IoT based Schistosomiasis Monitoring for More Efficient Disease Prediction and Control Model
Bibliographic record
Abstract
The urinary and intestinal schistosomiasis are a significant public health problem in Senegal with a prevalence rate varying between 0.3% and 1%. After malaria, bilharzia (or Schistosomiasis) is the second disease that calls for admission to hospital. In Senegal, treatment is based on "Praziquantel" that is not effective and may aggravate symptoms. In fact, schistosoma that transmits the illness lives in water points. Firstly, our proposed Sensors-Based Bilharzia Detection (SB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D) architecture uses data collected by wireless sensors network that are deployed in natural environment. SB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D is able to collect in real time different physical and chemical parameters such as solar irradiation, water temperature, water point pH and then predicts whether the environmental factors are favourable to bilharzia life cycle transmission. Secondly, event detection algorithms were developed in order to assess the transmission contamination risk when anomalies are detected. The obtained results show that Support Vector Machines (SVM) gives good anomalies detection rate compared to other anomalies detection test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".