1

1. Introduction
1.1 Introduction to System:-
Nowadays, customer service oriented companies facing difficulties of lengthy queues. These problems often occurred in the banks, post office, Restaurant, Super mall and airport and it became worsen when the time reached peak hour. In general, queuing is a line of people waiting to be serve and the movement is from a central to a specific place. Thus, a queue management in our system must handle and organized queue formation in the most efficient way and Crowed Analysis also perform for the better way to provide customer services.

We are going to use the Machine Learning and Artificial Intelligence to solve this problem and provide efficient services to customers. Basically, this is an analytics project, use to enhance the services provided by the department and fulfil the requirements of customers.

1.2 Objectives Of Project:-
Objectives of this project to enhance the customer services that build up customer satisfaction and decrease the average waiting time. The objectives of this project to:-
Investigate the current approaches that manage the Queue.
Design a working system for queue management system.
Design a system that work for crowed control.

Implement the system that provide suggestions based on analysis and detection.
Implement the system using mechatronic solution.
Evaluate the system for effectiveness.

1.3 Problem Specification:-
Many companies provide queue management system for controlling queues of people in various situations and locations in a queue area. Most of the techniques used are manually for a small space and simple flow and take decision after the situation arise. On the other hand, in this project has hybrid way to deal for a larger space and complex flow. The System produce the alert notification for manage queue before it become wide. These can be see widely used in banks, hospitals or clinics, super mall, airport and post offices.

2. Requirement Analysis
2.1 Study of current System:-
Many times, we have to wait in long queues for the getting services, which in return waste a lot of time and greater level of patience. Sometimes customers can miss some offers etc. Many companies provide queue management system for controlling queues of people in various situations and locations in a queue area. Most of the techniques used are manually for a small space and simple flow and take decision after the situation arise. System can’t provide suggestions before the situation become worst.

2.2 Requirement of new System:-
As in new system Artificial Intelligence is used so that it will analyze the customer’s activities and then take decision automatically from the detection and provide some effective suggestions to improve customer satisfaction. This system optimizes your on-time services to ensure customer satisfaction and make important business decisions based on real-time data. The aimed of data Analytics to provide guidelines for queue formation and organising it in most efficient way and provide some special offers. It is useful for estimate future bottlenecks and peaks thanks to predictive analytics.

2.3 Technology used
Artificial Intelligence:-
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

Machine Learning:-
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly. Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output. Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. 
Computer Vision:-
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include method for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information,. Computer vision is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly.

Python:-
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. PyCharm is the most popular IDE integrated development environments used for Python programming language. In addition, in the Professional edition, one can develop Django, Flask and Pyramid applications. Also, it fully supports HTML (including HTML5), CSS, JavaScript, and XML: these languages are bundled in the IDE via plugins and are switched on for you by default. 
2.4 Hardware-Software used:-
Microsoft SQL server:-
Microsoft SQL Server is a relational database management system developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network (including the Internet).SQL server aims to make the data management self-tuning, self-organizing and self-maintaining. SQL server also include support for structured and semi- structured data, including digital media formats for pictures, audio, video and other multimedia data. In current version multimedia data can be stored as BLOBs(Binary Large Objects),but they are generic bit stream.

Pycharm:-
PyCharm is the most popular IDE integrated development environments used for Python programming language. In addition, in the Professional edition, one can develop Django, Flask and Pyramid applications. Also, it fully supports HTML (including HTML5), CSS, JavaScript, and XML: these languages are bundled in the IDE via plugins and are switched on for you by default. 
SQLyog:-
SQLyog is the most powerful manager, admin and GUI tool for MySQL, combining the features of MySQL Query Browser, Administrator, phpMyAdmin and other MySQL Front Ends and MySQL GUI tools in a single intuitive interface. SQLyog is a fast, easy to use and compact graphical tool for managing your MySQL databases. SQLyog was developed for all who use MySQL as their preferred RDBMS. Whether you enjoy the control of handwritten SQL or prefer to work in a visual environment, SQLyog makes it easy for you to get started and provides you with tools to enhance your MySQL experience. Build complex queries using drag-n-drop interface. Visually create SQL statements without the need to remember column names.

