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Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that increase the risk of health disparities in medical applications. The under-representation of a specific group can lead to bias in the datasets that are being replicated in the AI models. These disadvantaged groups are disproportionately affected by bias because they may have less accurate algorithmic forecasts or underestimate the need for treatment. One solution to eliminate bias is to use synthetic samples or artificially generated data to balance datasets. Therefore, the purpose of this study is to review and evaluate how synthetic data can be generated and used to mitigate biases, specifically focusing on the medical domain. We explored high-quality peer-reviewed articles that were focused on synthetic data generation to eliminate bias. These studies were selected based on our defined inclusion criteria and exclusion criteria and the quality of the content. The findings reveal that generated synthetic data can help improve accuracy, precision, and fairness. However, the effectiveness of synthetic data is closely dependent on the quality of the data generation process and the initial datasets used. The study also highlights the need for continuous improvement in synthetic data generation techniques and the importance of evaluation metrics for fairness in AI models. [ABSTRACT FROM AUTHOR]

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management. However, vehicles come in a range of sizes, which is challenging to detect, affecting the traffic monitoring system's overall accuracy. Deep learning is considered to be an efficient method for object detection in vision-based systems. In this paper, we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5 (YOLOv5) detector combined with a segmentation technique. The model consists of six steps. In the first step, all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the images. These pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase. A single-stage detector YOLOv5 is used to detect and locate vehicles in images. Each detection was exposed to Speeded Up Robust Feature (SURF) feature extraction to track multiple vehicles. Based on this, a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching technique. Further, we implemented a Kalman filter to track multiple vehicles. In the end, the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking algorithm. The experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art systems. The proposed implemented system provided 94.1% detection precision for Roundabout and 96.1% detection precision for Vehicle Aerial Imaging from Drone (VAID) datasets, respectively. [ABSTRACT FROM AUTHOR]

Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density andworldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob detection was applied to mark each vehicle. For the validation of each detection, a vehicle verification method based on the structural similarity index is proposed. The tracking of vehicles across the image frames is done using the Identifier (ID) assignment technique and particle filter. Also, vehicle counting in each frame along with trajectory estimation was done for each object. Our proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone (VAID), 0.86 over AU-AIR, and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking (UAVDT) dataset during the experimental evaluation. The proposed system can be used for several purposes, such as vehicle identification in traffic, traffic density estimation at intersections, and traffic congestion sensing on a road. [ABSTRACT FROM AUTHOR]

The tracking of in situ-bioremediation of paranitrophenol through functional screening is time consuming. In this study we address a quick, reliable polymerase chain reaction-based method to detect the paranitrophenol monooxygenase gene from soil. All paranitrophenol degrading strains viz. Pseudomonas P19, Bacillus species C1, Bacillus species C2 and Arthrobacter species were isolated from contaminated agricultural soil. DNA was isolated from pure strains as well as soil inoculated with these strains. DNA extraction protocol was modified by including Cetyl Trimethyl Ammonium Bromide and sodium metabisulphite instead of polyvinylpolyprrolidone in polyethylene glycol method. Paranitrophenol monooxygenase gene was screened using degenerate primers designed to give 100 base pair amplification products. All strains used in study showed amplification product corresponding to target region using designed degenerate primers. Modified polyethylene glycol method leads to increase in DNA yield from soil. The assay technique is sensitive with broad specificity and has application in rapid identification of paranitrophenol monooxygenase gene. This method has advantage over DNA hybridization method as needs less DNA. The present study demonstrated the detection and monitoring of paranitrophenol monooxygenase gene from soil sample inoculated with either Gram-positive or Gram-negative bacteria using degenerate primers. [ABSTRACT FROM AUTHOR]