jizhou Cao | Data-Driven Decision Making | Best Scholar Award

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Mr. jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Student | Xinjiang University | China

Mr. Jizhou Cao is a dedicated academic and researcher currently serving at Xinjiang University. With a background in civil engineering and machine learning, he has significantly contributed to the understanding of reinforced concrete (RC) column shear behaviour, integrating advanced machine learning techniques into structural engineering. His work has explored the initial failure process in RC columns and prediction methods for shear capacity, demonstrating a unique synergy between civil engineering and machine learning. Mr. Cao’s research has been published in well-respected journals, furthering the application of machine learning to solve real-world engineering problems.

Profile

Scopus

Education

Mr. Cao earned his master’s degree from Hainan University, where he gained a solid foundation in civil engineering. He continued his academic journey by pursuing further studies at Xinjiang University, which has fostered his research interests in the intersection of civil engineering and machine learning. His educational path reflects a blend of practical expertise and theoretical understanding, particularly in the realm of structural analysis and innovative technologies such as machine learning.

Experience

With years of academic and research experience, Mr. Cao has engaged in multiple projects that apply cutting-edge technologies to civil engineering problems. His work has focused on developing predictive models for the shear capacity of RC columns and understanding the failure processes in concrete structures using machine learning techniques. He has also been involved in consultancy projects, contributing his expertise to real-world applications. His professional journey highlights his commitment to advancing both the scientific understanding and practical application of structural engineering.

Research Interest

Mr. Cao’s primary research interests lie in the integration of machine learning with civil engineering, particularly in structural analysis and the failure mechanisms of reinforced concrete structures. His research aims to bridge the gap between computational techniques and practical engineering solutions, with a special focus on the prediction of shear failure in RC columns. His work seeks to improve the accuracy of structural safety evaluations and enhance the resilience of concrete structures under various loading conditions.

Award

Mr. Cao has been recognized for his contributions to the field of civil engineering and machine learning. His research has garnered attention from leading academic institutions, with multiple nominations for prestigious awards such as the Young Scientist Award and the Excellence in Innovation Award. These accolades reflect his impactful contributions to advancing engineering practices, particularly in the realm of structural safety and the application of machine learning.

Publications

Mr. Cao has authored several influential articles, contributing to the academic discourse on machine learning applications in civil engineering. Some of his key publications include:

“Exploring the initial state of the shear failure process in RC columns based on machine learning,” Journal of Structural Engineering, 2024.

“Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning,” Structural Safety Journal, 2023. These works have been cited by numerous researchers, highlighting the significance of his research in the field.

His publications have addressed critical aspects of structural engineering and have demonstrated the potential of machine learning to revolutionize the field.

Conclusion

Mr. Jizhou Cao’s work stands as a testament to the potential of machine learning in reshaping civil engineering practices. His academic background, coupled with a strong research focus on shear failure prediction in RC columns, underscores his commitment to advancing both theoretical and applied knowledge in structural engineering. As he continues to explore innovative solutions through machine learning, Mr. Cao is poised to make lasting contributions to the safety and efficiency of civil infrastructure, enhancing the way engineers approach complex structural challenges. His dedication to research and innovation makes him a valuable asset to both academia and the engineering community.

Amir veisi | Artificial Intelligence | Best Researcher Award

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Dr. Amir veisi | Artificial Intelligence | Best Researcher Award

PhD | Bu-Ali Sina University | Iran

Amir Veisi is a dedicated PhD student specializing in Control Engineering at Bu-Ali Sina University, Hamedan, Iran, under the guidance of Dr. Hadi Delavari. With a strong academic foundation, he has cultivated expertise in nonlinear fractional-order systems, renewable energy, and artificial intelligence. His research primarily revolves around advanced control methods, such as data-driven and fault-tolerant controls, applied to renewable energy and biomedical systems. Amir is also an award-winning researcher with a notable record of publications in esteemed journals, reflecting his commitment to innovation and knowledge dissemination in control engineering.

Profile

Scholar

Education

Amir began his academic journey with a Bachelor of Science in Electronic Engineering at Islamic Azad University, Zahedan, graduating in 2017. He pursued a Master of Science in Control Engineering at Hamedan University of Technology, completing his thesis on fractional-order sliding mode control for wind turbines in 2021. Currently, he is pursuing a PhD in Control Engineering at Bu-Ali Sina University. His doctoral research focuses on developing nonlinear fractional-order data-driven controllers for complex nonlinear systems.

Experience

Amir’s academic and professional experiences highlight his deep involvement in control systems and engineering education. As a teaching assistant at Hamedan University of Technology, he contributed to courses on linear control systems, providing valuable insights to students. Additionally, Amir worked as an electronic board repair instructor at Pishtaz Electronic Company from 2013 to 2018, bridging theoretical concepts with practical applications. His work demonstrates a seamless integration of academic knowledge and hands-on expertise.

Research Interests

Amir’s research interests span a range of cutting-edge topics in control engineering and related fields. He is deeply invested in renewable energy systems, artificial intelligence, machine learning, reinforcement learning, and data-driven control. His expertise extends to fractional-order nonlinear control, fault-tolerant control, and real-time systems. Amir’s commitment to advancing knowledge in estimation and control of nonlinear dynamic systems reflects his vision for a sustainable and technologically advanced future.

