Peng Wang | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model
Peng Wang
Affiliation Beijing Zhijingling Technology Co., Ltd.
Country China
Article Title Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model
Google Scholar ID Rr1cJGoAAAAJ
Article Type Research Article
Article View 165
Reference Count 22
Award Category Best Paper Award
Event International Research Excellence and Best Paper Awards

The Best Paper Award recognizes scholarly excellence demonstrated through original research, methodological rigor, and meaningful contributions to the advancement of knowledge. Peng Wang received recognition for the article titled Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model, published in 2026 through MDPI. The research addresses emerging challenges in intelligent recommendation systems by integrating graph-based interaction modeling with large language model capabilities, offering a framework that supports more effective product bundling recommendations in complex digital environments.[1]

Abstract

This article examines an advanced recommendation framework that combines interactive graph representations with large language model technologies to improve product bundling performance. The study investigates how structured user–item relationships and semantic understanding can be integrated within a unified architecture to address limitations in traditional recommendation systems. Through the incorporation of graph-based interaction learning and contextual language modeling, the proposed approach enhances recommendation accuracy, relevance, and interpretability. The research contributes to ongoing developments in intelligent commerce systems by presenting a scalable methodology capable of supporting complex recommendation environments while improving user engagement and decision-making effectiveness.[1]

Keywords

Product Bundling; Large Language Model; Interactive Graph; Graph-To-Text Modeling; Recommendation System.

Introduction

Product bundling has become an important strategy within digital commerce platforms because it enables organizations to enhance customer experiences while increasing transaction value. As recommendation environments become increasingly complex, conventional algorithms often struggle to capture nuanced user preferences and contextual relationships. Recent advances in graph learning and language modeling have created opportunities for more adaptive recommendation frameworks capable of generating personalized and semantically meaningful bundle suggestions across large-scale datasets.[2]

Research Profile

Peng Wang is affiliated with Beijing Zhijingling Technology Co., Ltd. and has contributed to research within the field of computer science, particularly in intelligent recommendation systems and data-driven applications. According to the available academic profile, the researcher maintains a Google Scholar record with ten indexed publications, approximately 1,380 citations, and an h-index of seven. These indicators reflect continuing engagement with emerging computational methodologies and practical applications of artificial intelligence technologies.[3]

Scientific Background

The development of recommendation systems has evolved from rule-based approaches to sophisticated machine learning architectures capable of processing large volumes of behavioral and contextual information. Graph neural networks have demonstrated effectiveness in modeling relational structures among users and products, while large language models have introduced advanced semantic reasoning capabilities. Integrating these technologies offers opportunities to overcome challenges related to sparse data, contextual ambiguity, and recommendation diversity within commercial ecosystems.[2][4]

Methodology

The study employs a dual-enhancement architecture that combines interactive graph learning mechanisms with large language model representations. User behaviors, product attributes, and relational interactions are incorporated into graph structures that capture latent dependencies among entities. Simultaneously, language-based contextual understanding is utilized to enrich semantic representations. The integration process enables complementary learning between structural and contextual information sources, resulting in a unified recommendation framework designed to generate more accurate and interpretable product bundles.[1]

Key Findings

The findings indicate that combining graph-based interaction modeling with large language model capabilities improves recommendation quality across multiple evaluation measures. Enhanced semantic awareness allows the system to better understand product relationships, while graph representations strengthen the identification of user preferences. The resulting framework demonstrates improved predictive performance and contributes to more relevant product bundle generation, supporting practical deployment within intelligent commerce platforms and recommendation-driven applications.[1][4]

Scientific Contributions

This research contributes to the growing intersection of graph intelligence and language-based artificial intelligence by demonstrating how complementary computational paradigms can be integrated within recommendation systems. The proposed framework expands methodological possibilities for product bundling analysis, improves recommendation interpretability, and provides a foundation for future investigations into hybrid AI architectures. The work also highlights practical pathways for deploying advanced recommendation technologies within contemporary digital marketplaces.[1][5]

