Bathymetry Mapping With Unmanned Underwater Bots
Team Members: Devanshu Joshi
Guided by: Dr. Darshana patel, Prof. Dishita Mashru
Bathymetry is the measure of the depth of water in seas, oceans, rivers, or lakes. Oceans cover 71% of the surface of the earth and the bathymetry is mapped precisely up to a resolution of 1.5×1.5 km^2, whereas land topography is mapped up to 30×30 cm^2 resolution. This means that we cannot utilize our resources completely. Oceans are used all the time for laying network cables, shipping resources, food, and even crude oil mining from oil rigs. This makes it necessary to develop better maps so that fewer resources could be wasted. This paper then describes the approach of bathymetry mapping using unmanned bots and their architecture; not having or needing a crew or staff. A new industry is born, as soon as these maps are generated, saving resources, studying marine biology, and even developing new engineering techniques. Mapping could be done in several ways proposed in this paper. This paper focuses on generating bathymetric maps using multibeam sonar with the help of unmanned underwater bots with implemented work on small-scale underwater unmanned bots with some basic sensors, gathering insights like temperature profile, water quality level, and surface pattern on a real-time data set
Enhancing Face Detection and Recognition in Video using Deep Learning
Team Members : Nimesh Kadecha, Nakul Maniar, Vrajesh Patadiya, Shreya Ghetiya
Guided by : Dr. Darshana Patel, Prof. Aditiba Jadeja
In today’s world, deep learning is gaining significance due to its advancements in enabling automation and driving innovations across various fields. Face detection and recognition are crucial for security, surveillance, and user authentication, as they enable automated identification, access control, and enhanced safety in systems such as CCTV monitoring, biometric verification, and personalized services. Searching for specific individuals in security camera footage is a time-consuming process that demands constant human attention; besides, humans may make mistakes. This research aims to develop a system that can automatically search through video data by detecting the faces of people, making the process faster and easier compared to humans who do it manually. This involves the conversion of videos into frames using OpenCV for video processing. Then, DeepFace further accomplishes accurate face recognition by comparing each frame with the reference image that has been provided. The enhancement of this system has been achieved through parallel processing, which optimizes the handling of large video files by utilizing multiple CPU cores. This approach speeds up the face detection and recognition tasks by distributing the workload across several processing units, thereby improving efficiency and reducing the overall processing time.
Solar Panel Faults Detection System
Team Members: Harsh Devmurari, Shivam Ameta, Devalba Jadeja
Guided by: Dr. Darshana Patel, Prof. Aditiba Jadeja
In the face of growing global energy demands, solar photovoltaic (PV) modules have emerged as a vital renewable resource. However, environmental factors such as dust accumulation, bird droppings, and mechanical defects can significantly reduce power output. Traditional inspection methods, reliant on manual visual checks, are time-consuming and prone to human error. The adoption of deep learning, especially convolutional neural networks (CNNs), offers a powerful solution by automatically identifying anomalies in both infrared and electroluminescence images. Through feature extraction and classification algorithms like CNN, microcracks, hotspots, and other malfunctions can be accurately detected. This early detection paves the way for timely maintenance, minimizing downtime and prolonging module lifespan. Furthermore, advanced techniques such as data augmentation and explainable AI tools enhance model performance, transparency, and user confidence. By integrating meteorological data with historical power output, machine learning models can also predict the expected energy generation, triggering alerts when actual performance falls below the forecasted threshold. The result is a comprehensive fault detection system that not only optimizes power generation but also reduces operational costs. As solar adoption continues to rise, robust, AI- driven defect detection ensures greater reliability, improved efficiency, and sustainability forthe solar energy sector and fosters ongoing innovation. The paper uses different measures to evaluate the performance of the system such as confusion matrix, accuracy, loss and epochs considering different datasets.
Detection of Flaws in Machine Parts Using Ultrasonic Sensors
Team Members: Devanshu Joshi
Guided by: Dr. Darshana patel, Prof. Dishita Mashru
Several billions of machine parts are being manufactured per day in different parts of the world. Recalling the fact that only some things are perfect and considering just 2% of the average error rate we get millions of defective products. Some are crucial objects, which if used defective can cause mass destruction or fatality to human life. Some parts get errors in them while they are being transported from one place to another, hence a sophisticated mechanism is required for their testing. Some methods include the utilization of Artificial Intelligence with the help of camera vision which in turn results in an expensive approach to resolve the issue. This project provides a new, and economical prospect with the usage of Ultrasonic Sensors and provides a brief on how precise error detection could be achieved using these sensors. A 3D Point Cloud is generated from the distance data points captured and then compared with the ideal object.