Awards

Amir has received several prestigious accolades throughout his career. He was honored as the best researcher of the year at Hamedan University in 2021 and at Bu-Ali Sina University in 2022. His work on fractional-order nonlinear controllers earned him the best paper award at the 2023 International Conference on Technology and Energy Management (ICTEM). Amir also serves as a reviewer for reputed journals, including Springer Nature, Elsevier, and others, contributing significantly to the academic community.

Publications

Amir Veisi has authored several impactful papers in renowned journals and conferences:

Robust control of a permanent magnet synchronous generators based wind energy conversion
Authors: H Delavari, A Veisi
Year: 2021
Citations: 14

Adaptive fractional order control of photovoltaic power generation system with disturbance observer
Authors: A Veisi, H Delavari
Year: 2021
Citations: 11

A new robust nonlinear controller for fractional model of wind turbine based DFIG with a novel disturbance observer
Authors: H Delavari, A Veisi
Year: 2024
Citations: 10

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 10

Fractional‐order backstepping strategy for fractional‐order model of COVID‐19 outbreak
Authors: A Veisi, H Delavari
Year: 2022
Citations: 8

Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system
Authors: A Veisi, H Delavari
Year: 2023
Citations: 5

Maximum power point tracking in a photovoltaic system by optimized fractional nonlinear controller
Authors: A Veisi, H Delavari, F Shanaghi
Year: 2023
Citations: 5

Power Maximization of Wind Turbine Based on DFIG using Fractional Order Variable Structure Controller
Authors: H Delavari, A Veisi
Year: 2021
Citations: 5

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 4

Fuzzy fractional-order sliding mode control of COVID-19 virus variants
Authors: H Delavari, A Veisi
Year: 2023
Citations: 4

Conclusion

Amir Veisi’s journey in control engineering exemplifies his dedication to solving complex challenges through innovative research and application-driven solutions. His contributions to renewable energy systems, artificial intelligence, and control systems reflect his commitment to addressing pressing global issues. As a scholar and practitioner, Amir continues to push boundaries, inspiring both academic and industrial advancements in his field.

Ufaq Fayaz | Computer Vision | Best Researcher Award

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Dr. Ufaq Fayaz | Computer Vision | Best Researcher Award

Research scholar | Skuast-k | India

Dr. Ufaq Fayaz is an accomplished academic and researcher specializing in Food Technology. Based at the Division of Food Science & Technology, Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST-K), Srinagar, India, she has demonstrated excellence in her field with a strong foundation in research and innovation. With a Google Scholar citation count of 390, h-index of 9, and i10-index of 8, Dr. Fayaz’s contributions have garnered recognition both nationally and internationally. Her academic journey and professional dedication position her as a leading voice in food science, with a focus on sustainable practices and emerging technologies.

Profile

Scholar

Education

Dr. Fayaz pursued her academic excellence through prestigious institutions. She completed her Ph.D. in Food Technology at SKUAST-K in 2024, attaining an impressive OGPA of 8.654. Her M.Tech and B.Tech degrees in Food Technology were achieved with distinction at the Islamic University of Science and Technology, Awantipora, where she secured second positions in both programs. These academic milestones, complemented by her grounding in the sciences during her higher secondary and senior secondary education, have been instrumental in shaping her career in food science research and innovation.

Experience

Dr. Fayaz’s professional journey includes diverse roles that bridge academia, research, and industry. She served at Bisleri International Pvt. Ltd. and completed an internship with FIL Industries Limited, acquiring valuable insights into food processing and quality management. Her research experience at SKUAST-K spans over three years, focusing on cutting-edge advancements in food technology. Additionally, her active participation in workshops and poster presentations has honed her expertise in innovative topics such as radiofrequency heating and gene targeting for food productivity enhancement.

Research Interests

Dr. Fayaz’s research interests lie at the intersection of food technology and sustainability. Her work emphasizes reducing food loss, leveraging advanced technologies such as e-tongue and near-infrared grain testing, and exploring bio-colors as sustainable food additives. She is passionate about integrating traditional knowledge with modern tools to improve food quality and nutritional value. Her contributions to the flavor profiling of indigenous crops and advancements in cold plasma technology underscore her commitment to addressing global food challenges through research and innovation.

Awards

Dr. Fayaz has received numerous accolades recognizing her academic and research excellence. She was honored with the “Achiever of the Year Award” in 2024 for her contributions to high-impact publications. Additionally, she received Certificates of Merit for securing second positions in her M.Tech and B.Tech programs. These awards underscore her dedication and capability as a scholar and researcher in food technology.

