Extraction Approach Research Articles

Dynamic Japanese Sign Language Recognition Throw Hand Pose Estimation Using Effective Feature Extraction and Classification Approach.

Japanese Sign Language (JSL) is vital for communication in Japan's deaf and hard-of-hearing community. But probably because of the large number of patterns, 46 types, there is a mixture of static and dynamic, and the dynamic ones have been excluded in most studies. Few researchers have been working to develop a dynamic JSL alphabet, and their performance accuracy is unsatisfactory. We proposed a dynamic JSL recognition system using effective feature extraction and feature selection approaches to overcome the challenges. In the procedure, we follow the hand pose estimation, effective feature extraction, and machine learning techniques. We collected a video dataset capturing JSL gestures through standard RGB cameras and employed MediaPipe for hand pose estimation. Four types of features were proposed. The significance of these features is that the same feature generation method can be used regardless of the number of frames or whether the features are dynamic or static. We employed a Random forest (RF) based feature selection approach to select the potential feature. Finally, we fed the reduced features into the kernels-based Support Vector Machine (SVM) algorithm classification. Evaluations conducted on our proprietary newly created dynamic Japanese sign language alphabet dataset and LSA64 dynamic dataset yielded recognition accuracies of 97.20% and 98.40%, respectively. This innovative approach not only addresses the complexities of JSL but also holds the potential to bridge communication gaps, offering effective communication for the deaf and hard-of-hearing, and has broader implications for sign language recognition systems globally.

Effect of zinc doping on the radiation shielding properties of calcium ferrite nanoparticles synthesized via green extract approach

indent Nanoparticles (NPs) of calcium ferrite doped with Zinc in the range of 10–50 mol % were successfully synthesized using solution combustion method. Neem leaves extract played a crucial role as a reducing agent in the synthesis process, followed by calcination at 500 °C. The comprehensive characterization of these nanoparticles confirmed the formation of orthorhombic calcium ferrite, and distinct Bragg reflections were observed due to Zinc doping. Notably, the nanoparticles displayed irregular shapes and varying sizes, along with the presence of pores and voids within their morphology. The optical energy band gap, determined using Wood and Tauc’s relation, fell within the range of 2.85 to 2.96 eV. Interestingly, as the zinc substitution increased, the energy band gap increased while the crystallite size decreased. The evaluation of radiation protection capacity included an in-depth examination of shielding parameters, encompassing the mass attenuation coefficient and its derivatives like mean free path, half-value layer, and effective atomic number. Additionally, variations in the energy-absorbing buildup factor across different energy regions were thoroughly investigated. The research also explored neutron shielding and bremsstrahlung radiation parameters, taking into account the zinc concentration in calcium ferrite nanoparticles. This aspect is particularly crucial in the field of radiation dosimetry, emphasizing the importance of understanding how these nanoparticles, with varying zinc concentrations, influence radiation absorption and dosimetric outcomes.

Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers.

Automatic extraction of relations between drugs/chemicals and proteins from ever-growing biomedical literature is required to build up-to-date knowledge bases in biomedicine. To promote the development of automated methods, BioCreative-VII organized a shared task - the DrugProt track, to recognize drug-protein entity relations from PubMed abstracts. We participated in the shared task and leveraged deep learning-based transformer models pre-trained on biomedical data to build ensemble approaches to automatically extract drug-protein relation from biomedical literature. On the main corpora of 10,750 abstracts, our best system obtained an F1-score of 77.60% (ranked 4th among 30 participating teams), and on the large-scale corpus of 2.4M documents, our system achieved micro-averaged F1-score of 77.32% (ranked 2nd among 9 system submissions). This demonstrates the effectiveness of domain-specific transformer models and ensemble approaches for automatic relation extraction from biomedical literature.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics.

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

Feature extraction and learning approaches for cancellable biometrics: A survey

AbstractBiometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

ScMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.

