Augmented Reality on Sudoku Puzzle Using Computer Vision and Deep Learning

Syed, Azhar Talha; Merugu, Suresh; Kumar, Vijaya

doi:10.1007/978-981-15-3125-5_55

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…Using computer vision and deep learning, the authors, Syed, Azhar and Merugu, Suresh and Kumar, Vijaya (2020) [6] suggest a superior method for identifying Sudoku puzzles and using constraint programming and backtracking to solve them using an algorithm to present the finished puzzle as augmented reality. Along with this study, a performance comparison with earlier work is offered.…”

Section: Research Papers On Sudoku Solversmentioning

confidence: 99%

Sudoku Solver: A Comparative Study of Different Algorithms and Image Processing Techniques

Hira,

Bhagwatkar,

Agrawal

et al. 2023

Advances in Transdisciplinary Engineering

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This research paper aims to explore the different types of algorithms used for solving Sudoku puzzles and provide a comparative analysis of their efficiency and accuracy. Sudoku puzzles have become increasingly popular in recent years, and many people enjoy the challenge of solving these puzzles. However, for more difficult puzzles, finding a solution can be time-consuming and require a lot of trial and error. In this paper, we will examine the Backtracking approach, Backtracking + Hashmap approach, Constraint Propagation Algorithm, and Dancing Links Algorithm for solving Sudoku puzzles. We will also provide a detailed analysis of the advantages and disadvantages of each algorithm, based on their performance on different levels of difficulty of Sudoku puzzles. We will also compare the efficiency and accuracy of these algorithms to determine which approach is best suited for solving Sudoku puzzles. The proposed approach will be of interest to puzzle enthusiasts, researchers in the field of artificial intelligence, and anyone interested in exploring different approaches to solving complex problems using algorithms.

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Section: Research Papers On Sudoku Solversmentioning

confidence: 99%

Sudoku Solver: A Comparative Study of Different Algorithms and Image Processing Techniques

Hira,

Bhagwatkar,

Agrawal

et al. 2023

Advances in Transdisciplinary Engineering

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“…Some of the features as well as the challenges of the existing IDS are depicted in Table 1. Deep RNN [12,23] offers security against the cyber-attacks and it further effectively works in the real time environments. But, it results in high false alarm rates.…”

Section: Problem Statementmentioning

confidence: 99%

Designing Framework for Intrusion Detection in IoT Based on Spotted Hyena-Based ANN

Bathula

Muhuri

Merugu

et al. 2021

Lecture Notes in Electrical Engineering

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Internet of Things (IoT) is the platform for resource sharing amidst the computing platforms, mainly memory, power, large data, and storage. Securing the data in the IoT has become a prime concern as the hackers can access through the invaluable information from the IoT database. Once the attacks are incurred on the IoT environment, the loss of data is inevitable, and it can have a major effect on the progress of the IoT platform. In this paper, an Intrusion Detection System (IDS) with improved artificial intelligence is proposed. This paper makes use of standard benchmark datasets from diverse sources for performing the experiment. A wellperforming machine learning algorithm called Artificial Neural Network (ANN) is developed with improved network architecture. The usage of a renowned metaheuristic algorithm called Spotted Hyena Optimization (SHO) is used for selecting the optimal hidden neurons for ANN. The main objective of the improved training is to reduce the error difference between the target and the measured outputs to enhance the detection accuracy. Finally, the experimental outcomes and simulations prove the stability and robustness of the proposed model in terms of a variety of performance metrics over other machine learning models.

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