ARUN KUMAR

@veltech.edu.in

Computer Science and Engineering
Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology

ARUN KUMAR


                     Download Compact PDF  
https://researchid.co/arun06_83
Dr. S. Arun Kumar is an associate professor in Department of Computer Science and Engineering at Vel Tech Rangarajan and Dr. Sagunthala R & D institute of Science and Technology, Avadi, Chennai, Tamil Nadu. As a Doctorate and with a Master Degree in Software Engineering, he has 13 years of teaching experience in different educational institutions. He has registered 2 patents in his name and published 12 papers with Scopus Indexed. He holds a professional membership in ACM and IEEE. His research interests include Big Data Analytics, Distributed Computing, and Machine Learning.

EDUCATION

I am Doctorate and with a Master Degree in Software Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Multidisciplinary
17

Scopus Publications

1155

Scholar Citations

11

Scholar h-index

19

Scholar i10-index

Scopus Publications

  • Implementation of Edge Intelligent Deep Learning Approach for Management and Prediction of Disasters (IDLAPD) using Social Media Dataset
    T.M.S. Mekelarani, M.Giri, Nalipi Hemalatha, Talla Muni Keerthi, S Arun Kumar, Bodireddy Kethankumar
    Proceedings of the IEEE International Conference on AI Engineering and Innovations Aiei 2026, 2026
  • An Intelligent Computational Framework for Real Time Micro Moment Detection and Conversion Optimization in Smart Digital Marketing Systems
    Arun Kumar S, Taheseen Shaikh Abdul Aziz, Farhat Embarak, Joel T, Lokesh Gupta
    Journal of Machine and Computing, 2025
    In the rapidly shifting landscape of digital marketing and tailored services, identifying and responding to high-intent user micro-moments has become a necessity for user activation and conversion maximization. Existing deep learning techniques such as single CNNs, LSTMs, and transformer models are promising but are either temporally weak, non-interpretable, or resource-intensive. This paper proposes a novel Attention-Augmented LSTM for Micro-Moment Detection (AALSTM-MM) that integrates behavioral clustering, sessional temporalization, and attention-augmented LSTM network to effectively learn sequential dependencies and intent signals of user browsing sessions. The designed model was experimented and verified against a benchmark e-commerce user session dataset with 92.3% accuracy, 91.7% precision, 90.8% recall, 91.2% F1-score, and an AUC of 0.948. Above all, it posed a low inference latency of 42 milliseconds and therefore was feasible for real-time application. In addition, attention visualizations and behavior clustering ensured interpretability and pattern insights to make decision-making more transparent. This paper delivers a robust, scalable, and interpretable model capable of inferring micro-moment behavior at scale with high accuracy and low latency. Its use cases are in real-time user intent prediction industries such as advertising, recommender systems, and intelligent customer service. The strengths of modeling accuracy, efficiency, and interpretability merge to make AALSTM-MM a significant step toward human-centered, smart digital interaction systems.
  • Integrating Emotion Aware AI for Hyper Personalized Consumer Targeting in Next Generation Man Machine Computing Environments
    Taheseen Shaikh Abdul Aziz, Vinodha Ramalingam, Nandini Prasad K S, David Neels Ponkumar Devadhas, Arun Kumar
    Journal of Machine and Computing, 2025
    This study presents a user-driven, emotion-aware expert system designed for intelligent consumer targeting within man–machine computing environments. Traditional digital marketing frameworks rely heavily on generalized behavioral analytics, lacking real-time emotional awareness and failing to capture nuanced user intent. To address these limitations, we propose a next-generation AI architecture that integrates multimodal emotion detection—including facial expression analysis, vocal tone interpretation, and textual sentiment mining—into the targeting process. The system employs a hybrid deep learning framework combining Convolutional Neural Networks (CNN) for visual emotion recognition and Bi-directional Long Short-Term Memory (Bi-LSTM) for sequential audio-text analysis, enhanced by a dynamic attention mechanism. Implemented within a modular, Python-based platform, this expert system enables seamless integration with existing digital marketing ecosystems and supports real-time data processing. Experimental evaluations demonstrate a 21.6% improvement in targeting accuracy over behavior-only models and a 92.4% emotion recognition rate on standard benchmarks. Results show increased user engagement, improved personalization, and higher campaign effectiveness. This research contributes to the field of augmented intelligence and expert systems by advancing man–machine interaction and enabling emotionally adaptive consumer profiling for smarter, human-centered digital marketing strategies.
  • A FUTURE TREND ON 5G NETWORK SUB-SLICING TECHNIQUES FOR MACHINE LEARNING ALGORITHMS
    Journal of Theoretical and Applied Information Technology, 2025
  • AI Powered Sign Language Detection with a Modern Tech Stack
    S. Arun Kumar, N. Pranaya Deepika, N. Likhitha, U. Lakshmi Savya
    Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2025, 2025
    Human-Computer Interaction (HCI) enables effective and natural interaction using hand gestures. Computer vision, deep learning, and image processing technologies are utilized to translate gestures in real-time. The overall goal of this research is to create an efficient system for smooth gesture-based interaction for the hearing and speech disabled. The system utilizes the application of deep learning and convolutional neural networks(CNNs) to provide precise gesture recognition. To enable generalization to users and environments, the model is trained on a robust dataset with a large variety of hand poses and movements. Hand gesture identification and interpretation are done through real-time image capture, feature extraction, and classification. Potential applications of this technology are in robotics, virtual reality, smarts, assistive and intelligent control devices, enhancing accessibility and user interfaces. The system optimizes recognition efficiency and reduces errors by using advanced edge detection and background subtraction methods. Experimental results show high accuracy and real-time responsiveness, validating the effectiveness of the system for practical application. Future research will aim at model generalization, extending gesture repertoires, and bringing together multimodal inputs like speech and facial recognition in order to enrich interaction.
  • Revolutionizing Brain Tumor Classification with Fusion-Driven Deep Learning Models
    Katepaka Anil Kumar, Gunasundari B, Raghavendra M Ichangi, S Arun Kumar, Jeethu Philip, S Saranya
    Apci 2025 2025 International Conference on Advancements in Power Communication and Intelligent Systems, 2025
    The identification of brain tumors presents a significant medical challenge, as timely and precise diagnosis greatly influences patient survival rates. Brain tumors, caused by abnormal cell growth, vary in severity and often present subtle differences in medical imaging, making manual diagnosis by radiologists Labor-intensive and susceptible to mistakes. Magnetic resonance imaging (MRI) and Computed tomography (CT) scans plays a key role in enhancing tumor visibility and aiding diagnosis. However, the complexity of brain structures and the limitations of manual interpretation highlight the need for automated, efficient, and reliable diagnostic methods to support timely clinical decision-making. Multimodel approaches integrating information from various imaging techniques, including CT and MRI scans, have surfaced as a robust solution to enhance diagnostic accuracy. In this study, we propose a novel multimodel framework leveraging ResNet50, a deep convolutional neural network designed for the detection of brain tumors. Our approach integrates CT and MRI scan data, utilizing their complementary information to achieve superior performance. The proposed system incorporates a late fusion strategy, where features extracted from the CT and MRI scans by independent ResNet50 models are combined for final classification. The model underwent training and evaluation using a comprehensive dataset, achieving an accuracy of 97.5% in tumor detection, significantly outperforming single-modality approaches. This high accuracy emphasizes the potential of multimodel frameworks in enhancing the reliability of brain tumor diagnostics. Our research indicates that multimodal data integration, unified with the advanced feature extraction capabilities of ResNet50, can substantially strengthened the precision of automated brain tumor recognition systems. This research paves the way for more effective diagnostic tools within clinical contexts, it may significantly lower diagnostic error rates and lead to improved health outcomes.
  • Pneumonia Detection Enhancement Through Diverse Deep Learning Approaches
    T. Subburaj, S. Arun Kumar, K. Santha Kumari, B. Gunasundari, M. Kumaresan, R. Kannan
    Lecture Notes in Networks and Systems, 2025
  • Secure Remote Access: A Scalable VPN Framework using Machine Learning-based Intrusion Detection with Random Forest
    David Neels Ponkumar Devadhas, S Arun Kumar, Syed Mahammed Afzal, Mahmad Muskhan, Sangaraju Joshika, R Julian Menezes
    International Conference on Nexgen Networks and Cybernetics Ic2nc 2025 Proceedings, 2025
    As the volume of data exchanged is growing and the number of remote workers is also rising, the need for reliable and secure remote access solutions becomes much more critical in the IT domain. This research work presents a model for secure remote access by integrating a scalable VPN infrastructure with a machine learning-based Intrusion Detection System employing the Random Forest algorithm. The framework has been built in such a way that it can identify and block the access attempts made illegally in real time and thus ensure remote users an uninterrupted and secure network. The basic structure is not only supportive of scaling that is ultimately utilized to handle the various loads but is also highly intelligent that it adapts to new and changing attack patterns by continuously learning. The distinctiveness of this innovation comes from the fact that the Random Forest algorithm is seamlessly fused with the VPN structure to achieve the required level of security. The proposed method relies on the use of the Random Forest algorithm to classify the network traffic and to detect abnormal behavior while keeping the VPN performance unchanged. The Random Forest algorithm is taught using labelled network traffic data, which means that it can accurately determine if an activity is harmful or is legitimate. The method’s ensemble feature is responsible for increasing precision in the detection of the attack and therefore for reducing false positives which gives the detection of the threat a rapid effect. Moreover, the framework has also been adapted for a smooth distribution and deployment in different environmental settings which makes it a very practical and useful solution for companies looking for a secure and intelligent remote access.
  • A study on the impact of artificial intelligence on talent sourcing
    Varun Chand Hemachandran, Kurakula Arun Kumar, Syarul Azlina Sikanda, Seema Sabharwal, Sivaprakasam Arun Kumar
    Iaes International Journal of Artificial Intelligence, 2024
    <p class="p1"><span lang="EN-US">Talent sourcing is one of the most effective mechanisms to engage with the talent pool and convert a candidate into an applicant. Today, machine learning has emerged as a trend to assist employers in addressing recruitment challeng-es with the help of tools such as neuro-linguistic programming (NLP) and automated assessments. 80% of the executives strongly believe deep learning makes candidate screening highly efficient. Including current start-ups globally, only 15% use artificial intelligence (AI) and are expected to increase by 31%. The study focused on the impact of AI in recruitment process. There are a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on. Here we would like to analyze the impact of using AI in various phases of hiring in the organization.</span></p>
  • Optimizing Lung Cancer Diagnosis Through Dimensionality Reduction and Transfer Learning
    B Gunasundari, S Sathya, S Arun Kumar, Yarrabally Durga Bhargavi, Bojjodu Asha, V.S. Divya Sundar
    2024 2nd International Conference on Advances in Computation Communication and Information Technology Icaiccit 2024, 2024
    Globally, lung cancer is a major contributor to cancer-related deaths, making it one of the most fatal forms of the disease, primarily because diagnoses are often made in advanced stages due to the lack of early symptoms. Detecting diseases at an early stage is vital for increasing survival rates, as it significantly enhances treatment effectiveness and patient outcomes. Advances in diagnostic methods, including low-dose CT scans and machine learning models, are essential for detecting lung cancer earlier and improving survival chances. This study presents a system designed to address the challenge of using CT scan images To ensure prompt and accurate identification of lung cancer, a critical tool for identifying potential tumors. The proposed approach integrates UMAP for dimension reduction, which helps in managing the high-dimensional nature of CT scan images, and leverages transfer learning with fine-tuned EfficientNetB3 to enhance classification accuracy and efficiency. Dataset balancing is achieved through augmentation techniques to address class imbalance, a common issue in medical imaging datasets. Refining the parameters of the EfficientNetB3 model with pre-trained ImageNet weights, the model converges faster and delivers superior results. Additionally, a custom callback mechanism dynamically adjusts the learning rate as per the validation loss trends, aiding in the prevention of overfitting and ensuring effective optimization throughout training. The model records an accuracy level of $\mathbf{9 6. 3 \%}$, indicating its potential for accurate lung cancer detection. These results underscore the effectiveness of combining UMAP for dimension reduction, transfer learning, data balancing, and dynamic learning rate adjustments in overcoming the challenges of lung cancer diagnosis through computed tomography (CT) scan data.
  • A Method for Predicting Coronary Heart Disease Using Machine Learning Approaches
    A. Ramathilagam, S. Pradeepha, M. Preethi Ram, B. Sankara Lakshmi, S. Arun Kumar, D. David Neels Ponkumar
    Proceedings of 2024 2nd International Conference on Recent Trends in Microelectronics Automation Computing and Communications Systems Exploration and Blend of Emerging Technologies for Future Innovation Icmacc 2024, 2024
  • Discover Crypto-Jacker from Blockchain Using AFS Method
    T. Subburaj, K. Shilpa, Saba Sultana, K. Suthendran, M. Karuppasamy, S. Arun Kumar, A. Jyothi Babu
    Lecture Notes in Networks and Systems, 2023
  • Machine learning based cardiovascular disease prediction
    P. Chinnasamy, S. Arun Kumar, V. Navya, K. Lakshmi Priya, Siva Sruthi Boddu
    Materials Today Proceedings, 2022
  • Blockchain technology in smart-cities
    P. Chinnasamy, C. Vinothini, S. Arun Kumar, A. Allwyn Sundarraj, S. V. Annlin Jeba, V. Praveena
    Intelligent Systems Reference Library, 2021
  • BrownBoost Classifier-Based Bloom Hash Data Storage for Healthcare Big Data Analytics
    S. Arun Kumar, M. Venkatesulu
    Advances in Intelligent Systems and Computing, 2020
  • Gramian matrix data collection-based random forest classification for predictive analytics with big data
    S. Arun Kumar, M. Venkatesulu
    Soft Computing, 2019
  • Probabilistic scheduling based soft max map-reduce regression function for resource optimization with big data analytics
    Journal of Advanced Research in Dynamical and Control Systems, 2019