A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

Malware is one of the most common and severe cyber threats today. Malware infects millions of devices and can perform several malicious activities including compromising sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Recently, Deep Learning (DL) has emerged as one of the promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are discussed. We also present feature extraction approaches and a review of DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on DL-based malware detection including future research directions to further advance knowledge and research in this field.

Pickering extremulsion rich in lipophilic phytochemicals: Preparation, characterization and application

The stability and use convenience of lipophilic phytochemicals are needed in the food or medicine fields. In this work, a new and environmentally friendly process for efficient extraction and formulation of lipophilic phytochemicals from plant had been successfully developed. A medium system consisting of water, tributyrin oil and natural amphiphilic complex particles (SPICS) was established. Under the optimal conditions of the oil/water ratio 10%, the particle concentration 2%, solid-liquid ratio 35:1 (mg/mL), ultrasound time 90 s and ultrasound power 300 W, the stable and uniform Pickering extremulsion rich in phytochemical lipophilic cannabidiol（CBD）from industrial hemp leaves was prepared. The Pickering extremulsion with the highest CBD content 1.06 mg/g was obtained, which increased 1.17-fold to the traditional Soxhlet extraction. Meanwhile, the CBD-rich Pickering extremulsion had an excellent antibacterial, antioxidant activity and cell activity with bacteriostatic diameter 7.5–18 mm, DPPH clearance rate 61.8% and cell activity of more than 80%. Besides, CBD-rich Pickering extremulsion had good bioaccessibility. In conclusion, the study provided a green, efficient and low-cost approach for simultaneous extraction and preparation of lipophilic phytochemicals, and it should provide some valuable insights for its application as nutrient or active carriers in phytochemicals field.

Determination of bisphenols in beeswax based on sugaring out–assisted liquid–liquid extraction: Method development and application in survey, recycling and degradation studies

The methodology of sugaring out–assisted liquid–liquid extraction (SULLE) coupled with high-performance liquid chromatography–fluorescence detection was devised for quantifying bisphenol A (BPA) and bisphenol B (BPB) in beeswax. The effectiveness of SULLE was methodically explored and proved superior to the salting out–assisted liquid–liquid extraction approach for beeswax sample preparation. The analytical performance underwent comprehensive validation, revealing detection limits of 10 μg/kg for BPA and 20 μg/kg for BPB. The method developed was employed to analyse commercial beeswax (n = 15), beeswax foundation (n = 15) and wild-build comb wax (n = 26) samples. The analysis revealed BPA presence in four commercial beeswax samples and three beeswax foundation samples, with the highest detected residue content being 88 ± 7 μg/kg. For BPB, two beeswax foundation samples were positive, with concentrations below the limits of quantification and 85 ± 4 μg/kg, respectively. No bisphenols were detected in wild-build comb wax. Furthermore, the bisphenol removal efficacy of two recycling methods—boiling in water and methanol extraction—was assessed. The findings indicated that after four recycling cycles using water boiling, 9.6% of BPA and 29.2% of BPB remained in the beeswax. Whereas methanol extraction resulted in approximately 7% residual after one recycling process. A long-term study over 210 days revealed the slow degradation of bisphenols in comb beeswax. This degradation fitted well with a first-order model, indicating half-lives (DT50) of 139 days for BPA and 151 days for BPB, respectively. This research provides the first report on bisphenol contamination in beeswax. The low removal rate during the recycling process and the gradual degradation in beeswax underscore the significance of bisphenol contamination and migration in bee hives along with their potential risk to pollinators warranting concern. Furthermore, the developed SULLE method shows promise in preparing beeswax samples to analyse other analytes.

