The floor-planning problem is solved using genetic algorithms. Speedup is then achieved through parallelizing the algorithm through multiprocessing.

The algorithm could be extended to solve other combinatorial optimization problems.

Source 1

NA

Sarthak Choudhary

An e-commerce website facilitated with user login/logout, Facebook OAuth, Razorpay Payment API. Initially inspiration was to deploy both models to the website. But then only one ML model could be successfully deployed to website. ML model 1: Disease prediction based o symptoms ML model 2: Chatbot

Got a good job, rest you can see on link provided. (heroku baar baar un–deploy kr deta hai app bata dena re-deploy kar dunga)

Source 1 Source 2 Source 3

Link 1: (is course me course 3 app dev ka hai to can be skipped if not interested) There are three Udemy courses for ML 1. Pyhton bootcamp zero to hero 2. Python and R for Data Science 3. Machine Learning A to Z

Siddhartha Shukla

We solved inverse kinematics for a legged robot using simple machine learning at first then using ANN.

Apply machine learning to a legged robot to get more efficient functioning.

Many sources available on the internet. Search for medium articles particularly.

Rashmi Deep Mahawar

Automated Path Mapping Robot Sep '18- Oct '18 Constructed an autonomous robot that used an overhead camera and applied image processing techniques using MATLAB. Image Processing techiques were used to extract features from the image about the arena. The bot traversed the shortest path obtained from Dijkstra's Algorithm to perform some tasks.

NA

Just refer gfg for dijkstras also and basic image processing techniques(you can Google them)

Divya Gupta

The aim is to predict whether a person is COVID positive or not by examining the CT scan of the examinee.

NA

Source 1 Source 2 Source 3

Deep learning course: https://youtube.com/playlist?list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0

Eshaan Gupta

* Developed a CNN based model using Pytorch that classifies the input CT scan images as Covid infected or as healthy ( not infected ). * Input images are filtered using a GRU Layer for better accuracy. * The accuracy obtained from the proposed CR Net model is 79% which is slightly lesser than that of some of the pre - trained standard models. * The prediction time of proposed model is lesser than that of pre - trained standard models. * The model is lighter and is suitable to use on the web.

NA

Source 1

https://archive.physionet.org/physiobank/database/

Rohit Yadav

Created Cryptographically secure polling booth using blind and digital signatures. Implemented RSA, SHA 256 from scratch. Used Tkinter to make interactive application implementing the above algorithms and protocols. Course Exposure Studied DES( Data Encryption Standard) with different hyperparameters and demonstrated the Avalanche effect. Demonstrated CBC mode( Cipher Block Chaining) hides features better than ECB mode( Electronic Codebook) with Blowfish as an encryption algorithm on a binary image.

NA

Network security (open elective course offered by cse department) course slides. Sir uploads the pdf of all the semesters there and it is accessible to all the students of that course, so ask for the notes from your friends who have taken up that course

Divya Gupta

1. The task was to predict the level of diabetes of a patient by classification with the help of given data. 2. Data preprocessing and model training knowledge were a must to score well. I used a random forest classifier to train this model. 3. I used Flask to deploy the model on Heroku that was saved in a pickle file.

NA

Source 1

https://github.com/anshgoyal1/diabetes_prediction

Ansh Goyal

Detecting drowsiness of a driver from the facial expressions captured by a camera on the steering wheel, and thereby sounding an alarm

NA

Source 1

NA

Dinesh Easwaravaka

Hand gesture extraction using background elimination followed by recognition through convolutional neural networks.

NA

Source 1 Source 2

NA

Nishant Kumar

A CNN is made to detect if the person in live videostream is wearing a mask or not. Imp Libraries used : Keras, OpenCV

NA

Search GitHub for face mask detection project

Ashrita Agarwal

This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. This project aims to work on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network.

NA

Source 1

NA

Harsh Agarwal

FlappyBird agent learns to master the game using Neuroevolution!

NA

Source 1 Source 2

NA

Nishant Kumar

The task is to discover a new learning based approach that can potentially improve our computational imaging tasks or can be extended to the other types of computational imaging tasks with similar features. For the 3D particle field imaging using holography and demonstrate them with synthetic and experimental holograms.

Simulation of the hologram in the form of Images.

Source 1

Notebook +slides

Pushker Naresh

Categorised the action of the holder of the mobile into 6 different activities

Useable in tracking devices

Source 1

NA

Himanshu Balasamanta

1. Built a Human Emotion Detector which takes input from live video feed and detects the faces present at each instant and predicts emotions i.e. happiness, fear, sadness, disgust, anger, surprise and neutral. 2. Used the Kaggle FER-2013 dataset. 3. Built and trained a CNN model for emotion detection.