2.5 Hardware-Software Requirements:-
Hardware Requirements:-
Computer
Internet
External Hard Drive for Backup
Mobile device
Server
Software Requirements:-
Python
Notepad++, pycharm(editor)
SQL server
Web Browser (Google Chrome, Internet Explorer, Mozilla Firefox)
3. System Design
3.1 Use Case diagram:-
Admin:-

Diagram-1
User:-

Diagram-2
3.2 Activity Diagram:-
Admin:-

Diagram-3
User:-

Diagram-4
3.3 Sequence Diagram:-
Admin:-

Diagram-5
User:-

Diagram-6
3.4 State Diagram:-
Admin:-

Diagram-7
User:-

Diagram-8
3.5 Class Diagrams:-

Diagram-9

3.6 E-R Diagram:-

Diagram-10
3.7 Data Flow Diagram:-
3.7.1 Context level Diagram:-

Diagram-11
3.7.2 Level-1 DFD:-
Admin:-

Diagram-12
User:-

Diagram-13
4. Data Model and Description
4.1 Data Dictionary:-
Table_Login:-
Column_name Data_Type Length Constraint
Login_id Int – Primary Key
User_id Varchar 50 Not Null
Password Varchar 50 Not Null
User_Type Varchar 50 Not Null
Table-1
Table_Registration:-
Column_name Data_Type Length Constraint
User_id Varchar 15 Primary key
First name Varchar 15 Not Null
Last name Varchar 15 Not Null
Address Varchar 50 Not Null
Work-Location_id Varchar 20 Foreign key
Email-id Varchar 25 Not Null
Mobile no. Int – Not Null
Login_id Int – Foreign key
Password Varchar 20 Not Null
Table-2
Table_Dataset:-
Column_name Data_Type Length Constraint
Video_id Int – Primary key
Video File – Not Null
User_id Varchar(10) 10 Foreign key
Table-3
Table_ Work-Location:-
Column_name Data_Type Lenght Constraint
Work-Location _id Int 20 Primary key
Street_name varchar 20 Not Null
Landmark Varchar 10 Not Null
Area Varchar 20 Not Null
City Varchar 20 Not Null
Pincode Int – Not Null
State Varchar 20 Not Null
Country Varchar 20 Not Null
Table-4
Table_Complain:-
Column_name Data_Type Length Constraint
Complain_id Int – Primary key
Subject Varchar(20) 20 Not Null
Discription Varchar(50) 50 –
User_id Int – Primary key
Table-5
Table_Report:-
Column_name Data_Type Length Constraint
Report_id Int – Primary key
Report File – Not Null
User_id Varchar(10) 10 Foreign key
Table-6
Table_Feedback:-
Column_name Data_Type Length Constraint
Feedback_id Int – Primary key
Feedback Varchar(70) 70 Not Null
User_id Int – Foreign key
Table-7
5. Canvas Sheets
5.1 AEIOU Canvas :-

Diagram-14
Environment: –
Our project support multiple platform includes Linux, Windows.

Interaction: –
AdminWeb Portal
Mall Manager Web Portal
Customer Web Portal
AdminMall Manager
Mall ManagerCustomer
5.2 Empathy Canvas :-

Diagram-15
By this Empathy Canvas we know about users, stakeholders and activities which are directly and indirectly related with stakeholders and users.

User: –
Admin
Mall Manager
Customers
Staff
5.3 Product Development Canvas :-

Diagram-16
Purpose:-
The purpose of our project is to queue control and queue analysis for provide the better services to customer. Another purpose is to find the best for manage and organizing queue in efficient way and to do the crowed analysis and provide some suggestions.
Experience:-
It is effective solution for minimize the average waiting time of customer and also provide fast billing facility.

Product Function:-
By using this web application service provider can manage queue and additionally get the alert notification bell for critical situation.

Product Features:-
User Logistic Analytics System has many features Some of them are Auto Detect Queue, Auto Billing Using Barcode, Face Detection, View Report, Alert Notification.

Components:-
Components which are require to use User Logistic Analytics System are Mobiles, Tablets, Router, Computer, Modem etc.

5.4 Ideation Canvas:-

Diagram-17
People:-
The main people are Mall Manager, Admin, Customer, Occupant, Bank, Hospital and compatible to any casual user, Professional users.
Situation/Context/Location:-
How are users related with domain, where and why are they related to domain is visualized through situation/Context/Location.

Queue Analysis
Accuracy
Offer Suggestions
6. Future Enhancement
Future Extension:-
Cash -;taking cash by the system itself.

Minimal use of humans resources-;We can make less human power at any retro.

7. Conclusion and Reference
7.1 Conclusion:-
Design and development of User Logistic Analytics System starts with the understanding of the queue system itself. which is very crucial to broaden the horizon of understanding. Then, the consideration of the control strategy and component to be use plays important role as guidelines to accomplish this project. In addition, several articles have been review to investigate the current approaches for managing queue control. Although the current approaches have proven to ease and give benefits to service providers, yet there are space of improvement in order for a queue system function efficiently and customer analysis and suggestions for offers.

There are some improvement can be made such as addition of sensors of cameras that can detect customers at a certain time. This method can improve the efficiency of a queue system as it alerts manager to counter fast on the situation.

7.2 References:-
www.python.org
www.javatpoint.com
www.gtu.ac.in
www.tutorialspoint.comIntroduction to Machine Learning with python by Andreas C. Muller and Sarah Guido
www.w3school.com