Reduced Matrix Effects and Simultaneous Determination of Three Juvenile Hormone Analogs in Tea by Disperse Solid-Phase Extraction (DSPE) and Gas Chromatography – Tandem Mass Spectrometry (GC-MS/MS)

Juvenile hormone III (JH III) maintains larval characteristics in insects and promotes ovary development in adults. Hydroprene, kinoprene, and methoprene are synthesized analogs based on natural JH III. A novel and precise method was established to mitigate the matrix effects and concurrently determine these juvenile hormone analogs in tea employing gas chromatography-tandem mass spectrometry (GC-MS/MS). Water addition as a sample extraction approach was utilized and contrasted with protocols excluding water. Concurrently, the sought compounds underwent extraction with 1% (v/v) acetic acid-acetonitrile, followed by purification utilizing 0.1 g graphitized carbon black (GCB), 0.4 g primary secondary amine (PSA), and 0.4 g C18. Following separation via a DB-17MS column, detection of the analytes was conducted through GC-MS/MS in dynamic multiple reaction monitoring (DMRM) mode with quantification by a matrix-matching standard curve. Satisfactory linear relationships were established for the trio of juvenile hormone analogs within a 0.01–0.32 mg/L range, exhibiting correlation coefficients (R2) exceeding 0.999 and quantitation limits (LOQs) approximately spanning 0.005–0.01 mg/kg. Mean recoveries, spanning three concentrations (0.02, 0.1, 0.2 mg/kg), varied between 77.6% and 115.9%. The relative standard deviations (RSDs, n = 6) were from 0.5% to 5.8%. Detection of the juvenile hormone analogs was accomplished in tea samples. In summary, this investigation demonstrated a proficient approach in addressing complications by diminishing the matrix effects in a complex sample.

CO2‐NBs‐ assisted enzymatic hydrolysis as a new green approach for extraction of phenolic compounds from rhizomes of Polygonatum cyrtonema Hua

SummaryThe integration of carbon dioxide‐nanobubbles (CO2‐NBs) with cellulase has led to the development of an innovative extraction system for phenolic compound retrieval from P. cyrtonema (Polygonatum cyrtonema) Hua rhizomes. Four critical factors influencing on CO2‐NBs‐cellulase were methodically investigated and optimised through single‐factor experiments. The optimal experimental conditions are 30 cycles, 0.35 MPa, pH 5.45 and 0.025 g/mL cellulase aqueous solution. The stability of system was characterised meticulously, indicating the viability for at least 24 h. Comparative assessment of bioactive constituents and antioxidant activity demonstrated exceptional extraction efficiency and potential synergistic effects of CO2‐NBs‐cellulase. Synergistic effects and reaction mechanisms during extraction were explored using Fourier‐transform infrared spectroscopy, differential scanning calorimetry and scanning electron microscopy, which contributed to mechanism understanding with significant alterations in partial structure and heightened thermal stability we found. In essence, this study introduces an eco‐friendly, energy‐efficient extraction system paradigm based on a novel nanobubble‐enzyme binding approach.

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.

Deep encoder–decoder network based data-driven method for impact feedback rendering on head during earthquake

In safety training simulators, realistic haptic feedback is essential to make people construct accurate situation awareness through experiencing. In this regard, this paper presents a new and innovative system that provides the haptic experience of falling objects on user’s head during an earthquake. Special focus was on the accurate reproduction of impact feedback when various objects fall on the head. To this end, we propose a novel data-driven approach. This approach first collects 3-axis acceleration signals during real collision under several impact velocities. Afterward, 3D acceleration data is abstracted to a 1D acceleration profile using our novel max–min extraction approach. The impact signal for an arbitrary velocity is interpolated using a deep convolutional bidirectional long short-term memory encoder–decoder model. Rendering hardware is also implemented using high performance voice-coil vibrotactile actuator. Numerical and subjective evaluations are carried out to evaluate the performance of the proposed approach.Kindly check and confirm the edit made in the title.I confirm the edit is okay.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Authors Given name: [Joolekha Bibi] Last name: [Joolee], Given name: [Mohammad Shadman] Last name: [Hashem]. Also, kindly confirm the details in the metadata are correct.Yes, the author names are presented accurately and in the correct sequence.

