Mini Project Notice for 4th Semester Students
The following lists the mini-project topics for the 4th semester students of BTech-CSE/CSE-DS/CSE-ML/CSE-CC/CSE-IS/CE/CST/CE-SE/IT students who have NOT OPTED for the GCCF. Along with the project list, name(s) of faculty member is(are) given who would act as the resource person for the topic. Note that you are required to understand, study and implement the topic on your own. The Resource person may only be contacted if you need advice on things like dataset, suitable tools etc. Resource persons will NOT advice you in any way in the coding part, they will also NOT evaluate your projects.
Mini Projects will be done individually. No group work is allowed. Mini project is a compulsory part of your scheme, Absentees and Failed students have to resubmit as Back.
Expected Evaluation Date: 1st week of May. Exact dates will be announced later.
Marks: 100 marks.
Note: Students who have opted and selected for Mini Projects through GCCF modules will be evaluated through that mode only. This notice is for the Non GCCF students.
|Serial No 1 Resource person: Hemant Singh Pokhariya
|1. Investigating the Land Use Land Cover change Detection by using GIS and RS Techniques:
Change detection is widely used to estimate deforestation, LULC, urban growth, shifting cultivation, etc. If the change detection process on earth surface is carried out accurately and timely, then the interaction and relationship between humans and natural phenomena can be better investigated leading to a better utilization of the natural resources.
|Serial No 2-4 Resource person: Deepak Uniyal
|2. Misinformation: Detecting and combating misinformation on Social Media
|3. Detecting Offensive Content on Social Media:
Read the following articles to get an idea about the topic –
|4. Smart Dustbin – Garbage Management:
Please go through following articles and discuss the problem statement with me (Deepak Uniyal) before finalizing the statement. The links given below are for reference purpose and you should not exactly copy these ideas.
|Serial No 5-6 Resource person: Dr. Sachin Sharma
|5. IoT & Smart City Technology:
Smart cities use Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data. The cities then use this data to improve infrastructure, public utilities and services, and more.
|6. AI & ML in Smart Urban Areas:
Machine learning generally takes the data generated by several apps such as Health MD applications, internet-enabled cars, etc. and leverages it to identify patterns and learn how to optimize the given set of services. Its tools are able to personalize the smart city experience by aggregating information about the most used roads in a city and then apply it to a transportation system.
On the other hand, machine learning and AI can be helpful in waste collection and its proper management and disposal which is a vital municipal activity in a city. Thus, the technology for smart recycling and waste management provides a sustainable waste management system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives.
|Serial No 7-8 Resource person: Dr. Mohd. Wazid
|7. AI enabled authentication scheme in IoT:
Students can design AI enabled authentication schemes for various computing domains like smart homes, smart transportation system, smart healthcare systems.
|8. Access control schemes in Industry 4.0:
Students can design access control schemes for Industry 4.0 communication.
|Serial No 9 Resource person: Ankit Tomar
|9. Customer Segmentation in retail sectors:
Segment the customers based on their gender, age, interest. It is useful in customized marketing. Customer segmentation is an important practice of dividing customers based on individual groups that are similar.
|Serial No 10 Resource person: Garima Sharma
|10. Performance Analysis of Big Data and Data Science Tools:
Contrast different available tools and techniques in big data stack and analyze performance of each tool , handson on different programming techniques using real world use cases.
|Serial No 11 Resource person: Kireet Joshi
|11. Machine learning in Healthcare:
Machine learning has virtually endless applications in the healthcare industry. Today, machine learning is helping to streamline administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.
|Serial No 12 Resource person: Saurabh Mishra
|12. Voice based Email service for visually challenged people:
The main benefit of this system is that the use of keyboard is completely eliminated, the user will have to respond through voice and mouse click only.
|Serial No 13-14 Resource person: Ashwini Kumar
|13. Visual Question Answering using Deep Learning:
Visual Question Answering is a research area about building an AI system to answer questions presented in a natural language about an image.
A system that solves this task demonstrates a more general understanding of images: it must be able to answer completely different questions about an image, oftentimes even addressing different sections of the image.
|14. Social Media Sentiment Analysis using Twitter Dataset:
Twitter boasts 330 million monthly active users, which allows businesses to reach a broad audience and connect with customers without intermediaries. On the downside, there is so much information that it’s hard for brands to quickly detect negative social mentions that could harm their business.
That’s why sentiment analysis, which involves monitoring emotions in conversations on social media platforms, has become a key strategy in social media marketing.