It detects the faces present at each instant and predicts emotions i.e. happiness, fear, sadness, disgust, anger, surprise and neutral.

Source 1

https://github.com/ashuKS29/Human-Emotion-Detector

Ashutosh Kumar Singh

Development and design of a GIS based 3D navigation app of IIT BHU campus.

The web map application works on the server responsively and has in-depth detail about different places in our college campus. The webapp is much similar to google maps but is much more advanced and detailed. a lot of work has been completed and we are planning to host it from Geoinformatics lab IIT BHU once we're back in campus. It has many applications and can be integrated with many more advanced technologies like ML, Iot etc.

ArcGIS for developers (https://developers.arcgis.com/) ArcGIS documentation A bit of webdev knowledge. Esri training website And previously made open source projects on github.

Rishabh Purohit

a biomimetic bipedal robot with reinforcement learning as it core control algorithm

NA

Source 1 Source 2

NA

Nishant Kumar

Designed an autonomous bot with the help of image processing techniques which takes input from an overhead camera. Used Open CV functions for thresholding, contour finding and shape detection. Used HSV segmentation to detect position and breadth-first-search algorithm for path planning. Used Arduino Uno Microcontroller and L298 motor driver for controlling the bot's movement.

NA

Source 1

NA

Ritwik Ghorui

Used pre-trained coco-data set of 80 classes, to detect and further created warning on detection of anomalies. Deep Neural Network module of Open CV and YOLO_v3 algorithm were used for the detection part. The project is designed for the CCTV frame. Whenever there are anomalies get detected in the frame it will save the information's of anomalies with the timestamp and also creates warning by giving the alarm. Most of the time the CCTVs are used at post-event time to check the footage. This project will help in equipping the CCTV as a pre-event tool by creating alarm for suspected objects. Application of this project is vast for invigilation as well as security purposes at various places like exam hall, Jewelry shops, border area etc. Exposure: Learned Python, Open cv, Yolo_v3 object detection algorithm, Convolution Neural Network. Achievement: Got recognition from HOD of Department of Electrical Engineering and awarded by A grade in the exploratory project.

Successfully tested 80 classes of objects on web cam.

Source 1 Source 2

If you want, you can complete all these courses. But if you have less time, complete only the 4th course, i.e., "Convolution Neural Networks." This will help you understand the fundamentals of this project and give a short and theoretical aspect of the project.

Sanjul Kumar Sharma

Done in tata steel wom seasin 4 and technex robonex category

NA

Participate in hackathons

Sabhyata Chaudhary

Work on pos tagging using cross domain and cross task transfer learning

NA

Google it

Arpit mehta

https://docs.google.com/presentation/d/163E1KgP2KlJLB1TinNLNthlFOKrjEoHECpmbuE4sZQY/edit?usp=sharing

NA

Presentation Link Given

Ritwik Ghorui

Neutal Networks was used to detect amd locate fault

NA

Already made microgrid form matlab website

Manas Asati

Built a CNN model that predicts the emotion of the face and deploys it in Android

NA

Source 1

NA

Mobasshir Ali

Rates the cv of candidate by identifying keywords

NA

Source 1

NA

Kshitij Sharma

Tried to get the selective voice amplified and clear from a noisy crowd using a amplifier system and a ml model trained on specific voice Initiative to separate all the sound sources that are present in class and then amplify profs voice. It Is a very tough problem, so we started with signal separation algorithm(ICA) just applied that. It also needs various specifications. But was good learning. It really stands out in your resume. Believe me or not.

We got the separated voice of our choice but not will full accuracy as the part of amplification was not much focused upon.

Just Google the cocktail party problem Google independemt componet analysis-- unsupervised learning problem. Search about google voice, Alexxa etc.

Adarsh Srivastav

To build a bot that walks on a black path on a white background. Turning, speed, stopping automated by machine learning.

Finally have a fully autonomous working vehicle.

Yolo project is similar to it, you only look once project.

Forecast load with various model ARIMA, SARIMA, LSTM, unstacked LSTM, CNN, CNN LSTM .

Gain the knowledge of Time -series forecasting.

