Vipul K. Dabhi

Nitya Dhagat, Vipul K. Dabhi, Harshadkumar B. Prajapati et al.

This paper proposes a voice-powered AI-based banking system based on Google Conversational Agent, Dialogflow CX, which provides safe and convenient banking by phone. The system supports essential banking functions such as balance inquiries, transaction history retrieval, card activations, PIN-based authentication of sensitive tasks, smooth live agent handoff for complex and out-of-scope queries, and ensures seamless handover to human agents when required. These tests were performed with high-duration calls, high concurrency, and noisy environments; the system proved to be scalable, responsive, and resilient. All the data used is safely stored in the cloud environment for efficiency and security in real-time voice interactions. A voice-based banking solution that is efficient and easy to use can be provided through this.

Vishnu D. Burkhawala, Zankhana J. Barad, Harshadkumar B. Prajapati et al.

This paper presents an innovative approach that enables the users to capture their hand and try the jewel ring on their hand. The user captures the image of the hand using the React Native base GUI of the mobile application and selects the ring that the user wants to try, and the output image will have the user's hand with the ring image. This approach is implemented using a combination of MediaPipe hand point detection and YOLO-V8 custom object detection. The hand image uploaded by the user first undergoes mediapipe hand point detection. It will give the hand points and a Region of Interest mask where the ring is going to be placed. Then the ring is passed through YOLO object detection, in which ring points are detected, and background is removed. After that, using vector algebra, the angular discrepancy between the finger's reference axis and the ring's principal axis is computed. Also, ring size is rescaled according to finger thickness, preserving the aspect ratio to maintain perceptual realism. Then the ring is placed on the hand image and the output image is generated and shown on the user screen.

Dhruv Motwani, Ankush Tyagi, Vipul Dabhi et al.

Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data to cloud services to perform real-time face detection and recognition. Unlike existing solutions, our system accurately identifies attendees even when they are not looking directly at the camera, allowing natural movements, such as looking around or talking while walking. To the best of our knowledge, this is the first system to achieve high recognition rates under such dynamic conditions. Our system demonstrates overall 90% accuracy, with each video frame processed in 5 seconds, ensuring real time operation without frame loss. In addition, notifications are sent promptly to security personnel within the same latency. This system achieves 100% accuracy for individuals without facial obstructions and successfully recognizes all attendees appearing within the camera's field of view, providing a robust solution for attendee recognition in large-scale social events.

Ankush Tyagi, Dhruv Motwani, Vipul K. Dabhi et al.

The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as input and outputs the number of rice grains and size of each rice grain. The different steps, such as extraction of region of interest, segmentation of rice grains, and sub-contours removal, involved in the proposed approach are discussed. The approach was tested on rice grain images captured from different height using mobile phone camera. The obtained results show that the proposed approach successfully detected 95\% of the rice grains and achieved 90\% accuracy for length and width measurement.

Setu A. Bhatt, Harshadkumar B. Prajapati, Vipul K. Dabhi et al.

This paper presents an innovative approach that enables the user to find matching faces based on the user-selected face parameters. Through gradio-based user interface, the users can interactively select the face parameters they want in their desired partner. These user-selected face parameters are transformed into a text prompt which is used by the Text-To-Image generation model to generate a realistic face image. Further, the generated image along with the images downloaded from the Jeevansathi.com are processed through face detection and feature extraction model, which results in high dimensional vector embedding of 512 dimensions. The vector embeddings generated from the downloaded images are stored into vector database. Now, the similarity search is carried out between the vector embedding of generated image and the stored vector embeddings. As a result, it displays the top five similar faces based on the user-selected face parameters. This contribution holds a significant potential to turn into a high-quality personalized face matching tool.

