Browsing by Author "Sachchida Nand Chaurasia"

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6G networks for artificial intelligence-enabled smart cities applications: A scoping review
(Elsevier B.V., 2023) Prabhat Ranjan Singh; Vivek Kumar Singh; Rahul Yadav; Sachchida Nand Chaurasia
Due to the increasing need for time with respect to industrial growth and the speeding up of human day-to-day work, network evolution is always the center of focus for research organizations. Artificial intelligence (AI), on the other hand, is playing an increasingly important role in automating systems, increasing efficiency, and ensuring high dependability in complicated activities. Network and AI research are generating a boom in IoT applications, as firms strive to serve their consumers with ever-improving features and functionality. In this article, we look at the evolution of network technology across time as well as the role of AI in the next generation of networks. First, we will introduce the reader to the features of different network generations, technology used in. Second in the context of network evolution and AI, we will define the scope of IoT applications and their need in order to alleviate user demand. Consequently, we provide the requirements of advanced models in smart cities applications and insights on the development of future network for those applications. Our study showed that, though important features were introduced in the advanced automated networks, these are still to be tested under for automated applications that have strict requirements in terms of low latency, high reliability, and fast transmission. © 2023
A Comprehensive Analysis of Indian Legal Documents Summarization Techniques
(Springer, 2023) Saloni Sharma; Surabhi Srivastava; Pradeepika Verma; Anshul Verma; Sachchida Nand Chaurasia
In the Legal AI field, the summarization of legal documents is very challenging. Since the Indian case documents are much noisier and poorly organized, the summarization of legal documents can be useful for legal professionals, who often have to read and analyze large amounts of legal text. During the review process of the legal documents, a team of reviewers may be needed to understand and for taking further actions. A branch of text summarization called ‘legal text summarization’ which is concerned with summarizing legal texts, such as court opinions, contracts, and legal briefs may reduce the need of these reviewers. Legal text summarization aims to highlight the key points of a legal document and convey them in a concise form so that decisions can be made in quick manner. In this paper, we experimented on seven machine learning-based summarization models to analyse their performance on judgment report datasets that has been collected from Indian national legal portal. The models that are taken here for the analysis are BART, LexRank, TextRank, Luhn, LSA, Legal Pegasus, and Longformer. We experimented with these models to find which model may perform well on the legal data. As a result, we observed that Legal Pegasus outperforms over all other models in the case legal summarization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
An Efficient Hybrid Algorithm with Novel Inver-over Operator and Ant Colony Optimization for Traveling Salesman Problem
(Springer Science and Business Media Deutschland GmbH, 2024) Dharm Raj Singh; Manoj Kumar Singh; Sachchida Nand Chaurasia
In this research paper, we present a hybrid algorithm that merges the principles of Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Our algorithm consists of two distinct stages. In the first stage, we employ Ant Colony Optimization to establish an initial population, and we utilize the proposed Inver-over (IO) heuristic to obtain suboptimal solutions for the Euclidean Traveling Salesman Problem (TSP). The proposed Inver-over operator is used to refine the solution obtained through ACO. Subsequently, this refined solution is incorporated into the Genetic Algorithm (GA) for the second stage. In the second stage of our algorithm, we apply GA with our proposed crossover operator and a 2-optimal heuristic to further refine the solution with the goal of achieving global optimality. To assess the effectiveness of our proposed algorithm, we rely on standard benchmark data from TSPLIB. The experimental results indicate that our hybrid algorithm outperforms recent methods and exhibits greater efficiency when compared to other reported methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Genetic Algorithm Incorporating Group Theory for Solving the General Travelling Salesman Problem
(Springer, 2024) Dharm Raj Singh; Manoj Kumar Singh; Sachchida Nand Chaurasia; Anshul Verma
This paper presents a novel Genetic Algorithm (GA) designed to tackle the Travelling Salesman Problem (TSP) with remarkable efficacy. It integrates group theory into population initialization, employs Partially Matched Crossover (PMX), and adopts a 2-optimal mutation strategy. The pioneering approach harnesses algebraic structures in constructing group tours, utilizing integer addition modulo n within Zn to generate varied initial solutions. The initial population enhances diversity by ensuring that each individual/tour shares an identical node order but begins from a distinct starting node. This distinctiveness in starting nodes facilitates thorough exploration of the entire search space. The Partially mapped Crossover operator, grounded in order principles, is a crucial mechanism for transferring sequence and value characteristics from parental to offspring tour. This operation effectively guides a strong local search. Subsequently, applying a 2-opt optimal mutation seeks to refine the solution further, targeting a more globally optimal outcome. The effectiveness of this methodology is evaluated through experiments with TSP instances sourced from the widely recognized TSPLIB. Furthermore, the superiority of our proposed approach is demonstrated through comparisons with state-of-the-art methods developed within hybrid frameworks. Statistical analyses underscore the significance and effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
Hybrid Heuristic for Solving the Euclidean Travelling Salesman Problem
(Springer, 2024) Dharm Raj Singh; Manoj Kumar Singh; Sachchida Nand Chaurasia; Pradeepika Verma
This study introduces a hybrid methodology that integrates the ant colony optimization (ACO) with genetic algorithm (GA) techniques. ACO is employed first to create an initial population and to derive a sub-optimal solution for the TSP using a newly designed inver-over (IO) operator. The Proposed IO operator is utilized to improve the solution derived from the ACO. This refined solution is then employed in the GA, where a genetic operator is applied alongside other randomly selected members from the initial population during the second phase. GA is used with the proposed crossover operator and the 2-opt heuristic in this phase to achieve optimal solution refinement towards a global optimum. Our evaluation of the algorithm’s efficacy uses benchmark datasets from TSPLIB. The proposed approach gives superior solution quality, both the average and the best solution metrics, demonstrating enhanced performance with a lower percentage of best error and percentage of average error. Experimental results indicate that the hybrid approach outperforms the efficiency of other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.