Enhancing performance of big data applying similarity over detected community
Bibliographic record
Abstract
The enhancing Performance of Big Data applying Similarity over Detected Community using\nMachine learning (ML) allows social network analysis and the Internet of Things (IoT) to gain\nhidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly\nlooking for the knowledge and patterns. Without ML, social network analysis is ineffective,\nand IoT cannot withstand the future requirements of businesses, governments, and individual\nusers. The primary goal of IoT is to perceive what is happening in our surroundings and later\nautomate the decision making, which will mimic the decisions made by humans. Further,\nnetwork analysis is highly dependent on finding similarities across all communities. The\ncommunity can be strengthened with the help of content information. However, it is highly\nrestricted to the noise present on most networks, especially in the link structure. This thesis\noutlines an essential way to integrate content and link information into graph-based designs to\nfacilitate public access. It also attempts to reduce the impact of frequent noise on social\nnetworking sites and web-based information networks.\nWe propose to calculate signal strength between nodes across a network by combining the\npower of a link, which that link may lie within the community, by the content similarity that\ncan be measured using cosine similarity or Jaccard coefficient. In addition, we discuss the\nprocess of sampling in keeping the right edges in place of the whole element of the graph.\nGraph results can be compiled using standard algorithms used for public acquisition, such as\nMarkov-clustering and METIS. We have tried real-world data sets (Wikipedia, CiteSeer, and\nFlickr) that change sizes and parameters to understand the effectiveness of our method\ncompared to the existing one. We have tried to find a useful way to integrate content analysis\nand linking methods with the method of graph deviation.\nIn this thesis, we performed social network analysis, and we classify IoT and related ML\nliterature from three perspectives: data, application, and industry. In this thesis, we emphasize\nbringing awareness and enhancing the understanding of how ML can play a significant role in\nmaking our environment smarter and more intelligent. The thesis helps to understand better\nML's function and its effects in a broader context of social network analysis and IoT. This\nthesis also discussed emerging IoT trends: Internet of Behaviors (IoB), pandemic\nmanagement, connected autonomous vehicles, edge and fog computing, and deep learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".