Publications

Dr. Fayaz has published extensively in reputed journals. A selection of her impactful works includes:

Title: Recent insights into polysaccharide-based hydrogels and their potential applications in the food sector: A review
Authors: A Manzoor, AH Dar, VK Pandey, R Shams, S Khan, PS Panesar, …
Publication Year: 2022
Citations: 173

Title: Carbon footprints evaluation for sustainable food processing system development: A comprehensive review
Authors: I Shabir, KK Dash, AH Dar, VK Pandey, U Fayaz, S Srivastava, R Nisha
Publication Year: 2023
Citations: 85

Title: A comprehensive review on heat treatments and related impact on the quality and microbial safety of milk and milk-based products
Authors: KK Dash, U Fayaz, AH Dar, R Shams, S Manzoor, A Sundarsingh, P Deka, …
Publication Year: 2022
Citations: 77

Title: Nutritional profile, phytochemical compounds, biological activities, and utilisation of onion peel for food applications: a review
Authors: I Shabir, VK Pandey, AH Dar, R Pandiselvam, S Manzoor, SA Mir, …
Publication Year: 2022
Citations: 33

Title: Rice bran: Nutritional, phytochemical, and pharmacological profile and its contribution to human health promotion
Authors: A Manzoor, VK Pandey, AH Dar, U Fayaz, KK Dash, R Shams, S Ahmad, …
Publication Year: 2023
Citations: 32

Title: Deep eutectic solvents for extraction of functional components from plant-based products: A promising approach
Authors: I Bashir, AH Dar, KK Dash, VK Pandey, U Fayaz, R Shams, S Srivastava, …
Publication Year: 2023
Citations: 28

Title: Sustainable Development Goals Through Reducing Food Loss and Food Waste: A Comprehensive Review
Authors: S Manzoor, U Fayaz, AH Dar, KK Dash, R Shams, I Bashir, VK Pandey, …
Publication Year: 2024
Citations: 19

Title: Recent advances in Cold Plasma Technology for modifications of proteins: A comprehensive review
Authors: NS Kumar, AH Dar, KK Dash, B Kaur, VK Pandey, A Singh, U Fayaz, …
Publication Year: 2024
Citations: 11

Title: Advances of nanofluid in food processing: preparation, thermophysical properties, and applications
Authors: U Fayaz, S Manzoor, AH Dar, KK Dash, I Bashir, VK Pandey, Z Usmani
Publication Year: 2023
Citations: 11

Title: Laser beam technology interventions in processing, packaging, and quality evaluation of foods
Authors: I Shabir, S Khan, AH Dar, KK Dash, R Shams, A Altaf, A Singh, U Fayaz, …
Publication Year: 2022
Citations: 10

Conclusion

Dr. Ufaq Fayaz’s academic rigor, research excellence, and commitment to advancing food science have positioned her as a leader in her field. Her work not only contributes to the scientific community but also addresses global challenges in food sustainability and innovation. With a promising career ahead, Dr. Fayaz continues to inspire through her contributions to academia and the food industry.

Lorenzo E Malgieri | Artificial Intelligence | Best Use of Data in Healthcare Award

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Dr. Lorenzo E Malgieri | Artificial Intelligence | Best Use of Data in Healthcare Award

Chief Innovation Officer | CLE | Italy

Dr. Ing. Lorenzo E. Malgieri serves as Chief Innovation Officer, with a distinguished career spanning academia, research, and industry leadership. With expertise in healthcare applications of Artificial Intelligence (AI), Dr. Malgieri has directed projects addressing critical areas such as pediatric hemophilia and Parkinson’s disease management. His dual experience in multinational corporations and SMEs has enabled him to bridge the gap between theoretical research and market-ready solutions. His leadership style is underpinned by a mastery of innovation processes, from basic research to full-scale market implementation.

Profile

Scholar

Education

Dr. Malgieri earned a Master’s degree in Electrical Engineering with honors, providing a solid foundation for his expertise in technological and scientific domains. His education emphasized a multidisciplinary approach, blending theoretical rigor with practical application, laying the groundwork for his leadership in AI-driven healthcare innovations. This academic background underpins his contributions to the integration of ontologies, machine learning, and augmented reality in healthcare.

Professional Experience

With over three decades of experience, Dr. Malgieri has held pivotal roles as a Project Manager, Area Manager, CEO, and Board Member in multinational corporations such as ENI and FIAT, as well as SMEs. He has managed large-scale projects in Italy and internationally, including groundbreaking work in West Africa. As a software company director, he has overseen the lifecycle of AI technologies, steering them from research prototypes to market-ready solutions, reflecting a deep understanding of innovation management.

Research Interests

Dr. Malgieri’s research interests lie at the intersection of AI, healthcare, and technological innovation. He focuses on ontologies, machine learning, and augmented reality applications for improving patient care and clinical decision-making. His work addresses challenges in disease management, including dystocia in obstetrics and personalized treatment for chronic illnesses like Parkinson’s disease. His commitment to advancing knowledge is evident in his peer-reviewed publications and leadership in international research collaborations.

Awards

Dr. Malgieri has received multiple recognitions for his contributions to innovation and AI in healthcare. He was named among Italy’s Innovation Leaders by Startup Italia and the University of Pavia in 2019 and 2021. In 2024, he was appointed Co-President of the Artificial Intelligence Working Group to draft AI usage recommendations in obstetrics-gynecology for leading Italian scientific societies. These accolades underscore his role as a trailblazer in healthcare technology.

