Interface : i. In android interface is very useful concept with the interface we are able to access List of items in Recycler view adapter in simple word we can easily access multiple users like whatsapp we are able to send the message multiple users. ii. we are able to implement onClickListenr and onLongClick Listener into the same user with onclick listener we are able to go to the user location or another activity iii. with the on long Click listener we are able to perform another activity into the same account. iv, user is able to go to the location of his friend if he is lost v. Able to communicate multiple users at same time. Cloud Messaging : i. With the help of Retrofit api we are able to friend request to the another user ii. We are able to communicate with the users with this api and user is able to send to communicate with another user easily and able to send details over messaging platform. Code Interface : public interface ItemClickListener { void onClick(View view,int position); void onItemLongClick(View view,int position); } MyAdapeter.java public class MyAdapter extends RecyclerView.ViewHolder implements View.OnClickListener, View.OnLongClickListener { public TextView textEmail; ItemClickListener itemClickListener; public void setItemClickListener(ItemClickListener itemClickListener) { this.itemClickListener = itemClickListener; } public MyAdapter(@NonNull View itemView) { super(itemView); textEmail=itemView.findViewById(R.id.txt_email); itemView.setOnClickListener(this); } @Override public void onClick(View v) { itemClickListener.onClick(v,getAdapterPosition()); } @Override public boolean onLongClick(View v) { itemClickListener.onItemLongClick(v, getAdapterPosition()); return true; } } Mainn.java inside OnbindActivity holder.setItemClickListener(new ItemClickListener() { @Override public void onClick(View view, int position) { if(!model.getEmail().equals(FirebaseAuth.getInstance().getCurrentUser().getEmail())) { Toast.makeText(Online.this, "Lets Go", Toast.LENGTH_SHORT).show(); Intent map=new Intent(Online.this,MapsActivity.class); map.putExtra("email",model.getEmail()); map.putExtra("lat",mlocation.getLatitude()); map.putExtra("lng",mlocation.getLongitude()); startActivity(map); } } @Override public void onItemLongClick(View view, int position) { showDialogRequest(model); } }); Hire an android developer to get quick help for all your android app development needs. with the hands-on android assignment help and android project help by Codersarts android expert. You can contact the android programming help expert any time; we will help you overcome all the issues and find the right solution. Want to get help right now? Or Want to know price quote Please send your requirement files at contact@codersarts.com. and you'll get instant reply as soon as requirement receives

Project Objective Employers are required to report any serious work-related injuries and death to the authority. This information helps employers, workers and the authority to evaluate the safety of a workplace, understand industry hazards, and implement worker protections to reduce and eliminate hazards. In this mini-project, assume you are engaged by a client to perform text mining on the accident reports to help find answers to the following questions: 1. What are the major types of accidents reflected in the reports? No labels, supervised or non-supervised? Clustering or Topic modelling? All data or partial data? 2. Which type of accidents are more common? Frequency of doc wrt topic 3. Can we find out the more risky occupations in such accidents? Information Extraction, how to identify “occupations” words? 4. Which part of the body is injured most? (Optional) Information Extraction, how to identify “body” words? The dataset is in file “osha.txt“. Data understanding and cleaning Load the data file into R. – read.delim(), header=FALSE e.g. textdata <- read.delim("osha.txt", header=FALSE, sep="\t", quote = "", stringsAsFactors = FALSE) Explore your data: - How many records do you have? How many variables? - Examine the first few records in the datasets. - What information does the dataset contain? - Which fields are useful for your study? - How long are the reports generally? - How’s the data quality? - What are the contents of the reports roughly? [ Create a word cloud for the dataset ] Vectorsource, corpus, DTM Term frequency summary Wordcloud Contact us: If you have any doubt in this blog or need any project or programming related help related to machine learning then you can contact at here

In this example we are seeing how to develop Line Chart application using technologies JavaFX. A line chart or line graph displays information as a series of data points (markers) connected by straight line segments. A Line Chart shows how the data changes at equal time frequency. In JavaFX, a line chart is represented by a class named LineChart. This class belongs to the package javafx.scene.chart. By instantiating this class, you can create a LineChart node in JavaFX. Line.fxml LineController.java package application; import javafx.event.ActionEvent; import javafx.fxml.FXML; import javafx.scene.chart.LineChart; import javafx.scene.chart.XYChart; public class LineController { @FXML LineChart lineChart; public void generateLineChart(ActionEvent ae){ lineChart.getData().clear(); XYChart.Series series=new XYChart.Series(); series.getData().add(new XYChart.Data("Jan",200)); series.getData().add(new XYChart.Data("Feb",100)); series.getData().add(new XYChart.Data("Mar",300)); series.getData().add(new XYChart.Data("Apr",400)); series.setName("Month Pay"); lineChart.getData().add(series); } } Main.java package application; import javafx.application.Application; import javafx.fxml.FXMLLoader; import javafx.stage.Stage; import javafx.scene.Parent; import javafx.scene.Scene; public class Main extends Application { @Override public void start(Stage primaryStage) { try { Parent root=FXMLLoader.load(getClass().getResource("/application/Line.fxml")); Scene scene = new Scene(root); scene.getStylesheets().add(getClass().getResource("application.css").toExternalForm()); primaryStage.setScene(scene); primaryStage.show(); } catch(Exception e) { e.printStackTrace(); } } public static void main(String[] args) { launch(args); } }

In this example we are seeing how to develop Pie Chart application using technologies JavaFX. A pie-chart is a representation of values as slices of a circle with different colors. These slices are labeled and the values corresponding to each slice is represented in the chart. In JavaFX, a pie chart is represented by a class named PieChart. This class belongs to the package javafx.scene.chart. This class has 5 properties which are as follows − clockwise − This is a Boolean Operator; on setting this operator true, the data slices in the pie charts will be arranged clockwise starting from the start angle of the pie chart. data − This represents an ObservableList object, which holds the data of the pie chart. labelLineLength − An integer operator representing the length of the lines connecting the labels and the slices of the pie chart. labelsVisible − This is a Boolean Operator; on setting this operator true, the labels for the pie charts will be drawn. By default, this operator is set to be true. startAngle − This is a double type operator, which represents the angle to start the first pie slice at. PieChart.fxml PieChartController.java package application; import javafx.collections.FXCollections; import javafx.collections.ObservableList; import javafx.event.ActionEvent; import javafx.fxml.FXML; import javafx.scene.chart.PieChart; import javafx.scene.chart.PieChart.Data; public class PieChartController { @FXML PieChart pieChart; public void generateChart(ActionEvent ae){ ObservableList list=FXCollections.observableArrayList( new PieChart.Data("Java",50), new PieChart.Data("C",30), new PieChart.Data("C++",25), new PieChart.Data("Python",40) ); pieChart.setData(list); } } Main.java package application; import javafx.application.Application; import javafx.fxml.FXMLLoader; import javafx.stage.Stage; import javafx.scene.Parent; import javafx.scene.Scene; import javafx.scene.layout.BorderPane; public class Main extends Application { @Override public void start(Stage primaryStage) { try { Parent root=FXMLLoader.load(getClass().getResource("/application/PieChart.fxml")); Scene scene = new Scene(root); scene.getStylesheets().add(getClass().getResource("application.css").toExternalForm()); primaryStage.setScene(scene); primaryStage.show(); } catch(Exception e) { e.printStackTrace(); } } public static void main(String[] args) { launch(args); } }

(NLP) is a wide area of research where the worlds of artificial intelligence, computer science, and linguistics collide. It includes a bevy of interesting topics with cool real-world applications, like named entity recognition, machine translation, or machine question answering. Each of these topics has its own way of dealing with textual data. But before diving into the deep end and looking at these more complex applications, we need to wade in the shallow end and understand how simpler tasks such as text classification are performed. Text classification offers a good framework for getting familiar with textual data processing. There are many interesting applications for text classification such as spam detection and sentiment analysis. In this post, we will see some NLP techniques for text classification. The basics include: Structure extraction – identifying fields and blocks of content based on tagging Identify and mark sentence, phrase, and paragraph boundaries – these markers are important when doing entity extraction and NLP since they serve as useful breaks within which analysis occurs. Language identification – will detect the human language for the entire document and for each paragraph or sentence. Language detectors are critical to determining what linguistic algorithms and dictionaries to apply to the text. Tokenization – to divide up character streams into tokens which can be used for further processing and understanding. Tokens can be words, numbers, identifiers or punctuation (depending on the use case) Acronym normalization and tagging – acronyms can be specified as “I.B.M.” or “IBM” so these should be tagged and normalized. Lemmatization / Stemming – reduces word variations to simpler forms that may help increase the coverage of NLP utilities. Decompounding – for some languages (typically Germanic, Scandinavian, and Cyrillic languages), compound words will need to be split into smaller parts to allow for accurate NLP. Entity extraction – identifying and extracting entities (people, places, companies, etc.) is a necessary step to simplify downstream processing. There are several different methods: Regex extraction – good for phone numbers, ID numbers (e.g. SSN, driver’s licenses, etc.), e-mail addresses, numbers, URLs, hashtags, credit card numbers, and similar entities helps to identify the same. Dictionary extraction – uses a dictionary of token sequences and identifies when those sequences occur in the text. This is good for known entities, such as colors, units, sizes, employees, business groups, drug names, products, brands, and so