RECENT SCHOLAR PUBLICATIONS

  • Ethnopharmacological Insights on Plumeria obtusa L.: A Comprehensive Review of its Phytochemistry and Pharmacological Properties
    S Salar, M Singh, DK Yadav, V Kumar, R Ekbbal, S Kumar
    Current Bioactive Compounds 22 (3), e15734072350755 , 2026
    2026
  • MXene–TMD heterostructure photodetectors: engineering the Ti 3 C 2/SnS 2 interface for high-speed visible light detection
    C Das, S Kumar, J Gosai, MM Ganaie, A Sharma, M Kumar, A Solanki, ...
    Journal of Materials Chemistry C , 2026
    2026
    Citations: 1
  • Blu-WERP (Web Extraction and Refinement Pipeline): A Scalable Pipeline for Preprocessing Large Language Model Datasets
    S Rupesh, S Kumar, V Chaithanya
    arXiv preprint arXiv:2511.18054 , 2025
    2025
  • Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors
    A Manaithiya, R Bhowmik, R Ray, S Kumar, S Sharma, B Mathew, ...
    SAR and QSAR in Environmental Research 36 (11), 1041-1079 , 2025
    2025
    Citations: 1
  • Design of a MIMO Antenna for UWB Applications
    VNKR Devana, GL Durga, K Jithendra, KS Gowtham, S Kumar, ...
    Proceedings of International Conference on Computational Intelligence and … , 2025
    2025
  • WS 2 Nanoparticle-Decorated, Vertically Aligned SnS 2 -Based High-Performance Heterostructures for Ambient-Stable Ultrafast Photodetection
    C Das, S Kumar, A Sharma, M Kumar, AK Rath, S Sahu
    ACS Applied Nano Materials 8 (43), 21047-21056 , 2025
    2025
  • Green Synthesis of Nanoparticles using Pea Peel Biomass and Their Assessment on Seed Germination of Tomato, Chilli and Brinjal Crop
    A Kanwal, BJ Singh, S Kumar, R Sehgal, SK Upadhyay, R Singh
    Indian Journal of Agricultural Research 59 (10), 1608-1618 , 2025
    2025
    Citations: 3
  • Prediction of Chronic Kidney Disease (CKD) Using Hybrid Machine Learning
    SA Kumar, S Niveditha, KB Vikas, C Varshini
    Smart Trends in Computing and Communications: Proceedings of SmartCom 2025 … , 2025
    2025
  • Institutional Experience with Interstitial and Surface Mold Brachytherapy for Head and Neck Cancers: Efficacy, Toxicity, and Clinical Outcomes
    B Devnani, KA Nair, RP Nair, DR Poonia, S Kumar, D Aggarwal, ...
    International Journal of Radiation Oncology, Biology, Physics 123 (1), e331 , 2025
    2025
  • FORMULATION AND EVALUATION OF ACECLOFENAC SODIUM GEL-USING CARBOPOL 934 AND 940
    S Nandhini, M Sakthivel, SM Halith, SA Kumar, A Basheer
    2025
  • Climate-resilient strategies for wheat farming: minimizing climate impact, optimizing productivity, and maximizing profitability in the subtropical agroecological landscape of …
    VS Meena, RK Jat, S Durgude, S Kumar, RK Sohane, RK Jha, A Kumar, ...
    Frontiers in Sustainable Food Systems 9, 1564812 , 2025
    2025
    Citations: 2
  • Sex Determination of Human Remains Using Fingerprint Ridge Density.
    S Gupta, S Kumar, J Singh, SP Mandal
    Mymensingh Medical Journal: MMJ 34 (3), 914-920 , 2025
    2025
    Citations: 1
  • Assessing Global Performance of 15 and 22 MW Floating Wind Semi-Submersible (FWSS) Platforms Through Cyber-Physical Modelling and Simulation
