Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

Breast cancer: A comparative review for breast cancer detection using machine learning techniques.

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.

Nanocarrier Based Delivery of Berberine: A Critical Review on Pharmaceutical and Preclinical Characteristics of the Bioactive.

Berberine (BBR) is an isoquinoline alkaloid with several therapeutic properties, including anti-microbial, anti-diarrhea, anti-viral, anti-inflammatory, antihypertensive, anti-tumor, and anti-diabetes. However, its low water solubility, low absorption, first-pass metabolism, nontargeting, and poor bioavailability represent major hurdles to its successful therapeutic applications. Hence, researchers have attempted to enhance the biological and pharmacological activity of BBR to overcome its drawbacks by encapsulation of BBR in micro and nano delivery systems. For the preparation of nanostructured carrier systems of BBR, a range of methods has been developed, and each method has its benefits and characteristics. This review critically describes different types of nanocarriers like liposomes, niosomes, ethosomes, nanoemulsions, polymeric nanoparticles, micelles, dendrimers, and silver and gold nanoparticles that have been used for encapsulation of BBR for different therapeutic applications. The various pharmaceutical characteristics (size, shape, entrapment efficiency, zeta potential, drug release, and drug permeation) of these BBR-loaded nanocarriers have been discussed systematically. Preclinical studies of BBR nanoformulations involving animal models are also discussed.