Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Posted on by ScienceFatherLeave a Comment on Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Dr. Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Research Fellow at Islamic Azad University Science and Research Branch, Iranđź“–

Dr. Manijeh Emdadi is an accomplished Data Scientist and AI Specialist with 8 years of experience in designing, developing, and deploying machine learning models and data-driven solutions. Currently pursuing her Ph.D. in Artificial Intelligence at the Islamic Azad University, Tehran, her research focuses on exploring explainable AI models for healthcare decision support systems. Dr. Emdadi has a robust background in machine learning, neural networks, and deep learning, and she actively collaborates with cross-disciplinary teams to develop innovative AI solutions.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Artificial Intelligence (In Progress)
    Islamic Azad University Science and Research Branch, Tehran, Iran
    Research Focus: Exploring Explainable AI Models for Healthcare Decision Support Systems
  • Master of Science in Data Science / Artificial Intelligence
    Islamic Azad University Qazvin Branch, Qazvin, Iran
    Thesis: Optimizing Neural Network Architectures for Image Recognition Tasks
  • Bachelor of Science in Computer Engineering
    Iran University of Science and Technology (IUST), Tehran, Iran
    Relevant Courses: Advanced Algorithms

Professional Experience🌱

Dr. Emdadi has a strong professional background as a Data Scientist, collaborating with cross-functional teams to integrate predictive analytics into business workflows. Her expertise spans programming in Python, SQL, and Java, as well as working with data science tools such as Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch. Additionally, she has experience deploying AI/ML models on cloud platforms like Google Cloud. She also serves as a teaching assistant for graduate-level courses on deep learning, sharing her knowledge and expertise with the next generation of AI professionals.

Research Interests🔬

Dr. Emdadi’s primary research interests lie in the intersection of Artificial Intelligence, Machine Learning, and healthcare applications. She is particularly focused on exploring explainable AI models for decision support systems in healthcare, using machine learning and neural networks to solve complex problems in medical data analysis. Her research also includes advancements in deep learning and reinforcement learning, and she is dedicated to creating innovative AI solutions with real-world applications.

Author Metrics

Dr. Manijeh Emdadi has made significant contributions to the academic field, particularly in the domains of Artificial Intelligence, Machine Learning, and healthcare applications. She has authored several impactful publications in high-ranking journals, focusing on areas such as predictive modeling, explainable AI, and healthcare decision support systems. Notable works include her study on “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data,” published in Plos One (2024), and her exploration of grid synchronization methods in power converters, published in Electrical Engineering (2023). Additionally, Dr. Emdadi has authored research on key molecular mechanisms in papillary thyroid carcinoma and developed advanced AI models for predicting cancer metastasis. Her work has been well-received in both the academic and industry sectors, reflecting her expertise in applying AI and machine learning techniques to solve real-world challenges. Her research continues to have a notable impact, especially in healthcare, where her AI-driven models aim to advance personalized medicine and decision support systems.

Publications Top Notes đź“„

1. “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data”

  • Authors: SZ Vahed, SMH Khatibi, YR Saadat, M Emdadi, B Khodaei, MM Alishani, et al.
  • Journal: Plos One
  • Volume: 19
  • Issue: 8
  • Article Number: e0308531
  • Year: 2024
  • DOI: 10.1371/journal.pone.0308531
  • Summary: This paper applies SHAP (Shapley Additive Explanations) values to identify genes associated with lymph node metastasis in breast cancer patients, utilizing mRNA expression data for enhanced model interpretability.

2. “D-estimation method for grid synchronization of single-phase power converters: analysis, linear modeling, tuning, and comparison with SOGI-PLL”

  • Authors: H Sepahvand, M Emdadi
  • Journal: Electrical Engineering
  • Year: 2023
  • Summary: The study proposes a D-estimation method for grid synchronization in single-phase power converters. It provides a detailed analysis, linear modeling, tuning methods, and compares the performance with the traditional SOGI-PLL (Second-Order Generalized Integrator Phase-Locked Loop).

3. “Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm”

  • Authors: SM Hosseiniyan Khatibi, S Zununi Vahed, H Homaei Rad, M Emdadi, et al.
  • Journal: Plos One
  • Volume: 18
  • Issue: 11
  • Article Number: e0293335
  • Year: 2023
  • DOI: 10.1371/journal.pone.0293335
  • Summary: This research uses association rule mining to explore the molecular mechanisms involved in papillary thyroid carcinoma at various stages. The findings aim to reveal biomarkers for early diagnosis and targeted treatment strategies.

