Tadesse Tarik Tamir | Medicine | Editorial Board Member

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Mr. Tadesse Tarik Tamir | Medicine | Editorial Board Member

Senior Lecturer at College of Medicine and Health Sciences, University of Gondar | Ethiopia

Mr. Tadesse Tarik Tamir is a dedicated scholar and lecturer in Pediatrics and Child Health Nursing at the University of Gondar, Ethiopia, with a strong research footprint in maternal and child health. He has authored more than 65 peer-reviewed publications, contributing extensively to areas such as neonatal and infant mortality, childhood nutrition, immunization coverage, maternal vaccination, and pediatric mental health. His work is characterized by rigorous analytical approaches, including spatial, multilevel, and hierarchical modeling applied to large demographic and health survey datasets across Africa. Tadesse’s research offers critical insights into public health challenges affecting vulnerable populations, particularly mothers, infants, and young children in low- and middle-income settings. In addition to his academic role, he actively supports knowledge generation through teaching and community engagement, fostering a supportive environment for emerging researchers. Currently advancing his expertise through studies in epidemiology, he demonstrates a strong commitment to evidence-based practice and interdisciplinary collaboration. His contributions continue to inform policy, improve health service delivery, and shape interventions aimed at reducing preventable morbidity and mortality among children and women across sub-Saharan Africa.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Tamir, T. T., Zegeye, A. F., Workneh, B. S., Ali, M. S., Gonete, A. T., Techane, M. A., Wassie, M., Kassie, A. T., Ahmed, M. A., & Tsega, S. S., et al. (2025). Childhood wasting and associated factors in Africa: Evidence from standard demographic and health surveys from 35 countries. BMC Public Health.

Tamir, T. T., Tekeba, B., Techane, M. A., Alemu, T. G., Wubneh, C. A., & Wassie, Y. A. (2025). Coverage of complete basic childhood vaccination and its variation by basic characteristics among children aged 12–23 months in 41 low- and middle-income countries: A meta-analysis of demographic and health survey reports between 2015 and 2025. Vaccine.

Kassie, A. T., Zegeye, A. F., Bazezew, A. M., Mamo, E. Y., Gebru, D. M., & Tamir, T. T. (2025). Determinants of pressure to conceive among reproductive age women in sub-Saharan Africa: A multilevel analysis of recent demographic and health surveys in five countries. PLOS Global Public Health.

Mekonen, E. G., Workneh, B. S., Zegeye, A. F., & Tamir, T. T. (2025). Only three out of ten women received adequate postnatal care in sub-Saharan Africa: Evidence from 20 countries demographic and health surveys (2015–2022). BMC Pregnancy and Childbirth.

Tekeba, B., Tamir, T. T., & Zegeye, A. F. (2025). Prevalence and determinants of full vaccination coverage according to the national schedule among children aged 12–35 months in Ghana. Scientific Reports.

Jiawen Wang | Medicine | Best Researcher Award

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Dr. Jiawen Wang | Medicine | Best Researcher Award

Surgeon at Longhua Hospital Shanghai University of Traditional Chinese Medicine | China

Jiawen Wang is a distinguished surgeon and deputy chief physician at a leading hospital specializing in Traditional Chinese Medicine, with a focused expertise in colorectal surgery and integrative medicine. She has made significant contributions to the clinical management of anorectal disorders and inflammatory bowel disease, combining traditional Chinese medicine approaches with minimally invasive surgical and endoscopic interventional techniques. As a principal investigator and key researcher in multiple competitive research projects, she has led studies that bridge traditional practices with modern medical science, investigating clinical outcomes and underlying biological mechanisms. Her scholarly output includes numerous peer-reviewed publications, contributions to books, and active participation as a reviewer for several academic journals. Her work has garnered recognition through multiple awards and patents, reflecting innovation and clinical impact. Collaborating with prominent institutions both nationally and internationally, she has advanced the application of integrative medicine in gastroenterology and pelvic floor medicine. She is also actively involved in professional societies, holding leadership and committee roles that influence clinical standards, research priorities, and training programs in colorectal and integrative medicine. Her contributions are characterized by the integration of traditional methodologies with evidence-based research, promoting high standards in both clinical practice and academic scholarship. She has achieved a total of 116 citations, with 112 citations since 2020, an h-index of 4 overall and since 2020, and an i10-index of 2 both total and since 2020, reflecting the recognition and influence of her work within the scientific and medical community. Her research and clinical practice demonstrate a unique capacity to combine innovation, rigorous scientific validation, and practical applicability, establishing her as a leading figure in the advancement of integrative approaches to colorectal and gastrointestinal medicine.

