Projects

INTERNSHIP PROJECT 1: 

Project Title: Gas Detection and Identification Using MultiModal AI based Sensor Fusion

Student Members(names with branch details):

Shruti Mandaokar, Final Year BTech (ENTC) 

Pulkit Chandel, Final Year BTech (ENTC) 

Mentor Name:

Dr Rahee Walambe Associate Faculty ,SCAAI

Associate Professor,  Dept of E&TC , SIT 

Dr. Ketan Kotecha, Head, SCAAI 

Collaborator: Dr. George Ghinea, Brunel University, UK 

Objectives and Methodology (with schematic/ flowchart/diagram):

With the rapid developments in the industrialization and automated chemical plants, gas leakage is a common issue. Explosions, fires, spills, leaks, and waste emissions are some of the consequences of industrial accidents. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. The primary research question was - In a mixed gas environment, how to detect a particular gas and achieve better gas classification accuracy? 

In this work, we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied .The network architecture (CNN+LSTM)  consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. 

Contribution of this work - Develop a more reliable gas detection method involving two modalities and fusing them to achieve better results than existing methods.

DATA COLLECTION :

METHODOLOGY :

Early Fusion - 

Combines features extracted from raw data which have high correlation between them, which are passed through Concatenate layer and final predictions are made.

Late Fusion - 

Decisions are taken based on the individual modalities separately. Predictions from individual modalities are then combined using statistical methods like mean, mode, median.

Publication: Parag Narkhede, Rahee Walambe, Mandaokar Shruti, Pulkit Chandel, Ketan Kotecha, George Ghinea. Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion. MDPI Applied System Innovation, 2021.  https://www.mdpi.com/2571-5577/4/1/3

INTERNSHIP PROJECT 2: 

Project Title: International Trade Data Analysis using Machine Learning techniques 

Student Members:

1. Ananya Srivastava, Final year BTech E&TC
2. Zainuddin Saiyed, Final year BTech CS

Mentor Name:

Dr Rahee Walambe, Associate Faculty, SCAAI, 

Associate Professor, Dept of E&TC, SIT

Dr. Ketan Kotecha, Head, SCAAI 

Collaborator: Dr. Ishita Ghosh, Symbiosis School of Economics, Dr Jyoti Chandiramani, Director, Symbiosis School of Economics

Objectives and Methodology: 

The exchange of goods or services between territories is known as International trade. And in many cases International trade plays a very important role in constituting the total Gross Domestic Product (GDP) of that territory. The objective of this project is to forecast the total trade of India’s trading partners in vis-à-vis trade in services. Services are the economic output of intangible commodities that may be produced, transferred, and consumed at the same time. There are many factors that influence the overall trade of a territory. An analysis on these features is required to understand how they relate to each other and the impact of such features on the overall trade of a territory.

INTERNSHIP PROJECT 3: 

Project Title: Using ML based methods for predictions of COVID information 

Student Members:     Mohammed Hasan, CS & IT Department

Yagnik Bhargav, CS & IT Department

Mentor Name:     Dr. Rahee Walambe, Associate Faculty, SCAAI,

                            Associate Professor, Dept of E&TC, SIT.

                            Dr Ketan Kotecha, Head, SCAAI 

Objectives and Methodology:

Predicting and understanding the growth of Coronavirus was very important for the Government to plan and take necessary actions to prevent it. We created a dashboard for predicting various parameters like No. of cases, No. of deaths in India, Maharashtra, and the No. of Males and Females infected. The data was sourced from the Ministry of Health, Govt. of Maharashtra. Numerous statistical forecasting techniques like ARIMA, SARIMA, Prophet etc were implemented for the same dataset to obtain the least Root Mean squared error. The various approaches were tested and a hybrid forecasting models was created which could predict with an accuracy of 0.02% for the no. of cases in India.

