Quantum computing poses a fundamental threat to the classical cryptographic mechanisms that underpin contemporary cloud security, motivating an urgent transition toward post-quantum solutions. This study introduces Quantum-Lattice Synergy (QLS), a next-generation security blueprint that integrates the computational hardness of lattice-based cryptography with the information-theoretic guarantees of quantum cryptographic primitives. QLS is a mix of Learning with Errors (LWE)-based encryption, quantum key distribution (QKD), and quantum-generated randomness to offer robust and scalable with resilience to future protection of cloud infrastructures. The architecture consists of three modules which are closely interrelated, namely, QLS-KG to generate quantum-lattice hybrid keys, QLS-Enc for lattice-based encryption with quantum-derived keys, and QLS-Auth to achieve secure authentication with quantum hashing and lattice trapdoors. Through rigorous mathematical modelling, security proofs and complexity analysis, it has been demonstrated that QLS provides a dual-layered defence which provides, at the computational level, computational security based on hard lattice problems, and at the physical level, physical security based on quantum mechanics. It has been experimentally shown that QLS is more efficient in key generation, encryption, and decryption compared to the state-of-the-art post-quantum protocols, such as LWE-based encryption, NTRU Encrypt, and Kyber512, depending on the quantum-assisted keying and parameterizing lattices, and achieving the same level of classical symmetric-key security at 256 bits. This characterization of security is not intended to mean an AES-256 baseline or to exclude the ability of existent schemes to meet similar security levels in the right configurations, it simply indicates that QLS has a strength in its hybrid, defence-in-depth architecture. QLS is created in a way that it can be deployed in the context of existing cloud facilities, which tackles durability of confidentiality, future economics, scalability and reliability of trust during the post-quantum era.

Quantum key distribution (QKD) entanglement-based solutions have become effective solutions for providing security to communications owing to the increased challenges posed by quantum computing. Entanglement-based QKD protocols are especially suitable for deployment over existing metropolitan optical fibre infrastructure, particularly those that provide device-independent security. Nonetheless, their implementation is strongly hindered by environmental noise, decoherence by optical fibres, and interference by co-existent classical data traffic. This research paper provides an overall experimental report on the noise resistance of entanglement-based QKD, in this case, the BBM92 protocol of a metropolitan fibre network. To solve practical problems, this study presents a hybrid model with three adaptive algorithms: the Adaptive entanglement purification protocol (AEPP), noise-resilient key reconciliation algorithm (NRKRA), and optimised privacy amplification technique (OPAT). The AEPP constantly checks the entanglement fidelity and enhances it through the purification of pairs of photons, depending on the real-time channel conditions. The NRKRA builds upon efficient key reconciliation by dynamically using error correction coded to optimise the observed quantum bit error rate (QBER). OPAT also enhances system security because it can handle privacy amplification based on the degree of information leakage. Experimental findings indicate that such a combined method is able to produce low QBER, high secret key rates with improved entanglement fidelity, even in the fibre links of up to 50 km length and in the presence of environmental experiments. This work has shown that an adaptable and resilient structure of this kind goes far beyond the conceptual frameworks of QKD modelling and real-world applications to provide insights on the necessary performance standards and to provide viable implementation guidelines to large-scale quantum cryptographic systems in the future.

Breast cancer is a life-threatening disease that is very common among women in the world. The early and correct diagnosis is necessary to enhance the rate of survival and treatment. The conventional techniques such as mammography, ultrasound and MRI usually fail to differentiate between the benign and malignancy tumors in the presence of limited resources, particularly in the resource-poor environments. This paper presents a new methodology of breast cancer classification, Shape-Aware Angular Feature Learning (SAAFL), that merges machine and deep learning methods. We propose a method of Speckle-Reducing Anisotropic Diffusion (SRAD) filters to improve the quality of ultrasound images by reducing the number of speckle noise and maintaining the edges of the tumors. Our segmentation approach is robust, which is RBBSAM-RSF, using which we detect tumors automatically and then process the data with Angular Feature (AF) analysis in order to determine the specific features of the lesions. The hierarchical classification system combines the AF extraction and classifiers like Support Vector Machines (SVM) and Backpropagation Artificial Neural Networks (BPANN) to enhance the accuracy of the diagnostic. On 1293 breast ultrasound (BUS) images, our model manages to attain 95.38 % accuracy, which is better than the traditional texture-based and morphological models. This reduces false positives and unnecessary biopsies, making it suitable for near-real-time deployment in low-resource clinical settings without specialized hardware. By selectively integrating deep learning-assisted segmentation with shape-aware angular feature analysis and supervised machine learning, SAAFL advances noninvasive and interpretable breast cancer diagnostics.

