Precision and recall

model is (90.80) with weighted average precision (91.37) greater than recall (91.11) and F1-score (91.24). From the above results, it appears that Deep Learning classifier achieve higher accuracy, precision, recall, and F1-score. Figure 11: Clustering accuracy using Deep Learning Technique 1.5 Results Comparison Table 2: Performance Measures Comparison Model Decision Trees Naïve Bays Deep Learning Domain precision recall precision recall precision recall food 100.00 25.93 58.06 66.67 46

that the participants would inaccurately recall an

To evaluate our systems classication performance, i.e. the proportion of correctly classied tweets for a given test set, we used four common information retrieval (IR) evaluation measures, including recall (R), Precision (P), F1 Score, and Accuracy [73]. To explain these evaluation measures, we use a confusion matrix depicted in gure 5.1. 1 Classified Positive Classified Negative Actual Positive TP FN Actual Negative FP TN Figure 5.1: Confusion Matrix Where, TP (True Positive) ) Number of correct

as FN and FP speaks to the inaccurately grouped items. Fig 2.2. Precision and Recall Precision and recall for the most part go up against each other and the spotted lines inside the bend speak to the choice limit for high recall or high precision separately. On the off chance that the choice limit is moved to one side, there will be a greater amount of FN objects and less FP objects, bringing about low recall and higher precision. Accuracy: Accuracy is the most widely recognized execution measure

Developing A Diabetes Detection System by Using Big Data Technology 4 Problem Solution In this system, the performance of CBR Algorithm will be boosted based on MapReduce approach and to detect diabetes of a particular patient with improved CBR algorithm by using Apache Hadoop framework. Fig. 1. Framework of Case Based Reasoning Algorithm The biggest challenge of a CBR is finding an accurate indexing function. In diabetes dataset, there are five variables that influence the glycosylated

{\it Effectiveness-based}, {\it Complementary Dimensions of Quality} and {\it Domain Profiling}.\looseness=-1 \subsection{Effectiveness-based metrics} The main evaluation metrics are related to the accuracy, precision, and recall concepts \cite{herlocker2004evaluating}. Precision and recall are concepts that aim to quantify information

After performing various experiments in order to test the computational performance of FingerScanner to validate its viability. First, the performance of the communication system was measured to obtain the response times. Then, the behavior of the different components of the system was analyzed in depth. Fig1.1 Setting the software control panel Fig1.2 Swiping, Enrolling and Saving Fingerprint to disk Separating the results into three different categories. URIs, which only access the SQLite

Hello everyone, I am Haya Alhussin who have been in RIT 2 years ago. Before starting my major, I have been at English Language Center which I attended all levels that are requirement for me. After completing ELC, I started my bridge courses at ISTE department last Spring and I attempted all courses very successfully. Currently, this semester is a first semester as a graduate student and I am so excited to learn new knowledge in different culture. In fact, I have another two courses beside this course

First, data is prepared for being processed through sequential subsequent phases. First for every image per subject is loaded in the gray scale mode. Next, vein image is adjusted using a threshold detected adaptive for each image. The vein image is segmented to recognize the region of interest, vein region. For the vein region a feature extractor is used to extract the most power features exists. These features are stored labeled for the subject name for father classification and recognition. Dataset

ABSTRACT Opinions play a crucial role in the decision making process. Analysis in the field of making decisions and setting policies has shown that sentiment analysis and Opinion mining has become increasingly important in the field of Information Retrieval and Web analysis. In the past years, the growth of user generated data in web forums, social networking sites and other social platforms is tremendous, which diverts our study towards mining the opinions on web. In this paper, we have presented