Adversarial actions and a rapid climate change are disrupting operations of infrastructure networks (e.g., energy, water, and transportation systems). Unaddressed disruptions lead to system-wide shutdowns, emphasizing...
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Battery electric buses (BEBs) have received significant recognition as an environmentally conscious and sustainable means of transportation. Placement of charging stations to ensuring efficient and consistent charging...
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With the expansion of network traffic and the increasing experiences of cyber threats, the need for flexible as well as systematic network traffic attack detection systems has become foremost. Conventional signature-b...
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To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are parti...
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In our rapidly evolving technological landscape, AI tools have gained substantial power and integration across various domains. Through interviews and surveys conducted at a University in the Netherlands, we investiga...
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Multiscript handwriting recognition is used to recognize handwritten text from multiple scripts. It is a challenging task because each script has its own unique characteristics like writing direction, their character ...
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Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in d...
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Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing *** deep learning and machine learning techniques are available to detect phishing ***,most of the techniques did not use efficient optimization *** this context,our proposed model used random forest-based techniques to select the best features,and then the Brown-Bear optimization algorithm(BBOA)was used to fine-tune the hyper-parameters of the convolutional neural network(CNN)*** test our model,we used a dataset from Kaggle comprising 11,000+*** addition to that,the dataset also consists of the 30 features that are extracted from the website uniform resource locator(URL).The target variable has two classes:“Safe”and“Phishing.”Due to the use of BBOA,our proposed model detects malicious URLs with an accuracy of 93%and a precision of 92%.In addition,comparing our model with standard techniques,such as GRU(Gated Recurrent Unit),LSTM(Long Short-Term Memory),RNN(Recurrent Neural Network),ANN(Artificial Neural Network),SVM(Support Vector Machine),and LR(Logistic Regression),presents the effectiveness of our proposed ***,the comparison with past literature showcases the contribution and novelty of our proposed model.
Tongue bracing occurs when the lateral edges of the tongue maintain contact with the palate or upper molars. Bracing is pervasive in speech: previous EPG data analysis shows lateral contact to be maintained for 97.5% ...
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The widely used diagonal loading approach for robust adaptive beamforming (RABF) is based on maximizing the signal-to-interference plus noise ratio (SINR) while constraining the gain against white noise of the desired...
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Credit cards have greatly improved convenience in financial transactions, particularly in the realm of e-commerce, both online and offline. They enable cashless shopping worldwide, but this convenience has also led to...
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