Ikram ul Haq | Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Ikram ul Haq
BIT

Ikram ul Haq
Affiliation BIT
Country Pakistan
Documents 1479
Citations 30,887
h-index 77
Subject Area Artificial Intelligence
Event International AI Data Scientists Award

Ikram ul Haq is a distinguished researcher associated with BIT, Pakistan, whose scholarly contributions have significantly influenced the advancement of Artificial Intelligence and related computational disciplines. His extensive publication portfolio, high citation impact, and sustained academic productivity demonstrate a strong commitment to scientific inquiry and innovation. The breadth of his research output reflects continuous engagement with emerging technologies, interdisciplinary collaboration, and the practical application of intelligent systems across diverse domains.[1]

Abstract

This article presents an overview of the academic achievements and research influence of Ikram ul Haq. Through a substantial body of scholarly work, he has contributed to Artificial Intelligence research, producing publications that have received significant attention from the global scientific community. His research impact is reflected through extensive citations and a strong h-index, indicating sustained relevance and influence within the field.[1]

Keywords

Artificial Intelligence, Machine Learning, Computational Intelligence, Data Analytics, Research Excellence, Knowledge Discovery, Intelligent Systems.

Introduction

Artificial Intelligence has become one of the most influential scientific disciplines of the modern era, enabling advances in automation, predictive analytics, intelligent decision-making, and data-driven innovation. Researchers who contribute extensively to this field help shape future technologies and provide solutions to complex societal and industrial challenges. Ikram ul Haq has established a notable academic presence through sustained scholarly activity and impactful research contributions that support the continued evolution of intelligent systems.[2]

Research Profile

  • Affiliation: BIT, Pakistan.
  • Primary research domain: Artificial Intelligence.
  • Total scholarly documents: 1,479.
  • Total citations: 30,887.
  • h-index: 77.

Research Contributions

Ikram ul Haq’s research contributions span a broad range of Artificial Intelligence topics, including intelligent computing, machine learning methodologies, data analytics, and computational modeling. His work has contributed to the advancement of scientific understanding while supporting practical applications across academic and industrial environments. Through collaborative research efforts and consistent publication activity, he has participated in the global exchange of knowledge and technological innovation.[2]

Publications

With 1,479 scholarly publications, Ikram ul Haq demonstrates exceptional research productivity. His publication record reflects long-term engagement with emerging scientific challenges and highlights a commitment to advancing Artificial Intelligence through rigorous investigation and dissemination of findings. The diversity of topics covered within his research portfolio illustrates both depth and breadth of expertise.[1]

Research Impact

The impact of scholarly research is often measured through citation performance and recognition by the academic community. Accumulating more than 30,887 citations and maintaining an h-index of 77, Ikram ul Haq has achieved significant visibility within the research landscape. These indicators suggest that his work continues to influence subsequent studies and contributes meaningfully to ongoing developments in Artificial Intelligence and related fields.[1]

Award Suitability

The Best Researcher Award recognizes individuals who demonstrate sustained scholarly excellence, measurable scientific impact, and meaningful contributions to research advancement. Based on his publication volume, citation metrics, and influence within the Artificial Intelligence community, Ikram ul Haq represents a strong candidate for recognition. His achievements align closely with the objectives of the International AI Data Scientists Award, which promotes innovation, scientific excellence, and global research leadership.[3]

Conclusion

Ikram ul Haq’s academic career reflects a substantial contribution to Artificial Intelligence research through a combination of productivity, influence, and scholarly engagement. His extensive publication portfolio, strong citation record, and continuing impact on scientific literature illustrate a sustained commitment to advancing knowledge. These accomplishments support his consideration for the Best Researcher Award and highlight his role within the broader research community.[1]

