Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Chaotic Kbest gravitational search algorithm (CKGSA)

Abstract: Gravitational search algorithm is a popular adaptive search algorithm among nature-inspired algorithms and has been successfully used for optimizing many real-world problems. Gravitational search algorithm uses the law of Newton gravity for finding the optimal solution. The performance of gravitational search algorithm is controlled by exploration and exploitation capabilities and Kbest is one of its parameters that controls this trade-off. In this paper, a novel chaotic Kbest gravitational search algorithm has been proposed that uses the chaotic model in Kbest to balance the exploration and exploitation non-linearly. The proposed algorithm shows better convergence rate at later iterations with high precision and does not trap into local optima. The experimental results validate that the proposed algorithm outperforms.

A new measure of inaccuracy with its application to multi-criteria decision making under intuitionistic fuzzy environment

Abstract: Mathematics has evolved to study vague phenomena that do not show statistical stability. Intuitionistic fuzzy sets best represent these vague phenomena, and admit set operations that do not arise otherwise, because of the functions involved in their definition. This has greatly enriched mathematics and has potential new directions for quantitative studies and applications. There is need to define quantitative measures for contents, vagueness, distance, etc. over intuitionistic fuzzy sets. In this paper a measure of inaccuracy between two 'intuitionistic fuzzy sets' is introduced and studied. The measure is demonstrated to satisfy some very interesting properties, which prepare ground for applications in multi-criteria decision making problems. We develop a method to solve multi-criteria decision making problems with the help of new measure. Finally, three numerical examples are given to illustrate the proposed method to solve multi-criteria decision-making problem under intuitionistic fuzzy environment.

Nature Inspired Algorithm-Based Improved Variants of DV-Hop Algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks

Abstract: Localization is a significant challenge in the area of wireless sensor networks (WSNs). Distance Vector Hop (DV-Hop) algorithm is most preferable algorithm due to its low cost, distributed nature, and its feasibility for all kinds of sensor networks, but it suffers from high localization error. In order to reduce the problem of high localization error for 2-dimensional and 3-dimensional WSNs, two Nature Inspired Algorithm based improved variants have been proposed. The first one uses Grey-Wolf optimization (GWO-DV-Hop) to identify a better estimate of average distance per hop and second one, a weighted Grey-Wolf optimization (Weighted GWO-DV-Hop), finds average distance per hop as computed by each beacon node using grey wolf algorithm and then, a weighted approach is applied by each node to get weighted average distance per hop (weights based on distance from each beacon) so as to consider impact of all types of beacons. The results prove the superiority of proposed algorithms over traditional DV-Hop in terms of localization error.

Disaster and Pandemic Management Using Machine Learning: A Survey

Abstract: This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.