A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research

A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search Emergent Research Forum Paper

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research

Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding

Modern expert nding algorithms are developed under the assumption that all possible expertise evidence for a person is concentrated in a company that currently employs the person. The evidence that can be acquired outside of an enterprise is traditionally unnoticed. At the same time, the Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only

A novel improvement to google scholar algorithms through broad topic search.

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match and citation count to sort its results. This paper presents a novel improvement to Google Scholar algorithms by aggregating multiple synonymous searches into one set of results, offsetting the necessity to guess all potential search phrases for a research topic. This design science research method uses a broad topic analysis that examines search queries, finds synonymous phrases, and combines all keyword searches into one set of results based on current Google Scholar citation count algorithms. To support and evaluate this research-in-progress, several users will compare multiple niche search queries against old and new algorithms. The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes