The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such a...
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Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is t...
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Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.
The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules usin...
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Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-...
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With the development of network technology and the blockchain, the metaverse as an emerging network paradigm has received extensive attention. In metaverse, users can create multiple virtual avatars to obtain differen...
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Globally, neurodegenerative diseases like dementia and Alzheimer's represent a major danger to our healthcare system. Clinical evaluations, which may or may not be able to identify the modest cognitive changes tha...
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On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the...
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Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recent...
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Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are ...
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Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are at the highest risk of developing lung cancer. Early detection of lung cancer is crucial for starting early treatment and preventing the disease from spreading. Hence, it can improve people’s chances of survival. Imaging tests, such as a chest computed tomography (CT) scan, can detect lung cancer by providing a more detailed picture. However, the examination of chest CT scans is a challenging task and is prone to subject variability. For this, researchers have developed many computer-aided diagnostic (CAD) systems for the automatic detection of cancer using CT scan images. Misdiagnoses can occur in manual interpretation of images. An automated trained neural network on lung images from healthy and malignant lung cells helps lower the problem. Convolutional neural network (CNN)-based pretrained deep learning models have been used successfully to detect lung cancer. The accuracy of classification is significant to avoid false prediction. This research presents a metalearning based approach for identifying the common types of lung cancer tissues namely, Benign tissue, Squamous Cell Carcinoma, and Adenocarcinoma using LC25000 dataset. All the experiments have been conducted on a publicly available benchmark dataset for lung histopathological images. The features extracted from the penultimate layer (global average pooling) of the transfer learning-based CNN models, namely InceptionResNetV1, EfficientNetB7, and DenseNet121, have been fused together, and the dimensionality reduction has been applied to them before passing to the metaclassifier, which is the Support Vector Machine (SVM) classifier in our case. A quantitative analysis of the proposed algorithm has been conducted through classification accuracy and confusion matrix computation. When compared wit
Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseas...
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Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.
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