on, which helps to identify the same. Complex pattern-based extraction – good for people names (made of known components), business names (made of known components), and context-based extraction scenarios (e.g. extract an item based on its context) which are fairly regular in nature and when high precision is preferred over high recall. Phrase extraction – extracts sequences of tokens (phrases) that have a strong meaning which is independent of the words when treated separately. These sequences should be treated as a single unit when doing NLP. For example, “Big Data” has a strong meaning which is independent of the words “big” and “data” when used separately. All companies have these sorts of phrases that are in common usage throughout the organization and are better treated as a unit rather than separately. Techniques to extract phrases include: Part of speech tagging – identifies phrases from the noun or verb clauses Statistical phrase extraction - identifies token sequences which occur more frequently than expected by chance Hybrid - uses both techniques together and tends to be the most accurate method. Some Text Classification Algorithms: 1. Naive Bayes Naive Bayes is a family of statistical algorithms we can make use of when doing text classification. One of the members of that family is Multinomial Naive Bayes (MNB). One of its main advantages is that you can get really good results when data available is not much (~ a couple of thousand tagged samples) and computational resources are scarce. All you need to know is that Naive Bayes is based on Bayes’s Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event. This means that any vector that represents a text will have to contain information about the probabilities of the appearance of the words of the text within the texts of a given category so that the algorithm can compute the likelihood of that text’s belonging to the category. Support Vector Machines 2. Support Vectors Machines(SVMs): Support Vector Machines (SVM) is just one out of many algorithms we can choose from when doing text classification. Like naive Bayes, SVM doesn’t need much training data to start providing accurate results. Although it needs more computational resources than Naive Bayes, SVM can achieve more accurate results. In short, SVM takes care of drawing a “line” or hyperplane that divides a space into two subspaces: one subspace that contains vectors that belong to a group and another subspace that contains vectors that do not belong to that group. Those vectors are representations of your training texts and a group is a tag you have tagged your texts with. 3. Deep Learning: Deep learning is a set of algorithms and techniques inspired by how the human brain works. Text classification has benefited from the recent resurgence of deep learning architectures due to their potential to reach high accuracy with less need for engineered features. The two main deep learning architectures used in text classification are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). On the one hand, deep learning algorithms require much more training data than traditional machine learning algorithms, i.e. at least millions of tagged examples. On the other hand, traditional machine learning algorithms such as SVM and NB reach a certain threshold where adding more training data doesn’t improve their accuracy. In contrast, deep learning classifiers continue to get better the more data you feed them with. Applications and Examples of Text Classification: Text classification can be used in a broad range of contexts such as classifying short texts (e.g. as tweets, headlines, or tweets) or organizing much larger documents (e.g. customer reviews, media articles, or legal contracts). Some of the most well-known examples of text classification include sentiment analysis, topic labeling, language detection, and intent detection. Sentiment Analysis: Probably the most common example of text classification is sentiment analysis: the automated process of determining whether a test is positive, negative, or neutral. Companies are using sentiment classifiers on a wide range of applications, such as product analytics, brand monitoring, customer support, market research, workforce analytics, and much more. This is a pre-trained classifier using MonkeyLearn for classifying text in English according to their sentiment. Feel free to experiment and try different expressions to see the classifier makes the predictions: Topic Labeling: Another common example of text classification is topic labeling, that is, understanding what a given text is talking about. It’s often used for structuring and organizing data such as organizing customer feedback by its topic or organizing news articles according to their subject. Language Detection: Language detection is another great example of text classification, that is, the process of classifying incoming text according to its language. The text classification also helps us to know the language of the text. Use-cases: Some real-life use cases are mentioned below. Social media monitoring checking. Brand monitoring checking. Customer service. Call Center service. Changing languages. Voice of the customer. Google Translate. Thank You! Reference: https://machinelearningmastery.com/best-practices-document-classification-deep-learning/

