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In the modern era, researchers have focused a great deal of effort on multimedia security and fast processing to address computational processing time difficulties. Due to limited battery capacity and storage, Unmanned Aerial Vehicles (UAVs) must use energy-efficient processing. In order to overcome the vulnerability of time inefficiency and provide an appropriate degree of security for digital images, this paper proposes a new encryption system based on the bit-plane extraction method, chaos theory, and Discrete Wavelet Transform (DWT). Using confusion and diffusion processes, chaos theory is used to modify image pixels. In contrast, bit-plane extraction and DWT are employed to reduce the processing time required for encryption. Multiple cyberattack analysis, including noise and cropping attacks, are performed by adding random noise to the ciphertext image in order to determine the proposed encryption scheme’s resistance to such attacks. In addition, a variety of statistical security analyses, including entropy, contrast, energy, correlation, peak signal-to-noise ratio (PSNR), and mean square error (MSE), are performed to evaluate the security of the proposed encryption system. Moreover, a comparison is made between the statistical security analysis of the proposed encryption scheme and the existing work to demonstrate that the suggested encryption scheme is better to the existing ones.

Background The spread of misinformation of all types threatens people’s safety and interrupts resolutions. COVID-19 vaccination has been a widely discussed topic on social media platforms with numerous misleading and fallacious information. This false information has a critical impact on the safety of society as it prevents many people from taking the vaccine, decelerating the world’s ability to go back to normal. Therefore, it is vital to analyze the content shared on social media platforms, detect misinformation, identify aspects of misinformation, and efficiently represent related statistics to combat the spread of misleading information about the vaccine. This paper aims to support stakeholders in decision-making by providing solid and current insights into the spatiotemporal progression of the common misinformation aspects of the various available vaccines. Methods Approximately 3800 tweets were annotated into four expert-verified aspects of vaccine misinformation obtained from reliable medical resources. Next, an Aspect-based Misinformation Analysis Framework was designed using the Light Gradient Boosting Machine (LightGBM) model, which is one of the most advanced, fast, and efficient machine learning models to date. Based on this dataset, spatiotemporal statistical analysis was performed to infer insights into the progression of aspects of vaccine misinformation among the public. Finally, the Pearson correlation coefficient and p-values are calculated for the global misinformation count against the vaccination counts of 43 countries from December 2020 until July 2021. Results The optimized classification per class (i.e., per an aspect of misinformation) accuracy was 87.4%, 92.7%, 80.1%, and 82.5% for the “Vaccine Constituent,” “Adverse Effects,” “Agenda,” “Efficacy and Clinical Trials” aspects, respectively. The model achieved an Area Under the ROC Curve (AUC) of 90.3% and 89.6% for validation and testing, respectively, which indicates the reliability of the proposed framework in detecting aspects of vaccine misinformation on Twitter. The correlation analysis shows that 37% of the countries addressed in this study were negatively affected by the spread of misinformation on Twitter resulting in reduced number of administered vaccines during the same timeframe. Conclusions Twitter is a rich source of insight on the progression of vaccine misinformation among the public. Machine Learning models, such as LightGBM, are efficient for multi-class classification and proved reliable in classifying vaccine misinformation aspects even with limited samples in social media datasets.

In the modern era, researchers have focused a great deal of effort on multimedia security and fast processing to address computational processing time difficulties. Due to limited battery capacity and storage, Unmanned Aerial Vehicles (UAVs) must use energy-efficient processing. In order to overcome the vulnerability of time inefficiency and provide an appropriate degree of security for digital images, this paper proposes a new encryption system based on the bit-plane extraction method, chaos theory, and Discrete Wavelet Transform (DWT). Using confusion and diffusion processes, chaos theory is used to modify image pixels. In contrast, bit-plane extraction and DWT are employed to reduce the processing time required for encryption. Multiple cyberattack analysis, including noise and cropping attacks, are performed by adding random noise to the ciphertext image in order to determine the proposed encryption scheme’s resistance to such attacks. In addition, a variety of statistical security analyses, including entropy, contrast, energy, correlation, peak signal-to-noise ratio (PSNR), and mean square error (MSE), are performed to evaluate the security of the proposed encryption system. Moreover, a comparison is made between the statistical security analysis of the proposed encryption scheme and the existing work to demonstrate that the suggested encryption scheme is better to the existing ones.

