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The concept of frame is a generalisation of the concept of category of topological space open subsets. As a result, each frame acts as an open set in this context and the Pythagorean fuzzy sets is defined as a frame. The primary goal of this research unit is to investigate the behaviour of Pythagorean fuzzy frames. Pythagorean fuzzy structure space is defined using Pythagorean fuzzy frames. Pythagorean fuzzy closed sets, Pythagorean fuzzy dense set, Pythagorean fuzzy nowhere dense set, Pythagorean fuzzy somewhere dense set is established in order to investigate the Pythagorean fuzzy frames defined in Pythagorean fuzzy structure space. Further, Pythagorean fuzzy continuous function is explored in this manuscript. Separation axioms of the Pythagorean fuzzy structure space is established in order to comprehend the Pythagorean fuzzy frame. Additionally Pythagorean fuzzy fraction dense space and Pythagorean fuzzy space is defined and explored to examine the behaviour of defined Pythagorean fuzzy frames.

An important part of customer relationship management (CRM) is being able to read emails for emotional cues; this helps with both communication and keeping customers satisfied. This study aims to improve email emotion identification by creating a system combining visual clues, aural signals, and textual information. To analyze text and emoji, the system uses advanced affective computing techniques such as Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa), Convolutional Neural Networks (CNN) for images, Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM) for video, and Cross-Modal BERT for audio. Together, they enable a wider variety of emotional signals to be extracted and understood from email content, yielding more insightful results than text-based analysis could on its own. By facilitating better two-way communication and customer satisfaction, the technology intends to improve CRM by providing actionable information that firms can use to personalize responses. This research delves into the possible uses of multi-modal emotional analysis across different businesses dealing with customers and builds a strong foundation.

Magnesium-based matrix composites' attention has recently increased significantly in various engineering applications due to their high strength-to-weight ratio, good solidification, and good mechanical properties. During the fabrication process, oxidation and porosity was the main drawback and led to reduced composites' mechanical properties. Based on this, the AZ31B grade magnesium alloy composite was prepared with different weight fractions (0, 3, 5, 7, and 9 wt%) of zirconia nanoparticles (ZrO2) through a liquid state stir cast process under an inert argon atmosphere. The effect of inert atmosphere-operated ZrO2 on the microstructural and mechanical properties of AZ31 alloy nanocomposites was studied. The surface morphology of the developed composite showed homogenous particle distribution with a porous free structure. As a result, low porosity level (less than 1%) and enriched mechanical properties of composite and composite contained 5 wt% ZrO2 facilitate maximum yield, tensile, and impact strength of 208±2 MPa, 276±1.5 MPa, and 5.8±0.5J, respectively. However, the higher content of ZrO2 in AZ31B alloy offered a high hardness value of 74±1HV. The optimum results composite sample 3 (AZ31B/5 wt% ZrO2) is recommended for automotive roof frame application.

Since blockchain platforms still depend on classical cryptographic protocols, they become more and more vulnerable to the rapid advancement of quantum computing. However, the emergence of quantum attacks has placed the need to develop Quantum Key Distribution (QKD) protocols that can preserve security while eliminating the limitations of quantum information systems, such as noise and error mitigation. To address these needs, this study proposes a novel Hybrid Rainbow-Kyber QKD (HRK-QKD) Protocol, which uses the strength of multivariate quadratic equations in Rainbow to mask the classical keys and the efficiency of lattice-based encryption in Kyber for key encryption. An entanglement-assisted dynamic key synthesis protocol that combines matrix-based quantum noise filtering, lattice-based multi-dimensional transformations and adaptive private key rotations is utilized. The proposed methods provide real-time mitigation of quantum noise and minimal performance overhead for key generation. HRK-QKD achieves the highest scalability ratio ( S c = 2.7 ), the best noise resilience (0.90-0.99), and the highest quantum security measure ( Q S = 0.064881 ) with minimal information leakage probability (0.00001). This advancement also means blockchain remains a resilient technology against quantum threats, with an economical, scalable, and high-accuracy solution for next-generation secure communication systems.

This research proposes a learning-based efficient single-image dehazing method. Dehazing, discriminator, and fine-tuning networks build the end-to-end network model. These three techniques are independently trained on suitable datasets. An end-to-end network architecture improves dehazing. The dehazing network model estimates transmission map, atmospheric light, and parallel convolution layers to analyze the input hazy image. The discrimination network extracted a discriminated dehazing image. Finally, discriminator network model findings are used for fine-tuning. The suggested model is tested using foggy images from various datasets and performance measures including PSNR, SSIM, MSE, and Entropy. The suggested learning-based image dehazing is compared to existing approaches qualitatively and quantitatively. The suggested approach improves PSNR by 34.3% to 3.65% over previous works. The proposed work has a 24.9% higher average SSIM and a 76% lower MSE than current efforts. The entropy of the proposed work is improved by a maximum of 9.38%.