Listening to how customers feel on Twitter allows companies to understand their audience, keep on top of what’s being said about their brand, and their competitors, and discover new trends in the industry.
|Serial No 15-16 Resource person: Dr. Manoj Diwakar
|15. Multimodality medical image fusion:
Tools: MATLAB/Python, Description: An image fusion based on multimodal medical images renders a considerable enhancement in the quality of fused images. An effective image fusion technique produces output images by preserving all the viable and prominent information gathered from the source images without any introduction of flaws or unnecessary distortions.
|16. Medical Image denoising:
Tool: MATLAB/Python, Description: medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution or frequency dependent noise introduced by a device’s mechanism or signal processing algorithms. The presence of noise makes the images unclear and may perplex the identification and analysis of diseases which may result heavy losses including deaths. Hence, denoising of medical images is a mandatory and essential pre-processing technique for further medical image processing stages.
|Serial No 17 Resource person: Dr. Priya Matta
|17. IoT Based Home Automation:
This project will allow you to control the devices and appliances present at your home via an app or even via voice commands..
|Serial No 18-25 Resource person: Dr. Preeti Mishra
|18. Cloud based Android Malware Feature Extraction:
Write a script that performs the following function s(features extracted by static analysis)
a. For the apks present in dataset, diassemble the apk
b. Extract the permission features from the manifest files.
c. The features extracted should be converted into a csv file. Thus for each apk there should be a row in the csv. The columns of the csv will be the different permission. The value for each apk will be 0 or 1 based on permission present or absent.
* Use a cloud platform for performing project. Dataset- Use any dataset available at this link https://github.com/traceflight/Android-Malware-Datasets
|19. Android Dynamic Feature Extraction (API extraction):
Write a script that performs the following functions (features extracted by dynamic analysis)
a. For the apks present in dataset, Runs the apk in a secure environment (in a VM)
b. Using the tools extract the API calls (AndroidTamer->MobSF can be used here) that the apk makes during its execution.
c. Converts the obtained API calls details into a csv file. Thus for each apk there should be a row in the csv. The columns of the csv will be the different API calls. The value for each apk will be 0 or 1 based on API call present or absent
Android Tamer is a VM consisting of several Android analysis tools ranging from analysis to pentesting. 8GB RAM is must otherwise the speed of the application slows down w.r.t. responsiveness.
|20. Android APP penetration testing:
Perform penetration testing of the web based mobile apps (use the BurpSuite for this). Exploit the XSS vulnerability by using browser on the emulator. You will need to set up proxy setting on the emulator. Setting will also be made in the Burpsuite to intercept the requests from app and then these requests can be tampered. Create a demo app(web based) and include a search text box in this. When you query using this search box, you can intercept this search string using Burp and then also tamper it.
|21. Andoid APP penetration testing and security breach:
Try to exploit the vulnerable components of an app (benign app) from another app (malicious app). Do the following
a. Create a demo app with a login screen
b. Once valid credentials are provided it should take you to the next screen/page.
c. Try to open the second screen/page directly from the malicious app.
d. Then also provide a fix for this vulnerability.
e. Once done, now again try to access the second screen/page from the malicious app by encoding permissions in the malicious app.
This scenario demonstrates how a malicious app tries to access secure area of a genuine app with code vulnerabilities.
|22. Performance analysis of Web Browser Forensic Tools:
Demonstrate and compare the performance of any three web browser forensics tools. Analysis the activity logs of any one web browser.
|23. IoT Penetration Testing:
Find the vulnerabilities in any IoT device using penetration testing tools. Parse the logs and generate some report as an output.
|24. Cloud based Web application development:
Develop and host some innovative web application using cloud services such as AWS EC2, AWS S3 and RDS services etc.
|25. Generate and Parse Cloud User Activity Logs:
Extract the logs of services associated with your account. Parse them and convert into some meaningful format using python APIs. You can use CloudTrail, CloudWatch etc. to monitor the activity logs. Write a script to extract and parse such logs.
|Serial No 26-27 Resource person: Sarishma
|26. Comparative analysis of different forensic tools:
Purpose: To compare and evaluate the performance of different tools by using quantifiable data. To find out the best tool after running test cases on 4-5 different forensic tools.
Questions to be answered:
i)The tool’s features: How does it process, index, search, etc.?
ii)The tool’s ability to obtain artifacts: How many artifacts?, Location of those artifacts, How many relevant artifacts? , How quickly were artifacts obtained? (time)
iii)The tools searching speed: What differences are present between the tool’s searches?, Test under different scenarios
iv)The tool’s ability to recover deleted files: What data can be recovered?, Can it be used to create a time series analysis with support of artifacts?
v)The tool’s level of difficulty: Is it easy for beginners?, Designing test cases is easy?, Evaluate the analysis provided by the tool.
Hardware used: Use virtual machines for testing. Do not use your personal system as there are high chances of damage. Use a virtual machine via VMware or VMWorkstation. Run the test case scenario to setup the scene. Create a copy of the VM by using the tool FTK Imager.
ii)FTK 6.1 (Forensic Toolkit)
iii)SANS Investigative Forensic Toolkit Workstation
iv)EnCase Forensic v8
|27. Image Steganalysis for forensic investigation of hidden artefacts:
JPEG (other formats) – analysis of image formats in order to find out hidden information which might be stored. It can be extended by doing analysis of various other multimedia files including audio, video, network packets. A detailed study of formats along with their threat vectors has to be done.