Source 1

NA

Manohar Kumar

Proposed a short term load forecasting model based on deep residual networks focuses on the forecasting of loads from several minutes upto one week into the future. Exposure: Python, Keras ,Residual networks

NA

Source 1

NA

Sanika jain

Classifies spam and non spam messages

NA

Coursera deep learning course

Pratyush Amrit

Data of many places at different points of time was organised into different time and space bins so that it can be used to analyse trends and behaviour of the region wrt a particular event

You will learn how we can study the region statistics to understand various trends so that appropriate plans can be made accordingly with regards to infrastructure, management etc. (this project can be used for work in the areas of healthcare, pollution, demographics, public amenities, business strategies and economic development etc.)

ArcGIS tutorials on ESRI website and youtube, several other spatio temporal analysis project

Avani Singh

If u have an interest in doing ML project but sometimes B.Tech project is more specific towards your branch than this would be perfect for you.

In intern if you sitting for ML field you will have one project extra than other that is Btech project. And with this you will learn about ML algo.

Contact me if you want to do this project, I will provide my Jupyter Notebook and pdf submitted.

Arushikha Tayal

What is MPA? Mycophenolic Acid(MPA) is an immunosuppressant. (In simple words, if I have a kidney, liver or lung transplant, my immune system will identify the new organ as a foreign system and may attack it, to prevent this MPA suppresses this action till the organ gets adjusted and till my body believes that this organ is not "foreign" stuff), What's an issue with this, & our aim? MPA is a little expensive, our project aims to reduce its production cost. What will we do? In simple words, There are microorganisms which when you expose them in certain conditions, produce MPA. So, we set up different combinations of conditions to produce this to end up with the set of conditions that produce optimized results. To enhance this journey we 1. Understand the mechanism by which Microorganism produces so as know what changes we make to get optimized results. 2. Conduct a literature review to know about better conditions to adapt. 3. Use statistical methods to optimize this process of arriving at optimized results. Besides, the computational and simulation aspect of this project is yet explored, which we aim to do, so as to create a library/tool to help people who work on similar lines. Here above, point 1 and point2 have already done a Ph.D. student mentor and got the paper published. I was instructed to replicate and understand it. The 3rd point and last objective is what entrusted to me, and will be doing it almost alone. I'll this section to describe my lame exploratory project one line which involved, was collecting fruit waste samples from fruit/juice corners of IIT(BHU), then characterizing what all microorganisms are there.

1. Ideally, to have MPA prices down in the market. (But will just end up publishing a paper, unless anyone has the will to convert research results to influence the market). 2. End up making a 'go-to' computational, simulation tools or statistical methods to help other people working on a similar project but don't have a strong engineering background.

Source 1 Source 2

NA

Prajwal M

A typical multi-agent setup features wheeled robots (such as Khepera robots, e-Puck robots) and a tracking system to provide global position data to the robots (to close the feedback control loop). The GRITSBot aims at replicating such a setup albeit at a much lower cost as well as with robots significantly reduced in size without limiting the capabilities of the individual robots. Therefore, the GRITSBot is a wheeled differential-drive microrobot that resembles state-of-the-art platforms while closely replicating typical capabilities like wheeled locomotion, wireless communication to a host computer, infrared-based distance sensing, accelerometer and gyroscope, and onboard power for operation up to 5 hours.

NA

Source 1

NA

Deepesh arya

Designed a user friendly web page using HTML, CSS, JavaScript. Implemented minimax algorithm based functional paradigm ,used appropriate heuristics and optimized the code using alpha beta pruning to reduce its time complexity.

Web page of the game works efficiently , prints win/loss and draws messages depending upon the situation.

Source 1 Source 2

NA

Ainy

An unbeatable tic tac toe where player cannot win against the computer

Game should run without any error, in any case the game should end in either a draw or with machine winning the game

Google

Swati

Using GANs for open-set person re-identification

NA

Source 1

NA

Dhvani Shah

The task is to implement an automatic number plate recognizer in an unconstrained condition that considers occlusion, poor quality of images, and other spatial variations in image data.

Text obtained from images of Number plates

Source 1

NA

Pushker Naresh

a novel formulation titled Federated Deep Q Networks (F-DQN) to perform distributed learning for Deep RL algorithms.

Faster Learning than vanilla methods

Source 1

NA

Nishant Kumar

Ive attached my github account link , I've done tons of projects so would be hard to put all in form , please see them

.

Source 1 Source 2 Source 3 Source 4

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Ayush Agarwal

Apply neural networks to images for classification/identification

NA

kaggle, coursera(Andrew NG), analytics vidhya

Akarsh Vatsa