One of the most common types of human malignancies is skin cancer, which is chiefly diagnosed visually, initiating with a clinical screening followed by dermoscopic analysis, histopathological assessment, and a biopsy. Due to the fine-grained differences in the appearance of skin lesions, automated classification is quite challenging through images. To attain highly segregated and potentially general tasks against the finely grained object categorized, deep convolutional neural networks (CNNs) are used. In this paper, we propose a new prediction model that classifies skin lesions into benign or malignant lesions based on a novel regularizer technique. Hence, this is a binary classifier that discriminates between benign or malignant lesions. The proposed model achieved an average accuracy of 97.49%, which in turns showed its superiority over other state-of-the-art methods. The performance of CNN in terms of AUC-ROC with an embedded novel regularizer is tested on multiple use cases. The area under the curve (AUC) achieved for nevus against melanoma lesion, seborrheic keratosis versus basal cell carcinoma lesion, seborrheic keratosis versus melanoma lesion, solar lentigo versus melanoma lesion is 0.77, 0.93, 0.85, and 0.86, respectively. Our results showed that the proposed learning model outperformed the existing algorithm and can be used to assist medical practitioners in classifying various skin lesions.INDEX TERMS Convolutional neural network, skin lesion, novel regularizer, AUC-ROC.
Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news. However, the credibility of the news posted on these platforms has become a significant issue. In other words, written news that contains inaccurate information aiming to mislead readers has been rapidly disseminated on these platforms. In the literature, this news is called fake news. Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news. A hybrid model comprising a recurrent neural network (RNN) and support vector machine (SVM) is incorporated to detect real and fake news. An RNN with bidirectional gated recurrent units was used to encode textual data, including news content and comments, to numerical feature vectors. The encoded features were fed to an SVM with radial basis function kernel to classify the given input of real and fake news. Experiments on the real-world dataset yield encouraging results and demonstrate that the proposed framework outperforms state-of-the-art methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.
In the recent years, an alarming rise in the incidence of cyber attacks has made cyber security a major concern for nations across the globe. Given the current volatile socio-political environment and the massive increase in the incidence of terrorism, it is imperative that government agencies rapidly realize the possibility of cyber space exploitation by terrorist organizations and state players to disrupt the normal way of life. The threat level of cyber terrorism has never been as high as it is today, and this has created a lot of insecurity and fear. This study has focused on different aspects of cyber attacks and explored the reasons behind their increasing popularity among the terrorist organizations and state players. This study proposes an empirical model that can be used to estimate the risk levels associated with different types of cyber attacks and thereby provide a road map to conceptualize and formulate highly effective counter measures and cyber security policies.
In this paper, we proposed a novel triple algorithm based on RSA (Rivest-Shamir-Adleman), AES (Advanced Encryption Standard), and TwoFish in order to further improve the security of Bluetooth that is currently using only 128-bit AES for encryption in its latest versions (Bluetooth 4.0-5.0). Furthermore, older Bluetooth 1.0A-3.0 + HS (High-Speed) devices use E0 stream cipher for encryption that has been shown to be weak by numerous researchers and thus it could be considered insufficient for high security purposes nowadays. In our novel approach, the triple protection of AES, RSA, and TWOFISH would enhance the level of security, which shields the data transmission in the Bluetooth. As the first step of our novel approach, we first encrypted the message by using AES with 128-bit key and then further encrypted it by using Twofish with the same 128-bit key. Finally, the 128-bit key generated in the beginning will be encrypted by using RSA with 1024-bit key to protect its over-the-air transfer. In the receiving end, the decryption process goes in reverse order compared with encryption process. We showed with experimental figures that our novel algorithm improved the security of Bluetooth encryption by eliminating all known weaknesses and thus made data exchange between Bluetooth devices secure.
Relying on social networks to follow the news has its pros and cons. Social media websites indeed allow the spread of information among people quickly. However, such websites might be leveraged to circulate low-quality news full of misinformation, i.e., "fake news." The wide distribution of fake news has a considerable negative impact on individuals and society as a whole. Thus, detecting fake news published on the various social media websites has lately become an evolving research area that is drawing great attention. Detecting the widespread fake news over the numerous social media platforms presents new challenges that make the currently deployed algorithms ineffective or not applicable anymore. Basically, fake news is deliberately written on the first place to mislead readers to accept false information as being true, which makes it difficult to detect based on news content solely; consequently, auxiliary information, like user social engagements on social media websites, need to be taken into account to help make a better detection. Using such auxiliary information is challenging because users' social engagements with fake news produce noisy, unstructured, and incomplete Big-Data. Due to the fact that fake news detection on social media is fundamental, this research aims at examining four well-known machine learning algorithms, namely the random forest, the Naïve Bayes, the neural network, and the decision trees, distinctively to validate the efficiency of the classification performance on detecting fake news. We conducted an experiment on a widely used public dataset i.e. LIAR, and the results show that the Naïve Bayes classifier defeats the other algorithms remarkably on this dataset.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.