Conclusion

The recognition of Peng Wang through the Best Paper Award reflects the scholarly significance of research that advances recommendation technologies through interdisciplinary innovation. By integrating interactive graph structures with large language model capabilities, the study presents a meaningful contribution to computer science and intelligent commerce research. Its methodological insights and practical implications support continued exploration of scalable, context-aware recommendation frameworks capable of addressing evolving challenges within digital ecosystems.[1]

References

  1. Wang, P. (2026). Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model. Electronics, MDPI.
    https://doi.org/10.3390/electronics15122659
  2. MDPI. (2026). Electronics Journal: Research in intelligent systems and recommendation technologies.
    https://www.mdpi.com/journal/electronics
  3. Google Scholar. (n.d.). Author Profile: Peng Wang, Scholar ID Rr1cJGoAAAAJ.
    https://scholar.google.com/citations?hl=en&user=Rr1cJGoAAAAJ
  4. P Wang, J Xu, B Xu, C Liu, H Zhang, F Wang, H Hao. (2015). Semantic clustering and convolutional neural network for short text categorization.
    https://doi.org/10.3115/v1%2FP15-2058
  5. Peng Wang, Heng Zhang, Bo Xu, Chenglin Liu & Hongwei Hao. (2014). Short text feature enrichment using link analysis on topic-keyword graph.
    https://doi.org/10.1007/978-3-662-45924-9_8

Arti | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Arti
Sanatan Dharma College, Ambala Cantt, India

Arti
Affiliation Sanatan Dharma College, Ambala Cantt
Country India
Scopus ID Research Profile Available
Documents 12
Citations 2
h-index 1
Subject Area Computer Science
Event Best Paper Awards

The Best Paper Award recognition highlights the scholarly contributions of Arti, a researcher affiliated with Sanatan Dharma College, Ambala Cantt, India. The recognition reflects participation in academic research activities within the field of Computer Science and acknowledges contributions demonstrated through peer-reviewed publications, scholarly dissemination, and engagement with contemporary research topics. The award evaluation considers publication quality, originality, methodological rigor, relevance to emerging technological challenges, and the broader academic significance of the research work.

Abstract

This article presents an academic overview of Arti’s research profile and suitability for recognition under the Best Paper Award framework. The assessment is based on scholarly productivity, citation performance, publication record, and research relevance within Computer Science. Particular emphasis is placed on the quality of published work, methodological soundness, innovation potential, and contribution to ongoing scientific discourse. The profile reflects active engagement in research activities and demonstrates alignment with the objectives of academic excellence and knowledge dissemination.

Keywords

Computer Science, Research Excellence, Scholarly Publications, Academic Recognition, Scientific Contribution, Citation Analysis, Best Paper Award, Research Evaluation, Innovation, Knowledge Dissemination.

Introduction

Recognition through a Best Paper Award is generally reserved for research that demonstrates originality, technical rigor, clarity of presentation, and meaningful contribution to its respective discipline. Within Computer Science, award-winning research often addresses emerging challenges, proposes innovative methodologies, or advances theoretical and practical understanding of technological systems. Arti’s academic profile reflects participation in this broader scholarly ecosystem through published research outputs and contributions to scientific communication.

Research Profile

Arti is affiliated with Sanatan Dharma College, Ambala Cantt, India, and has established a developing scholarly record within the Computer Science domain. The available bibliometric indicators show a publication portfolio consisting of 12 indexed documents, supported by citation activity and an h-index of 1. Such indicators provide measurable evidence of academic engagement and demonstrate the visibility of research contributions within scholarly databases.

Research Contributions

The papers collectively address issues associated with modern computing environments, digital transformation, information processing, algorithmic approaches, and emerging technological trends. Such research contributes to the broader objective of enhancing efficiency, innovation, and problem-solving capacity within computing systems. The documented work further reflects adherence to scholarly publication standards, including peer review, methodological transparency, and academic integrity.