In this example we are seeing how to develop CRUD (Create, Read, Update and Delete) operation application using technologies JavaFX. In this example all operations are performed on Employee basic properties like employee id, employee name,department name,mobile number and employee salary . Application main aim is adding employee details to DB using user interface, and performing multiple operations like update, viewing and deleting. Employee.fxml EmployeeController.java package application; import java.io.IOException; import java.sql.Connection; import java.sql.PreparedStatement; import java.sql.ResultSet; import java.sql.SQLException; import javafx.collections.FXCollections; import javafx.collections.ObservableList; import javafx.event.ActionEvent; import javafx.fxml.FXML; import javafx.fxml.FXMLLoader; import javafx.scene.Parent; import javafx.scene.Scene; import javafx.scene.control.Label; import javafx.scene.control.TableColumn; import javafx.scene.control.TableView; import javafx.scene.control.TextField; import javafx.scene.control.cell.PropertyValueFactory; import javafx.stage.Stage; public class EmployeeController { @FXML private TextField ename; @FXML private TextField dept; @FXML private TextField mobNo; @FXML private TextField salary; @FXML private Label lavel; @FXML private TableView table; @FXML private TableColumn eid; @FXML private TableColumn name; @FXML private TableColumn department; @FXML private TableColumn salary1; @FXML private TableColumn mobile; @FXML private TextField id; Employee emp; Connection con=null; ResultSet rs=null; PreparedStatement pstmt=null; // create table employee ( id number,name varchar2(15),department varchar2(15),mobileNo varchar2(10),salary number); public void register(ActionEvent ae){ emp=new Employee(); emp.setName(ename.getText()); emp.setDepartment(dept.getText()); emp.setMobileNo(mobNo.getText()); emp.setSalary(Float.parseFloat(salary.getText())); if(emp.getMobileNo().length()<10 || emp.getMobileNo().length()>10){ lavel.setText("please enter correct mobile No"); return; } try { con=DBUtil.getConnection(); String sql="insert into employee values((select nvl(max(id),0)+1 from employee),?,?,?,?)"; pstmt=con.prepareStatement(sql); pstmt.setString(1,emp.getName()); pstmt.setString(2,emp.getDepartment()); pstmt.setString(3,emp.getMobileNo()); pstmt.setFloat(4, emp.getSalary()); rs=pstmt.executeQuery(); if(rs.next()){ lavel.setText("Register Sucessfully."); eid.setCellValueFactory(new PropertyValueFactory("eid")); name.setCellValueFactory(new PropertyValueFactory("name")); department.setCellValueFactory(new PropertyValueFactory("department")); salary1.setCellValueFactory(new PropertyValueFactory("salary1")); mobile.setCellValueFactory(new PropertyValueFactory("mobile")); ObservableList data = FXCollections.observableArrayList( new Employee(101,emp.getName(),emp.getDepartment(),123,"12312323") ); table.getItems().addAll(data); } } catch (SQLException e) { e.printStackTrace(); } System.out.println(emp.getSalary()+"..........."); } public void showAll(ActionEvent ae){ try { con =DBUtil.getConnection(); String sql="select * from employee"; pstmt=con.prepareStatement(sql); rs=pstmt.executeQuery(); while(rs.next()){ eid.setCellValueFactory(new PropertyValueFactory("eid")); name.setCellValueFactory(new PropertyValueFactory("name")); department.setCellValueFactory(new PropertyValueFactory("department")); salary1.setCellValueFactory(new PropertyValueFactory("salary1")); mobile.setCellValueFactory(new PropertyValueFactory("mobile")); ObservableList data = FXCollections.observableArrayList( new Employee(rs.getInt("id"),rs.getString("name"),rs.getString("department"),rs.getFloat("salary"),rs.getString("mobileNo")) ); table.getItems().addAll(data); } } catch (SQLException e) { e.printStackTrace(); } } public void deleteEmployee(ActionEvent ae){ try { con =DBUtil.getConnection(); String sql="delete from employee where id=?"; pstmt=con.prepareStatement(sql); pstmt.setInt(1, Integer.parseInt(id.getText())); rs=pstmt.executeQuery(); if(rs!=null){ lavel.setText("Record deleted "); }else{ lavel.setText("please check employee id"); } }catch(Exception e){ e.printStackTrace(); } } public void update(ActionEvent ae) throws IOException{ Stage primaryStage= new Stage(); Parent root =FXMLLoader.load(getClass().getResource("/application/Update.fxml")); // Parent root = FXMLLoader.load(getClass().getResource(arg0)) Scene scene = new Scene(root); scene.getStylesheets().add(getClass().getResource("application.css").toExternalForm()); primaryStage.setScene(scene); primaryStage.show(); } } Employee.java package application; public class Employee { private int eid; private String name; private String department; private float salary; private String mobileNo; public Employee() { super(); } public Employee(int eid, String name, String department, float salary, String mobileNo) { super(); this.eid = eid; this.name = name; this.department = department; this.salary = salary; this.mobileNo = mobileNo; } public int getEid() { return eid; } public void setEid(int eid) { this.eid = eid; } public String getName() { return name; } public void setName(String name) { this.name = name; } public String getDepartment() { return department; } public void setDepartment(String department) { this.department = department; } public float getSalary() { return salary; } public void setSalary(float salary) { this.salary = salary; } public String getMobileNo() { return mobileNo; } public void setMobileNo(String mobileNo) { this.mobileNo = mobileNo; } } Update.fxml UpdateController.java package application; import java.sql.Connection; import java.sql.PreparedStatement; import java.sql.ResultSet; import java.sql.SQLException; import javafx.collections.FXCollections; import javafx.collections.ObservableList; import javafx.event.ActionEvent; import javafx.fxml.FXML; import javafx.scene.control.Label; import