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events. The early diagnosis of DR plays a significant role in preventing CVD. However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. The proposed work introduces Improvised Deep Belief Network named I-DBN to resolve the above mentioned problems and mainly to concentrate on improving the entire performance of the model leading to the unbiased output. We used Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) algorithm for feature extraction and selection respectively. Five performance metrics have been used to assess the proposed model. The results of I-DBN outperform other state-of-the-art methods. The result validation ensures that I-DBN can deliver trustworthy recommendations to doctors to treat the patients by enhancing the accuracy of CVD prediction up to 98.95%.

Multifunctional materials have garnered significant attention in the modern electronics and energy storage fields because of their diverse properties and wide range of applications. In this study, we synthesized TiO2/Ag3VO4 hybrid composite materials that possess dual functionality, enabling them to perform multiple tasks or exhibit distinct behaviors simultaneously. Herein, we synthesized TiO2/Ag3VO4 at different weight percentages of Ag3VO4, namely 0%, 10%, 15%, and 20% Ag3VO4-incorporated TiO2 hybrid composite material, by a facile hydrothermal approach. A mixture of anatase and rutile crystalline phases of TiO2 and the cubic phase of Ag3VO4 was confirmed by powder x-ray diffraction (XRD) spectra. The direct band gap of pristine TiO2 (3.15 eV) was markedly decreased to 2.41 eV for the 20% Ag3VO4-incorporated TiO2 sample. Scanning electron microscopy (SEM) results for the composite demonstrate that the appropriate amount of Ag3VO4 effectively disperses the sheets and prevents the aggregation of TiO2 nanospheres. The elemental composition and stoichiometric ratios of hybrid samples were analyzed using energy-dispersive x-ray analysis (EDAX). Current–voltage (I–V) characteristic analysis showed that the sensing parameters of the TiO2 thin films were improved by Ag3VO4 incorporation. The highest conductivity and carrier concentration were achieved with 20% Ag3VO4-incorporated TiO2, with values of 4.753 × 103 S/cm and 7.46 × 1019 cm−3, respectively. For solar energy conversion applications, the efficiency of TiO2/Ag3VO4 as a photoanode in dye-sensitized solar cells (DSSCs) was investigated. Interestingly, 20% Ag3VO4-incorporated TiO2 exhibited improved photo-conversion efficiency of 8.76% relative to pristine TiO2 and other hybrid samples. This improved performance of the TiO2/Ag3VO4 hybrid material in terms of photo-detection and photoanode behavior can be attributed to the synergistic effect between the two components, which leads to enhanced light harvesting properties. The valuable insights from our study can guide the design of materials for photosensors and photoanodes, with broad implications for the energy storage and electronics industries. These findings hold potential benefits for diverse applications in these fields.

The demand for automated music organization and the ever-increasing volume of digital audio recordings has both contributed to a surge in interest in deep learning-based genre classification. The purpose of this research is to examine the feasibility of using CNNs and RNNs, two types of deep learning architectures, for the task of audio track genre classification. The proposed models aim to achieve high accuracy and robustness in genre classification tasks by leveraging features extracted from raw audio signals and spectrogram representations. A comprehensive dataset comprising diverse music genres is utilized for training and evaluation, with performance metrics such as accuracy, precision, and recall assessed to ensure reliability. The results demonstrate that deep learning approaches significantly outperform traditional methods, providing insights into the underlying characteristics of musical styles. Potentially useful in areas such as music discovery platforms, playlist creation, and recommendation services, this study adds to the body of knowledge on automated music classification systems.

Image inpainting can be used to fix broken images and get rid of distracting elements. In exemplar based methods, patch priority computation and exemplar patch selection are crucial to the success of image inpainting technique. The dropping effect occurred in the highest patch priority computation and matching error in the best patch selection are the major issues in the exemplar inpaint methods. In this paper, the enhanced priority calculation technique is employed to avoid the dropping effect and introduced the new similarity measuring process, Mean Squared Error (MSD). The efficacy of the proposed techniques is estimated by comparing with the available methods in the literature qualitatively. It shows that proposed methods outperformed existing techniques.