    H Santo, KY Chia, CT Liong, M Cai, C Zhang, S Liu, CK Yeo, QH Ng, ...
    Offshore Technology Conference, D031S043R007 , 2025
    2025
    Citations: 2
  • Comments on «The use of robotic surgery for the management of urethral strictures and bladder neck contractures: A systematic review».
    R Mehta, S Kumar
    Actas Urologicas Espanolas 49 (5), 501739-501739 , 2025
    2025
  • Assessment of thermal conductivity prediction models for compacted bentonite-based backfill material
    PK Sah, SS Kumar
    International Journal of Environmental Science and Technology 22 (6), 4571-4582 , 2025
    2025
    Citations: 5
  • P162–Correlation of sexual hormones with various stages of chronic kidney disease
    S Kumar, M Singh, D Bhirud, SC Navariya, GR Choudhary, AS Sandhu
    European Urology 87, S164 , 2025
    2025
  • A0312–Correlation between Urethral Stricture Disease (USD) and serum testosterone levels, a tertiary care centre study
    M Singh, K Sharma, S Kumar, D Bhirud, SC Navariya, GR Choudhary, ...
    European Urology 87, S1117 , 2025
    2025
  • Materiality of antimony doping in Ge–S amorphous chalcogenides in nonlinear switching
    A Kumar, S Shukla, S Kumar, R Gupta
    The European Physical Journal Plus 140 (2), 146 , 2025
    2025
    Citations: 1
  • Highly scalable accelerator
    PR Lantz, S Kumar, RM Sankaran, S Gayen
    US Patent App. 18/749,130 , 2025
    2025
  • Assessment of CO2 storage potential of deccan volcanic provinces (DVP), India using Python
    RK Singh, NP Nayak, S Kumar, V Vishal
    EAGE Workshop on Carbon Capture and Storage (CCS) in Basalts 2025 (1), 1-7 , 2025
    2025
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Blockchain technology in smart-cities
    P Chinnasamy, C Vinothini, S Arun Kumar, A Allwyn Sundarraj, ...
    Blockchain technology: Applications and challenges, 179-200 , 2021
    2021.0
    Citations: 62
  • Preventing erosion of X-chromosome inactivation in human embryonic stem cells
    M Cloutier, S Kumar, E Buttigieg, L Keller, B Lee, A Williams, ...
    Nature communications 13 (1), 2516 , 2022
    2022.0
    Citations: 43
  • Machine learning based cardiovascular disease prediction
    P Chinnasamy, SA Kumar, V Navya, KL Priya, SS Boddu
    Materials Today: Proceedings 64, 459-463 , 2022
    2022.0
    Citations: 37
  • Discover crypto-jacker from blockchain using AFS method
    T Subburaj, K Shilpa, S Sultana, K Suthendran, M Karuppasamy, ...
    Proceedings of Fourth International Conference on Computer and Communication … , 2023
    2023.0
    Citations: 15
  • Elasticity of nanoparticles influences their blood circulation, phagocytosis, endocytosis, and targeting, ACS Nano 9 (2015) 3169–3177
    AC Anselmo, M Zhang, S Kumar, DR Vogus, S Menegatti, ME Helgeson, ...
    Citations: 14
  • Effect of some metallic impurities on the density of localized states in a-Se80Te20 thin films
    SP Singh, S Kumar, A Kumar
    Vacuum 75 (4), 313-320 , 2004
    2004.0
    Citations: 13