Image steganography often faces the issue of noticeable embedding patterns, which render conventional LSB-based techniques vulnerable to both statistical and machine-learning steganalysis. The present research offers a solution to this problem by presenting a Secure Random Pixel Distribution (SRPD) framework that integrates AES-based cryptographic randomization along with a lightweight module LSB approach to make pixel changes unpredictable and with minimal distortion at the same time. The technique offers the option of encrypted coordinates, which can be used for either embedding or external deployment, providing flexibility for various deployment situations. Experimental testing on ten benchmark images reveals the excellent performance of SRPD, with PSNR values ranging from 70 to 85 dB, SSIM scores exceeding 0.998, and MSE values as low as 0.0002. The security check through chi-square and RS analysis provides p-values greater than 0.85 and RS deviations less than 0.03%, demonstrating that SRPD is still "NOT DETECTED" by conventional steganalytic methods. Furthermore, the method's linear computational complexity is accompanied by a very low overhead from AES encryption. In summary, SRPD is a steganographic system that is secure, imperceptible, and cost-effective in terms of computational resources; thus, it can be used for secret communication and privacy-preserving multimedia applications.

Navigating complex environments poses a significant challenge for visually impaired individuals who often rely on traditional aids such as guide dogs and white canes. This aids in the lack of real-time, precise spatial feedback. This paper proposes an advanced place recognition system that utilizes YOLO-based object detection and 3D audio feedback to enhance spatial awareness. The system provides a portable camera and deep-learning algorithms to detect nearby objects and generate spatialized audio cues that guide users safely. The results show an improvement in object recognition accuracy, with the Enhanced YOLO with Attention model achieving 91

Anomaly detection is a critical task in various domains, such as cybersecurity, healthcare, and finance, where identifying rare and irregular patterns is essential. Traditional methods often struggle with imbalanced data, high dimensionality, and temporal dependencies, limiting their applicability in real-world scenarios. This study aims to develop a robust and scalable hybrid anomaly detection framework that effectively handles the complexities of multidomain data and improves detection accuracy and efficiency. We propose a novel hybrid model that integrates three complementary components: Isolation Forest for efficient outlier partitioning, autoencoder for latent feature extraction and reconstruction error analysis, and ConvLSTM for capturing spatial–temporal patterns in sequential data. The model was evaluated on diverse datasets, including synthetic Gaussian data, KDD Cup 1999 (network intrusion), credit card fraud detection, and breast cancer (healthcare), along with additional IoT and streaming datasets. A comprehensive preprocessing pipeline and hyperparameter optimisation strategy were employed to enhance performance. The proposed model achieved up to 99.5

The communal impact of Li+ and Mg2+ ions on the photoluminescent characteristics, optical thermometry attributes, and anti-counterfeiting potential of CaWO4: Er3+/Yb3+ phosphors. Infusing rare-earth ions Er3+/Yb3+ into calcium tungstate, CaWO4, exhibits considerable promise for optoelectronic applications. Nevertheless, the augmentation of alkali and alkaline earth metal ions represents a strategic avenue to elevate the performance and versatility of these phosphors. A series of CaWO4:Er3+/Yb3+ co-doped phosphors, incorporating Li+ and Mg2+ ions, was successfully synthesized via a high-temperature solid-state reaction method. Structural and morphological evaluations were studied with XRD & FE-SEM, and elemental analysis through energy-dispersive X-ray spectroscopy (EDS). Photoluminescence spectroscopy was employed to scrutinize the luminescent attributes of the phosphors. Our findings underscore a profound influence of Li+ and Mg2+ ions on the luminescence properties of CaWO4: Er3+/Yb3+ phosphors. We recorded excellent temperature-dependent upconversion, indicating the potential of these phosphors for optical thermometry applications. Additionally, the optimized phosphor sample demonstrated efficacy in latent fingerprint detection and security ink applications. These revelations offer crucial insights into conceptualizing and fabricating innovative luminescent materials, with implications spanning a broad spectrum of optical temperature sensing and security-oriented endeavors.

