Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization.

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

Enhancing fraud detection in auto insurance and credit card transactions: a novel approach integrating CNNs and machine learning algorithms.

Fraudulent activities especially in auto insurance and credit card transactions impose significant financial losses on businesses and individuals. To overcome this issue, we propose a novel approach for fraud detection, combining convolutional neural networks (CNNs) with support vector machine (SVM), k nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT) algorithms. The core of this methodology lies in utilizing the deep features extracted from the CNNs as inputs to various machine learning models, thus significantly contributing to the enhancement of fraud detection accuracy and efficiency. Our results demonstrate superior performance compared to previous studies, highlighting our model's potential for widespread adoption in combating fraudulent activities.

Assessing the acceptability of COVID-19 vaccine and its booster dose.

BACKGROUND AND OBJECTIVE: Coronavirus disease of 2019 (COVID-19) vaccinations are essential to control the pandemic and prevent severe COVID-19 infections. This study aims to assess the acceptability of the COVID-19 vaccine and the factors that impact the intention to take the COVID-19 vaccine and its booster dose. METHODS: A cross-sectional study was conducted in Saudi Arabia and Jordan. The study used a self-administered web-based survey (questionnaire) for data collection that was distributed via social media platforms from May 2022 to July 2022. RESULTS: In this study, among 518 participants, 54.4% had already received two doses of the COVID-19 vaccine, and out of the participants who didn't receive the booster dose, 19.9% declared a definite willingness to receive it, while 42% had already taken a booster dose, which indicated good acceptance. After adjustment for significant background characteristics, a significant association between the country and receiving the COVID-19 vaccine, the intention to get the vaccine, and infection with COVID-19 were found, in addition to a significant association between the country and the participants' opinion that electronic applications helped them to follow their vaccine schedule were found (p < .001). Also, the results showed that participants' attitudes were significantly associated with educational level and age groups (p ≤ .001, p = .032, respectively). There was a significant association between the intention to receive the vaccine booster dose and the country (p < .001). The Saudi participants were willing to get the booster dose seven times more than the Jordanians, furthermore, there was a significant association between taking the vaccine booster dose in the country, as well as age group, working in the medical field, previous COVID-19 infection, and the intention to vaccinate the children (p < .001, p = .030, .031, .025, < .001, respectively). CONCLUSION: Overall, our results emphasize a positive response and a positive attitude toward COVID-19 vaccination. In addition, define the groups to be targeted with effective communication regarding the COVID-19 vaccine and its booster dose.

Enabling Secure Communication in Wireless Body Area Networks with Heterogeneous Authentication Scheme.

Thanks to the widespread availability of Fifth Generation (5G) wireless connectivity, it is now possible to provide preventative or proactive healthcare services from any location and at any time. As a result of this technological improvement, Wireless Body Area Networks (WBANs) have emerged as a new study of research in the field of healthcare in recent years. WBANs, on the one hand, intend to gather and monitor data from the human body and its surroundings; on the other hand, biomedical devices and sensors interact through an open wireless channel, making them exposed to a range of cyber threats. However, WBANs are a heterogeneous-based system; heterogeneous cryptography is necessary, in which the transmitter and receiver can employ different types of public key cryptography. This article proposes an improved and efficient heterogeneous authentication scheme with a conditional privacy-preserving strategy that provides secure communication in WBANs. In the proposed scheme, we employed certificateless cryptography on the client side and Identity-Based Cryptography on the receiver side. The proposed scheme employs Hyperelliptic Curve Cryptography (HECC), a more advanced variation of Elliptic Curve Cryptography (ECC). HECC achieves the same level of security with a smaller key size and a more efficient approach than its counterpart methods. The proposed scheme not only meets the security and privacy standards of WBANs but also enhances efficiency in terms of computation and communication costs, according to the findings of the security and performance analysis.

A Conditional Privacy Preserving Generalized Ring Signcryption Scheme for Micro Aerial Vehicles.

Micro Aerial Vehicles (MAVs) are a type of UAV that are both small and fully autonomous, making them ideal for both civilian and military applications. Modern MAVs can hover and navigate while carrying several sensors, operate over long distances, and send data to a portable base station. Despite their many benefits, MAVs often encounter obstacles due to limitations in the embedded system (such as memory, processing power, energy, etc.). Due to these obstacles and the use of open wireless communication channels, MAVs are vulnerable to a variety of cyber-physical attacks. Consequently, MAVs cannot execute complex cryptographic algorithms due to their limited computing power. In light of these considerations, this article proposes a conditional privacy-preserving generalized ring signcryption scheme for MAVs using an identity-based cryptosystem. Elliptic Curve Cryptography (ECC), with a key size of 160 bits, is used in the proposed scheme. The proposed scheme's security robustness has been analyzed using the Random Oracle Model (ROM), a formal security evaluation method. The proposed scheme is also compared in terms of computation cost, communication cost and memory overhead against relevant existing schemes. The total computation cost of the proposed scheme is 7.76 ms, which is 8.14%, 5.20%, and 11.40% schemes. The results show that the proposed scheme is both efficient and secure, proving its viability.