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The increasing volumes and sophistication of cyber threats, particularly Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks, pose significant dangers to contemporary network structures, particularly the Internet of Things (IoT) environment. Conventional Intrusion Detection Systems (IDS) are also becoming obsolete because they perform detection in a built-in manner and are unable to capture the time trends of dynamic changes of threats. To eliminate such shortcomings, a new hybrid deep learning architecture named the Neural Turing Machine-Gated Recurrent Unit (NTM-GRU) model is proposed in this paper that incorporates the external memory of NTMs and extra temporal learning power of GRUs. The architecture supports analysis on dual timescales, which in turn captures short- and long-term dependencies, exposing the model to unravel complex, low, slow, and zero-day intrusions with recall. Huge testing on the standard sets (UNSW-NB15 and BoT-IoT) and actual (CICIDS2017 and CSE-CID-IS2018 ) demonstrate the high effectiveness of the usage of the model, reaching an accuracy of 99.98%, F1-scores of up to 96% on unknown threats, and the low false positive rates (less than 0.4%). The proposed framework can be applied in both industrial settings and high-speed network settings, where the real-time inference speed was measured at 2.3 milliseconds. The model also incorporates interpretability aspects, making it suitable for Security Operation Centres (SOCs). This work, through the merger of complex memory neural-network structures with cybersecurity needs and requirements encountered in the world, can be realized as providing a scalable, adaptive, and interpretable intrusion detection module, establishing a new state-of-the-art standard for securing next-generation networks.

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model’s efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model’s performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives.

In the past few years, there have been many advancements in the field of Generative Adversarial Networks (GANs). The paper talks about the various types of GANs developed along with focusing on one specific application of generating a human face using the given text description which is a less-explored area. GANs are a class of machine learning models designed for generative tasks, such as creating realistic images, music, or text. GANs are a powerful tool in the field of deep learning. This paper explains StackGAN, AttentionalGAN, MirrorGAN, CycleGAN, etc. Beyond this, the paper also comprises the various embedding techniques, their advantage, models, and disadvantages. Through this paper, we also got insight into how to improve the performance of models just by improving the embeddings or by pre-training the models in the case of MirrorGAN.

Preserving privacy is a critical concern in medical imaging, especially in resource limited settings like smart devices connected to the IoT. To address this, a novel encryption method for medical images that operates at the bit plane level, tailored for IoT environments, is developed. The approach initializes by processing the original image through the Secure Hash Algorithm (SHA) to derive the initial conditions for the Chen chaotic map. Using the Chen chaotic system, three random number vectors are generated. The first two vectors are employed to shuffle each bit plane of the plaintext image, rearranging rows and columns. The third vector is used to create a random matrix, which further diffuses the permuted bit planes. Finally, the bit planes are combined to produce the ciphertext image. For further security enhancement, this ciphertext is embedded into a carrier image, resulting in a visually secured output. To evaluate the effectiveness of our algorithm, various tests are conducted, including correlation coefficient analysis ( . < or negative), histogram analysis, key space [(10 ) ] and sensitivity assessments, entropy evaluation [ ( ) > 7.98], and occlusion analysis. Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.

The classification of brain tumors from medical imaging is pivotal for accurate medical diagnosis but remains challenging due to the intricate morphologies of tumors and the precision required. Existing methodologies, including manual MRI evaluations and computer-assisted systems, primarily utilize conventional machine learning and pre-trained deep learning models. These systems often suffer from overfitting due to modest medical imaging datasets and exhibit limited generalizability on unseen data, alongside substantial computational demands that hinder real-time application. To enhance diagnostic accuracy and reliability, this research introduces an advanced model utilizing the Xception architecture, enriched with additional batch normalization and dropout layers to mitigate overfitting. This model is further refined by leveraging large-scale data through transfer learning and employing a customized dense layer setup tailored to effectively distinguish between meningioma, glioma, and pituitary tumor categories. This hybrid method not only capitalizes on the strengths of pre-trained network features but also adapts specific training to a targeted dataset, thereby improving the generalization capacity of the model across different imaging conditions. Demonstrating an important improvement in diagnostic performance, the proposed model achieves a classification accuracy of 98.039% on the test dataset, with precision and recall rates above 96% for all categories. These results underscore the possibility of the model as a reliable diagnostic tool in clinical settings, significantly surpassing existing diagnostic protocols for brain tumors.