Virtual Reality (VR) technology offers a scalable and cost-effective approach to overcome the challenges of traditional medical training, including high expenses, limited resources, and ethical issues. This study presents a VR-based medical training platform that combines high-fidelity 3D models, real-time haptic feedback, and AI-driven adaptive learning to deliver interactive and immersive instructions for a wide range of medical procedures, from basic tasks to complex surgeries. In a randomized controlled trial, participants trained with the VR platform demonstrated significant gains over those using conventional methods, with a 42% improvement in procedural accuracy, a 38% reduction in training time, and better skill retention. The VR system also led to a 45% decrease in error rates and a 48% increase in trainee confidence. These findings highlight the platform's capacity to personalize training according to individual performance, resulting in superior learning outcomes and enhanced procedural skills. By providing consistent, standardized, and immersive learning experiences, the platform effectively bridges the gap between theory and practice, representing an innovative and scalable advancement in medical education.

In this manuscript, GdF3:Ho3+/Yb3+upconversion phosphor particles have been prepared via the chemical co-precipitation method. The concentration of Ho3+ and Yb3+ dopants have been fixed while the components Zn2+ and Ca2+ ions concentration varied with the difference of 5 mol

Greenhouses are controlled area environment to grow plants. As the limitation of existing greenhouse plants is that it is not operated automatically and has to be operated manually with different records. In order to achieve the optimum growth of plants, the continuous monitoring and controlling of environmental parameters such as temperature, humidity, soil moisture, light intensity etc. are necessary for our greenhouse system. This paper demonstrates a checking and control system for nursery through Internet of Things (IOT). The system will screen the undeniable common conditions, for instance, moistness, soil immersion, temperature, closeness of fire, etc. All the environment parameters data are sent to cloud using WiFi module NodeMCU esp8266. If any condition crosses certain limits related actuator will be turned ON. The microcontroller will as such turn on the motor if the earth stickiness isn't generally a particular worth. The user can screen and control parameters through mobile and computer. The model was attempted under various blends of obligations to our examination office and the test outcomes were found as expected. Keywords: 1.Arduino IDE program software. 2.Development board Arduino uno 3.Communication devices. 4. Sensors. 5. Watering devices.

This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89 a_i, b_i via quantum sensor data, encoding variables like soil moisture into rotation gates R_y ( π s_ij ) with < 2

This paper explores the integration of blockchain technology and smart contracts in the development of nextgeneration digital identity solutions. As the demand for secure, privacy-preserving, and user-centric identity management systems increases, blockchain and smart contracts offer a promising framework that enhances transparency, automation, and user control. We outline the methodology employed to assess the effectiveness of blockchain and smart contracts in digital identity management, focusing on aspects such as security, interoperability, and user empowerment. Through comprehensive data analysis, we present the results of our study, demonstrating the potential benefits and challenges associated with implementing blockchain-based identity systems augmented by smart contracts. Our findings contribute to the ongoing discourse on digital identity and provide insights for future research and practical applications.

Online shops use recommendation systems to show users products they might like based on their interests and actions. This makes people more interested and happier, and helps businesses make more money. There are different types of recommendation systems, like content-based, collaborative filtering, and hybrid systems. Content-based systems suggest similar things based on product features, while collaborative filtering systems suggest items based on the preferences of similar users. Hybrid systems use both methods for better suggestions. To create an effective recommendation system, businesses need to collect and analyze user data, such as browsing history, purchase history, and search queries, as well as consider factors like user demographics, item popularity, and seasonality. Evaluation of recommendation systems can be done through various metrics, including accuracy, diversity, novelty, and serendipity, which measure the system's ability to predict user preferences, recommend a variety of items, suggest new items to users, and recommend interesting but unexpected items. The deployment of recommendation systems in e-commerce platforms brings several benefits, including increased user engagement and customer loyalty, improved user experience, and increased revenue through cross-selling and upselling. However, there are also challenges and ethical concerns associated with recommendation systems, such as the potential for reinforcing biases and discrimination. Hence, it's crucial for businesses to ensure that their recommendation systems are fair and inclusive by eliminating any biases in the data and algorithm.

