In an era where data security is paramount, encrypting sensitive information is ubiquitous. Traditional encryption methods often rely on complex algorithms and keys, making decryption an intricate and computationally intensive task. This paper introduces a novel approach to signal recovery and decryption, leveraging data-driven techniques, specifically Long Short-Term Memory (LSTM) neural networks. Our methodology is not limited to any specific encryption algorithm, allowing it to be applicable in various domains. We explore using LSTM networks as powerful tools for deciphering encrypted signals without prior knowledge of the encryption process. The critical insight behind our approach is the ability of LSTMs to capture intricate patterns and dependencies in the encrypted data, thus enabling the reconstruction of the original signal. We present experimental results that showcase the effectiveness of our data-driven decryption approach in a range of scenarios. This research signifies a paradigm shift in signal recovery and decryption, offering an alternative to traditional cryptanalysis techniques. By harnessing the power of data-driven modelling, we open new avenues for retrieving valuable information from encrypted signals, with potential applications in data cybersecurity and beyond.
This Chaos theory has long been a fascinating realm of study, offering insights into systems characterized by sensitivity to initial conditions and complex, unpredictable behaviour. Among these intriguing systems, the "Capsule-Shaped Equilibrium Curve Chaotic System" stands out due to its distinctive and intricate dynamics. In this paper, we present a novel approach to understanding and predicting the behaviour of this complex chaotic system through the application of Recurrent Neural Networks (RNNs). Our investigation begins with a thorough examination of the capsule-shaped equilibrium curve chaotic system, revealing its underlying principles and revealing its chaotic nature. We employ the scalability of neural networks to propose an innovative approach for predicting the temporal progression of this system. A promising avenue for modeling the dynamic behavior of chaotic systems with a high degree of precision is offered by the RNN framework, which is capable of capturing temporal dependencies. We delve into the details of our prediction methodology, including data preprocessing, network architecture, and training strategies tailored to the unique characteristics of the capsule-shaped equilibrium curve chaotic system. We conduct extensive experiments and provide quantitative evaluations of prediction precision and dependability to evaluate the predictive capabilities of our neural network-based strategy. Furthermore, this investigation contributes to the comprehension of chaotic systems and opens the door to practical applications in diverse fields, such as physics, engineering, and finance, where precise predictions of chaotic dynamics are essential for making decisions and controlling systems
KEYWORDS: Image encryption, Computer security, Medical imaging, Complex systems, Data communications, Medicine, Data storage, Network security, Digital watermarking, Data privacy
The proliferation of digital medical imaging has ushered in unprecedented advancements in healthcare diagnostics and treatment planning. However, this digital era has also raised significant concerns regarding the security and privacy of sensitive medical image data, particularly in the context of the Digital Imaging and Communications in Medicine (DICOM) standard. In response to these concerns, this paper presents a novel Chaos-Driven Encryption (CDE) system designed to fortify the confidentiality and integrity of DICOM medical images. The CDE system harnesses chaotic systems' inherent unpredictability and complexity to create a robust encryption framework. Chaos-based encryption offers a formidable defence against conventional cryptographic attacks due to its nonlinearity and sensitivity to initial conditions. We propose a specific chaotic map and key management scheme tailored for DICOM images, ensuring that the encryption process remains secure and efficient. This paper comprehensively analyzes the CDE system, including its encryption process, key generation, and decryption procedure. We assess the security of CDE through extensive cryptographic analysis, demonstrating its resistance to known attacks and vulnerabilities. Moreover, we evaluate the computational performance of CDE in terms of encryption/decryption speed and resource utilization. Our experimental results highlight the feasibility and effectiveness of Chaos-Driven Encryption in safeguarding DICOM medical images against unauthorized access and tampering. By presenting this innovative encryption system, we contribute to the ongoing dialogue on healthcare data security and privacy, offering a promising solution for the protection of sensitive medical image information in the digital age.
Background: The evolution of AI applications in dental imaging, covering caries detection, anatomical structure segmentation, and pathology identification, highlights the importance of high-quality datasets for effective detection models. This paper focuses on optimizing dataset quality for real-time AI-based dental bitewing radiograph detection.
Methods: We systematically analyze preprocessing methods suitable for dental bitewing radiographs, covering image enhancement, noise reduction, and contrast adjustment. These techniques are strategically chosen to address common challenges in dental radiograph images, including variations in lighting, contrast disparities, and noise fluctuations. We employ optimized algorithms to meet real-time constraints, ensuring efficient model training and inference.
Results: Our study assesses the impact of each preprocessing step on dataset quality and its influence on AI model performance. Practical recommendations are provided to empower researchers and practitioners in creating datasets optimized for dental bitewing radiograph detection tasks, aiming to improve AI model accuracy while adhering to real-time requirements. In addition, a comparative analysis is conducted, evaluating datasets enhanced using conventional methods against the ResNet18 model for the segmentation of bitewing dental images.
Conclusion: This paper serves as a valuable guide for the dental imaging community, offering insights into preprocessing steps that elevate dataset quality for AI-driven dental bitewing radiograph detection. By emphasizing the relevance of real-time performance and providing a comparison with conventional enhancements on the ResNet18 model, we contribute to advancing early diagnosis and enhancing oral healthcare outcomes.
Approximately 15% of the world's population faces some form of disability, with 2-4% experiencing significant challenges in using their hands and legs to meet their daily needs. This global estimate for disabilities is rising, primarily due to an aging population and the increasing prevalence of chronic diseases. Nonetheless, individuals with disabilities can still contribute as self-reliant members of society. In this paper, we present a system designed to empower people with disabilities by enabling them to independently perform daily tasks by precisely controlling their home devices using only their eye movements. The system comprises an infrared (IR) camera and a Raspberry Pi, which processes live video captured by the IR camera and performs eye-tracking tasks using the OpenCV library for Python. A microcontroller (Arduino) is linked to the home devices, enabling them to be controlled based on commands received from the Raspberry Pi.
With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enable automatic and remote sensing, processing, and execution. IoT ensures that information is easily available and accessible from any location around the world. The IoT applications improve the visibility, scalability, and cost-effectiveness of solar energy generation and service. A bibliometric analysis of scientific publications in the field of solar PV and IoT applications was conducted using the Scopus database between the years 2011 and 2023. Many studies of technological development have been discovered, and some insights can still be approached in such a way that the practical implementation of photovoltaic solar systems is improved. Since 2013, there has been an increase in the rate of publications. The majority of these studies were conducted in India, and the most common IoT applications reported were in the fields of computer science and engineering. This article identifies knowledge gaps to inform the community, industry, and government officials about IoT research directions in the solar energy field.
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