Experimental investigation and validation on an air-source heat pump frosting state recognition method based on fan current fluctuation signal and machine learning

Thick frost on the evaporators of air-source heat pumps and refrigeration systems can degrade system performance. Defrosting in time based on precise frosting state recognition method is of great importance. Indirect measuring recognition method with low accuracy is widely used in commercial systems, for cheapness and simpleness, needing improvement urgently. While accurate direct measuring methods are usually expensive and complicated. Therefore, a novel frosting state recognition method with high accuracy is proposed, which can be realized with a simple current sensor. The method is based on the micro fluctuation in the evaporator fan current (rather than current amplitude) caused by the perturbed air due to frost. An experimental setup is built to obtain fan current samples. Combining three feature extraction approaches and three classifiers, four frost state recognition methods using fan current fluctuation are merged. They are compared and studied based on the experimental samples. Results show original signal + 1D-CNN method has the best identification performance, reaching 95.74 ± 1.73 % accuracy at −10 °C evaporator air temperature. It reveals 94.53 ± 1.06 % accuracy in a temperature range of −5∼-20 °C, and 94.73 ± 1.00 % accuracy for another fan with the same model. The method has significant improvement in performance and good potential for further study and application.

Single-molecule analysis of purified proteins and nuclear extracts: Insights from 8-oxoguanine glycosylase 1

By observing one molecule at a time, single-molecule studies can offer detailed insights about biomolecular processes including on rates, off rates, and diffusivity of molecules on strands of DNA. A recent technological advance (Single-molecule Analysis of DNA-binding proteins from Nuclear Extracts, SMADNE) has lowered the barrier to entry for single-molecule studies, and single-molecule dynamics can now be determined directly out of nuclear extracts, providing information in an intermediate environment between purified proteins in isolation and the heterogeneity of a nucleus. To compare and contrast the single-molecule DNA binding dynamics in nuclear extracts versus purified proteins, combined optical tweezers and fluorescence microscopy experiments were performed with purified GFP-tagged 8-oxoguanine glycosylase 1 (OGG1), purified GFP-OGG1 spiked into nuclear extracts, and nuclear extracts from human cells overexpressing GFP-OGG1. We observed differences in undamaged DNA binding during DNA damage search in each of the three conditions. Purified GFP-OGG1 engaged undamaged DNA for a weighted average lifetime of 5.7 s and 21% of these events underwent DNA diffusion after binding. However, unlike other glycosylases studied by SMADNE, OGG1 does not bind non-damaged DNA efficiently in nuclear extracts. In contrast, GFP-OGG1 binding dynamics on DNA substrates containing oxidative damage were relatively similar in all three conditions, with the weighted average binding lifetimes varying from 2.2 s in nuclear extracts to 7.8 s with purified GFP-OGG1 in isolation. Finally, we compared the purified protein and nuclear extract approaches for a catalytically dead OGG1 variant (GFP-OGG1-K249Q). This variant greatly increased the binding lifetime for oxidative DNA damage, with the weighted average lifetime for GFP-OGG1–249Q in nuclear extracts at 15.4 s vs 10.7 s for the purified protein. SMADNE will provide a new window of observation into the behavior of nucleic acid binding proteins only accessible by biophysicists trained in protein purification and protein labeling.

An Imbalanced Sequence Feature Extraction Approach for the Detection of LTE-R Cells with Degraded Communication Performance

Within the Shuo Huang Railway Company (Suning, China ) the long-term evolution for railways (LTE-R) network carries core wireless communication services for trains. The communication performance of LTE-R cells directly affects the operational safety of the trains. Therefore, this paper proposes a novel detection method for LTE-R cells with degraded communication performance. Considering that the number of LTE-R cells with degraded communication performance and that of normal cells are extremely imbalanced and that the communication performance indicator data for each cell are sequence data, we propose a feature extraction neural network structure for imbalanced sequences, based on shapelet transformation and a convolutional neural network (CNN). Then, to train the network, we set the optimization objective based on the Fisher criterion. Finally, using a two-stage training method, we obtain a neural network model that can distinguish LTE-R cells with degraded communication performance from normal cells at the feature level. Experiments on a real-world dataset show that the proposed method can realize the accurate detection of LTE-R cells with degraded communication performance and has high practical application value.