AUTO BILLING AND INVOICING SHOPPING CART
Team Members : Hetvi Karavadiya
Guided by : Dr. Darshana Patel, Prof. Dishita Mashru
This is an Auto Billing Invoicing Shopping Cart. There is a general problem, standing in a queue whenever you visit any shopping mall or shopping mart. The people especially the old aged mass cannot stand for a longer time in a queue. So, to overcome this problem we have invented the auto billing cart. This Cart can be used at many places such as shopping mall, Airports, Shopping marts etc. This Cart has wonderful features. One of the most important features of this cart is that it has an automated system of billing. So, when you keep the items, you want to purchase, into the cart, it will show the total price of the items. It is having a LED Display that will display the price. And, it has a wonderful another feature that if you do not want to pay the amount in cash you can also pay through your card. There will be a QR Code Scanner on the cart so by which you can pay online. By this all features it is so helpful to everyone regardless of their age. The display of the cart will be charged by the battery cells. So, one of the small disadvantages of this trolley is one must keep an eye on the battery cells. If the battery will not be working properly than it will not function properly. So, one must change the battery cells regularly. So, when used properly it is the best invention so far.
Smart Medicine Box
Team Members: Samarth Vekariya
Guided by: Dr. Darshana Patel, Prof. Dishita Mashru
A smart medicine box is a device that helps individuals keep track of their medication schedules and ensures that they take their medication as prescribed. The box uses technology to provide reminders and alerts to patients or caregivers, so they don’t miss a dose or take the wrong medication. The smart medicine box typically features compartments or slots for each medication, which are programmed with a specific
medication schedule. The device uses buzzer or alerts to remind patient when to take medicine. User can schedule particular time for particular medicines, as the time for medicine was scheduled
buzzer will rang or alerts will be sent. Smart medicine boxes can be connected to a smartphone app or web portal, allowing patients and caregivers to monitor medication adherence and receive alerts or notifications even when they are not at home. The app can also provide detailed reports on medication usage and adherence over time, allowing healthcare providers to track the patient’s progress and adjust the treatment plan as needed. Overall, smart medicine boxes can be a useful tool for individuals who take multiple medications or have difficulty remembering to take their medication as prescribed. They can help reduce medication errors and improve medication adherence, which can lead to better health outcomes and reduce the risk of complications. Smart pill boxes have the potential to become an important tool in the management of chronic conditions and the promotion of patient self-care. They can help reduce medication errors and improve medication adherence, which can lead to better health outcomes and reduce the risk of complications.
Path Darshak
Team Members: Labdhi Gathani, Smit Doshi, Brinda Davda, Varshaba Jadeja, Vedant Raval, Mihir Chavda Guided by: Prof. Dishita Mashru
This project is an android app built for blind people to show directions while their commutations. Specifically, this app gives a voice output if it detects any obstacle on the way and also gives the direction to move left or right for the smooth commutation.
This is a Deep Learning based project which made the use of YOLO model for detecting object on runtime on the android app and once the object is detected, a voice notification is sent to the person using the app along with the direction in which any object is not detected and is safe to commute.
Speech Emotion Recognition With Sentiment Analysis Using Deep Learning
Team Members : Labdhi Gathani, Brinda Davda, Priyal Panara Guided by : Dr. Darshana Patel, Prof. Dishita Mashru
The integration of speech emotion recognition using MLP classifier, speech to text using ASR, and sentimental analysis using VADER can provide several advantages in understanding and analyzing spoken content. MLP classifier can accurately classify speech signals into different emotional categories. When combined with speech to text using ASR, the resulting textual data can be analyzed to determine the emotional sentiment expressed by the speaker using sentimental analysis. The overall accuracy of the model is compared with Acoustic model and it can be concluded that the algorithm used by our model is better.
Overall, this process can be useful in a variety of applications, such as customer feedback analysis, market research, and opinion mining. The ability to extract valuable information from spoken content can provide a significant advantage for organizations seeking to optimize business operations, improve customer satisfaction, and make data-driven decision.
AI Code Convertor
Team Members: Keval Mehta Guided by: Prof. Dishita Mashru
This project is a platform created by the student that takes a snippet of code as input, and provides various options for the user in terms of coding language to convert that code into. For example, I can give a C code as an input and select from the given options like Java, Python, NodeJS, etc. and covert that C code into any of the languages given in the option, without changing the logic and the output of the code in C.
Corona Virus Tracker App
Team Members: Shruti Santoki Guided by: Prof. Dishita Mashru
CSS Component Library
Team Members : Himadri Shah Guided by : Prof. Dishita Mashru
CSS Component Library is a collection of various types of components that are used by developers in their day-to-day lives for the purpose of designing and developing a website.
Work From Home Application and Attendance Management
Team Members:Yasar Khadera Guided by: Prof. Akshita Shah
WORK FROM HOME APPLICATION AND ATTENDANCE MANAGEMENT is a platform where the User Can store the records of employees. Initially User has to register their employees and then they can use this software. They can store the full time and part time employee details.It is allow employees to apply for work from home and they can see leave balance too. This software is very useful to maintain attendance. User can store details of Employees like From When they started working and for how many hours they work. Which employees are on leave who take half day leave or full day leave. Information like Birthdays , Holidays can also seen on dashboard.
This website provides users to search and store the data and maintain the attendance details.