javafx.scene.control.TableColumn; import javafx.scene.control.TableView; import javafx.scene.control.TextField; import javafx.scene.control.cell.PropertyValueFactory; public class UpdateController { @FXML private TextField ename; @FXML private TextField dept; @FXML private TextField mobNo; @FXML private TextField salary; @FXML private TextField id; @FXML private Label lavel; @FXML private TableView table; @FXML private TableColumn eid; @FXML private TableColumn name; @FXML private TableColumn department; @FXML private TableColumn salary1; @FXML private TableColumn mobile; Employee emp; Connection con=null; ResultSet rs=null; PreparedStatement pstmt=null; public void updateEmployee(ActionEvent ae){ emp=new Employee(); emp.setEid(Integer.parseInt(id.getText())); emp.setName(ename.getText()); emp.setDepartment(dept.getText()); emp.setMobileNo(mobNo.getText()); emp.setSalary(Float.parseFloat(salary.getText())); if(emp.getMobileNo().length()<10 || emp.getMobileNo().length()>10){ lavel.setText("please enter correct mobile No"); return; } try { con=DBUtil.getConnection(); String sql="update employee set name=?,department=?,mobileno=?,salary=? where id=?"; pstmt=con.prepareStatement(sql); pstmt.setString(1,emp.getName()); pstmt.setString(2,emp.getDepartment()); pstmt.setString(3,emp.getMobileNo()); pstmt.setFloat(4, emp.getSalary()); pstmt.setInt(5, emp.getEid()); rs=pstmt.executeQuery(); if(rs.next()){ lavel.setText("Update Sucessfully."); eid.setCellValueFactory(new PropertyValueFactory("eid")); name.setCellValueFactory(new PropertyValueFactory("name")); department.setCellValueFactory(new PropertyValueFactory("department")); salary1.setCellValueFactory(new PropertyValueFactory("salary1")); mobile.setCellValueFactory(new PropertyValueFactory("mobile")); ObservableList data = FXCollections.observableArrayList( new Employee(emp.getEid(),emp.getName(),emp.getDepartment(),emp.getSalary(),emp.getMobileNo()) ); table.getItems().addAll(data); } } catch (SQLException e) { e.printStackTrace(); } } } DBUtil.java package application; import java.sql.Connection; import java.sql.DriverManager; import java.sql.SQLException; public final class DBUtil { private static boolean isDriverLoaded = false; static{ try{ Class.forName("oracle.jdbc.driver.OracleDriver"); System.out.println("Driver Loaded"); isDriverLoaded = true; }catch(ClassNotFoundException e){ e.printStackTrace(); } } private final static String url="jdbc:oracle:thin:@localhost:1521:XE"; private final static String user="SYSTEM"; private final static String password="system"; public static Connection getConnection() throws SQLException{ Connection con = null; if(isDriverLoaded){ con = DriverManager.getConnection(url,user,password); System.out.println("Connection established"); } return con; } public static void closeConnection(Connection con) throws SQLException{ if(con!=null){ con.close(); System.out.println("connection closed"); } } } Main.java package application; import javafx.application.Application; import javafx.fxml.FXMLLoader; import javafx.stage.Stage; import javafx.scene.Parent; import javafx.scene.Scene; import javafx.scene.layout.BorderPane; public class Main extends Application { @Override public void start(Stage primaryStage) { try { Parent root = FXMLLoader.load(getClass().getResource("/application/Employee.fxml")); Scene scene = new Scene(root); scene.getStylesheets().add(getClass().getResource("application.css").toExternalForm()); primaryStage.setScene(scene); primaryStage.show(); } catch(Exception e) { e.printStackTrace(); } } public static void main(String[] args) { launch(args); } }

Note: In order to do this week's assignment, you will need to download this file, extract its contents, and use it as a base for your assignment. In other words the three folders - fonts, images, and text_files - must all be stored at the root level of your project folder. You will add any other folder you need to (e.g. docs) as you go along. For this assignment, you will need to submit a ZIP file containing all of the project files in this assignment that does the following: Opens up the XML file in the "text_files" folder Cleans out any non-ASCII characters from the file Saves the cleaned up file back to the file system under a new name Opens the cleaned up XML file For each employee in the XML file, adds name and title information to the file listed in the "profile_pic" field. All original images are stored in the "images" folder. All modified images must be stored under the "images/output_files/" folder The rest of your implementation details are up to your discretion (including font, color, size, positioning), applying the things you have learned about Python so far. Please note, however, that you must organize any classes\functions you create into modules and produce documentation (using Pydoc) for your work using the principles discussed earlier in the class. Your code must also be flexible so it could handle any XML and set of images thrown at it. Be sure to put comments in your code that clearly mark how you are performing your program logic. In the submission comments of this assignment, please place the repository URL of your file submission. Contact us at: contact@codersarts.com and get help related to python by our highly educated and experienced professionals