INTERNSHIP PROJECT 4: 

Project Title: Using AI methods to generate malicious network traffics undetectable by Intrusion Detection Systems

Student Member:

Jugal Shroff, Second Year BTech (CS)

Mentor Name:

Dr. Rahee Walambe 

Associate Faculty, SCAAI and Associate Professor, Dept of E&TC, SIT

Dr. Ketan Kotecha, Head, SCAAI 

Objective:

Generative adversarial network is a technique employed in the field of Artificial Intelligence which attempts to fool models through malicious inputs. The objective is to generate malicious network traffic instances (like DDoS) looking similar to benign ones using adversarial AI which can bypass the Intrusion Detection System without creating any suspicion. 

Methodology:

Generative Adversarial Networks (GANs) architecture is used for this purpose. Initially, the model is trained using a random noise vector and benign network traffic instances. This way, the Generator finally learns how the non-malicious network traffic instances looks like and tries to produce the malicious traffic instances looking similar to the benign ones.

For testing and evaluation purpose, we first measure how different the output generated by the Generator is w.r.t the original benign network traffic instances. Then we pass this Generator output to the classifier (IDS) and see what the classifier’s accuracy is. One of the objectives is to keep this accuracy as low as possible.

1. Training     

       2. Testing

INTERNSHIP PROJECT 5: 

Project Title: Email Spam Detection using Domain Adaptation

Student Members:

Avani Mundra, Third Year Btech (CS) 

Ria Gohel, Third Year Btech (CS)

Tanvi Pathak, Third Year Btech (IT)

Mentor Name:

Dr. Rahee Walambe

Associate Faculty, SCAAI and Associate Professor, Dept of E&TC, SIT

Objectives and Methodology:

The primary objective of this project is to develop a spam filter which segregates the spam emails from the ham(non-spam) emails using Recurrent Neural Network (RNN) and domain adaptation. Every email account’s inbox today is filled with unwanted emails from various companies including promotional emails, emails from social media websites, emails containing links to unsecure websites etc. which hampers the authenticity and outreach of important emails for the user. This project aims to solve this real world problem by integrating a classic spam classifier with domain adaptation technique to achieve state-of-the-art spam filtering system. To implement this, we used SpamAssassin dataset as our source domain and our own emails dataset as target domain. Data preprocessing and text cleaning was carried out on both the datasets followed by feature extraction to convert the words to floats. We used an embedding layer followed by RNN to create the neural network for predictions. To incorporate domain adaptation, we used Domain Adversarial Neural Networks (DANN) and trained it on the labelled source dataset to achieve high accuracy on the unlabeled target dataset.

INTERNSHIP PROJECT 6: 

Project Title: Detection of Hateful Speech for Hinglish Data on Social Media

Student Members:

Ananya Srivastava, Final Year BTech (E&TC)

Bhargav Yagnik, Final Year BTech (CS)

Mohammed Hasan, Final Year BTech (CS)

Mentor:

Dr Rahee Walambe – Associate Faculty, SCAAI, Associate Professor, Dept. of E&TC, SIT

Dr. Ketan Kotecha, Head, SCAAI 

Objective / Methodology:

The rise in the usage of social media has led to escalation of contrasting ideologies and hateful speech on social media. Opinions that an individual would earlier desist from due to fear of repercussions from society have now found their way online. With most of the urban population in India being bilingual, the use of Hindi-English code mixed – Hinglish language is quite prevalent across various social media platforms. Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages. This makes it essential to have an efficient hate speech detection algorithm which can be used to deal with such content in Indian social media space. 

The objective of this project was to detect hateful speech in Hinglish language. For this, fine tuning based approaches were applied by utilizing contextual based embeddings such as ELMo, FLAIR and transformer-based BERT. The project involved creating an annotated hate speech dataset in Hinglish, building a generic pre-processing pipeline along with development and validation of deep learning based approaches for detection of hate speech in Hinglish text. 

Publication : Bhargav Yagnik, Mohamed Hasan, Ananya Shrivastava, Rahee Walambe, Ketan Kotecha. Detection of Hateful Speech for Hinglish Data on Social Media. In Lecture Notes in Electrical Engineering series, Springer , accepted for publication in March 2021. 

