E-GOVERNANCE COMPLAINT APPLICATION

Abstract

In our daily life we face several issues in our society We have different portal for a particular domains like electrical complaints, water supply complaints etc… While a user is registering a complaint in our portal, this complaint text and images were being analyze using KNN algorithm which can be used for both classification and regression problems. The KNN algorithm uses 'feature similarity' to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set, if this complaint already exits complaint will be wrapped under the existing previous complaint . Once the complaint is solved, it indicates the complaint status to all users. If the complaint did not previously exist in any complaints, it will be saved as a new complaint. Furthermore, if the same type of complaint registers multiple times, they will be analyze the severity of the complaint using the SVM algorithm of emotional prediction to identify the severity of the complaint. This algorithm helps us identify the motive and emotions behind a complaint, gain many more insights, and eventually predict the future response and after the prediction of the severity appropriate action will be taken. If the majority of complaints continuously arises, they will be analysed using KNN algorithm. The KNN algorithm is used to classify by finding the K-nearest matches in training data and then using the label of closest matches to predict, after analysing report of reason will send to the admin.

Specification

Description:Technical field

Document Term Matrix (DTM) driven by TFIDF (Term Frequency - Inverse Document Frequency) to pre-process raw textual data. Inverse Document Frequency), the embedding model Word2Vec, and the psycholinguistic method Linguistic Inquiry and Word Count (LIWC). A paired test performed on the AUC values obtained from different classifiers revealed that the LIWC in combination with the Random Forest and Nave Bayes techniques yields the statistically significant performance. Smart mobile devices and websites, which can serve as a foundation for a system for managing complaints, have become more affordable over the past few years and established themselves in the lives of regular people.

Background

E-governance refers to the use of digital technologies to enhance the delivery of government services and engage citizens in decision-making processes. As governments worldwide strive for greater transparency, efficiency, and responsiveness, e-governance has emerged as a vital tool for modern public administration.
One of its key components is the development of complaint management systems that enable citizens to report issues and seek resolutions.
Importance of Complaint Management
Complaint management is a critical function of public service delivery. Citizens often face various issues related to utilities, public services, and infrastructure. Efficient complaint management systems empower citizens to voice their concerns, allowing governments to address problems more effectively and improve service quality.
Role of Technology in Complaints Handling With advancements in technology, traditional methods of complaint management are increasingly being replaced by digital platforms. These platforms allow for streamlined processes, making it easier for citizens to submit complaints, track their status, and receive updates. Technology not only facilitates quicker resolutions but also helps in gathering data for analysis and decision-making.
Features of E-Governance
Complaint Applications E-governance complaint applications typically include features such as user registration, complaint submission (text and images), status tracking, and feedback mechanisms. They may also incorporate advanced technologies like machine learning and data analytics to enhance functionality and responsiveness, thereby improving user experience.
Integration of Multi-Channel Access To cater to a diverse population, e-governance complaint applications often support multiple channels for complaint submission, including web portals, mobile apps, and social media platforms. This multi-channel approach ensures that citizens can easily access services, regardless of their technological proficiency or resources.
Data Analytics for Insightful Decision-Making By leveraging data analytics, e-governance complaint applications can identify trends and recurring issues within communities.
This data-driven approach enables governments to prioritize resource allocation, implement preventive measures, and develop policies that address the root causes of complaints.
Enhancing Transparency and Accountability E-governance complaint applications promote transparency by providing citizens with real-time updates on the status of their complaints. This visibility fosters trust in government processes and holds public officials accountable for their actions, ultimately enhancing the overall governance framework.
Citizen Engagement and Empowerment These applications empower citizens by providing a platform for their voices to be heard. By actively participating in the complaint resolution process, citizens feel more engaged with their local governance, leading to a stronger sense of community and civic responsibility.
Challenges in Implementation Despite the numerous benefits, implementing e-governance complaint applications comes with challenges. Issues such as digital divide, lack of digital literacy, data security concerns, and resistance to change within governmental structures can hinder the effectiveness of these systems.
Case Studies of Successful Implementations Various countries have successfully implemented e-governance complaint applications, showcasing their potential to improve public service delivery. For example, cities that have adopted mobile apps for utility complaints have reported reduced response times and increased citizen satisfaction, serving as models for others to follow.
Role of Machine Learning and AI Integrating machine learning and artificial intelligence into complaint management systems can enhance predictive capabilities, enabling authorities to forecast trends and allocate resources more efficiently.
These technologies can analyze complaint data to identify patterns and prioritize issues based on severity and frequency.
Impact on Public Policy E-governance complaint applications provide valuable insights that can influence public policy. By analyzing the types of complaints received, governments can identify pressing community needs, guiding policy development and resource allocation in a way that is responsive to citizen concerns.
Future Trends in E-Governance
As technology continues to evolve, e-governance complaint applications are likely to incorporate more advanced features, such as chatbots for immediate assistance, blockchain for enhanced security, and enhanced user interfaces for improved accessibility. These innovations will further streamline processes and enhance citizen engagement.
Importance of User-Centric
Design To maximize effectiveness, e-governance complaint applications must prioritize user-centric design. Ensuring that the applications are intuitive, accessible, and user-friendly will encourage greater participation and satisfaction among citizens, ultimately leading to more effective complaint resolution.
Conclusion
In conclusion, e-governance complaint applications represent a significant advancement in how governments engage with citizens and manage public service issues. By leveraging technology, these applications enhance efficiency, transparency, and responsiveness, ultimately contributing to improved governance and stronger community relationships. As e-governance continues to evolve, these systems will play a crucial role in shaping the future of public administration.

