Anomaly Detection in Drones with Machine Learning Algorithms

Shaw, Soumya; Joshi, Kartik; Pathak, Akhil; Thyagarajan, Abitha K.; Vidya, Gayatri; Shah, Rahul Hemal; Kishan, V. Ram; Alex, John Sahaya Rani

doi:10.1007/978-981-16-4625-6_42

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The k-NN algorithm can be easily applied to various fields such as agriculture, finance, medicine, and engineering (Alkhatib et al, 2013;Huda & Chowanda, 2021;Mukid et al, 2018;Pacheco & Lopez, 2019;Ramteke & Monali, 2012;Shaw et al, 2021;Xing &Yilin, 2017). This algorithm has some limitations such as the wrong choice of the distance or the value of k degrades the performance (Cherif, 2018).…”

Section: K-nn Algorithmmentioning

confidence: 99%

Forecasting sustainable development level of selected Asian countries using M-EDAS and k-NN algorithm

Özarı

DEMİRKALE

ALICI

2023

International Journal of Social Sciences and Education Research

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This study aims to forecast the sustainable development levels of countries with the least possible parameters based on social, economic, and environmental dimensions. For this purpose, a hybrid model consisting of multi-criteria decision-making and machine learning methods is proposed. First, using the M-EDAS method, selected Asian countries were ranked based on the main goals of the Sustainable Development Report. By using ranking findings, sustainability development levels were determined for 2017–2020. Using the last two years before the relevant year as a training dataset, the sustainable development levels determined for 2019-2020 were estimated using two basic macroeconomic variables. 2020 forecast findings are not successful as 2019. Additionally, the findings obtained from the ranking analysis were evaluated using Spearman's correlation to compare the periods before and during the COVID-19 pandemic.

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Section: K-nn Algorithmmentioning

confidence: 99%

Forecasting sustainable development level of selected Asian countries using M-EDAS and k-NN algorithm

Özarı

DEMİRKALE

ALICI

2023

International Journal of Social Sciences and Education Research

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A Generic Review on Anomaly Detection

Bansal

Pahuja

2022

Lecture Notes in Electrical Engineering

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Biosensor-Based Drones Anomaly Detection Integration for Sustainable Agriculture Development

Shafik

2024

Advances in Environmental Engineering and Green Technologies

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Recently, biosensor-based drones (BBD) have emerged and have proven to be highly influential in the convergence of modern technology and agriculture. These drones possess the capacity to bring about significant changes in the realm of sustainable agriculture development. This study presents a comprehensive insight into the crucial element of anomaly detection (AD) in integrating BBD and investigates their diverse uses in promoting sustainability in agriculture. With various advanced sensor technologies, BBD collects and transmits real-time data for accurate monitoring of agricultural crops, soil quality, and environmental parameters. The utilization of a diverse range of sensors, including multispectral, hyperspectral, thermal infrared, global positioning system (GPS), light detection and ranging (LiDAR), environmental, chemical, and crop health sensors, provides farmers with the capability to make informed decisions based on data. The management of extensive datasets produced by these sensors presents a considerable obstacle. The utilization of AD techniques is crucial to exploit the capabilities of drones equipped with biosensors fully. Machine learning (ML) algorithms and artificial intelligence (AI) systems significantly impact the processing and interpretation of sensor data. They are essential in detecting deviations from anticipated trends and notifying farmers of abnormalities that could indicate crop stress, illnesses, or pest presence. Detecting issues early enables prompt action, decreasing crop production losses and reducing reliance on chemical treatments. This, in turn, supports the adoption of sustainable farming methods. The utilization of BBD in advancing sustainable agriculture encompasses a wide range of applications. The practices encompass precision irrigation management, targeted fertilization, disease and insect control, land optimization, and minimization of environmental impact. These applications collectively enhance resource efficiency, augment agricultural yields, mitigate environmental impact, and promote sustainable agriculture. Selected studies were obtained from six top academic research databases. The authors used an exhaustive data extraction technique, focusing on the study objectives and type of AD in a smart operation, such as smart agriculture, transportation and smart things settings. According to this analysis, several studies have shown that deep learning (DL) and ML are more employed in preventing point and collective anomalies. Statistical approaches are more applicable in contextual and collaborative AD. The study presents an AD summary of ML, DL, and statistical-based approaches. Conclusively, the study identifies AD's future research directions from a drone operations perspective.

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Anomaly Detection in Drones with Machine Learning Algorithms

Cited by 3 publications

References 14 publications

Forecasting sustainable development level of selected Asian countries using M-EDAS and k-NN algorithm

Forecasting sustainable development level of selected Asian countries using M-EDAS and k-NN algorithm

A Generic Review on Anomaly Detection

Biosensor-Based Drones Anomaly Detection Integration for Sustainable Agriculture Development

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