Publications

The publication portfolio consists of 12 documented research outputs indexed within scholarly databases. These publications represent sustained academic participation and provide a foundation for assessing research productivity, impact, and contribution to the field. Publication quality remains an important criterion in academic award evaluations because it reflects both scientific rigor and relevance.

 

Research Impact

Research impact may be assessed through citation activity, scholarly visibility, publication quality, and influence on subsequent studies. With documented citations and indexed publications, the available evidence suggests that Arti’s work has contributed to academic discussions and has achieved measurable recognition within the research community. While bibliometric indicators represent only one dimension of impact, they remain widely accepted tools for evaluating scholarly influence.

Award Suitability

Based on the available academic indicators, publication activity, and demonstrated commitment to scholarly research, Arti exhibits characteristics commonly associated with Best Paper Award consideration. The profile demonstrates research productivity, engagement with scientific inquiry, and contribution to knowledge development within Computer Science. The documented body of work supports evaluation under criteria such as originality, technical merit, academic relevance, and scholarly communication effectiveness.

Conclusion

Arti’s academic profile reflects meaningful participation in Computer Science research through published scholarly work, measurable bibliometric indicators, and contributions to the advancement of scientific knowledge. The combination of publication output, citation activity, and research engagement provides a reasonable basis for recognition within the Best Paper Award framework. Continued scholarly activity is expected to further strengthen the visibility and impact of future research contributions.

References

  1. Digital Twin Applications in Agriculture: Emerging Prospects and Opportunities.
    https://link.springer.com/chapter/10.1007/978-981-95-5915-2_13

  2. Deep learning-based facial recognition: A comparative study of CNN, VGG-16, and MobileNetV2.
    https://www.researchgate.net/publication/405125071_Deep_learning-based_facial_recognition_A_comparative_study_of_CNN_VGG-16_and_MobileNetV2

Sarvesh Tanwar | Computer Science | Excellence in Research Award

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Prof. Dr. Sarvesh Tanwar | Computer Science | Excellence in Research Award

Professor | Amity University | India

Dr. Sarvesh Tanwar is an accomplished researcher and academic with a strong background in cryptography, cybersecurity, blockchain, and computer network security. She earned her Ph.D. in Computer Science, where her research focused on securing IoT networks and blockchain-based systems. Over the years, she has gained extensive professional experience as a faculty member, project lead, and mentor for undergraduate and graduate students, contributing to multiple national and international research projects. Her research interests span cybersecurity, public key infrastructure, intrusion detection systems, secure communication protocols, and blockchain applications in digital security. She possesses advanced research skills in cryptographic algorithm design, network security analysis, blockchain architecture, IoT security frameworks, and data-driven cybersecurity solutions. Dr. Tanwar has an impressive record of publications in top-tier journals and conferences, including IEEE, Scopus-indexed journals, and Springer, reflecting her ability to address complex security challenges with innovative approaches. Her contributions have been recognized through multiple awards and honors, including research excellence recognitions, best paper awards, and memberships in prestigious professional organizations such as IEEE and CRSI. She has also served as a resource person, guest editor, and technical program committee member, demonstrating leadership in the academic and research community. With a strong focus on mentoring, global collaboration, and advancing secure computing research, Dr. Tanwar continues to make high-impact contributions to both academia and industry. Her work not only advances theoretical knowledge but also emphasizes practical applications in secure digital systems, demonstrating her commitment to societal and technological advancement. Overall, her educational background, professional achievements, research expertise, and recognized contributions establish her as a leading figure in cybersecurity and blockchain research, making her highly deserving of recognition and awards in the field.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Tanwar, S., & Bojarajulu, B. (2023). Intelligent IoT-BOTNET attack detection model with convolutional neural network. Computers, Materials & Continua, 70(2), 2077–2093.Citations: 44

  2. Tanwar, S., Choudhary, V., Choudhury, T., & Kotecha, K. (2024). Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset. Computational Intelligence and Neuroscience, 2024, Article ID 11107349.Citations: 11

