The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life *** of the countries was ready to face this emerging health *** professionals were not able to predict its ri...
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The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life *** of the countries was ready to face this emerging health *** professionals were not able to predict its rise and next move,as well as the future curve and impact on lives in case of a similar pandemic situation *** created huge chaos globally,for longer and the world is still struggling to come up with any suitable *** the better use of advanced technologies,such as artificial intelligence and deep learning,may aid healthcare practitioners in making reliable COVID-19 *** proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia,and also empowering health professionals to distinguish chest CT scans of healthy *** efforts done by the Saudi government for the management and control of COVID-19 are remarkable,however;there is a need to improve the diagnostics process for better *** used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT *** proposed methodology was compared to current models and found to be more accurate(93 percent)than the existing methods.
Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyb...
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Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potent...
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In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
Federated Learning (FL) allows healthcare organizations to train models using diverse datasets while maintaining patient confidentiality collaboratively. While promising, FL faces challenges in optimizing model accura...
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Background: COVID-19 prompted a global shift to online learning, including video conference-assisted online learning (VCAOL), which necessitated educators understanding students' perspectives. Objective: This stud...
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Background: COVID-19 prompted a global shift to online learning, including video conference-assisted online learning (VCAOL), which necessitated educators understanding students' perspectives. Objective: This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. Material and methods: Synthetic Minority Over-sampling Technique (SMOTE) and data augmentation were used to handle imbalanced data from small-scale datasets. The prediction model was developed using Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). SHAP model-agnostic interpretability was used to interpret and comprehend prediction findings. The data was gathered from September 2022 to January 2023, resulting in 361 records. The research variables included students' academic performance as the dependent variable, and the video conference application (VC), learning material (LM), internet connection (IC), students' ability to learn (SL), and student knowledge (SK) as independent variables, which were mapped into 28 attributes. Result: The SMOTE improved the performance of three algorithms, with RF outperforming SVM and GNB in almost all tests, achieving an accuracy of 79.45%, precision of 75.71%, and recall of 79.45%. SHAP bar plots ranked attributes by importance demonstrated that "Performance," "Frequency Constraint," and "Increase Value" had a significant impact on prediction results. When we mapped the three attributes to our study perspective, we determined that SK and SL were the most important views for students to perform well in VCAOL. SHAP's beeswarm revealed students' performance in VCAOL was positively correlated with "Performance", "Increase Value", "Completing Project", "Adequate Method", "User Interface", and "Feature". As we mapped the three attributes to our study perspective, we found that SK, LM, SL, and VC were positively related to students' performance in VCAOL. Conclusion: T
As the Internet has evolved rapidly, Learning Management systems in recent years, particularly during the pandemic era, have become increasingly popular and can effectively override time and gives people new insights ...
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Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data,...
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Pneumonia is a major worldwide health concern, particularly among children, and early and correct diagnosis is critical for successful treatment. This study addresses the challenges in classifying pediatric pneumonia ...
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In wireless sensor networks,sensor nodes are deployed to collect data,perform calculations,and forward information to either other nodes or sink ***,geographic routing has become extremely popular because it only requ...
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In wireless sensor networks,sensor nodes are deployed to collect data,perform calculations,and forward information to either other nodes or sink ***,geographic routing has become extremely popular because it only requires the locations of sensor nodes and is very ***,the local minimum phenomenon,which hinders greedy forwarding,is a major problem in geographic *** phenomenon is attributed to an area called a hole that lacks active sensors,which either prevents the packet from being forwarded to a destination node or produces a long detour *** order to solve the hole problem,mechanisms to detect holes and determine landmark nodes have been *** on the proposed mechanisms,landmark-based routing was developed in which the source node first sends a packet to the landmark node,and the landmark node then sends the packet to the ***,this approach often creates a constant node sequence,causing nodes that perform routing tasks to quickly run out of energy,thus producing larger *** this paper,a new approach is proposed in which two virtual ellipses are created with the source,landmark,and destination *** guide the forwarding along the virtual ***,a recursive algorithm is designed to ensure a shortcut even if there are multiple holes or a hole has multiple ***,the proposed approach improves both geographic routing and energy efficiency *** experiments show that the proposed approach increases the battery life of sensor nodes,lowers the end-to-end delay,and generates a short path.
Diabetes, recognized as a chronic noncommunicable condition, has become increasingly prevalent and now affects an estimated 425 million individuals worldwide as of 2020. This escalating global prevalence underscores t...
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