For this assignment, you are going to write a basic file management script to the following specifications: Your file manager needs to take command-line arguments. One of the arguments (-m) will be the mode the script needs to run in - basic, elevated, and admin. This argument is required; without it, the program should terminate. The other argument your program should take (-d) is optional and should be a directory path where you wish to browse files. If provided, your script will open that directory by default. If not, your script should open the default folder where it runs scripts. Your command-line arguments should be able to be accepted in any order. You need to ensure that no bad -m values get put in and that provided directory paths do not contain dot-dot folders. When your script loads, offer the user a menu of different things they can do. In basic mode, your script should allow the users to change the current directory to one of their choosing (you will need to prompt for this) and list items in the current directory only. When you list items, please include file size and distinguish between directories and files. In elevated mode, your script should allow users to do all of the above, plus be able to copy files and directories from one place to another (you will again need to prompt the user for details on how to accomplish this). In admin mode, your script should allow users to do all of the above, plus be able to move and delete files and directories (again, prompt the user for any details needed in order to make these operations work properly). Your script must log all commands to a file off the root of your working hard drive to a file called "system_log.txt" that's stored in a folder called "Python_Log". So, if you are working in Windows, your file would be stored at C:\Python_Log\system_log.txt You do not need to get overly granular here. If you are copying a file, just say "<name of the file> was copied to <destination>". If a directory was copied, just say "<name of the directory> was copied to <destination>". In the case where a directory is copied, you don't need to list the individual files that were copied...just the name of the folder. For any delete operation, you need to first copy whatever you're deleting to a folder off the root of your working hard drive to a folder called "backups". Then, once you've copied everything over, rename each file\directory so that the names are prefixed by "deleted_". Thus a file named "file.txt" would be renamed to "deleted_file.txt". Details on the above instructions are as follows: Your program must include appropriate error checking to make sure the source and destination folders do/don't exist as appropriate, as well as handle any other potential errors. If the destination folder doesn't exist when performing an operation, your program must block the operation and ask the user to try again. Your script must be designed to work equally well on Windows, Mac, and Linux platforms. The rest of your implementation details are up to your discretion, applying the things you have learned about Python so far. Please note, however, that you must organize any classes\functions you create into modules and produce documentation (using Pydoc) for your work using the principles discussed earlier in the class. You do not need to create test cases. Be sure to put comments in your code that clearly mark how you are performing your program logic. In the submission comments of this assignment, please place the repository URL of your file submission. contact us: contact@codersarts.com to get any system programming assignemnt help related to python, java or Operating System.

GUI Programming Assignment For this assignment, you will need to submit a ZIP file containing all of the project files for this assignment. Functional requirements for this assignment are as follows: The project you are going to build is intended to be used by helpdesk personnel for configuring a server-side application that pulls its settings in from a JSON file (your JSON file will look just like a Python dictionary). The items the app needs to control are as follows: -Max threads (allow to choose 1, 2, or 4 only) -Event log file location -Types of events to log (can choose application, security, error, input/output - these are not mutually exclusive) -Supported file types (can choose .doc, .docx, .ppt, .pptx, .xls, .xslx, .rtf, .pdf, .txt, .jpg, .png, .gif, .xml, .html, .zip, .mp4, .mov - these are not mutually exclusive) -Whether or not to turn debug mode on -Server port (single value in the range from 50,000 to 50,500) Your program must have the ability to save a user's settings to a properly formatted JSON file (you are going to need to create a format for this that works for you). Your program must have the ability to load a JSON file from the filesystem. When it does, it should pre-set all of the form fields on your application to the values that are in the file you loaded. Your program must use either buttons or a menu system to achieve saving/loading functionality. If you use buttons, make sure that they use images on them. Use the appropriate dialog widgets for opening files and saving back to the file system. You should make every effort to ensure your application is fault-tolerant and free from software bugs. The rest of your implementation details are up to your discretion (including font, color, size, positioning, which widgets to use, etc.), applying the things you have learned about Python so far. Please note, however, that you must organize any classes\functions you create into modules and produce documentation (using Pydoc) for your work using the principles discussed earlier in the class. Be sure to put comments in your code that clearly mark how you are performing your program logic. In the submission comments of this assignment, please place the repository URL of your file submission. Contact us: contact@codersarts.com to get instant help.

