Few Observations for Research

## Dataset Download

```
While using any public dataset, collect information regarding :
1. Size of Dataset,
2. No. of paper citations ( Usage of Dataset)
3. State of the art algorithms,
4. Constraints of the data
5. Errors Identified with data
```

## Literature Survey

```
Collect following information for chosen domain
1. Top 10 - Conferences - Also their acceptance criteria and Deadlines
2. Top Journals for domain
3. State of The Art Papers in the domain/ related
4. Initial Paper/Discovery which led to the domain
5. Challenges faced in each paper
6. Dataset used for the paper
7. Open Issues and Future work of paper
```

## Calculations

```
accuracy = (correctly predicted class / total testing class) × 100%
The accuracy can be defined as the percentage of correctly classified instances (TP + TN)/(TP + TN + FP + FN). where TP, FN, FP and TN represent the number of true positives, false negatives, false positives and true negatives, respectively.
also you can use standard performance measures:
1. Sensitivity = TP / TP + FN
2. Specificity = TN / TN + FP
3. Precision = TP / TP + FP
4. True-Positive Rate = TP / TP + FN
5. False-Positive Rate = FP / FP + TN
6. True-Negative Rate = TN / TN + FP
7. False-Negative Rate = FN / FN + TP
```