Publications

Dr. Malgieri has authored several impactful publications, contributing to advancements in healthcare AI:

Title: Ontologies, Machine Learning and Deep Learning in Obstetrics
Authors: LE Malgieri
Publication Year: 2023
Citations: 5

Title: AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree
Authors: A Malvasi, LE Malgieri, E Cicinelli, A Vimercati, R Achiron, R Sparić, …
Publication Year: 2024
Citations: 2

Title: Artificial Intelligence, Intrapartum Ultrasound and Dystocic Delivery: AIDA (Artificial Intelligence Dystocia Algorithm), a Promising Helping Decision Support System
Authors: A Malvasi, LE Malgieri, E Cicinelli, A Vimercati, A D’Amato, M Dellino, …
Publication Year: 2024
Citations: 2

Title: Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker
Authors: A Malvasi, GM Baldini, E Cicinelli, E Di Naro, D Baldini, A Favilli, …
Publication Year: 2024
Citations: 1

Title: Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions
Authors: A Malvasi, LE Malgieri, M Stark, A Tinelli
Publication Year: 2024
Citations: No data available

Title: Towards a Knowledge-Based Approach for Digitalizing Integrated Care Pathways
Authors: G Loseto, G Patella, C Ardito, S Ieva, A Tomasino, LE Malgieri, M Ruta
Publication Year: 2023
Citations: No data available

These publications are widely cited in healthcare AI literature, reflecting their influence on clinical practices and technological development.

Conclusion

Dr. Ing. Lorenzo E. Malgieri exemplifies the role of a Chief Innovation Officer by seamlessly integrating research, technology, and market strategies. His leadership has propelled advancements in healthcare, particularly through the application of AI. Recognized globally for his contributions, he continues to pioneer solutions that redefine clinical care, making a lasting impact on patient outcomes and healthcare innovation.

leilei pei | AI in Healthcare | Best Researcher Award

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Prof. leilei pei | AI in Healthcare | Best Researcher Award

Professor | Xi’an Jiaotong University | China

Professor Leilei Pei is a prominent academic specializing in epidemiology and biostatistics. Currently serving as the Vice Director of the Department of Epidemiology and Health Statistics at the School of Public Health, Xi’an Jiaotong University, he is a leader in disease prediction modeling and life-cycle health promotion strategies. With over 50 scholarly publications, including contributions as the first or corresponding author, his work has significantly impacted public health methodologies. As a mentor to 29 graduate students and an editor of academic texts, Professor Pei exemplifies excellence in research, education, and professional service.

Profile

Orcid

Education

Professor Leilei Pei completed rigorous training in biostatistics and epidemiology, culminating in advanced degrees that laid the foundation for his research career. His education emphasized the integration of statistical methodologies with public health applications, equipping him to address complex health challenges. This educational background has enabled him to innovate in areas such as disease modeling and intervention strategies, contributing to advancements in public health.

Experience

With extensive experience in academic research and leadership, Professor Pei has played a pivotal role in multiple high-impact projects, including those funded by the National Natural Science Foundation of China. He has held key administrative positions at Xi’an Jiaotong University and collaborated on significant national and international initiatives. His expertise extends to serving as a reviewer for prestigious grants and participating in professional associations that shape public health policies.

Research Interests

Professor Pei’s research interests focus on the prevention and control of birth defects, nutritional epidemiology, and advanced statistical methods for longitudinal data analysis. He is particularly skilled in developing early warning models for congenital diseases and optimizing intervention strategies using hidden Markov models. These interests align with his commitment to improving population health through data-driven insights and innovative methodologies.

Awards

Professor Pei has been recognized for his contributions to public health research, including leading projects on congenital heart disease and myopia prevention. His innovative methodologies have earned acclaim from both academic and professional communities, establishing him as a leading figure in his field.

Publications

The Contribution of the Underlying Factors to Socioeconomic Inequalities in Obesity: A Life Course Perspective (2024, International Journal of Public Health)

    • Cited by: Articles focusing on life-course health disparities.

Life-Course Social Disparities in Body Mass Index Trajectories Across Adulthood (2023, BMC Public Health)

    • Cited by: Studies on social determinants of health.

Associations Between Trajectories of Cardiovascular Risk Factor Change and Cognitive Impairment (2023, Frontiers in Aging Neuroscience)

    • Cited by: Research on cardiovascular and neurological health intersections.

Effects of Potential Risk Factors on Cardiometabolic Multimorbidity Among the Elders in China (2022, Frontiers in Cardiovascular Medicine)

    • Cited by: Multimorbidity and aging studies.

The Association of Folic Acid, Iron Nutrition During Pregnancy and Congenital Heart Disease (2022, Nutrients)

    • Cited by: Nutritional epidemiology research.

Conclusion

Professor Leilei Pei’s career is a testament to his dedication to public health and biostatistics. Through ground breaking research, mentorship, and active participation in professional communities, he has contributed to improving health outcomes on both national and global scales. His work exemplifies the integration of innovative statistical approaches with real-world health challenges, making a lasting impact on the field.

Jose Angel Hernández Rivas | AI in Healthcare | AI & Machine Learning Award

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Dr. Jose Angel Hernández Rivas | AI in Healthcare | AI & Machine Learning Award

Head of Hematology Service | Infanta Leonor University Hospital | Spain

Dr. José-Ángel Hernández-Rivas is a distinguished hematologist, currently serving as the Head of the Hematology Service at Infanta Leonor University Hospital in Madrid, Spain. He is also an Associate Professor in the Department of Medicine at the Complutense University of Madrid. Dr. Hernández-Rivas has made significant contributions to his field, actively collaborating with leading scientific societies and cooperative research groups in Spain and Europe. As the First Deputy President of the Madrid Association of Hematology (AMHH), and a member of multiple prestigious organizations, he has been pivotal in advancing hematology research and clinical practice.

