Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

Faculty at North Eastern Mindanao State University | Philippines

Dr. Sheilla Ann Pacheco is an accomplished academic and researcher with extensive experience in computer science, particularly in the fields of machine learning, image processing, and adversarial defense. With over nine years of academic service, she has established herself as a dedicated educator, mentor, and innovator who contributes significantly to both research and teaching. Her work spans practical and theoretical domains, addressing challenges in privacy-preserving AI, biometrics, and medical applications such as breast cancer prediction. Dr. Pacheco is actively involved in presenting her research at national and international conferences, where she has received recognition for her contributions. She is also engaged with professional organizations such as IEEE and ACM, which allows her to remain connected with global advancements in her field. Combining strong technical expertise, leadership in research, and dedication to academic growth, she continues to advance computer science while inspiring students and peers alike.

Professional Profiles 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Sheilla Ann Pacheco has pursued her academic journey with determination and excellence in the field of computer science. She earned her Bachelor of Science in Computer Science from Surigao del Sur State University, laying the foundation for her career in research and academia. She further advanced her studies by completing her Master of Science in Computer Science at the same institution, where she deepened her knowledge of programming, data processing, and research methodologies. To further enhance her expertise, she is currently completing her Doctor of Philosophy in Computer Science at the Technological Institute of the Philippines, focusing on advanced topics such as machine learning, adversarial defense, and computational intelligence. Her academic path highlights her continuous commitment to lifelong learning and growth in her field. Through her education, she has developed the strong theoretical and practical background that now underpins her teaching, supervision, and impactful research contributions.

Professional Experience

Dr. Sheilla Ann Pacheco has built a solid professional career as an academic and researcher in the field of information technology. She currently serves as an Assistant Professor at North Eastern Mindanao State University, where she teaches a variety of courses, supervises research, and contributes to the development of the academic community. Over the years, she has guided students in their research projects, emphasizing innovation and practical applications of computer science in areas such as artificial intelligence and data processing. Her experience is not only limited to classroom teaching but also extends to participation in academic conferences, workshops, and seminars where she presents her work and collaborates with other professionals. Her professional journey demonstrates a balance of academic leadership, technical expertise, and a commitment to advancing knowledge. Through her role, she continues to inspire students and colleagues while contributing to the university’s mission of research and innovation.

Research Interest

Dr. Sheilla Ann Pacheco’s research interests lie in the fields of machine learning, image processing, adversarial defense, and privacy-preserving artificial intelligence. She has a particular focus on developing intelligent solutions that enhance the security and accuracy of biometric recognition systems, as reflected in her work on SARGAN-based face recognition and hidden adversarial attacks on facial biometrics. In addition, she explores federated learning models that aim to protect user privacy while enabling effective AI applications. Her research also extends to healthcare, where she has contributed to studies such as breast cancer prediction using ensemble techniques. These areas highlight her commitment to addressing real-world challenges through innovative technologies. By integrating theoretical models with applied solutions, her research contributes both to the scientific community and to society at large. Her future directions aim to expand collaborations in international research networks and further explore secure, ethical, and intelligent AI applications.

Research Skill

Dr. Sheilla Ann Pacheco possesses a wide range of research skills that enable her to excel in both academic and applied studies. Her expertise includes image processing, machine learning, and adversarial defense, which she has applied in developing innovative solutions for biometric recognition and healthcare prediction models. She is proficient in programming, data analysis, and the use of advanced computational tools, allowing her to conduct rigorous and high-quality research. Her skills in academic writing and presentation have enabled her to publish and present her work at reputable conferences and to effectively communicate her findings to diverse audiences. She is also skilled in research supervision, guiding students through the research process and fostering a culture of inquiry and innovation. Combined with strong organizational and leadership skills, she demonstrates the ability to collaborate with peers, contribute to multidisciplinary projects, and advance knowledge in her field through impactful and practical research outcomes.

