The FT232R is the latest device to be added to FTDI?s range of USB UART interface Integrated Circuit Devices. The FT232R is a USB to serial UART interface with optional clock generator output, and the new FTDIChip-ID? security dongle feature.
You must be logged in to post a review.
This site uses Akismet to reduce spam. Learn how your comment data is processed.
?24,000.00?Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It?s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
Shipping : 4 to 5 days from the date of purchase
Warranty : 3 Months
100 in stock
The main objective of this project is to analyse previous year?s student?s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions using Machine Learning Algorithms.
The objective of the project is to understand the concepts of natural language processing and create a tool for text summarization. The concern in automatic summarization is increasing broadly so the manual work is removed. The project concentrates on creating a tool that automatically summarizes the document.
In this project, we create a model to do the accurate prediction of heart disease problems in health care applications. Easier to analyse the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.
The main objective of this project is to predict the employee attrition rate using Machine Learning Algorithms such as SVM and Naive Bayes algorithms. After the results obtained, the performance of the model is evaluated by calculating the accuracy score and showing it in the form of a confusion matrix.
In this concept, we create Machine Learning Model for Smart Farming. Smart Farming Prediction and the recommendation can be made using Space Vector Modulation Classification and Neural Network Algorithm.
Churn Analysis is one of the worldwide used analyses on Subscription Oriented Industries to analyse customer behaviours to predict the customers which are about to leave the service agreement from a company. The proposed model ?rst classi?es churn customers data using classi?cation algorithms, in which the Random Forest (RF) and Decision tree (DT) algorithm performed well with 90.44% correctly classi?ed instances.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective of the project to find the Network attacks using KDD Datas and Data Mining Approach.
To prevent malware attacks, researchers and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.?We can analyse cyber threats using two techniques, static analysis, and dynamic analysis, the most important thing is that these are the approaches to get the features that we are going to use in data science.
The proposed framework focuses on merging the demographic and study related attributes with the educational psychology fields, by adding the student’s psychological characteristics. After surveying, we picked the most relevant attributes based on their rationale and correlation with the academic performance.
We apply the ML model on datasets like Twitter, Flickr, and YouTube. It will predict a similar type of hashtag with a detailed description. Unsupervised word embedding methods train with a reconstruction objective, in which the embedding is used to predict the original text.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective is to create a ML Model by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. This latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.
In this Project, we are building a Machine Learning Model to detect the Credit Card Fraud using Random Forest Algorithm. Random Forest is an algorithm for classification and regression. Summarily, it is a collection of decision tree classifiers. The random forest has an advantage over the decision tree as it corrects the habit of overfitting to their training set. A subset of the training set is sampled randomly so that to train each individual tree and then a decision tree is built, each node then splits on a feature selected from a random subset of the full feature set.
The main objective is to detect fake news, which is a classic text classification problem with a straightforward proposition. It is needed to build a model that can differentiate between Real news and Fake news using Machine Learning Algorithm.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. During this process, we are going to realize economic technique which may predict stock movement additional accurately. Social media offers a robust outlet for people’s thoughts and feelings it’s a fast-ever-growing supply of texts starting from everyday observations to concerning discussions
Performance analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of students’ interests.? The proposed framework analyse the students demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers, and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project to create a ML Model to predict the liver disease from the huge liver disease datasets using the algorithms. Time complexity and accuracy can measured by various machine learning models ,so that we can measures different. Risky factors can be predicted early by machine learning models.
The primary goal of this project is to extract patterns from a common loan-approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. The historical data of the customers like their age, income, loan amount, employment length etc. will be used in order to do the analysis.
This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. To Analysis of the language in the typical datasets to get hate speech by features in the ?long tail? in a dataset using Machine Learning.
Models for the prediction of water table depth were developed based on Artificial Neural Networks (ANN) with different combinations of hydrological parameters. The best combination was confirmed with factor analysis. The input parameters for groundwater level forecasting were derived using Time Series Analysis (TSA).
Models are created using accident data records which can help to understand the characteristics of many features like driver’s behaviour, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.
Customer Reviews
There are no reviews yet.