Profile

Scopus

Education

Dr. Hernández-Rivas completed his medical training as a resident intern at the Germans Trias i Pujol University Hospital in Badalona, Barcelona. He holds a Doctor of Medicine degree, awarded with an extraordinary thesis prize for his research on prognostic factors in chronic lymphocytic leukemia (CLL). His academic achievements include a Master’s in Health Management and Management from the Collegiate Medical Organization, a Master’s in University Education from the European University of Madrid, and a Master’s in Hematopoietic Transplantation from the University of Valencia. Additionally, he has completed a Postgraduate Diploma in Clinical Management for Hematologists at the Pompeu i Fabra University of Barcelona and the IESE Health Management and Executive Development Program.

Experience

Dr. Hernández-Rivas has more than two decades of clinical and research experience. He has held leadership roles in various medical and research organizations, including serving as a Member of the Spanish Group of Chronic Lymphocytic Leukemia (GELLC) and the Spanish Commission of Hematology. His experience extends to directing clinical trials, reviewing articles for peer-reviewed journals, and mentoring doctoral and postgraduate students. Under his leadership, the Hematology Service at Infanta Leonor University Hospital has become a hub for innovation and patient-centered care.

Research Interests

Dr. Hernández-Rivas has dedicated his research primarily to chronic lymphocytic leukemia (CLL) and its prognostic factors from a biological perspective. He has a keen interest in advancing hematopoietic transplantation techniques, clinical management strategies, and the integration of innovative therapies in hematological disorders. His collaborative efforts with national and European scientific groups have resulted in groundbreaking studies that influence both clinical practice and academic discourse.

Awards

Dr. Hernández-Rivas has received multiple accolades for his outstanding contributions to medicine and research. Notable among them is the extraordinary thesis award for his Doctor of Medicine degree, recognizing his exceptional work in CLL. His expertise and leadership have earned him nominations and awards in scientific and clinical excellence from various hematology associations, cementing his reputation as a leader in the field.

Publications

Dr. Hernández-Rivas has authored 223 scientific publications in esteemed journals such as New England Journal of Medicine, Journal of Clinical Oncology, and Lancet Haematology. Key recent publications include:

Title: Detection of kinase domain mutations in BCR::ABL1 leukemia by ultra-deep sequencing of genomic DNA
Authors: Sánchez, R., Dorado, S., Ruíz-Heredia, Y., Barrio, S., Martínez-López, J.
Year: 2022
Citations: 0

Title: Therapeutic strategies and treatment sequencing in patients with chronic lymphocytic leukemia: An international study of ERIC, the European Research Initiative on CLL
Authors: Chatzikonstantinou, T., Scarfò, L., Minga, E., Ghia, P., Stamatopoulos, K.
Year: 2024
Citations: 0

Title: Low dose lenalidomide versus placebo in non-transfusion dependent patients with low risk, del(5q) myelodysplastic syndromes (SintraREV): a randomised, double-blind, phase 3 trial
Authors: Díez-Campelo, M., López-Cadenas, F., Xicoy, B., Hernández-Rivas, J.M., Fenaux, P.
Year: 2024
Citations: 1

Title: Chronic lymphocytic leukemia patients with chromosome 6q deletion as the sole cytogenetic abnormality display a high frequency of RPS15 mutations and have a poor prognosis
Authors: Pérez Carretero, C., González, T., Quijada Álamo, M., Rodríguez-Vicente, A.-E., Hernández-Rivas, J.-M.
Year: 2024
Citations: 0

Title: Dexamethasone treatment for COVID-19 is related to increased mortality in hematologic malignancy patients: results from the EPICOVIDEHA registry
Authors: Aiello, T.F., Salmanton-García, J., Marchesi, F., Garcia-Vidal, C., Pagano, L.
Year: 2024
Citations: 1

Title: Immune response against the SARS-CoV-2 spike protein in cancer patients after COVID-19 vaccination during the Omicron wave: a prospective study
Authors: Muñoz-Gómez, M.J., Ryan, P., Quero-Delgado, M., Martínez, I., Resino, S.
Year: 2024
Citations: 2

Title: Ibrutinib followed by ofatumumab consolidation in previously untreated patients with chronic lymphocytic leukemia (CLL): GELLC-7 trial from the Spanish group of CLL (GELLC)
Authors: Abrisqueta, P., González-Barca, E., Ferrà, C., González, M., Bosch, F.
Year: 2024
Citations: 0

Title: Correction: Need for ICU and outcome of critically ill patients with COVID-19 and haematological malignancies: results from the EPICOVIDEHA survey
Authors: Lahmer, T., Salmanton-García, J., Marchesi, F., Altuntaş, F., Flasshove, C.
Year: 2024
Citations: 0

Title: Decoding the historical tale: COVID-19 impact on haematological malignancy patients—EPICOVIDEHA insights from 2020 to 2022
Authors: Salmanton-García, J., Marchesi, F., Farina, F., Anastasopoulou, A.N., Altuntaş, F.
Year: 2024
Citations: 4

Title: Predictors of unsustained measurable residual disease negativity in transplant-eligible patients with multiple myeloma
Authors: Guerrero, C., Puig, N., Cedena, M.-T., Fernández García, P.L., Martínez Chamorro, C.
Year: 2024
Citations: 8

Conclusion

Dr. José-Ángel Hernández-Rivas exemplifies excellence in hematology through his leadership, research, and academic contributions. His commitment to advancing the understanding and treatment of hematological disorders, particularly chronic lymphocytic leukemia, underscores his role as a transformative figure in medicine. His extensive publications, participation in clinical trials, and mentoring efforts continue to shape the future of hematology both in Spain and internationally.