Vipul K. Dabhi, Sanjay Chaudhary

This paper describes Postfix-GP system, postfix notation based Genetic Programming (GP), for solving symbolic regression problems. It presents an object-oriented architecture of Postfix-GP framework. It assists the user in understanding of the implementation details of various components of Postfix-GP. Postfix-GP provides graphical user interface which allows user to configure the experiment, to visualize evolved solutions, to analyze GP run, and to perform out-of-sample predictions. The use of Postfix-GP is demonstrated by solving the benchmark symbolic regression problem. Finally, features of Postfix-GP framework are compared with that of other GP systems.

Siddharth Shroff, Vipul Dabhi

Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. We aim to use Gene Expression Programming for modelling of dew point. Generally, accuracy of the model is the only objective used by selection mechanism of GEP. This will evolve large size models with low training error. To avoid this situation, use of multiple objectives, like accuracy and size of the model are preferred by Genetic Programming practitioners. Solution to a multi-objective problem is a set of solutions which satisfies the objectives given by decision maker. Multi objective based GEP will be used to evolve simple models. Various algorithms widely used for multi objective optimization, like NSGA II and SPEA 2, are tested on different test problems. The results obtained thereafter gives idea that SPEA 2 is better than NSGA II based on the features like execution time, number of solutions obtained and convergence rate. We selected SPEA 2 for dew point prediction. The multi-objective base GEP produces accurate and simpler (smaller) solutions compared to solutions produced by plain GEP for dew point predictions. Thus multi objective base GEP produces better solutions by considering the dual objectives of fitness and size of the solution. These simple models can be used to predict future values of dew point.

Siddharth Shroff, Vipul Dabhi

The processes occurring in climatic change evolution and their variations play a major role in environmental engineering. Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing, at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. This research aims to use Gene Expression Programming for modelling of dew point. Generally, accuracy of the model is the only objective used by selection mechanism of GEP. This will evolve large size models with low training error. To avoid this situation, use of multiple objectives, like accuracy and size of the model are preferred by Genetic Programming practitioners. Multi-objective problem finds a set of solutions satisfying the objectives given by decision maker. Multiobjective based GEP will be used to evolve simple models. Various algorithms widely used for multi objective optimization like NSGA II and SPEA 2 are tested for different test cases. The results obtained thereafter gives idea that SPEA 2 is better algorithm compared to NSGA II based on the features like execution time, number of solutions obtained and convergence rate. Thus compared to models obtained by GEP, multi-objective algorithms fetch better solutions considering the dual objectives of fitness and size of the equation. These simple models can be used to predict dew point.

Tejashvi R. Naik, Vipul K. Dabhi

In the field of empirical modeling using Genetic Programming (GP), it is important to evolve solution with good generalization ability. Generalization ability of GP solutions get affected by two important issues: bloat and over-fitting. Bloat is uncontrolled growth of code without any gain in fitness and important issue in GP. We surveyed and classified existing literature related to different techniques used by GP research community to deal with the issue of bloat. Moreover, the classifications of different bloat control approaches and measures for bloat are discussed. Next, we tested four bloat control methods: Tarpeian, double tournament, lexicographic parsimony pressure with direct bucketing and ratio bucketing on six different problems and identified where each bloat control method performs well on per problem basis. Based on the analysis of each method, we combined two methods: double tournament (selection method) and Tarpeian method (works before evaluation) to avoid bloated solutions and compared with the results obtained from individual performance of double tournament method. It was found that the results were improved with this combination of two methods.

Hardik M. Parekh, Vipul K. Dabhi

Premature convergence is one of the important issues while using Genetic Programming for data modeling. It can be avoided by improving population diversity. Intelligent genetic operators can help to improve the population diversity. Crossover is an important operator in Genetic Programming. So, we have analyzed number of intelligent crossover operators and proposed an algorithm with the modification of soft brood crossover operator. It will help to improve the population diversity and reduce the premature convergence. We have performed experiments on three different symbolic regression problems. Then we made the performance comparison of our proposed crossover (Modified Soft Brood Crossover) with the existing soft brood crossover and subtree crossover operators.