  3. Tanwar, S., & Kumar, K. (2024). Deep-learning-based cryptanalysis through topic modeling. Engineering, Technology & Applied Science Research, 14(1), 12524–12529.Citations: 4

  4. Tanwar, S., & Kumar, K. (2024). MAN-C: A masked autoencoder neural cryptography based encryption scheme for CT scan images. Engineering, Technology & Applied Science Research, 14(1), 12524–12529.Citations: 4

  5. Tanwar, S., & Kumar, K. (2024). Generation and evaluation of datasets for anomaly-based intrusion detection systems in IoT environments. Engineering, Technology & Applied Science Research, 14(1), 12524–12529.Citations: 4

Jamshir Qureshi | Cybersecurity | Best Researcher Award

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Mr. Jamshir Qureshi | Cybersecurity | Best Researcher Award 

Vice President, at Purdue University Global, United States.

Summary

Jamshir Qureshi is a seasoned Solution Architect based in Dallas/Fort Worth, Texas, with over two decades of experience in FinTech and regulated industries. Specializing in developing resilient, secure, and scalable enterprise architectures, he has led transformative projects in modern application platforms, cloud-native solutions, and cybersecurity frameworks. His extensive knowledge extends to healthcare information systems and compliance-driven solutions, supporting operational excellence in highly regulated environments. In addition to his technical contributions, Jamshir actively engages in thought leadership, authoring works on AI-driven innovations and their impact on digital transformation. His recent paper, “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, showcases his commitment to integrating AI into cloud technologies for digital business growth. Recognized for his leadership in AI-powered cybersecurity solutions, he also served as a judge for the Globee Awards in Cybersecurity. Jamshir is passionate about mentoring and driving continuous improvement across teams and aligning strategies with business priorities.

Professional Profile

Education

🎓 Jamshir Qureshi holds a Master of Science in Information Technology from Purdue University Global, USA. This advanced degree has equipped him with the knowledge and technical prowess to tackle complex architectural and technological challenges in today’s fast-evolving digital landscape. Beyond his formal education, Jamshir is dedicated to continuous learning, demonstrated by his professional certifications. He is an AWS Certified Solutions Architect – Professional, showcasing his commitment to cloud excellence and proficiency in designing and implementing solutions on Amazon Web Services. His memberships in prominent organizations like the National Society of Leadership and Success (NSLS) and the American Association for the Advancement of Science reflect his passion for professional development and leadership in technology. These credentials, combined with his diverse experiences, have empowered Jamshir to build solutions that meet rigorous standards in highly regulated industries, including FinTech and healthcare.

Experience

💼 Over his 20+ year career, Jamshir Qureshi has built a strong foundation in enterprise architecture, focusing on scalable, secure, and resilient systems across various global firms. Currently, he serves as Vice President of Software Engineering at MUFG Bank in Dallas, Texas, where he drives technology initiatives and leads engineering teams. His career includes significant roles, such as Architect at Charles Schwab (2016-2021) and Lead Software Engineer at CVS Caremark (2011). He has also worked with prestigious organizations like Target and OM Software Ltd., spanning locations from India to South Africa and the U.S. Jamshir’s expertise in cloud-native platforms, microservices, DevOps practices, and cybersecurity has allowed him to implement successful strategies that align with business objectives, compliance standards, and operational excellence. His leadership in regulated industries has been recognized as transformative, focusing on innovation and team mentorship.

Research Interests

🔬 Jamshir’s research interests center on the transformative applications of artificial intelligence (AI) and cloud technology in FinTech, e-commerce, and healthcare systems. His research addresses emerging trends in AI-driven solutions, exploring how AI impacts human cognition, emotion, and healthcare. His paper “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, delves into how AI integration with cloud technologies can enhance digital business strategies. His interest in AI deepfakes, illustrated in his work “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion,” explores the ethical and psychological implications of deepfake technology. Jamshir is also intrigued by the role of AI in healthcare, analyzing both risks and benefits in publications such as “AI Deepfakes in Healthcare: A Double-Edged Sword.” His work underscores his dedication to leveraging AI for innovation while recognizing the ethical considerations tied to this technology.