References

  1. Google Scholar. (n.d.). Scholar profile and citation metrics of Ikram ul Haq.
    https://scholar.google.com/citations?user=tIrmMlYAAAAJ&hl=en&oi=sra
  2. XAAI-ledger: An explainable CNN-transformer-based multi-modal deep learning framework for early detection of melanoma and non-melanoma skin cancers using dermoscopic and clinical data.
    https://doi.org/10.1016/j.bspc.2026.110410
  3. International AI Data Scientists Award. (n.d.). Award evaluation and recognition framework.
    https://aidatascientists.com/

Preety Shoran | Christ University | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Preety Shoran
Christ University
Preety Shoran
Affiliation Christ University
Country India
Scopus ID 58100727600
Documents 46
Citations 54
h-index 4
Subject Area Artificial Intelligence
Event International AI Data Scientists Award
ORCID 0000-0003-3873-4600

Preety Shoran of Christ University, India. Her academic activities are associated with research in Artificial Intelligence and related computational domains. Based on available bibliometric indicators, the researcher has contributed peer-reviewed publications and demonstrated measurable scholarly engagement through citations and academic dissemination.[1]

Abstract

This article summarizes the academic profile of Preety Shoran, emphasizing research productivity, scholarly visibility, and contributions within Artificial Intelligence. The profile reflects publication activity, citation performance, and participation in contemporary research initiatives relevant to data-driven technologies.[1]

Keywords

Artificial Intelligence, Machine Learning, Data Analytics, Computational Intelligence, Research Impact.

Introduction

Artificial Intelligence has emerged as a transformative discipline influencing science, industry, and society. Researchers contribute by developing methods, models, and applications that support intelligent decision-making and automation. Academic recognition programs acknowledge sustained contributions to these developments.[2]

Research Profile

Preety Shoran is affiliated with Christ University and has established a documented research record comprising 46 indexed publications. The available citation count of 54 and an h-index of 4 indicate ongoing scholarly engagement and contribution to academic literature.[1]

Research Contributions

The research portfolio demonstrates participation in studies related to Artificial Intelligence and computational methodologies. Such work supports knowledge generation, interdisciplinary collaboration, and technological advancement within emerging digital ecosystems.[3]

Publications

  • Peer-reviewed articles in Artificial Intelligence and related fields.
  • Conference proceedings and scholarly communications.
  • Collaborative research publications contributing to scientific literature.

Research Impact

Research impact is commonly evaluated using publication output, citation indicators, and academic visibility. The documented metrics suggest a developing research profile with contributions recognized through citations and scholarly dissemination channels.[1]

Award Suitability

Based on documented academic activities, publication output, and engagement within Artificial Intelligence research, the profile demonstrates attributes commonly considered during evaluations for researcher recognition programs. Assessment remains subject to established award criteria and review procedures.[4]

Conclusion

Preety Shoran’s scholarly profile reflects active participation in academic research and publication activities within Artificial Intelligence. The documented metrics, institutional affiliation, and research engagement provide a foundation for professional recognition and continued scholarly development.

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Preety Shoran, Author ID 58100727600. Scopus.
    https://www.scopus.com/pages/authors/58100727600
  2. Orcid author details: Preety Shoran.
    https://orcid.org/0000-0003-3873-4600
  3. Artificial Intelligence Research Overview.
    https://doi.org/10.1016/j.artint.2023.104001
  4. International AI Data Scientists Award Evaluation Framework.
    https://aidatascientists.com/

Farhat Nasim | Artificial Intelligence | Best Researcher Award

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Ms. Farhat Nasim | Artificial Intelligence | Best Researcher Award

ASSISTANT PROFESSOR GUEST at Jamia Millia Islamia, India

Ms. Farhat Nasim is a dedicated academician and researcher in the field of Control Systems and Instrumentation. With a keen interest in power system optimization and intelligent control methodologies, she has made significant contributions to the development of control strategies for wind power systems. Currently pursuing her Ph.D. at Jamia Millia Islamia, she focuses on designing and implementing intelligent controllers for wind power applications. Her research is driven by a commitment to advancing sustainable energy solutions through novel control techniques. Alongside her research, she serves as an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches various electrical engineering subjects and undertakes additional academic responsibilities.

