Payroll Management System Project in PHP with Source Code . This system is meant to supply the power to line up all the tasks of employee payment. At first, the user has got to undergo login system to urge access, then the user can add, list, update and take away the employee’s record. This system deals with the financial aspects of employee’s Salary, Deductions, allowances, Net pay. The user can view the account of each and every employee’s and update their payments, and the user can also manage deductions, modify overtime and salary rate. Each and every detail about employee’s payment is displayed which includes: Name with deduction, overtime, bonus and net pay. This system makes easier to the user for managing payroll system as it is not time-consuming. This project is not difficult to operate and understood by the users.
Features Payroll Management System Project
Login System
Add, Edit, Remove, View Employees record
Manage Department
Employee Personal Details
Employee Company Details
Add Payslip
Mange Payslip
PDF Format Payslip Generate
Settings
Manage Bonus and Deductions
List Income
Manage Over Time and Salary Rates
Brief overview of the technology:
Front end: HTML, CSS, JavaScript
HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
Bootstrap : responsive design mobile freindly site
JavaScript: it is a programming language, commonly use with web browsers.
Back end: PHP, MySQL
PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.
Software Requirement(any one)
WAMP Server
XAMPP Server
MAMP Server
LAMP Server
Installation Steps
1. Download zip file and Unzip file on your local server. 2. Put this file inside "c:/xamp/htdocs/" . 3. Database Configuration Open phpmyadmin Create Database named payroll_system.sql Import database art_gallry.sql from downloaded folder(inside database) 4. Open Your browser put inside "http://localhost/Projectworlds Payroll Management System/"
Courier Management system Project in PHP . In this project, you can simply perform the operations to manage the couriers and the parcels. . You can log in as the system admin and also can add and delete the Courier. you can also Update Status for courier delivered or not .
Courier Management System is the simplest solution for Courier & Cargo Tracking Business. This Courier Management System project will have different modules. The login section will have login facility for the admin who will operate this system and online tracking system of consignment and shipping detail for domestic shipping. While taking orders from its customers, it will take all the details of its customers who is placing the orders and all the details for the recipient such as its address,name,mobile number.
During billing process, system generates a consignment number for their products. Through this consignment no. customers or its recipient will able to track their products from any location using internet.
It will provide status of the product after placing orders.
This Courier Management System project will provide information recipient with following detail – where the current consignment is,till when it will reached its final destination, date of placing consignment , final date to reach its destination etc
Actors /Users of the System
Admin
Customers
ADMIN
Login
Admin can manage & update whole data
Manage Shipment
Add Shipper info, Receiver info and Shipment info.
Edit/Update Shipment
List all Shipment
Search By Consignment Number
Reports of the project
Report of all customer
Report of all consignment
Report of all shipper
Report of all pickup Date/Time
Report of all status
Customers(Users)
With Limited Access
Users can check status of their product after placing orders.
Brief overview of the technology:Online Examination System in PHP
Front end: HTML, CSS, JavaScript
HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
Bootstrap : responsive design mobile freindly site
JavaScript: it is a programming language, commonly use with web browsers.
Back end: PHP, MySQL
PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.
Software Requirement(any one) : Online Examination System in PHP
WAMP Server
XAMPP Server
MAMP Server
LAMP Server
Installation Steps
1. Download zip file and Unzip file on your local server. 2. Put this file inside “c:/xampp/htdocs/” . 3. Database Configuration Open phpmyadmin Create Database named courier_db. Import database courier_db.sql from downloaded folder(inside database) 4. Open Your browser put inside “http://localhost/Projectworlds Courier Management Sytem in PHP Mysql/“
Online reviews play a very important role for decision-making in today's e-commerce. Large parts of the population, i.e. customers read product or store reviews before deciding what to buy or where to buy and whether to buy or not. Because writing fake / fraudulent reviews comes with monetary gain, online review websites there has been a huge increase in tricky opinion spam. Basically, an untruthful review is a fake review or fraudulent review or opinion spam. Positive reviews of a target object can attract more customers and increase sales; negative reviews of a target object can result in lower demand and lower sales. Fake review detection has attracted considerable attention in recent years. Most review sites, however, still do not filter fake reviews publicly. Yelp is an exception that over the past few years
has filtered reviews. Yelp's algorithm, however, is a business secret. In this work, by analyzing their filtered reviews, we try to find out what Yelp could do. The results will be useful in their filtering effort for other review hosting sites. Filtering has two main approaches: supervised and unmonitored learning. There are also about two types in terms of the characteristics used: linguistic characteristics and behavioral characteristics. Through supervised learning approach we have tried to make a model which can identify the fake review with almost 70 percent accuracy.