4. “Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data”

  • Journal: International Journal of Fuzzy Systems
  • Year: 2024
  • Summary: This paper introduces a Graph Fuzzy Attention Network (GFAN) model for predicting metastasis in prostate cancer using mRNA expression data. The model leverages the strengths of fuzzy logic and graph-based learning for enhanced prediction accuracy.

5. “Load-aware Channel Assignment and Routing in Clustered Multichannel and Multi-radio Mesh Networks”

  • Authors: M Emdadi, MR Shahsavari, MD TakhtFouladi
  • Year: Unspecified
  • Summary: This work discusses the optimization of channel assignment and routing protocols in clustered multi-channel and multi-radio mesh networks, with a focus on load-awareness for efficient resource utilization and network performance.

Conclusion

Dr. Manijeh Emdadi is exceptionally well-suited for the Best Researcher Award due to her pioneering work in artificial intelligence and its application to healthcare decision-making systems. Her strong academic background, innovative research, and commitment to advancing AI for healthcare make her an outstanding candidate. By enhancing collaborations with the industry and expanding her research scope, Dr. Emdadi can continue to build upon her current achievements and make even more significant contributions to both academic and real-world advancements in AI and healthcare.

In summary, Dr. Emdadi’s impressive AI expertise, innovative healthcare solutions, and strong academic contributions strongly align with the qualities sought for the Best Researcher Award.

Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Posted on by ScienceFatherLeave a Comment on Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Dr. Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Postdoc Researcher at Mashhad University of Medical Sciences, Mashhad, Iranđź“–

Dr. Raheleh Ghouchan Nezhad Noor Nia is a Senior Data Scientist and Postdoctoral Researcher specializing in the application of machine learning, artificial intelligence, and medical informatics. She is currently based at the Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Her extensive academic and professional experience spans multiple domains, including medical informatics, AI in medicine, and data science.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

  • Postdoc in Medical Informatics – Data Science & AI in Medicine (2022 – Present), Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Ph.D. in Computer Engineering – Software (2016 – 2022), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Master’s in Computer Engineering – AI and Robotics (2013 – 2015), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Bachelor’s in Computer Engineering – Software (2009 – 2013), Department of Computer Engineering, Azad University, Mashhad, Iran.

Professional Experience🌱

Dr. Ghouchan Nezhad Noor Nia currently serves as a Postdoctoral Researcher and Senior Data Scientist at Mashhad University of Medical Sciences, where she is involved in several pioneering research projects related to AI in healthcare. In addition to her role as a researcher, she is a lecturer at various institutions, including the Department of Computer Engineering at Mashhad Azad University, Khayyam University, and Toos University. She is also actively contributing as a reviewer for prestigious journals such as Materials Today Communication and the Medical Informatics Europe conferences.

Her collaborative efforts extend internationally, having worked with prominent researchers at Karlsruhe Institute of Technology, Germany. Dr. Ghouchan Nezhad Noor Nia has also led and contributed to numerous conferences and workshops focused on AI in medical sciences and health technology.

Research Interests🔬

Dr. Ghouchan Nezhad Noor Nia’s research interests include the intersection of AI, machine learning, and medical informatics. Her focus is on big data mining, social mining, graph mining, material science, and AI applications in medical diagnostics, specifically in diseases like lupus nephritis and pulmonary thromboembolism. She is also interested in ontology engineering, metadata management, health social networks, deep learning, and point-of-interest recommendation systems

Author Metrics

Dr. Ghouchan Nezhad Noor Nia has contributed to numerous high-impact publications and has an active research profile with publications in reputed journals and conferences. Her work focuses on innovative solutions and machine learning methods to solve complex challenges in healthcare and material science. She has co-authored papers presented at various prestigious international conferences, including the 12th Neuroscience Congress and International Health Literacy Congress. Her Google Scholar profile reflects her growing influence in the field.

Publications Top Notes đź“„

1. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 16
  • Article ID: 8021
  • Year: 2022
  • DOI: 10.3390/app12168021
  • Summary: This paper introduces a graph-based k-nearest neighbor (KNN) algorithm for phase prediction in high-entropy alloys, leveraging machine learning techniques for material science applications.

2. Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Mail, Y Ivanisenko, C KĂĽbel
  • Journal: ACS Omega
  • Volume: 7
  • Issue: 15
  • Pages: 12978-12992
  • Year: 2022
  • DOI: 10.1021/acsomega.2c02625
  • Summary: This work focuses on community detection in a high-entropy alloy interaction network using machine learning methods, exploring the structure and relationships between various alloy elements.

3. Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors

  • Authors: A Arzehgar, RG Nezhad Noor Nia, V Dehdeleh, F Roudi, S Eslami
  • Journal: Healthcare Transformation with Informatics and Artificial Intelligence
  • Pages: 503-506
  • Year: 2023
  • Summary: This paper proposes a novel methodology for diagnosing non-alcoholic fatty liver disease (NAFLD) by considering multiple influencing factors and utilizing advanced informatics and artificial intelligence.

4. RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

  • Authors: RG Nezhad Noor Nia, M Jalali
  • Journal: Computational Intelligence and Neuroscience
  • Article ID: 8714870
  • Year: 2022
  • DOI: 10.1155/2022/8714870
  • Summary: The paper presents RecMem, a time-aware recommender system that integrates a memetic evolutionary clustering algorithm, aiming to improve recommendation accuracy in dynamic environments.

5. A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in Material Science

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Journal of Iranian Association of Electrical and Electronics Engineers
  • Volume: 21
  • Issue: 1
  • Year: 2024 (upcoming)
  • Summary: This study proposes a community detection-based approach within social networks to enhance equation analysis methods used in material science, specifically in the context of high-entropy alloys.

Conclusion

Dr. Raheleh Ghouchan Nezhad Noor Nia is a highly deserving candidate for the Best Researcher Award. Her contributions to AI, machine learning, and medical informatics are transformative and have the potential to address critical healthcare challenges. She exhibits strengths in multidisciplinary research, with a particular focus on medical diagnostics and healthcare innovation. With further growth in clinical and practical applications, Dr. Ghouchan Nezhad Noor Nia’s work could have an even broader and more profound impact on both academia and real-world healthcare solutions. Her dedication to research, teaching, and international collaboration makes her an exemplary figure in her field.

Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Posted on by ScienceFatherLeave a Comment on Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Dr. Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Associate Professor, Princess Nourah Bint Abdulrahman University, Saudi Arabiađź“–

Dr. Nithya Rekha Sivakumar is an accomplished academician and researcher, currently serving as an Associate Professor of Computer Science at the College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. She holds a Ph.D. in Computer Science from Periyar University, India, specializing in Mobile Computing and Wireless Networks with Fuzzy and Rough Set Techniques, funded by a prestigious UGC BSR Fellowship. Dr. Sivakumar also earned her M.Phil. in Data Mining, MCA in Computer Applications, and B.Sc. in Computer Science. With over 15 years of academic experience, she has served in diverse roles across reputed institutions in India and Saudi Arabia. Her research interests include wireless networks, mobile computing, data mining, and intelligent systems, with extensive contributions as a researcher, reviewer, and speaker in international conferences and journals. A recipient of multiple awards, including the “Best Distinguished Researcher Award,” she has secured research grants and actively evaluates Ph.D. theses globally. Dr. Sivakumar is also a member of IEEE and IAENG and continues to contribute to advancements in computing through teaching, research, and scholarly activities.

Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Rekha earned her Ph.D. in Computer Science from Periyar University, India, in 2014, supported by the prestigious UGC BSR Fellowship. Her doctoral research focused on mobile computing and wireless networks with fuzzy and rough set techniques. She also holds an M.Phil. in Computer Science from PRIST University (2009), an MCA from IGNOU (2007), and a B.Sc. in Computer Science from Bharathiar University (1996).

Professional Experience🌱

Dr. Rekha has over 15 years of academic and research experience. She has been with Princess Nourah Bint Abdul Rahman University since 2017, progressing from Assistant to Associate Professor. Prior to this, she served as an Assistant Professor at Qassim Private Colleges, Saudi Arabia, and held teaching roles in leading Indian institutions such as Vivekanandha College of Arts and Sciences and Excel Business School. She has also contributed to non-academic roles, including as a Java Programmer and high school teacher.