Retinal vessel segmentation is a critical task in fundus image analysis, providing essential insights for diagnosing various retinal diseases. In recent years, deep learning (DL) techniques, particularly Generative Adversarial Networks (GANs), have garnered significant attention for their potential to enhance medical image analysis. This paper presents a novel approach for retinal vessel segmentation by harnessing the capabilities of GANs. Our method, termed GANVesselNet, employs a specialized GAN architecture tailored to the intricacies of retinal vessel structures. In GANVesselNet, a dual-path network architecture is employed, featuring an Auto Encoder-Decoder (AED) pathway and a UNet-inspired pathway. This unique combination enables the network to efficiently capture multi-scale contextual information, improving the accuracy of vessel segmentation. Through extensive experimentation on publicly available retinal datasets, including STARE and DRIVE, GANVesselNet demonstrates remarkable performance compared to traditional methods and state-of-the-art deep learning approaches. The proposed GANVesselNet exhibits superior sensitivity (0.8174), specificity (0.9862), and accuracy (0.9827) in segmenting retinal vessels on the STARE dataset, and achieves commendable results on the DRIVE dataset with sensitivity (0.7834), specificity (0.9846), and accuracy (0.9709). Notably, GANVesselNet achieves remarkable performance on previously unseen data, underscoring its potential for real-world clinical applications. Furthermore, we present qualitative visualizations of the generated vessel segmentations, illustrating the network’s proficiency in accurately delineating retinal vessels. In summary, this paper introduces GANVesselNet, a novel and powerful approach for retinal vessel segmentation. By capitalizing on the advanced capabilities of GANs and incorporating a tailored network architecture, GANVesselNet offers a quantum leap in retinal vessel segmentation accuracy, opening new avenues for enhanced fundus image analysis and improved clinical decision-making.

Stuck valve is a very rare and severe complication that occurs in mechanical valve replacement patients with ineffective anticoagulation. However, with COVID-19 restriction measures, it became challenging to regularly assess INR to make sure it falls within the target therapeutic range to prevent this complication. We present a series of 10 patients who either underwent transthoracic echocardiography for a suspected stuck valve or were seen at the outpatient valve clinic with the residual consequences of a stuck valve during the COVID-19 restriction measures in our institute. Stuck prosthetic valves incident has increased significantly during this period, particularly those in the mitral position for which urgent replacement and prolonged hospitalization were necessary. Particularly with the COVID-19 restrictions in place, these cases highlight the need for physicians to be aware of the dramatic increase in the incidence of stuck prosthetic valves in patients on chronic warfarin therapy.

Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing. To address these concerns, this paper presents the mathematical and computer modeling of a novel two-dimensional (2D) chaotic system for secure key generation in quantum image encryption (QIE). The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence, named as Trigonometric-Rational-Saturation (TRS) map. Through rigorous mathematical analysis and computational simulations, the map is extensively evaluated for bifurcation behaviour, chaotic trajectories, and Lyapunov exponents. The security evaluation validates the map’s non-linearity, unpredictability, and sensitive dependence on initial conditions. In addition, the proposed TRS map has further been tested by integrating it in a QIE scheme. The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation (NEQR) technique, the TRS map is used for the generation of secure diffusion key, which is XOR-ed with the quantum-ready image to obtain the encrypted images. The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks. The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62% and 33.47% results for Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI). The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats, making it suitable for secure image encryption in IoT and edge-based applications.

The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network.