  • The non-exponential decay pattern of the weak luminescence from seedling of Cicer arietinum L. stimulated by pulsating electric fields
    BG Mathew, S Kumar
    Experientia 48 (3), 309-310 , 1992
    1992.0
    Citations: 13
  • Results of radical radiotherapy in carcinoma of the uterine cervix stage I-III
    BM Biswal, BK Mohanti, GK Rath, L Kumar, A Kriplani, T Ganesh, ...
    Clinical Oncology 6 (6), 356-360 , 1994
    1994.0
    Citations: 12
  • Futuristic non-antibiotic therapies to combat antibiotic resistance: a review. Front Microbiol. 2021; 12: 609459
    M Kumar, DK Sarma, S Shubham, M Kumawat, V Verma, PB Nina, ...
    2021.0
    Citations: 11
  • RETRACTED ARTICLE: Gramian matrix data collection-based random forest classification for predictive analytics with big data: S. Arun Kumar, M. Venkatesulu
    S Arun Kumar, M Venkatesulu
    Soft Computing 23 (18), 8621-8631 , 2019
    2019.0
    Citations: 11
  • Liquid crystal photoalignment on As 2 S 3 chalcogenide thin films
    NV Sheremet, L Sharpnack, M Gelbaor-Kirzhner, DM Agra-Kooijman, ...
    Journal of Physics D: Applied Physics 50 (6), 065306 , 2017
    2017.0
    Citations: 11
  • Formulation and evaluation of solid dispersions of an anthelmintic drug for enhancement of dissolution rate
    A Patil, S Kumar
    JIPBS 4 (3), 71-74 , 2017
    2017.0
    Citations: 11
  • Effect of kohlrabi on the quality characteristics of chicken sausages
    FA Zargar, S Kumar, ZF Bhat, P Kumar
    Indian Journal of Poultry Science 51 (3), 333-337 , 2016
    2016.0
    Citations: 11
  • Fenestrated posterior inferior cerebellar artery with concomitant vertebro-basilar junction fenestration and vertebral artery aneurysm
    S Kumar, EM Justin, NK Mishra
    Clinical neuroradiology 22 (3), 235-237 , 2012
    2012.0
    Citations: 11
  • Computer-assisted evaluation of stress and time parameters of serrated flow in mild steel
    S Kumar, H Weinhandl, E Pink
    Materials Science and Engineering: A 212 (2), 213-221 , 1996
    1996.0
    Citations: 11
  • Interactive display of parametric Spline Surfaces
    S Kumar
    PhD thesis, University of North Carolina , 1996
    1996.0
    Citations: 11
  • Commiphora wightii based novel edible film for improved lipid oxidative and microbial stability of meat products
    R Sharma, ZF Bhat, A Kumar, S Kumar, A Bekhit, Z Naqvi
    Journal of Food Safety 10 , 2021
    2021.0
    Citations: 10
  • Exploring packaged microvesicle proteome composition of Chinese hamster ovary secretome
    N Kumar, DG Gupta, S Kumar, P Maurya, A Tiwari, B Mathew, ...
    Journal of Bioprocessing & Biotechniques 6 (4), 1000274 , 2016
    2016.0
    Citations: 10
  • Spontaneous pneumoperitoneum associated with colonic pseudo-obstruction
    S Prasannan, S Kumar, YA Gul
    Acta Chirurgica Belgica 104 (6), 739-741 , 2004
    2004.0
    Citations: 10
  • Implications of the microstructure on strength-ductility synergy in a novel B2-strengthened medium manganese advanced high strength steel
    S Das, S Kumar, SK Pradhan, TK Bandyopadhyay, S Mandal
    Materials Science and Engineering: A 924, 147798 , 2025
    2025.0
    Citations: 9