Data Issues in High-Definition Maps Furniture – A Survey

The rapid advancements in sensing techniques, networking, and artificial intelligence (AI) algorithms in recent years have brought autonomous driving vehicles closer to common use in vehicular transportation. One of the fundamental components to enable autonomous driving functionalities are High-Definition (HD) maps – a type of map that carries highly accurate and much richer information than conventional maps. The creation and use of HD maps rely on advances in multiple disciplines, such as computer vision/object perception, geographic information systems, sensing, simultaneous localization and mapping, machine learning, etc. To date, several survey papers have been published describing the literature related to HD maps and their use in specialized contexts. In this survey, we aim to provide (1) a comprehensive overview of the issues and solutions related to HD maps and their use without attachment to a particular context; (2) a detailed coverage of the important domain knowledge of HD map furniture, from acquisition techniques and extraction approaches, through HD map–related datasets, to furniture quality assessment metrics, for the purpose of providing a comprehensive understanding of the entire workflow of HD map furniture generation, as well as its use.

A textual question answering and handwritten answer evaluation system for hindi language

Textual Question Answering targets answering questions defined in natural language. Question Answering Systems offer an automated approach to procuring answers to queries expressed in natural language. The need for Multilingual Question Answering without performing machine translation is ever existing. Besides that, automating tasks with the help of technology to assist humans, has been the main aim of research in recent years. This paper presents an automated answer evaluation system for reading comprehension-based questions in the Hindi language without requiring translation in any other language. The system accepts text, question, and handwritten answer of a student in the form of an image for answer evaluation. This is accomplished by developing a textual question-answering system for reading comprehension. It is an extractive approach that utilizes RoBERTa transformer model and fine-tunes it for Hindi question-answering. The answer to the question is extracted as a span from the provided text. Further, a handwritten text recognizer model is developed employing a Convolutional Recurrent Neural Network with Connectionist Temporal Classification module along with two layers of Bidirectional LSTM. Experimentation is performed using existing as well as self-created datasets to show the effectiveness of the proposed approach. An accuracy of 98.69% is obtained on the self-created Hindi-QA dataset and the proposed system outperformed the other existing methods. The paper also discusses potential research directions in the field.

Sustainable coloration and analysis of cellulosic viscose fabric incorporating Rosa rubiginosa extraction and pre-mordanting approaches

Dyeing with natural ingredients has gained popularity as a sustainable option because of its numerous useful applications and favorable environmental impacts. The purpose of this study was to determine the optimal conditions for extracting natural dyestuff from the red rose flower (Rosa rubiginosa) by observing the coloring of viscose fabric. The parameters examined were the M:L ratio (1:3), temperature (80 °C), and duration (30 min). Infrared dyeing procedures were conducted on viscose fabric, employing a diverse range of mordants, both natural and synthetic, which included materials such as orange and lemon peels, tannic acid, copper sulfate, and ferrous sulfate, at different mordanting ratios. The highest results of the mordants were achieved by a single mordanting process called pre-mordanting, and their performances were compared for the first time. Here, utilizing orange peel as a natural mordant in a pre-mordanting procedure for dyeing viscose fabrics yields better results and enhanced performance than using any of the other mordants used. The CIELab results were calculated using a variety of measurement conditions, and the ideal value was determined by an experiment on the color strength (K/S) properties. The FTIR spectra confirmed the interaction of colored particles with viscose fabrics, and the SEM investigation confirmed the dye's ability to be absorbed into the fabric's surface. Elemental mapping and EDX spectroscopy both showed that there were dye particles floating about the surface of the colored fabric. The colorfastness ratings, including a rating of 4 for rubbing, 4–5 for washing, and 4 for lightfastness, were found to be satisfactory when the fabric was treated with metal mordants. Hence, Rosa rubiginosa used as a coloration of viscose fabric is a very promising alternative option to synthetic dyestuff, which can be used to satisfy the actual need for coloring and functionality of viscose fabric in the dyeing and finishing industry.