Summary of the Invention

In our daily life we face several issues in our society we have different portal for a particular domains like electrical complaints, water supply complaints.
While a user is registering a complaint in our portal, this complaint text and images were being analyse using KNN algorithm which can be used for both classification and regression problems, if this complaint already exits complaint will be wrapped under the existing previous complaint. Once the complaint is solved, it indicates the complaint status to all users. If the complaint did not previously exist in any complaints, it will be saved as a new complaint.
Furthermore, if the same type of complaint registers multiple times, they will be analyze the severity of the complaint using the SVM algorithm of emotional prediction to identify the severity of the complaint and after the prediction of the severity appropriate action will be taken.
If the majority of complaints continuously arise, they will be analysed using KNN algorithm. After analysing report of reason will send to the admin.
By using our portal users can register various department complaints like electricity complaints, waste management complaints, sewage complaints etc...
So users can able to track all the complaints made by him in the single portal. While a user is registering a complaint in our portal, if the complaint already exists, the complaint will be wrapped under the existing complaint. Once the complaint is solved, it indicates the complaint status to all users.
If the complaint did not previously exist in any projects, it will be saved as a new complaint.
This is possible through the analysis of text and images from user complaints.
if the same type of complaint registers multiple times, they will be analyse the severity of the complaint and take appropriate action. he major complaints are repeated again and again, so the reason of why major complaints repeated and the report is submitted to the admin.
User Login Authentication:
The first step is to create a database to store user information such as username, password, and email address. We can use a database management system such as MySQL.
You will need to create a user registration form where users can create their account by providing their username, email, and password.
You should also implement password policies such as minimum length, special characters, and numbers. If user successfully registered they will get an confirmation email. After we confirm a mail to redirect the user login page.
User Complaint: Create a complaint form that allows users to provide detailed information about their complaint.
The form should include fields for the user's name, email address, a description of the complaint, and the attachments of images for reference, etc...
Implement a tracking system that assigns a unique reference number to each complaint.
User can verify the captcha code they are enable to submit the application. Else the captcha code verification is failed they are again redirect to the user complaint module.
Admin Panels:
Create a secure admin login page that requires a user id and password to access.
This will prevent unauthorized access to the admin panel. In the admin panel is divided into two types Master admin and department admin.
Master admin can view the overall complaints categories and the count of the complaints in the panel.
When user is not satisfied with solution provided by the department in charge person the complaint report is generated to the admin.
When complaint is closed every time the report for the complaint will be generated to the admin. the user registers a complaint a notification will be sent to the admin.
Analysing the duplication of complaints, so they are reduce overburden of duplicate complaints to the concern department in charge.
The severity of complaint is analyse they will severity based taken the action.
If then the complaints are continuously arrived the system will analyse and then send to the reason of report to admin
Tracking complaint:Assign a unique reference number to each complaint as soon as it is received from submitting the complaint. This will allow users to enter the tracking id and then track the progress of their complaint and provide updates if necessary.
, Claims:1. Efficient Complaint Management: The system streamlines the complaint registration process by automatically classifying and categorizing complaints using the KNN algorithm, reducing redundancy and improving response times.
2. Real-time Similarity Analysis: By leveraging feature similarity, the KNN algorithm ensures that new complaints are linked to existing ones, providing a comprehensive view of recurring issues and enhancing the resolution process.
3. Emotional Insight through SVM: The use of the Support Vector Machine (SVM) algorithm for emotional prediction allows for a deeper understanding of the motives and severity behind complaints, enabling more empathetic and effective responses.
4. Proactive Issue Identification: The system analyzes patterns in recurring complaints, allowing administrators to identify and address systemic issues before they escalate, promoting proactive problem-solving.
5. Data-Driven Reporting: By generating reports based on KNN analysis of complaint trends, the project equips administrators with actionable insights, facilitating informed decision-making and resource allocation for issue resolution

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