Awards

🏆 Jamshir Qureshi has received notable recognition for his contributions to technology and innovation. He was honored as a judge for the prestigious Globee Awards in Cybersecurity, reflecting his expertise in AI-powered cybersecurity solutions. His leadership in advancing secure, resilient, and scalable architectures in highly regulated environments has set a standard for excellence. Jamshir’s work on integrating AI into enterprise systems has garnered industry attention, positioning him as a thought leader in cloud-based e-commerce and AI-driven digital transformation. Additionally, his influence extends to the academic and professional communities, where he has shared his expertise through publications and conference presentations. His accolades underscore his commitment to enhancing technology-driven solutions and his ability to shape the future of cybersecurity, artificial intelligence, and enterprise architecture.

Top Noted Publications

📚 Below are some of Jamshir Qureshi’s notable publications, illustrating his contributions to AI and digital transformation:

  • “AI-Powered Cloud-Based E-Commerce: Driving Digital Business Transformation Initiatives”
    • Published in: IACIS 2024 Conference
    • Summary: This paper explores how artificial intelligence (AI) integrated with cloud technologies can drive digital business transformation in e-commerce. It discusses the potential of AI in enhancing business operations, customer experience, and overall business agility, with an emphasis on cloud-native platforms, scalability, and resilience. The paper provides insights into how AI applications can reshape business strategies and models in the digital age.
    • Link: IACIS 2024 Conference Paper
  • “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion”
    • Published in: Journal of Applied Artificial Intelligence, 2024
    • Summary: This paper delves into the psychological and cognitive implications of AI-generated deepfakes. It explores how deepfakes influence human perception, trust, and emotions, addressing the ethical concerns and risks associated with their use. The paper discusses both the dangers and potential applications of deepfakes in various sectors, including media and healthcare.
    • Link: Journal of Applied Artificial Intelligence
  • “Artificial Intelligence (AI) Deepfakes in Healthcare Systems: A Double-Edged Sword? Balancing Opportunities and Navigating Risks”
    • Published in: Preprints, 2024
    • Summary: This paper examines the dual nature of AI deepfakes in healthcare. While deepfakes offer transformative potential in areas like medical imaging and patient simulation, they also pose significant risks related to misinformation, privacy, and ethical concerns. The paper explores strategies for navigating these challenges while harnessing the benefits of AI in healthcare.
    • Link: Preprints – AI Deepfakes in Healthcare
  • “How Artificial Intelligence Technology Can Be Used to Treat Diabetes”
    • Published in: Preprints, 2024
    • Summary: This paper investigates how AI technologies can be applied in the treatment of diabetes, focusing on data analytics, predictive modeling, and personalized medicine. It reviews AI-driven advancements in monitoring glucose levels, improving diagnostic accuracy, and designing tailored treatment plans to enhance patient outcomes.
    • Link: Preprints – AI in Diabetes Treatment
  • “How Artificial Intelligence and Machine Learning Can Impact Market Design”
    • Published in: OPAST Publishers, 2024
    • Summary: This paper explores the role of AI and machine learning in transforming market design. It discusses how these technologies can improve market efficiency, enhance decision-making processes, and optimize resource allocation. The paper highlights the future of AI-driven market solutions, particularly in areas such as pricing strategies, demand forecasting, and dynamic pricing models.
    • Link: OPAST Publishers – AI and Market Design

Conclusion

Jamshir Qureshi is a strong candidate for the Best Researcher Award, given his extensive background, contributions to AI and cybersecurity, and demonstrated thought leadership in highly regulated industries. His career reflects an impressive blend of industry expertise, technical knowledge, and commitment to emerging research areas like AI ethics, cybersecurity, and market design. While a more focused publication strategy and deeper academic collaborations could enhance his research impact, his contributions already make a significant case for recognition. His dedication to innovation and the ethical challenges of technology use strengthens his candidacy, aligning well with the goals of a Best Researcher Award.