Profile

Scopus

Education

Ms. Farhat Nasim’s academic journey is marked by excellence in the field of electrical engineering and control systems. She is currently a Ph.D. candidate in Control Systems and Instrumentation at Jamia Millia Islamia, Central University, Delhi, with a dissertation titled “Design and Implementations of Intelligent Controllers for Wind Power System.” Prior to her doctoral studies, she earned her Master of Technology (M.Tech) in Control and Instrumentation from the same institution, further strengthening her expertise in control methodologies. She also holds a Bachelor of Technology (B.Tech) in Electrical Engineering from Jamia Millia Islamia, where she built a strong foundation in electrical power systems and control engineering.

Professional Experience

Ms. Nasim is currently an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches a range of subjects, including Electrical Power Generation, Basics of Electrical Engineering, DC and Synchronous Machines, Control Systems, and Advanced Control Systems. Her commitment to academic excellence extends beyond teaching, as she actively engages in administrative and organizational responsibilities. She has served as the Coordinator for the 6th Semester B.Tech students’ Industrial Visit at Losung Automation Pvt. Ltd., Associate Editor for the Departmental Magazine, Co-convener for the Workshop on Syllabus Revision of the B.Tech (EE) program, and Attendance Compiling In-Charge for all B.Tech semesters. Additionally, she has contributed significantly to laboratory coordination, including managing the Control System Lab and Project Lab for NBA accreditation.

Research Interests

Ms. Nasim’s research interests lie at the intersection of power system optimization, intelligent control, and renewable energy integration. Her primary focus is on the design and implementation of advanced control strategies for wind energy systems, particularly Double-Fed Induction Generators (DFIG). She has worked extensively on hybrid ANFIS-PI-based optimization techniques to enhance power conversion efficiency in wind turbines. Her research also explores Ziegler-Nichols-based controller optimization and crowbar protection mechanisms for DFIG systems. Through her work, she aims to develop more efficient and robust control solutions that contribute to the reliability and sustainability of renewable energy sources.

Awards and Achievements

Ms. Nasim has received recognition for her contributions to research and academia. She has successfully published her work in high-impact journals and presented her findings at reputed international conferences. Her role in academic coordination and syllabus revision has been instrumental in improving the curriculum for electrical engineering students at Jamia Millia Islamia. Her dedication to mentoring students and enhancing laboratory infrastructure has further solidified her reputation as a committed educator and researcher.

Publications

Nasim, F., Khatoon, S., Ibraheem, Urooj, S., Shahid, M., Ali, A., & Nasser, N. (2025). Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine. Sustainability, 17(6), 2454. https://doi.org/10.3390/su17062454 (SCI)

Nasim, F., Khatoon, S., Shahid, M., Baranwal, S., & Ahmad Wani, S. (2024). Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines. Tuijin Jishu/Journal of Propulsion Technology, 45(2). https://doi.org/10.52783/tjjpt.v45.i02.6966 (SCOPUS)

Nasim, F., et al. (2022). Effect of PI Controller on Power Generation in Double-Fed Induction Machine. 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE. doi: 10.1109/ICAC3N56670.2022.10074573.

Nasim, F., et al. (2024). Implementation of Crowbar Protection in DFIG. Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press. (Taylor and Francis Conference)

Nasim, F., et al. (2023). Field Control Grid Connected DFIG Turbine System. International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE. doi: 10.1109/PIECON56912.2023.10085726.

Conclusion

Ms. Farhat Nasim’s dedication to research and education reflects her commitment to advancing knowledge in control systems and renewable energy. Her work in optimizing wind power systems through intelligent control strategies has significant implications for sustainable energy solutions. As an educator, she continues to inspire and mentor students, ensuring that future engineers are equipped with the skills and knowledge necessary to address contemporary challenges in electrical engineering. With her strong academic background, research contributions, and teaching excellence, Ms. Nasim remains a key contributor to the field of control systems and instrumentation.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

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Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.