Modeling signal power path loss (SPPL) for deployment of wireless communication systems (WCSs) is one of the most time consuming and expensive processes that require data collections during link budget analysis. Radio frequency (RF) engineers mainly employ either deterministic or stochastic approaches for the estimation of SPPL. In the case of stochastic approach, empirical propagation models use predefined estimation parameters for different environments such as reference distance path loss PL(d 0 )(dB), path loss exponent (n), and log-normal shadowing (X σ with N (σ, µ = 0)). Since empirical models broadly classify the environment under urban, suburban, and rural area, they do not take into account every micro-variation on the terrain. Therefore, empirical models deviate significantly from actual measurements. This paper proposes a smart deployment method of WCS to minimize the need for predefined estimation parameters by creating a 3-D deployment environment which takes into account the micro-variations in the environment. Tree canopies are highly complex structures which create micro-variations and related unidentified path loss due to scattering and absorption. Thus, our proposed model will mainly focus on the effect of tree canopies and can be applied to any environment. The proposed model uses a 2-D image color classification to extract features from a 3-D point cloud and a machine learning (ML) algorithm to predict SPPL. Empirical path loss models have received signal level (RSL) errors in the range of 6.29%-16.9% from the actual RSL measurements while the proposed model has an RSL error of 4.26%.

The growth and development of generative organs of the tomato plant are essential for yield estimation and higher productivity. Since the time-consuming manual counting methods are inaccurate and costly in a challenging environment, including leaf and branch obstruction and duplicate tomato counts, a fast and automated method is required. This research introduces a computer vision and AI-based drone system to detect and count tomato flowers and fruits, which is a crucial step for developing automated harvesting, which improves time efficiency for farmers and decreases the required workforce. The proposed method utilizes the drone footage of greenhouse tomatoes data set containing three classes (red tomato, green tomato, and flower) to train and test the counting model through YOLO V5 and Deep Sort cutting-edge deep learning algorithms. The best model for all classes is obtained at epoch 96 with an accuracy of 0.618 at mAP 0.5. Precision and recall values are determined as 1 and 0.85 at 0.923 and 0 confidence levels, respectively. The F1 scores of red tomato, green tomato, and flower classes are determined as 0.74, 0.56, and 0.61, respectively. The average F1 score for all classes is also obtained as 0.63. Through obtained detection and counting model, the tomato fruits and flowers are counted systematically from the greenhouse environment. The manual and AI-Drone counting results show that red tomato, green tomato, and flowers have 85%, 99%, and 50% accuracy, respectively.

The impact of Covid 19 cases is increasing worldwide due to not complying with social distancing and mask-wearing rules in congested areas such as hospitals, schools, and malls where people have to be together. Although the authorities have taken various precautions to prevent not wearing masks, it is challenging to inspect masks in crowded areas. People who do not wear masks can be unnoticed by visual inspections, which is a critical factor in the increase of the epidemic. This study aims to create an Artificial Intelligence (AI) based mask inspection system with the YOLO V7 deep learning method to ensure that overcrowded public areas are protected from the Covid-19 epidemic.

The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively.