arXiv subjects

Kusum Deep

Publications and source records attributed to Kusum Deep.

3 recordsLinked to original sources

arXiv2024

Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture.

cs.LG↗

arXiv2024

Circle Packing Problem Using Nature-Inspired Optimization Techniques

This paper deals with the problem of circle packing, in which the largest radii circle is to be fit in a confined space filled with arbitrary circles of different radii and centers. A circle packing problem is one of a variety of cutting and packing problems. We suggest four different nature-inspired Meta-heuristic algorithms to solve this problem. Algorithms are based on the social behavior of other biology species such as birds, wolves, fireflies, and bats. Moreover, recent advancements in these algorithms are also considered for problem-solving. The circle packing problem is one of the NP-hard problems. It is challenging to solve NP-hard problems exactly, so the proposed algorithms provide an approximate solution within the allotted time. Standard statistical parameters are used for comparison, and simulation and results indicate that the problem is highly non-linear and sensitive.

math.OC↗

arXiv2023

Ensemble Laplacian Biogeography-Based Sine Cosine Algorithm for Structural Engineering Design Optimization Problems

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian Biogeography-Based Optimization (LX-BBO) and the Sine Cosine Algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA, and LX-BBO but also other state-of-the-art metaheuristic algorithms.

math.OC↗