For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of problem space. An efficient strategy for adjusting can be established with use greedy search and Swarm intelligence algorithms. The Random Search Grid optimization techniques show promise efficiency this task. small population solutions used at outset, costly goal functions by these searches, lead slow convergence or execution time in some cases. In research, we propose using...

Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth changed way consumers do online shopping. Using Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce entrepreneurs make assist achieving their business goals. In regard, an survey was conducted from purchasers of firms. The Partial Least Square (PLS) Smart used examine data....

The present study is done to perform the optimal design of structural components buildings against unwanted wind load exerted on their outer face. To this end, case research wall made a one-floor building modeled as rectangular plate with only one free edge and three clamped ones. It assumed that sandwich whose core auxetic material dace-sheets are reinforced nanoparticles graphene platelets (GPL). Differential equations governing system's motion obtained within background plate's...

Digitalization has brought a significant improvement in process optimization and decision-making processes, particular pursuing the goal of sustainability. This study examines how digitalization affected towards sustainability, focusing on Pakistan’s manufacturing sector. also moderating role environmental regulations between sustainable practices. is based quantitative methodology. Purposive sampling was used to gather primary data from 554 managers engineers working industries Pakistan...

Abstract This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed machine-learning model capable of detecting images through their embedded text content. Our leverages GloVe word embeddings enhance the model's predictive accuracy. also utilized Graph Convolutional Networks (GCNs) effectively analyze intricate relationships inherent in data. The practical implications our work are significant, despite some limitations...

This research aims to find out the factors affecting adoption of Metaverse in healthcare. study explores effect perceived ease use, usefulness, and trust on adopting healthcare by keeping digital division metaculture as moderating variables. The philosophical foundation is rooted positivism paradigm, methodology quantitative, approach used deductive. Data was collected Pakistan China through judgmental sampling from 384 respondents. Partial Least Square Structural Equation Modelling...

Information technology is one of the most rapidly growing technologies globally. Over last decade, its usage in healthcare has been remarkable. The study examines impact various factors as barriers to adopting information system healthcare. These are categorized into three major types: external attacks, which include phishing attacks and ransomware; employee factors, including lack skills issue misuse; technological complexity vulnerability. findings show that main systems, while have no...

Digitization has completely changed the landscape of supply chain management, which enables businesses to streamline their processes and attain higher levels profitability sustainability. This study investigates relationships between digitalization elements, particularly integration, communication, operation, distribution, effects on corporate The research is based an empirical investigation conducted through a questionnaire survey agri-food industries in Pakistan. PLS-SEM was used for...

Abstract Problem Breast cancer is a leading cause of death among women, and early detection crucial for improving survival rates. The manual breast diagnosis utilizes more time subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques. Distinct imaging tools have been utilized in works such as mammography MRI. However, these costly less portable than imaging. non-invasive method...

Background StudyUltrasound is the most widely used medical imaging technique during pregnancy for monitoring developing fetus and assessing maternal fetal health. Conventional 2D, as well advanced 3D 4D technologies, provide valuable diagnostic insights. Recent advancements in machine learning, particularly deep learning techniques, have enhanced interpretation of ultrasound images, enabling early predictions improving healthcare. However, limited integration Transformers Convolutional...

[This retracts the article DOI: 10.1016/j.heliyon.2024.e28778.].

<title>Abstract</title> Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of fetus image quality fluctuation, its interpretation is quite challenging. Although deep learning include Convolution Neural Networks (CNNs) have been promising, they largely limited one task or other, such as segmentation detection structures, thus lacking integrated solution that accounts for intricate interplay between anatomical...

As the planet faces challenge of global warming, every individual and organization must adopt green practices to protect nature. The automobile industry is one primary industries which can contribute significantly towards sustainability. This study aims examine impact behavior perceived benefits on buying behaviors automobiles. research also explores moderating influence environmental awareness mechanism. based a quantitative method for data was gathered from 406 respondents across Pakistan,...

This research paper presents novel condensed CNN architecture for the recognition of multispectral images, which has been developed to address lack attention paid neural network designs and hyperspectral photography in comparison RGB photographs. The proposed is able recognize 10-band images fewer parameters than popular deep designs, such as ResNet DenseNet, thanks recent advancements more efficient smaller CNNs. trained from scratch, it outperforms a comparable that was on terms accuracy...

Capsule Endoscopy (CE) is considered an established tool for the exploration and investigation of small intestine. There are a large number different capsules which have been launched in medical field by vendors such as Given Imaging, Olympus, IntroMedic, CapsoVision. To find experts GI that able to designate three four hours viewing one patient video will be very hard unfeasible economically. In this research, feature extraction techniques, Color Moment RGB, HSV, Histogram, LBP, Statistical...

The ability to detect and track fetal growth is greatly aided by medical image analysis, which plays a crucial role in parental care. This study introduces an attention-guided convolutional neural network (AG-CNN) for maternal–fetal ultrasound comparing its performance with that of established models (DenseNet 169, ResNet50, VGG16). AG-CNN, featuring attention mechanisms, demonstrates superior results training accuracy 0.95 testing 0.94. Comparative analysis reveals AG-CNN’s outperformance...

&lt;abstract&gt;&lt;p&gt;Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose GC at initial stages itself save lives. Histopathological analysis remains gold standard for accurate diagnosis of disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years diseases, challenging apply this case, due lack accessible gastric histopathological image databases. With a rapid progression Computer Vision (CV)...

&lt;p&gt;Echo cardiography is one of the major imaging modalities for quantifying heart functionality, providing advantages such as real-time features, cost-reflectiveness, efficiency, and safety compared to other image modalities. Many cardiac procedures diagnosis treatment disorders require segmentation endocardium walls robustness potential imperfections (such shadows, speckle noise possible movements). Manually segmenting boundary a time-consuming process, in emergency cases, results are...