Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era

A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS

In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients

RFID Medication Management System in Hospitals

One of the primary concerns of the World Health Organization is the improvement of medical care by reducing adverse events in the medication process and enhancing the safety of patients. These issues are mainly related to the management of expensive and high-risk medicines in hospitals. In this paper, we enhanced medication management by minimizing the possibility of medication errors from its prescription-validation to its preparation. For this purpose, we designed a hospital and pharmacy services management system by employing digital signature using Radio Frequency IDentification technology. Our proposed system is equipped with ESP8266 modules, RFID readers and tags which allow the detection of taking medications and ensures an automated medication management

RSS-based Indoor Positioning Using Convolutional Neural Network

Indoor Positioning has come under the spotlight in the last decade due to the increasing of location-based services demands. RSS Wi-Fi based positioning using the fingerprinting technique is widely used due to its low hardware requirements and simplicity. However, multi-path and fading cause random fluctuations of collected RSS values which affects the positioning accuracy. For this purpose, we propose an indoor positioning system based on RSS and convolutional neural network. This approach aims to improve accuracy by reducing the noise and the randomness of collected RSS values from a wireless sensor network. We implemented and evaluated our system using a single floor and multi-grid dataset. Our proposed approach provides a room and grid prediction accuracies of 100% and a mean error of location estimation of 0.98 m.</p

RSS-based Indoor Positioning Using Convolutional Neural Network

Indoor Positioning has come under the spotlight in the last decade due to the increasing of location-based services demands. RSS Wi-Fi based positioning using the fingerprinting technique is widely used due to its low hardware requirements and simplicity. However, multi-path and fading cause random fluctuations of collected RSS values which affects the positioning accuracy. For this purpose, we propose an indoor positioning system based on RSS and convolutional neural network. This approach aims to improve accuracy by reducing the noise and the randomness of collected RSS values from a wireless sensor network. We implemented and evaluated our system using a single floor and multi-grid dataset. Our proposed approach provides a room and grid prediction accuracies of 100% and a mean error of location estimation of 0.98 m