Arrhythmia is a fatal cardiac clinical condition that risks the lives of millions every year. It has multiple classes with variable prevalence rates. Some rare arrhythmia classes are equally critical as common ones, yet are very hard to detect due to limited training samples. While several methods accurately detect Arrhythmia's multi-class, minority class accuracy remains low and these methods are resource-intensive. Therefore, most of the existing detection systems ignore minority classes in their classification or focus on binary classification. In this study, we introduce RL-ECGNet, a resource-efficient reinforcement learning-based optimization for multi-class arrhythmia detection, encompassing minority classes, through ECG signal analysis. RL-ECGNet uses raw ECG signals, processes them to extract the temporal ECG features, and utilizes Reinforcement Learning (RL) to optimize the training and network hyperparameters of the Deep Learning (DL) models while reducing resource consumption. For evaluation, four DL models, namely, MLP, CNN, LSTM, and GRU, are trained and optimized. Moreover, time and memory usage are minimized to optimize resource consumption. Throughout the evaluation of the four DL models, the proposed RL model achieved accuracies ranging from 88.45% to 96.41% for all 9 arrhythmia classes, including minority classes. In addition, the proposed RL method improved performance by a factor ranging from 1.28 to 1.39 in terms of accuracy. Moreover, the optimized DL models had reduced training time, as well as minimized memory usage. The proposed method achieved resource consumption reduction ranging from 1.36 to 1.925 times for training time, and from 1.179 to 1.815 times for memory usage.

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications.

To improve the accuracy of malware detection on the Internet of Battlefield Things (IoBTs), a class of malware detection techniques transforms the benign and malware files into control flow graph (CFG) for better detection of malwares. In the construction process of CFG, the binary code of a file is transformed into opcodes using disassemblers. Probability CFGs are generated where vertices represent the opcodes and the edges between the opcodes represent the probability of occurrence of those opcodes in the file. Probability CFGs are fed to the deep learning model for further training and testing. The accuracy of deep learning model depends on the probability of CFGs. If the graph generation techniques reflectorize the binary file more accurately, then the result of the deep learning malware detection model is likely to be more accurate. In this research, we identify the limitations of the existing probability CFG techniques, propose a new probability CFG generation technique which is the combination of crisp and heuristic approaches called HeuCrip, and compare the proposed technique with the existing state-of-the-art schemes. The experimental results show that the HeuCrip achieved 99.93% accuracy, and show significant improvement in performance as compared to the existing state-of-the-art schemes.

The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that can be used in the future for fake news classification. Two different publicly available datasets containing political news are utilized for performing experiments. Sixteen supervised learning algorithms are used, and their results show that conventional Machine Learning (ML) algorithms that were used in the past perform better on shorter text classification. In contrast, the currently used Recurrent Neural Network (RNN) and transformer-based algorithms perform better on longer text. Additionally, a brief comparison of all these techniques is provided, and it concluded that transformers have the potential to revolutionize Natural Language Processing (NLP) methods in the near future.

Purpose - The purpose of this paper is to describe an adaptive approach for diagnosing faulty nodes in a wireless mobile environment.Design methodology approach - Based on a diagnosis approach that has been previously developed for wired networks. A discussion of the novel diagnosis protocol and its correctness is also included.Findings - In this paper, the author presents a new implementation of a failure detection service for wireless ad hoc and sensor systems that is based on an adaptation of a gossip-style failure detection protocol and the heartbeat failure detector. The authors show that the failure detector is eventually perfect - that is, there is a time after which every faulty mobile is permanently suspected by every fault-free host, and no host will be suspected before it crashes.Research limitations implications - The main limitations of this work is the lack of simulations results. Future works will be directed at providing such performance evaluations.Practical implications - The work introduced in this paper aims mainly at identifying faulty nodes in a wireless mobile environment. This service has been identified as one of the basic building blocks for dependable mobile ad hoc networks (MANETs).Originality value - The paper is first work that adapts a previously developed failure detection service to wireless and MANETs.

Purpose - The purpose of this paper is to describe a novel diagnosis approach, using neural networks (NNs), which can be used to identify faulty nodes in distributed and multiprocessor systems.Design methodology approach - Based on a literature-based study focusing on research methodology and theoretical frameworks, the conduct of an ethnographic case study is described in detail. A discussion of the reporting and analysis of the data is also included.Findings - This work shows that NNs can be used to implement a more efficient and adaptable approach for diagnosing faulty nodes in distributed systems. Simulations results indicate that the perceptron-based diagnosis is a viable addition to present diagnosis problems.Research limitations implications - This paper presents a solution for the asymmetric comparison model. For a more generalized approach that can be used for other comparison or invalidation models this approach requires a multilayer neural network.Practical implications - The extensive simulations conducted clearly showed that the perceptron-based diagnosis algorithm correctly identified all the millions of faulty situations tested. In addition, the perceptron-based diagnosis requires an off-line learning phase which does not have an impact on the diagnosis latency. This means that a fault set can be easily and rapidly identified. Simulations results showed that only few milliseconds are required to diagnose a system, hence, one can start talking about \"real-time\" diagnosis.Originality value - The paper is first work that uses NNs to solve the system-level diagnosis problem.