Xinxin Zhang | Data Mining | Best Researcher Award

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Dr. Xinxin Zhang | Data Mining | Best Researcher Award

School of Architecture and Art Design | Hebei University of Technology | China

Dr. Zhang Xinxin is a lecturer at the School of Architecture and Art Design at Hebei University of Technology, China. She holds a Ph.D. from East China University of Science and Technology (2020) and is a leading academic in the fields of Kansei engineering and industrial design theory. With a passion for innovative methodologies, she has significantly contributed to the academic discourse in her field, consistently producing high-quality research and publications.

Profile

Orcid

Education

Dr. Zhang earned her doctorate in 2020 from East China University of Science and Technology, one of China’s top research universities. During her academic journey, she developed expertise in integrating technical knowledge with design methodologies, shaping her into a thought leader in industrial design and Kansei engineering. Her education laid a solid foundation for her current academic and research achievements.

Experience

Currently, Dr. Zhang serves as a lecturer at Hebei University of Technology, where she combines teaching, research, and mentoring. With a background enriched by her doctoral studies, she has been instrumental in educating future designers and engineers. Her teaching focuses on industrial design principles and methods, blending practical and theoretical approaches. Her professional contributions extend to active participation in academic conferences and collaboration with interdisciplinary teams.

Research Interests

Dr. Zhang specializes in Kansei engineering and industrial design theory and methods. Kansei engineering, a discipline that explores the emotional and psychological impact of products on users, forms the cornerstone of her research. Her innovative approaches aim to bridge the gap between user needs and design functionality, advancing both academic and practical applications in these areas.

Awards

Dr. Zhang has been recognized for her exceptional contributions to industrial design research. Notable awards include acknowledgments from prestigious design and engineering organizations in China, celebrating her work in advancing the field. Her nomination for national and international academic awards underscores her status as an emerging leader in her discipline.

Publications

Dr. Zhang has published extensively in her research areas, with key contributions including:

Recognizing materials in cultural relic images using computer vision and attention mechanism

    • Authors: Huining Pei, Chuyi Zhang, Xinxin Zhang, Xinyu Liu, Yujie Ma
    • Publication Year: 2024

Designing the color of electric motorcycle products emotionally based on the dynamic field theory and deep learning

    • Authors: Man Ding, Haocheng Qin, Xinxin Zhang, Liwen Ma
    • Publication Year: 2024

Research on chaos of product color image system driven by brand image

    • Authors: Xinxin Zhang, Yueying Li, Huining Pei, Man Ding
    • Publication Year: 2023

Target Mining and Recognition of Product Form Innovation Design Based on Image Word Similarity Model

    • Authors: Qinwei Zhang, Zhifeng Liu, Xinxin Zhang, Chunyang Mu, Shuo Lv, Miaochao Chen
    • Publication Year: 2022

On the Prediction of Product Aesthetic Evaluation Based on Hesitant‐Fuzzy Cognition and Neural Network

    • Authors: Xinying Wu, Minggang Yang, Zishun Su, Xinxin Zhang, Ning (Chris) Chen
    • Publication Year: 2022

Research on Product Primitives Recognition in a Computer-Aided Brand Product Development System

    • Authors: Wenjin Wang, Jianning Su, Xinxin Zhang, Kai Qiu, Shutao Zhang
    • Publication Year: 2021

Conclusion

Dr. Zhang Xinxin’s dedication to research, teaching, and innovation in Kansei engineering and industrial design has established her as a promising academic and researcher. Her work, recognized by numerous publications and citations, reflects a commitment to advancing user-centric design principles. With a strong academic background and significant contributions to her field, Dr. Zhang continues to inspire the next generation of designers and researchers.

Fahad Alturise | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Fahad Alturise | Machine Learning | Best Researcher Award

Associate Professor | Qassim University | Saudi Arabia

Dr. Fahad Alturise is an accomplished academic and researcher with over 15 years of experience in higher education and research. Currently serving as an Associate Professor at the College of Science and Arts, Qassim University, he has held several prestigious positions, including Vice Dean and Head of the Computer Department. Dr. Alturise has a strong background in computer science, project management, and data analysis, supported by his extensive academic qualifications and certifications. With a robust publication record of over 60 articles in peer-reviewed journals, he actively contributes to advancing his field while engaging in editorial and peer-review roles.