This research presents an innovative approach to revolutionize IoT service development in medical education, specifically designed to empower individuals with physical disabilities. By integrating digital twin technology, we offer dynamic virtual representations of tangible assets, facilitating real-time simulation, monitoring, and feedback. A unique visual response algorithm has been developed to enhance the processing of visual vector data, resulting in a more efficient IoT service development process. Our method demonstrates superior performance over traditional techniques, particularly in achieving higher intrinsic variable merging values, which is critical for accurate and accessible visualization. The practical applications of this technology are highlighted through case studies that demonstrate how physically disabled students can benefit from interactive and immersive educational experiences. For instance, students can engage with the digital twins of medical equipment, allowing them to practice procedures and gain hands-on experience in a virtual environment without physical barriers. This approach not only improves accessibility but also personalizes learning experiences, adapting to the unique needs of each student. The research underscores the importance of inclusive design in developing IoT services, ensuring higher inclusivity rates and addressing diverse learning patterns. The findings suggest that the integration of IoT and digital twin technologies can significantly enhance medical education, making it more accessible, effective, and inclusive for physically disabled individuals. This study lays the groundwork for future advancements in this field, highlighting the potential for ongoing technological innovations to further transform medical education.

This research paper explores the pioneering role of augmented reality (AR) and virtual reality (VR) in reshaping medical education within the metaverse, focusing particularly on their remarkable benefits for individuals with disabilities. This research examines how these immersive technologies can be customized to meet the unique needs of those with disabilities, including those with mobility. It demonstrates how AR and VR enable these individuals to actively participate in medical simulations, offering them a deeper understanding of intricate medical procedures. This article highlights the critical importance of ethical considerations, privacy measures, and adherence to accessibility standards in the deployment of AR and VR in medical training and a robust framework for harnessing the transformative capabilities of AR and VR in medical health education. It delves into the various ways in which AR and VR facilitate experiential learning, providing an immersive, hands-on approach to medical education. This research highlights the role of AR and VR in supporting remote diagnostics and mental health services, showcasing their capability to enhance doctor–patient interactions and support. This article represents that AR and VR in the metaverse have the potential to empower individuals with disabilities, leading to more inclusive and effective medical training.

Virtual reality (VR) used in rehabilitation has the potential to enhance the quality of life for individuals with various medical conditions. As a result of this novel approach, there has been an increase in the number of individuals who are now giving their attention and actively engaging in rehabilitation programmes. This study aims to assess the effectiveness and advantages of virtual reality-based rehabilitation programmes in comparison to traditional educational methods for enhancing and strengthening talents. The creative capacity of VR was assessed through a study involving 50 participants who are going through regular traditional therepy methods. Virtual reality therapy enhances cognitive functions. As a result of the changes, there was a 30-40% increase in growth using proposed mathamatical model compared to traditional methods. The study revealed that the use of virtual reality-based personalised rehabilitation resulted in enhanced cognitive function and improved retention of knowledge among the participants.

An innovative methodology for predicting obesity levels has been devised by leveraging advanced Machine Learning techniques, specifically logistic regression, for forecasting obesity levels using a diverse dataset inclusive of demographic details, dietary patterns, and physical activity levels. Logistic regression, chosen for its well-established and interpretable nature, serves as the algorithm to model the correlation between input features and the likelihood of obesity occurrence. Meticulously selected variables within the dataset capture pivotal factors influencing obesity, enabling logistic regression to estimate an individual's probability of belonging to the obese category.

Multimedia tools and immersive technology have enormous potential for actively engaging students and improving learning quality. This research aims into the use of virtual reality (VR) in physical training and sports rigorous training to give students a new perspective on college training and educational training, as well as to improve the professional level and training excellence of college athletes. We present a revolutionary Virtual Reality-based Physical Training (VR-PT) technology for effective virtual reality instruction on a digital application. The semi-supervised learning framework was used to implement movement inputs and interactive reality methods. Virtual simulation and differentiated selection techniques based on Q statistical data are used first to identify sports students with excellent independent learning capacity, followed by the classifier's neighbour reliability. Experiment results indicate that this strategy can successfully support physical training practices while also improving students' learning performance. The effectiveness of students in athletics has increased by 30%. Simultaneously, two-thirds of people believe their involvement in athletic training has increased by 80%, and 90% of college coaches believe that using VR technology throughout physical training is extremely important for improving the technical aspect as well as the reliability of college sports training.