As the Internet continues to grow in size and importance, the quantity and impact of online reviews is increasing continuously. Reviews can influence people across a wide range of industries, but they are particularly important in e-commerce, where comments and reviews on products and services are often the most convenient, if not the only, way for a buyer to decide whether to buy them.
Model training
Refer to the Jupyter notebooks in research folder to know the steps taken for preprocessing, model development and algorithms used. Although we experemented with different models, we found Naive Bayes to be most accurate with F1 score of 77%.
Installing and running this app:
Requirements: Use pip install/conda install to download following packages
Numpy, pandas
sklearn
spacy
Django 2.1
pickle
tqdm
running the app:
Go to folder containing manage.py and run command: python manage.py runserver
Fake News Detection using Machine Learning Natural Language Processing . A NLP and Machine Learning based web application used for detecting fake news. Uses NLP for preprocessing the input text. Uses XGBoost model for predicting whether the input news is Fake or Real.
here are tons of stories articles, where the news is fake or cooked up. With numerous advances in tongue Processing and machine learning, we will actually build an ml model which is in a position to detect if a bit of stories ... Here we'll be using artificial neural network models to verify the genuinity of the article.
Driver Distraction Prediction Using Machine Learning”, where given driver images, each taken during a car with a driver doing something within the car (texting, eating, talking on the phone, makeup, reaching behind, etc). The goal was to predict the likelihood of what the driving force is doing in each picture.
Driving a car may be a complex task, and it requires complete attention. Distracted driving is any activity that takes away the driver’s attention from the road. Several studies have identified three main sorts of distraction: visual distractions (driver’s eyes off the road), manual distractions (driver’s hands off the wheel) and cognitive distractions (driver’s mind off the driving task).
Dataset details -
Image Size - 480 X 640 pixels
Training Images count - 22424 images
Test Images count - 79726 images
Image type - RGB
Image field of view - Dashboard images with view of Driver and passenger
DL Model - CNN's build from scratch ( 6 Conv Layer, 5 Dropout Layer, 3 Dense Layer)
Framework - Keras / Pytorch version in the process.
CNN Model Visualization/Model Interpretability - GradCAM
Final Accuracy -Train acc - 99.06%, Val acc-99 .46%
GRAD-CAM implementation for a test image with label drinking
GRAD-CAM is a technique to highlight how a model classifies new instanes by creating a heat map which highlights only the area which has contributed the most in prediction. As seen in below image model classifies driver as distracted by drinking by highlighting the hand and glass.
Multiple Disease Prediction using Machine Learning . This Web App was developed using Python Flask Web Framework . The models won’t to predict the diseases were trained on large Datasets. All the links for datasets and therefore the python notebooks used for model creation are mentioned below during this readme. The WebApp can predict following Diseases:
Diabetes
Breast Cancer
Heart Disease
Kidney Disease
Liver Disease
Malaria
Pneumonia
Models with their Accuracy of Prediction
Disease
Type of Model
Accuracy
Diabetes
Machine Learning Model
98.25%
Breast Cancer
Machine Learning Model
98.25%
Heart Disease
Machine Learning Model
85.25%
Kidney Disease
Machine Learning Model
99%
Liver Disease
Machine Learning Model
78%
Malaria
Deep Learning Model(CNN)
96%
Pneumonia
Deep Learning Model(CNN)
95%
Steps to run the WebApp in local Computer
Step-1: Download the files in the repository. Step-2: Get into the downloaded folder, open command prompt in that directory and install all the dependencies using following command
pipinstall-rrequirements.txt
Step-3: After successfull installation of all the dependencies, run the following command
Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions. The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.
Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
You can download the product after the purchase by a direct link on this page.
Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
You can download the product after the purchase by a direct link on this page.
Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.
A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.
A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.
Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
You can download the product after the purchase by a direct link on this page.