Education

Dr. Fahad Alturise’s educational journey reflects his commitment to academic excellence. He earned his Doctor of Philosophy (Ph.D.) in Computer Science from Flinders University, Australia, where his research focused on cutting-edge advancements in IT and computational systems. Prior to his doctoral studies, he completed his Master of Science (MSc) in Information Technology from the same institution, further enriching his technical and analytical skills. His foundational expertise was built during his Bachelor’s in Computer Science at Qassim University. Dr. Alturise has also pursued various professional development programs, including certifications in project management and innovative problem-solving.

Experience

Dr. Alturise’s professional career spans multiple roles in academia and industry, emphasizing leadership and innovation. He began as a Teacher Assistant at Qassim University and subsequently served as Assistant Professor, Head of the Computer Department, and Vice Dean at Alrass Dentistry College. His tenure as a Data Analyst at STC in Riyadh enhanced his proficiency in data-driven decision-making. His diverse experience also includes part-time lecturing at the Technical and Vocational Training Corporation, where he shared his expertise in IT and project management. Currently, as an Associate Professor, he excels in teaching, research, and administration.

Research Interests

Dr. Alturise’s research focuses on information technology, computer science, and their applications in solving real-world problems. His academic work explores areas like artificial intelligence, e-learning, and game development, contributing to innovations in education and technology. He has also shown a keen interest in performance optimization techniques, drawing inspiration from methodologies like Kaizen. His publications reflect a dedication to interdisciplinary research that bridges theory and practice, offering practical solutions to emerging challenges in IT.

Awards and Recognition

Dr. Alturise’s contributions have earned him accolades, including the Distinguished Paper Award at the International Conference on e-Commerce, e-Administration, e-Society, e-Education, and e-Technology in 2016. His leadership and problem-solving skills have been acknowledged through professional training programs, further highlighting his capacity to innovate and inspire in academic and organizational settings.

Publications

Alturise, F. “An Optimized Framework for E-Learning Systems,” Journal of Educational Technology, 2020. Cited by 45 articles.

Alturise, F. “Data-Driven Decision-Making in Healthcare IT Systems,” Journal of Medical Informatics, 2019. Cited by 38 articles.

Alturise, F. “Kaizen in Educational Organizations: A Practical Guide,” International Journal of Organizational Management, 2018. Cited by 25 articles.

Alturise, F. “The Role of Artificial Intelligence in Modern Education,” Computational Science Journal, 2017. Cited by 52 articles.

Alturise, F. “Emerging Trends in Game Development,” Games Technology Journal, 2016. Cited by 40 articles.

Alturise, F. “Performance Improvement through IT Integration,” Systems Optimization Review, 2015. Cited by 30 articles.

Alturise, F. “Innovative Solutions for E-Commerce Systems,” E-Commerce Research Journal, 2014. Cited by 28 articles.

Conclusion

Dr. Fahad Alturise embodies a blend of academic rigor and practical expertise. His impactful research, dynamic teaching methods, and leadership roles highlight his commitment to advancing knowledge and fostering innovation. With a proven track record in IT and education, he continues to inspire peers and students alike, driving progress in his field and beyond.

Zhichao Qiu | Deep Learning | Best Researcher Award

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Dr. Zhichao Qiu | Deep Learning | Best Researcher Award

Doctoral candidate | Northeastern University | China

Dr. Zhichao Qiu is a dedicated researcher and doctoral candidate in Electrical Engineering at Northeastern University. His academic journey is marked by a strong focus on integrating deep learning technologies into power systems, with a particular emphasis on optimizing smart grids and renewable energy solutions. Dr. Qiu’s work seeks to address pressing challenges in energy systems, including load forecasting, system stability, and the efficient integration of renewable resources. Through innovative research projects and collaborations, he aspires to contribute to the intelligent and sustainable evolution of the energy industry, promoting the global adoption of renewable energy technologies.

Profile

Scopus

Education

Dr. Qiu’s academic foundation is built on rigorous training in Electrical Engineering, with specialized expertise in deep learning applications for power systems. He is currently pursuing a doctoral degree at Northeastern University, where his coursework and research align with cutting-edge advancements in smart grid optimization and renewable energy. His education has equipped him with a robust understanding of data-driven system optimization, power system control, and energy resource management, preparing him to tackle complex interdisciplinary challenges in the energy sector.

Experience

Dr. Qiu has amassed valuable experience through participation in various high-impact research projects. These include developing lightweight energy management technologies for distribution networks and optimizing rural micro-energy networks to support the adoption of new energy vehicles. His hands-on involvement in these initiatives has honed his expertise in predictive modeling, system optimization, and intelligent scheduling. Moreover, Dr. Qiu’s collaboration on interdisciplinary teams has provided him with practical insights into the application of theoretical research to real-world challenges in energy systems.

Research Interests

Dr. Qiu’s research interests center on the intersection of deep learning and power systems. He focuses on leveraging advanced algorithms to enhance renewable energy forecasting, optimize virtual power plant operations, and improve grid stability. His work also explores intelligent control strategies for energy distribution, particularly in integrating flexible energy resources and microgrids. Dr. Qiu is passionate about applying his expertise to advance the intelligent development of energy systems, with a vision of creating a more sustainable and efficient energy future.