Profile: Orcid | Google Scholar

Featured Publications

1. Wang, J. W., Pan, Y. B., Cao, Y. Q., Wang, C., Jiang, W. D., Zhai, W. F., & Lu, J. G. (2020). Loganin alleviates LPS‐activated intestinal epithelial inflammation by regulating TLR4/NF‐κB and JAK/STAT3 signaling pathways. The Kaohsiung Journal of Medical Sciences, 36(4), 257–264.

2. Wang, J., Pan, Y., Cao, Y., Zhou, W., & Lu, J. (2019). Salidroside regulates the expressions of IL-6 and defensins in LPS-activated intestinal epithelial cells through NF-κB/MAPK and STAT3 pathways. Iranian Journal of Basic Medical Sciences, 22(1), 31.

3. Jiang, X., Chen, X., Dong, R., Wang, J., Pan, Y., & Cao, Y. (2023). Establishment of a mouse model of inflammatory bowel disease using dextran sulfate sodium. Advances in Clinical and Experimental Medicine, 32(5), 563–573.

4. Luo, Q., Wang, J., Ge, W., Li, Z., Mao, Y., Wang, C., & Zhang, L. (2024). Exploration of the potential causative genes for inflammatory bowel disease: Transcriptome-wide association analysis, Mendelian randomization analysis and Bayesian colocalisation. Heliyon, 10(7), [Article 7].

5. Liao, W., Luo, Q., Zhang, L., Wang, H., Ge, W., Wang, J., & Zuo, Z. (2024). Genetic overlap between inflammatory bowel disease and iridocyclitis: Insights from a genome-wide association study in a European population. BMC Genomic Data, 25(1), 92.

Jatinder Kumar | Health Care Services | Best Researcher Award

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Dr. Jatinder Kumar | Health Care Services | Best Researcher Award

IT Professional at PGIMER, Chandigarh, India

Dynamic IT professional with over 25 years of experience in software development, network infrastructure, and IT solutions. Specializes in full-stack development, cloud computing (AWS, Azure), network security, and virtualization technologies. Experienced in integrating business strategy with technology to enhance IT operations. Holds extensive expertise in hospital information systems, data analytics, and artificial intelligence applications in healthcare.

Publication Profile 

Scopus

Educational Background 🎓

  • PhD in Computer Science, Chitkara University (2024)
  • Master of Philosophy (Computer Science), MMU, Sadopur, Ambala, Haryana (2013)
  • Master of Business Administration (MBA), Panjab University, Chandigarh (2006)
  • Master of Computer Applications (MCA), Panjab University, Chandigarh (1997)
  • Bachelor of Science (B.Sc.), M.A.M. College, Jammu (1994)

Professional Experience 💼

  • Software Engineer, Trident InfoTech Corporation Limited, Chandigarh
    • Developed software solutions and contributed to system design and deployment.
    • Worked on enterprise-level applications for various industries.
  • Computer Programmer, PGIMER, Chandigarh (Since 2001)
    • Key contributor to the implementation of the Hospital Information System (HIS) for a 2,500-bed hospital.
    • Designed and developed the inventory module using process flow diagrams and entity-relationship diagrams.
    • Enhanced patient care, reduced wait times, and streamlined administrative workflows.
    • Involved in system integration, cloud-based solutions, and data migration strategies.

Research Interests 🔬

  • Artificial intelligence and deep learning applications in healthcare.
  • Medical imaging and thyroid nodule analysis using deep learning models.
  • Multi-agent system frameworks for efficient hospital administration.
  • Cloud computing and security solutions for healthcare IT systems.
  • e-Governance and digital transformation in public healthcare.

Awards and Honors🏆✨

  • Best Paper Presentation, TELEMEDICON 2024, PGIMER, Chandigarh.
  • Best Paper Presentation, ICAAIML 2024, Hyderabad, India.
  • First Prize in Yoga Competition, International Yoga Day 2024, PGIMER, Chandigarh.
  • Represented Chandigarh in National Yoga Competitions in Telangana and Rajasthan.
  • Conducting yoga classes and educational support for underprivileged children in Chandigarh.
  • Various professional certifications in hardware, Oracle, and MCSE.

Conclusion🌟

With an extensive background in IT, healthcare systems, and artificial intelligence, the author has made significant contributions to hospital information systems and deep learning applications in medical imaging. His research and professional work have enhanced patient care and operational efficiency in healthcare institutions. Recognized for academic excellence and community service, he continues to drive innovation in AI-driven healthcare solutions and IT infrastructure development.

Publications 📚

📖 Kumar, J., Panda, S.N., & Dayal, D. (2023, December). Deep Learning for Improved Thyroid Nodule Analysis in Ultrasound Images. INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR.ORG), Volume 10, Issue 4.

📖 Kumar, J., Panda, S.N., & Dayal, D. (2023). Utilizing Deep Learning Models for the Classification of Thyroid Nodules in Ultrasound Images. The International Journal of Engineering and Science (IJES), Volume 12, Issue 11, Pages 18-31.

📖 Kumar, J., Panda, S.N., & Dayal, D. (2023, December). An Overview of Deep Learning Methods for Segmenting Thyroid Ultrasound Images. International Journal of Advanced Engineering and Nano Technology (IJAENT), ISSN: 2347-6389 (Online), Volume 10, Issue 12.

📖 Kumar, J., Panda, S.N., Dayal, D., & Sharma, M. (2024). Enhancing Thyroid Nodule Assessment with Deep Learning and Ultrasound Imaging. e-Prime – Advances in Electrical Engineering, Electronics, and Energy (Scopus). (Submitted after revision, awaiting acceptance letter.)

🎙️ Kumar, J., Panda, S.N., & Dayal, D. (2023, November). Deep learning routes to thyroid ultrasound image segmentation: A review. AIP Conference Proceedings, Vol. 2878, No. 1. AIP Publishing.

🎙️ Kumar, J., Panda, S.N., Dayal, D., & Sharma, M. (2023, September). Review of Deep Learning Techniques Over Thyroid Ultrasound Image Segmentation. 2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), pp. 320-326. IEEE.

🎙️ Kumar, J., Panda, S.N., & Dayal, D. (2023). Pediatric Thyroid Ultrasound Image Classification Using Deep Learning: A Review. Chitkara University Doctoral Consortium – CUDC 2023.

🎙️ Kumar, J., Panda, S.N., & Dayal, D. (2024). Improving Thyroid Nodule Evaluation Using Deep Learning and Ultrasound Imaging. National Telemedicine Conference – TELEMEDICON 2024, held in Chandigarh (November 28-30, 2024).

 

 

 

 

Hailong Yan | Energy Storage | Best Researcher Award

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Mr. Hailong Yan | Energy Storage | Best Researcher Award

Professor at Nanyang Normal University, China

Shahzeb Khan 🎓✨ is a passionate Data Scientist and Assistant Professor with a flair for innovation and research. With expertise in AI, ML, and data science, he blends academic rigor with practical problem-solving skills. He has been recognized for his research excellence, including the prestigious Best Researcher Award (2024), and holds qualifications like GATE 2020 and UGC-NET 2023. Beyond his professional pursuits, Shahzeb enjoys reading, writing, cooking, solo traveling, swimming, and singing. 🌍📖🎤

Publication Profile : 

Scopus

Educational Background 🎓

Shahzeb Khan is a dedicated Data Scientist and educator with a strong background in information systems analysis, programming, teaching, and research. He earned his M.Tech in Computer Science from Mahatma Gandhi Central University, Bihar, in 2023 with an impressive 8.5 CGPA, after completing his B.Tech from AKTU Uttar Pradesh in 2020 with a 6.8 CGPA. Shahzeb demonstrated academic excellence from an early age, achieving 9.2 CGPA in matriculation (CBSE) in 2013 and 75% in his intermediate studies in 2015.

