Pt2520 Unit 4.5 Assignment 1

The security I use a database management program such as PHPMyAdmin (with the WEE extension), select the field you need to encrypt by its name from a menu, select the public key and press the encrypt button, the protected information is ready to be stored in the database.

In addition to translate some of the article, I was also given the task to update the stock of merchandise to be ordered by the customer. In this task I do not get into trouble like a previous assignment, to update their own merchandise needed to open access database, by Kak Aty therefore as one of the public relations team staff gave me access to open merchandise updates via

To fulfill all the requirements, Boots decided to use Customer Data Analysis System (CDAS) by giving advice from IBM. According to the support of this system, most queries response times were 30 times faster than before even though the database has reached 1.200 GB. Because of this, the analysts of Boots were delighted. CDAS includes IBM’s intelligent Miner for Data being used for more advanced data mining such as segmentation and

The TLE GADOE (2016) states that the Individual Assessment of Number (IKAN) helps to identify student’s basic number knowledge. In contrast, the Global Strategy Stage Assessment (GloSS) assesses a student’s capability to use math strategies and identifies the mental processes students use to answers and solve operational problems with numbers (TLE GADOE, 2016). The assessments consist of a series of interview strategy and number questions which should be administered to individual students which is administered at least three times a year. The students are then assigned an overall Strategy Stage based on their responses to the questions in the interview (TLE GADOE, 2016). The series of questions increase in

The following steps suggest one way of finding the best classifier. Note that this is just one way for doing the project. You can definitely make improvements or use other ways to get a classifier with higher accuracy.

Abstract—This report compares the performance of different type of databases and general the normal way to improve the performance of the database.

specify the algorithm performance on the new data. With this model, a test accuracy of

In this step, the products are classified based on their textual representation. Each product is classified by using a base classifier. The base classifier does not have any aware about the taxonomies. Machine learning techniques such as Naïve Bayes and Logistic Regression are used. The features of the product are extracted from the textual representation of the product.

The existing classification methods have limitation in accuracy, exactness and require manual interaction. So, designing automated system using image segmentation techniques helps make the detection accurate and efficient.

Feature plays a very important role in the area of image processing. Different feature extraction

exploitation human based mostly behavior analysis. Then the malwares are classified into malware families Worms and Trojans. The limitation of this work is that customization using human analysis isn't potential for today’s real time traffic that is voluminous and having a range of threats.

Feature extraction is the third stage in medical image processing application, after image pre-processing. In feature extraction, the features like the shape, colour, texture are used to describe an image content[bio2].features can be short relevance or strong relevant ones. Short relevant features give only little information about the image, while strong relevant features provide significant information about the image. Finding these strong relevant features are time consuming and hence good techniques has to be developed.

The three popular classification techniques are explained: (1) K-nearest neighbor (KNN), (2) support vector machine (SVM), and (3) naïve Bayes classifier (NBC). The given algorithms here include both deterministic and probabilistic classification approaches.

SVM classifiers worked the best in majority of papers. In this project, SVM classifier with linear kernel is used.

Pattern recognition is a technique to differentiate different pattern into classes through the help of supervised or unsupervised technique. We have developed highly sophisticated skills for sensing the environment and taking actions according to what we observe. This sensing and understanding is mostly dependent on ability to differentiate between patterns. The pattern recognition ability if with the help of machine learning can be applied in machine. The machine ability to make decision like human being will be enhanced. Many applications such as data mining, web searching, face recognition etc has already been in uses which are based on the pattern recognitions. The objective of this review paper is to summarize and compare some of the well-known methods and application used in pattern recognition system.