In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but ...
In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (H&E) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied.
Edge computing is a network topology made up of three layers: Cloud Server Layer (CSL), Edge Server Layer (ESL), and Edge Device Layer (EDL). Therefore, it is vulnerable since the processing capacity of loT devices or...
Edge computing is a network topology made up of three layers: Cloud Server Layer (CSL), Edge Server Layer (ESL), and Edge Device Layer (EDL). Therefore, it is vulnerable since the processing capacity of loT devices or mobile devices is insufficient to withstand and they are unable to implement high- level security measures such as using robust security protocols like HTTP/HTTPS, FTP, or SMTP at this layer. Hence, it poses considerable security risks, particularly the Slow Distributed Denial of Service (DDoS) attacks. Various detection methods have been investigated in this study, and three machine learning algorithms that are classified as anomaly-based detection techniques have been chosen, such as CDAAE (Conditional Denoising Adversarial AutoEncoder), CNN (Convolutional Neural Network), and deep learning using ‘relu’ and ‘softmax’ in detecting slow DDoS attacks. These models are evaluated with the CICIDS2017 dataset and show that CNN has achieved better overall performance with shorter training times and high detection accuracy.
Breast cancer is causing a significant increase in the number of deaths every year. It is the most common kind of cancer and the leading cause of death in women all over the world. Any improvement in cancer illness pr...
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There are many methods of machine learning. This paper shows an application of basic machine learning methods like bag of words, random forest and naive Bayes on classification task of assigning sentences to members a...
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The public e-Procurement system in Malaysia is a crucial component of the e-Government services. Its primary function is to provide a platform for government agencies to efficiently procure goods and services from bus...
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Electronic-commerce (e-commerce) has evolved since its inception in the nineties, and it is still evolving with the growth of new technological solutions [1]. Globally, e-commerce has revolutionized the trading indust...
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Diabetic Retinopathy (DR) is an eye condition triggered by Diabetes Mellitus (DM). It could be detrimental to diabetic patients since it may lead to permanent blindness if they do not receive early treatment. However,...
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Malware is very harmful software and contains threats to any computer, whether a traditional computer or mobile device. Malware is short-term malicious software that a computer device could easily contract without any...
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Modern large-scale information systems often use multiple database management systems, not all of which are necessarily relational. In recent years, NoSQL databases have gained acceptance in certain domains while rela...
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The paper is focused on automatic morphological annotation and its evaluation. The most common evaluation method is described as well as its main issues. Then, based on the theoretical part, a tool for quantitative co...
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