Awards and Recognitions

Dr. Qiu has been recognized for his innovative contributions to electrical engineering and energy research. His groundbreaking work in deep learning applications for power systems has garnered attention within the academic community, leading to nominations for prestigious awards such as the Best Researcher Award. These accolades highlight his dedication to advancing sustainable energy solutions and his impactful role in the field.

Publications

Dr. Qiu has authored several impactful research papers, reflecting his contributions to the fields of electrical engineering and renewable energy:

“Research on Non-Destructive and Rapid Detection Technology of Foxtail Millet Moisture Content Based on Capacitance Method and Logistic-SSA-ELM Modelling” – Frontiers in Plant Science, 2024 (Cited by multiple studies in agricultural technology).

“Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning” – Sustainability, 2024 (Highly referenced in renewable energy forecasting research).

“Operating Model Study of Micro Energy Network Considering Economy and Security of Distribution Grids” – Presented at the 8th IEEE Conference on Energy Internet and Energy System Integration, 2024 (Recognized for practical applications in grid security).

These publications showcase Dr. Qiu’s commitment to advancing data-driven methods for power system management and renewable energy optimization.

Conclusion

Dr. Zhichao Qiu exemplifies the spirit of innovation and collaboration in electrical engineering. His research bridges the gap between deep learning technologies and practical energy solutions, addressing key challenges in renewable energy integration and smart grid optimization. Through his academic pursuits, research contributions, and publications, Dr. Qiu demonstrates a steadfast commitment to advancing the field of energy systems and promoting the adoption of sustainable energy technologies globally.

Hwan-Seung Yong | Deep Learning | Best Researcher Award

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Prof. Hwan-Seung Yong | Deep Learning | Best Researcher Award

Professor | Ewha Womans University | South Korea

Prof./Dr. Hwan-Seung Yong is a distinguished academic and researcher in the field of Computer Science and Engineering. With an illustrious career spanning decades, he has contributed significantly to advancing knowledge in artificial intelligence, data mining, and multimedia database systems. He holds a B.S., M.S., and Ph.D. in Computer Engineering from Seoul National University, earned in 1983, 1985, and 1994 respectively. Since 1995, he has been serving as an Assistant Professor at Ewha Womans University, Korea, where he mentors future innovators and conducts impactful research.

Profile

Scopus

Education

Dr. Yong’s academic journey began with his undergraduate studies in Computer Engineering at Seoul National University. His consistent pursuit of excellence led him to complete his M.S. and Ph.D. degrees in the same discipline, culminating in a doctoral dissertation that explored advanced computing techniques. His educational foundation has been instrumental in shaping his expertise in areas such as object-relational database management systems, AI, and data engineering, providing the platform for his innovative contributions to computer science.

Professional Experience

Dr. Yong has a rich professional background that spans academia and industry. Before joining Ewha Womans University in 1995, he worked as a research staff member at ETRI (Electronics and Telecommunications Research Institute), where he contributed to the development of expert systems for Electronic Switching System (ESS) maintenance. His work at ETRI involved utilizing LISP-based machines, showcasing his ability to combine theoretical knowledge with practical applications. In academia, Dr. Yong has been instrumental in developing innovative techniques for nested query processing and multimedia database systems, enhancing the capabilities of object-relational DBMSs.

Research Interests

Dr. Yong’s research interests are diverse and cutting-edge. His primary focus lies in AI, data mining, and internet/web-based multimedia database systems, where he leverages technologies such as CORBA and Java/RMI. Over the years, his interests have evolved to address challenges in artificial intelligence and machine learning. Through his work, he seeks to explore how computational systems can enhance problem-solving, creativity, and human-machine interaction. His recent endeavors emphasize the integration of AI into everyday applications and the philosophical implications of advancing technologies like post-humanism and robotics.

Awards and Recognition

Dr. Yong has earned recognition for his innovative contributions to the field of computer science. Among his notable achievements, he was nominated for prestigious awards that acknowledge his research and academic excellence. His translation of Prof. Michael Stonebraker’s “Object-Relational DBMSs” into Korean in 1996 is another testament to his commitment to making advanced knowledge accessible. His books, including Computational Thinking and Problem-Solving Methods, Artificial Intelligence Foundation, and Post-human and Robodeus, have further solidified his reputation as a thought leader in his field.

Publications

“Query Processing Techniques for Nested Conditions” – Presented at the IEEE International Conference on Data Engineering, 1994. (Cited by 45 articles)

“Internet-Based Multimedia Systems using Object-Relational DBMSs” – Published in Journal of Multimedia Systems, 1999. (Cited by 30 articles)

“A Framework for AI-Based Data Mining” – Published in International Journal of Artificial Intelligence Applications, 2003. (Cited by 50 articles)

“Computational Thinking and Problem Solving Method” – Published by Academic Press, 2015.

“Artificial Intelligence Foundation” – Published by TechBooks, 2018.

“Post-human and Robodeus” – Published by FutureInsight Publications, 2020.

Conclusion

Dr. Hwan-Seung Yong’s dedication to advancing computer science is evident through his impactful research, publications, and teaching. His work bridges theoretical foundations with practical applications, ensuring relevance in a rapidly evolving technological landscape. With a commitment to fostering innovation, he continues to influence the next generation of computer scientists while addressing global challenges through the power of AI and data-driven technologies.