Professional Experience 💼

Shahzeb is currently serving as an Assistant Professor in Computer Science and Applications at Sharda University, Greater Noida, where he teaches Artificial Intelligence, Machine Learning, Big Data Analytics, Data Science, and Data Analytics (since September 2023). His professional journey also includes a web development internship at IIY Software Pvt. Ltd. in 2019 and key coordination roles, such as the TSML (Time Series Machine Learning) Summer Training Program at MGCUB in 2023. Shahzeb is actively involved in conducting workshops and seminars, including those at IIT Delhi, where he engaged in IoT and Machine Learning training.

Research Interests 🔬

Shahzeb’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, Deep Learning, and Statistical Modeling. He is particularly focused on predictive modeling, hybrid model development, and comparative analysis of advanced algorithms for time-series data. His notable work includes a publication on a novel Hybrid GRU-CNN model for PTB diagnostic ECG time series data in the Biomedical Signal Processing and Control journal, which boasts a 5.9 impact factor.

Publications 📚

  1. Khan, S. (2024). A Novel Hybrid GRU-CNN and Residual Bias (RB) Based RB-GRU-CNN Models for Prediction of PTB Diagnostic ECG Time Series Data. Biomedical Signal Processing and Control, Impact Factor: 5.9.

 

 

Shahzeb Khan | AI in Healthcare | Best Researcher Award

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Mr. Shahzeb Khan | AI in Healthcare | Best Researcher Award

Assistant professor at Sharda University, India

Shahzeb Khan 🎓✨ is a passionate Data Scientist and Assistant Professor with a flair for innovation and research. With expertise in AI, ML, and data science, he blends academic rigor with practical problem-solving skills. He has been recognized for his research excellence, including the prestigious Best Researcher Award (2024), and holds qualifications like GATE 2020 and UGC-NET 2023. Beyond his professional pursuits, Shahzeb enjoys reading, writing, cooking, solo traveling, swimming, and singing. 🌍📖🎤

Publication Profile : 

Google Scholar

Educational Background 🎓

Shahzeb Khan is a dedicated Data Scientist and educator with a strong background in information systems analysis, programming, teaching, and research. He earned his M.Tech in Computer Science from Mahatma Gandhi Central University, Bihar, in 2023 with an impressive 8.5 CGPA, after completing his B.Tech from AKTU Uttar Pradesh in 2020 with a 6.8 CGPA. Shahzeb demonstrated academic excellence from an early age, achieving 9.2 CGPA in matriculation (CBSE) in 2013 and 75% in his intermediate studies in 2015.

Professional Experience 💼

Shahzeb is currently serving as an Assistant Professor in Computer Science and Applications at Sharda University, Greater Noida, where he teaches Artificial Intelligence, Machine Learning, Big Data Analytics, Data Science, and Data Analytics (since September 2023). His professional journey also includes a web development internship at IIY Software Pvt. Ltd. in 2019 and key coordination roles, such as the TSML (Time Series Machine Learning) Summer Training Program at MGCUB in 2023. Shahzeb is actively involved in conducting workshops and seminars, including those at IIT Delhi, where he engaged in IoT and Machine Learning training.

Research Interests 🔬

Shahzeb’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, Deep Learning, and Statistical Modeling. He is particularly focused on predictive modeling, hybrid model development, and comparative analysis of advanced algorithms for time-series data. His notable work includes a publication on a novel Hybrid GRU-CNN model for PTB diagnostic ECG time series data in the Biomedical Signal Processing and Control journal, which boasts a 5.9 impact factor.

Publications 📚

  1. Khan, S. (2024). A Novel Hybrid GRU-CNN and Residual Bias (RB) Based RB-GRU-CNN Models for Prediction of PTB Diagnostic ECG Time Series Data. Biomedical Signal Processing and Control, Impact Factor: 5.9.