This App is Design for real time location tracking for for two user this shows the real time location of both users, We have Implemented firebase Recycler Adapter of and to get both the users details on to the CradView and design a menu bar onto the top of corner and there is we have created two activity meet and logout when we click on meet, we can add user and send them request for for Accept and decline if user accept the request then map show the path of user if user decline the request we are not able to follow the path of user. that is basic map and there is my live location shows and above searchbar is given where you can search the second user details splash Activity : Below when you open the app the splash activity shows you for 3 second Live Location of Users : Their is Splash Activity where first splash message show for 3 second and after that main activity open for sign Up there is an option you have Sign Up with Email,Google and Facebook you can signIn with any of them. after that their is Crad View were we have get the details of users. Sign Up : Menu : Hire an android developer to get quick help for all your android app development needs. with the hands-on android assignment help and android project help by Codersarts android expert. You can contact the android programming help expert any time; we will help you overcome all the issues and find the right solution. Want to get help right now? Or Want to know price quote Please send your requirement files at contact@codersarts.com. and you'll get instant reply as soon as requirement receives

Summary description and objectives for the assessment POLY Given a set of points in the XY-plane, find a (non-self-intersecting) polygon that covers ALL points trying to reduce the area it spans. The vertices of the computed polygon MUST belong to the set points. Examples of invalid and valid cases are shown in Figure 1. Figure 1. (a) An example of an input set of points (10 points). (b) An invalid polygon solution for (a) as its vertices are not points of the input data. (c) An invalid polygon solution for (a) as one of its vertices is not a point of the input data. (d) An invalid polygon solution for (a) as not all points of the input data are covered. (e) A valid polygon solution for (a) as all input points are covered and the vertices are all part of the input data. (f) A better valid solution for (a) as the resulting area is less than that in (e) and all conditions are met. Please observe that self-intersecting polygons are not valid. This individual project will be assessed through your code and a test. The test will use one input set of up to 1000 points to check whether your proposed algorithm generates valid solutions. You are provided with three Python scripts, namely, ‘main.py’, ‘test.py’, and ‘utils.py’, to examine your algorithm in similar conditions to those in the test. These scripts also provide useful tools such as a function to check whether a solution is valid, and a visualization tool to display a solution. The Python scripts are available here. Simple mock examples of invalid and valid solutions are provided in the scripts. It is your responsibility to familiarize with the use of the provided scripts, so please start as soon as possible! OBJECTIVES The main objective for this assignment is to verify that you are capable of Applying algorithm analysis techniques to determine the running time of algorithms and how a programme performs and scales with problem size. Explaining the basic principles and foundational techniques for designing algorithmic solutions for unseen problems. Using general design methods for devising programmes that are asymptotically efficient. Implement well-known, high-level algorithms and adapt them efficiently to new applications. Designing, implementing, and analyzing your own algorithms and data structures. Submission requirements Failure to comply with any of the requirements below will result in the assignment being marked 0%. You are required to submit a ZIP file that includes the source code of your proposed solution in Python 3. The main script of the algorithm should be called ‘main.py’ which includes the main solution function called ‘poly’ as described in subsection CODE SUBMISSION FORMAT The ZIP file needs to be sent via email to the module leader (Nicolas Rojas, n.rojas@imperial.ac.uk) before Tue 1 Sep 2020 4pm UK time. The module leader will acknowledge the receipt of your submission by replying to your email. It is your responsibility to keep a copy of this confirmation. CODE SUBMISSION FORMAT The main script of your proposed solution should be called ‘main.py’ which must include a main function called ‘poly’ as shown below: def poly(points): """ This function uses the input set of points, and finally outputs your polygon :param points: set of points :return: solution polygon covering the input set of points """ your code … return solution This ‘poly’ function takes an arbitrary set of points of the xy-plane points as input, and then generates and returns a polygon solution as output. Specifically, points is a list of coordinates provided by the module leader to represent the set of points to be covered. Each element in the list points represents a xy-coordinate using a tuple (x, y). For example, in Figure 1(a), the set of points is represented as: points = [(72, 69), (67, 30), (88, 58), (94, 15), (18, 77), (14, 6), (42, 11), (83, 21), (13, 20), (22, 1)] Based on the input set of points points, the ‘poly’ function will need to generate and return a list of coordinates solution to represent a polygon that covers the set. The coordinates in solution must be coordinates of points (i.e., the vertices of the computed polygon MUST belong to the set points). Moreover, the first and last coordinate in the list must be the same. For example, the solution polygon in Figure 1(c) is represented as: solution = [(22, 1), (14, 6), (13, 20), (18, 77), (72, 69), (95, 60), (94, 15), (22, 1)] which is invalid as one vertex (coordinate) of the polygon, namely (95, 60), is not part of the input set of points points. Similarly, the solution polygon in Figure 1(e) is represented as: solution = [(22, 1), (14, 6), (13, 20), (18, 77), (72, 69), (88, 58), (94, 15), (22, 1)] which is a valid solution. USE OF EXTERNAL LIBRARIES Python external libraries (e.g., NumPy, matplotlib) can be used in the proposed solution. However, appropriate comments explaining the use of functions/methods from these libraries have to be included in the code (where their call occurs). Please also include a statement at the top of the ‘main.py’ file summarising the external libraries and functions/methods used (if any). TEST POLICY The number of points of the input set will never exceed 1000. The running time of your algorithm MUST NOT exceed 10 minutes; if the algorithm cannot find a solution within that time in the test, the result will count as invalid. contact us at: cotact@codersarts.com to get any project or assignment related help at here with and affordable prices.

