CHAPTER TWO
LITERATURE REVIEW
2.1 RELATED WORK
2.2 OVERVIEW OF DEEP LEARNING
2.3 HISTORCAL BACKGROUND OF DEEP LEARNING
2.4 OVERVIEW OF NEURAL NETWORKS
CHAPTER THREE
METHODOLOGY
3.1 INTRODUCTION
3.2 WEB SEARCH ENGINES
3.3 SPATIAL SEARCH VARIATION
3.4 TIME SEARCH VARIATION
3.5 NEURAL NETWORKS
3.6 NEURAL NETWORKS IN WEB SEARCH
3.7 NEURAL NETWORKS IN LEARNING TO RANK ALGORITHMS
CHAPTER FOUR
RESULT ANALYSIS
4.1 RESULTS
4.2 RANKING
4.3 RANKING ALGORITHMS
CHAPTER FIVE
5.1 CONCLUSION
5.2 REFERENCES
Web Search Optimization Using Deep Learning Techniques. (n.d.). UniTopics. https://www.unitopics.com/project/material/web-search-optimization-using-deep-learning-techniques/
“Web Search Optimization Using Deep Learning Techniques.” UniTopics, https://www.unitopics.com/project/material/web-search-optimization-using-deep-learning-techniques/. Accessed 20 September 2024.
“Web Search Optimization Using Deep Learning Techniques.” UniTopics, Accessed September 20, 2024. https://www.unitopics.com/project/material/web-search-optimization-using-deep-learning-techniques/
Here’s a typical structure for Web Search Optimization Using Deep Learning Techniques research projects:
- The title page of Web Search Optimization Using Deep Learning Techniques should include the project title, your name, institution, and date.
- The abstract of Web Search Optimization Using Deep Learning Techniques should be a summary of around 150-250 words and should highlight the main objectives, methods, results, and conclusions.
- The introduction of Web Search Optimization Using Deep Learning Techniques should provide the background information, outline the research problem, and state the objectives and significance of the study.
- Review existing research related to Web Search Optimization Using Deep Learning Techniques, identifying gaps the study aims to fill.
- The methodology section of Web Search Optimization Using Deep Learning Techniques should describe the research design, data collection methods, and analytical techniques used.
- Present the findings of the Web Search Optimization Using Deep Learning Techniques research study using tables, charts, and graphs to illustrate key points.
- Interpret Web Search Optimization Using Deep Learning Techniques results, discussing their implications, limitations, and potential areas for future research.
- Summarize the main findings of the Web Search Optimization Using Deep Learning Techniques study and restate its significance.
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