Research and Service🔬

Dr. Rekha’s research interests span mobile computing, e-governance, and advanced data mining techniques. She has evaluated over 20 Ph.D. theses as a foreign examiner and served as a reviewer for esteemed journals such as IEEE Access, Springer, and Elsevier. A sought-after speaker, she has been invited to international seminars and conferences across the globe, sharing her expertise in computational science and emerging technologies.

Dr. Rekha continues to inspire through her teaching, research, and unwavering commitment to advancing the field of computer science.

Author Metrics 

Dr. Nithya Rekha Sivakumar has an impressive author profile, with a strong presence in international research communities. She has published over 40 papers in reputed journals and conferences, many indexed in Scopus and Web of Science, reflecting her contributions to fields like wireless networks, mobile computing, and data mining. Her work has garnered significant recognition, with an h-index of 12 and over 400 citations, underscoring the impact and relevance of her research. She has authored and co-authored book chapters published by renowned publishers such as Springer and Wiley, further highlighting her expertise. As a sought-after reviewer for top-tier journals, she actively contributes to maintaining the quality of scientific publications. Dr. Sivakumar’s research outputs, combined with her active engagement in scholarly dissemination, establish her as a leading voice in her domain.

Honors and Research Grants

Dr. Rekha has received numerous accolades, including the “Best Distinguished Researcher Award” (2015-2016) and multiple research grants from Princess Nourah Bint Abdul Rahman University, amounting to SAR 40,000 through the Fast Track Research Funding program. She has also been recognized for her doctoral research by the University Grants Commission, India, and secured a travel grant from the Indian Department of Science and Technology to present her work internationally

Publications Top Notes đź“„

“Increasing Fault Tolerance Ability and Network Lifetime with Clustered Pollination in Wireless Sensor Networks”

  • Authors: TKNVD Achyut Shankar, Nithya Rekha Sivakumar, M. Sivaram, A. Ambikapathy
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2020
  • Impact: The paper focuses on improving the fault tolerance and lifespan of wireless sensor networks through an innovative clustered pollination-based approach.

“Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks”

  • Author: NR Sivakumar
  • Journal: Computational Materials and Continua
  • Volume: 64
  • Pages: 81-96
  • Year: 2020
  • Impact: This paper addresses energy stabilization in wireless sensor networks by proposing techniques to manage energy distribution across unequal clusters, enhancing network sustainability.

“Enhancing Network Lifespan in Wireless Sensor Networks Using Deep Learning-based Graph Neural Network”

  • Authors: NR Sivakumar, SM Nagarajan, GG Devarajan, L Pullagura, et al.
  • Journal: Physical Communication
  • Volume: 59
  • Article No.: 102076
  • Year: 2023
  • Impact: The paper investigates how deep learning-based graph neural networks can be used to enhance the lifespan of wireless sensor networks, marking a significant contribution to AI-powered network optimization.

“Simulation and Evaluation of the Performance on Probabilistic Broadcasting in FSR (Fisheye State Routing) Routing Protocol Based on Random Mobility Model in MANET”

  • Authors: NR Sivakumar, C Chelliah
  • Conference: 2012 Fourth International Conference on Computational Intelligence
  • Year: 2012
  • Impact: This study explores the performance of the Fisheye State Routing (FSR) protocol in mobile ad hoc networks (MANETs), with an emphasis on the effects of random mobility models on network behavior.

“An IoT-based Big Data Framework Using Equidistant Heuristic and Duplex Deep Neural Network for Diabetic Disease Prediction”

  • Authors: NR Sivakumar, FKD Karim
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2023
  • Impact: This paper presents an IoT-based framework utilizing big data and deep learning for predicting diabetic diseases, offering a new approach to healthcare prediction systems through advanced technologies.

Conclusion

Dr. Nithya Rekha Sivakumar is a deserving candidate for the Best Researcher Award. Her impressive research accomplishments, strong publication record, innovative contributions to wireless networks and mobile computing, and active engagement in the academic community make her an outstanding researcher. Although there are areas for improvement, particularly in interdisciplinary collaboration and public outreach, her overall research trajectory and impact are exemplary. Dr. Sivakumar’s continuous pursuit of excellence in her field and her ability to address contemporary challenges in mobile computing, data mining, and wireless networks position her as a leading researcher in her domain. She is highly recommended for the Best Researcher Award.