In an emergency healthcare situation, the time delay between injury and treatment is crucial for patient survival. Real-time processing of ambulance data can significantly reduce diagnosis and pre-treatment delays, re...
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The volume of information produced by Internet of Things (IoT) gadgets has significantly expanded along with how many of these gadgets are connected to the Internet. Now a day's Edge-Cloud computing has a major ro...
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Indoor localization using deep learning has emerged as a promising approach due to its high accuracy in mapping and predicting user locations for complex datasets. However, the inherent complexity of deep learning mod...
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In this paper, we have conducted a cryptographic analysis of the most widely used Android social media applications. In Android social media applications, cryptography plays a vital role in protecting data and communi...
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At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhance...
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At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.
Dermoscopic image analysis has gained significant importance for dermatological applications due to its potential in eliminating observer bias. Segmenting lesion areas automatically in these images is crucial for expe...
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Diabetes is a chronic disease that affects millions of people worldwide. Early detection and timely intervention are crucial for preventing severe complications. Traditional diagnostic methods can be time-consuming an...
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Sentiment analysis on social media platforms has gained immense popularity in recent years due to its ability to analyze and classify users' emotions and opinions on different topics. This paper proposes a sentime...
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Balancing the protection of sensitive data with maintaining its utility for analysis remains a pressing challenge in industries such as healthcare, finance, and government, where large-scale data processing is integra...
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Arabic dialect identification plays a crucial role in natural language processing. It serves as a foundational step in various language processing applications like machine translation, Sentiment Analysis, and cross-l...
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