Introduction: Image classification can be accomplished by any machine learning algorithms( logistic regression, random forest, and SVM). But all the machine learning algorithms required proper features for doing the classification. If you feed the raw image into the classifier, it will fail to classify the images properly and the accuracy of the classifier would be less. CNN ( convolution neural network ) extracts the features from the images and it handles the entire feature engineering part. In normal CNN architecture, beginning layers are extracting the low-level features and end level layers extract high-level features from the image. Before CNN, we need to spend time selecting the proper features for classifying the image. There are so many handcrafted features available( local feature, global feature), but it will take so much time to select the proper features for a solution( image classification) and selecting the proper classification model. CNN handles all these problems and the accuracy of CNN is higher compared with the normal classifier. Different CNN Architectures are listed below LetNet AlexNet VGG16 VGG19 Google Inception Resnet. In this Blog, a simple CNN model and VGG-16(Transfer Learning) Model is discussed. 1. Simple CNN Architechture: Fig-1: CNN Architecture Fig-2: Simple CNN Architecture(With more labels) CNN Architecture steps: Let the image size is 100X100, and stride=1, with ongoing 1st convolution with 200 filters and then 2nd convolution with 100 filters Input Layer: The input images are loaded to this layer & the produced output is used to feed the convolution layers. Several image processing techniques such as resizing of the image to 40x40 pixels from 50X50 are applied. In our case, the dataset consists of the image and parameters are defining the image dimensions (100X100 pixels) and the number of channels is 3 for RGB. Convolutional Layer: In this layer, the convolution between input images with a set of learnable features or filters is carried out. By finding rough feature matches, in the same position for two images, CNN gets a lot better at seeing the similarity than whole image matching schemes. There are two convolution layers in our model with the receptive fields or kernel size of 3X3, the stride is set to 1. The first convolution layer learns 200 filters and the second one learns 100 filters. ReLU Layer: This layer removes the negative values from the filtered images and replaces them with zeros. For a given input value y, the ReLU layer computes output f(y) as y if y>0, else 0, or simply the activation is the threshold at zero. The first layer is the Conv2D layer with 200 filters and the filter size or the kernel size of 3X3. In this first step, the activation function used is the ‘ReLu’. This ReLu function stands for the Rectified Linear Unit which will output the input directly if is positive, otherwise, it will output zero. The input size is also initialized as 100X100X3 for all the images to be trained and tested using this model Pooling Layer: In this layer, the image stack is shrunk into a smaller size. The chosen window size is 2X2 with a stride of 1. For moving each window across the image, the maximum value is taken. Convolutional Layer: The next layer is again a Conv2D layer with another 100 filters of the same filter size 3X3 and the activation function used is the ‘ReLu’. This Conv2D layer is followed by a MaxPooling2D layer with pool size 2X2. Flatten(): In the next step, we use the Flatten() layer to flatten all the layers into a single 1D layer Fully Connected Layer(FCN): This is the final layer where the actual classification happens. All filtered & shrunk images are stacked up. It passes the flattened output to the output layer where the SoftMax classifier is used to predict the labels. At last, The dropout layers are added after the convolution & pooling layers to overcome the problem of overfitting to some extent. In our case it 0.5. This layer arbitrarily turns off a fraction of neurons during the training process, which reduces the habituation on the training set by some amount. The fraction of neurons to turn off is decided by a hyperparameter, which can be tuned accordingly. The loss function used here is categorical cross-entropy and the Keras optimizer used is Adam. Data Augmentation methods are also applied for image data such as horizontal & vertical flip, range of rotation, width and height shift, etc. It is required to obtain more data for training in general and adds it to the training set, also used to reduce overfitting. The process diagram is shown below 2. VGG-16(Transfer Learning): Fig-3: VGG-16 Architecture VGG-16 model is used in case of the limited size of datasets, VGG-16 is a pretrained model used in transfer learning techniques, for this reason, we have to ignore the training the layers which are already trained. The working process of VGG-16 has shown below Working: Due to the limited size dataset, it is difficult for learning algorithms to learn better features. As deep learning-based methods often require larger datasets, transfer learning is proposed to transfer learned knowledge from a source task to a related target task. Transfer learning has helped with learning in a significant way as long as it has a close relationship. First, we import the necessary libraries then we labeled the images as 0 and 1, in case of multiclass-classification we can use more. Pre-processing steps include resizing to 224×224 pixels, conversion to array format, and scaling the pixel intensities in the input image to the range [-1, 1]. Here the input image shape is 224X224X3. And we trained it using MobileNetV2 with weights of ImageNet.(In transfer learning VGG-16 Model the image size should be resized into 224X224) Then the MaxPooling layer is used with pool size 7X7. In the next step, we use the Flatten() layer to flatten all the layers into a single 1D layer. After the Flatten layer, we use the Dropout (0.5) layer to prevent the model from overfitting. Finally, towards the end, we use the Dense layer with 128 units, and the activation function as ‘ReLu’. The last layer of our model will be another Dense Layer, with only two units and the activation function used will be the ‘Softmax’ function. The softmax function outputs a vector which will represent the probability distributions of each of the input units. Here, two input units are used. The softmax function will output a vector with two probability distribution values. After building the model, we compile the model and define the loss function and optimizer function. In this model, we use the ‘Adam’ Optimizer and the ‘Binary Cross Entropy’ as the Loss function for training purposes. Finally, the VGG-16 model along with the cascade classifier is trained for 10 epochs with two classes. (In case of multi-class we have to train using multi-classes). So In this manner, these are the two methods/Techniques explained above that can be used in Image classification. Thank You!