INTERNSHIP PROJECT 7: 

Project Title: "Materials for Aerospace Applications: Machine Learning Methods for Titanium Alloys Classification"

Student Members:

Bhargav Yagnik , Final Year (CS)

Varad Nerlekar , Third Year (EnTC)

Nishita Agrawal, Second Year (CS)

Mentor Name:

Dr Rahee Walambe Associate Faculty ,SCAAI

Associate Professor,  Dept of E&TC , SIT

Dr. Ketan Kotecha, Head, SCAAI 

Collaborator : Dr. Meena Laad, HOD and Professor, Dept of Applied Sciences, SIT

Objectives and Methodology

Development of various advanced and multifunctional materials has been on the forefront of the material science research for past two decades. With excellent strength‐to‐weight ratio, exceptional corrosion resistance, good fatigue strength and crack propagation resistance, titanium and its alloys are the most used materials for aerospace applications.  This work proposes a classifier based on machine learning techniques such as Decision Tree, Random Forest, Support Vector Machine and Artificial Neural Network.  The model shows good classification accuracy and correlation between various thermal properties of pure metals and Titanium alloys.

Figure : Pipeline for the classification task

Publication : Meena Laad, Rahee Walambe, Ketan Kotecha, Bhargav Yagnik, Varad Nerlekar, Nishita Agrawal, Soham Hudnurkar. Materials for Aerospace Applications: Machine Learning Methods for Titanium Alloys Classification. Advances in Astronautical Sciences, UNIVELT Inc. USA, accepted for publication in March 2021.

INTERNSHIP PROJECT 8: 

Project Title :Using Generative models for Generating the suitable actions in reinforcement learning tasks

Student members:

Neelabh Paul (B.Tech E&TC, 2018-22)

Vaibhav Tasgaonkar (B.Tech E&TC, 2019-23)

Mentor:

Dr. Rahee Walambe, Associate Faculty, SCAAI Associate Professor, Dept. of E&TC, Symbiosis Institute of Technology

Dr. Ketan Kotecha, Head, SCAAI 

Collaborator : Saurabh Deshpande, SAAS

Objective and methodology:

The main idea behind this research is to generate RL agents with distinct behaviors. This is achieved by generating next best action using generative models. The first agent is trained in environment using q-learning which produces q-values for every state action pair. 

Our GAN network is trained such that it is able to produce similar but different q-values from noise vectors. And several noise vectors in the RL environment, we can produce new agents with distinct “behaviors”, although not all the generated agents will be able follow optimal policy and perform maximum reward collection. The main objective of this work is to utilize one agent’s learning to come up with different new agents that have similar way of interacting with the environment but not the same as the first agent.

INTERNSHIP PROJECT 9: 

Project Title : Agricultural trade analysis between India and African countries for food security post pandemic

Student Name : Ria Gohel, Third year BTech(CS)

Mentor Name : Dr Rahee Walambe Associate Faculty, SCAAI, 

                         Associate Professor,  Dept of E&TC , SIT 

Collaborator: Dr Ishita Ghosh, SSE

Objectives and Methodology

India and African countries have a long-standing agriculture trade relationship that also ensures food security in regions of Africa. During the pandemic, many countries decided to protect their own endowments and production domestically. So they either raised trade tariffs or took to other measures of protectionism. However, given that African countries are heavily dependent on other countries for food import, protectionism is not a very practical way of approaching food security. In this work we consider this problem in the context of India and Africa. We have a manually created dataset which has various features including the economic parameters and the demographic parameters  of 13 African countries over the last decade, and our aim is to predict with which countries is trade possible leading to food security. We apply various ML methods to approach this problem. 

In one of the methods, we use multiple linear regression to first decide how and what are the factors affecting trade, and then we use the features to make predictions about the country individually. In the second method, we use the clustering algorithm to divide the countries into two clusters, one in which food security is possible and the other where it is not possible. In the third method, we use simple extrapolation to check if the value of the target variable (Export) is increasing or decreasing and based on that we can predict which countries have possibility of trade and security and which ones don’t.

Method 1

Method 2