Publications Top Notes

Title: Enhanced content-based image retrieval using multivisual features fusion
Authors: SAB Pacheco, M Goyani, ZG Rehman, SF Rehman, T Champaneria, …
Year: 2025
Citation: 1

Title: Robust Face Recognition Under Adversarial Attack Using SARGAN Model and Improved Cross Triple MobileNetV1
Authors: SAB Pacheco, JE Estrada, MM Goyani
Year: 2025

Title: Least Variance based Modeling of Heart Disease Prediction System using Ensemble Technique
Authors: SA Pacheco, JP Bangoy, ZG Rehman, SF Rehman, SV Goyani, …
Year: 2025

Title: Hidden adversarial attack on facial biometrics – a comprehensive survey
Authors: MMG Sheilla Ann Bangoy Pacheco, Jheanel Espiritu Estrada
Year: 2025

Title: A Comprehensive Survey on Federated Learning and Its Applications in Health Care
Authors: SAB Pacheco
Year: 2024

Title: Performance of Students in Computer Programming: An Analysis
Authors: IB Christian, SA Pacheco
Year: 2023

Title: Breast Cancer Prediction using Ensemble Technique
Authors: SAB Pacheco
Year: 2022

Title: Trends and Analysis of Graduate Programs
Authors: SAB Pacheco
Year: 2022

Conclusion

Dr. Sheilla Ann Pacheco is a deserving candidate for the Best Researcher Award due to her impactful contributions in machine learning, image processing, and adversarial defense, which address critical challenges in biometrics, privacy-preserving AI, and healthcare applications. Her research outputs, academic leadership, and active involvement in professional organizations highlight her commitment to advancing both scientific knowledge and the research community. With her strong academic foundation, proven dedication, and potential for expanding her influence through future international collaborations and innovative projects, she is well-positioned to make even greater contributions to research and society in the years ahead.

Rohan Duppala | Machine Learning | Young Researcher Award

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Mr. Rohan Duppala | Machine Learning | Young Researcher Award

Student at VIT-AP University, India

Rohan Duppala is an emerging researcher and technologist with a strong foundation in artificial intelligence, machine learning, deep learning, and natural language processing. As a final-year B.Tech Computer Science student at VIT-AP University, he has demonstrated exceptional research capabilities, developing innovative solutions in healthcare, education, and smart transportation. Rohan has published multiple papers in reputed journals, including Scientific Reports and MDPI, and has worked on diverse AI-driven projects, from infant cry classification and Alzheimer’s detection to generative AI-based educational tools. His ability to integrate advanced AI models with real-world applications reflects a rare combination of academic rigor and practical insight. In addition to academic work, Rohan has engaged with leading technologies like Gemini, Llama 3, and Weights & Biases, earning several certifications and accolades. With a forward-thinking mindset and a passion for impactful research, he aspires to contribute meaningfully to global challenges through AI and interdisciplinary innovation.

Professional Profile 

Education🎓

Rohan Duppala is currently pursuing his Bachelor of Technology in Computer Science and Engineering at VIT-AP University in Andhra Pradesh. His education has provided a rigorous grounding in core computer science principles while enabling him to explore advanced technologies such as artificial intelligence, machine learning, and natural language processing. Prior to his undergraduate studies, he completed his intermediate education in the Mathematics, Physics, and Chemistry (MPC) stream at Narayana Junior College in Visakhapatnam. He also completed his schooling at Sri Chaitanya School in Palasa, Andhra Pradesh. Throughout his academic journey, Rohan has demonstrated consistent excellence and a strong inclination toward analytical thinking and computational problem-solving. This solid educational background has laid the foundation for his research endeavors and technical accomplishments in AI, edge computing, and intelligent systems.

Professional Experience📝

Rohan gained hands-on industry experience as an IoT Specialist at Prayana Electric between June and August 2024. During his tenure, he was instrumental in integrating IoT solutions into electric bicycles, leveraging microcontrollers, GPS, and LoRa technologies to enable real-time monitoring and smart navigation. He also spearheaded the development of a pothole detection system, optimized specifically for edge deployment on Raspberry Pi Pico, seamlessly integrating it into the e-bike ecosystem. This professional experience not only expanded his understanding of smart transportation and embedded systems but also allowed him to apply theoretical AI knowledge to practical, scalable solutions. Rohan’s work at Prayana Electric reflects his ability to bridge the gap between academic research and industry requirements, highlighting his skills in system design, data analysis, and sensor integration. His initiative, problem-solving abilities, and adaptability in a real-world setting underscore his potential as a well-rounded researcher and future technology leader.

Research Interest🔎

Rohan Duppala’s research interests lie at the intersection of artificial intelligence, healthcare, education, and smart systems. He is particularly focused on building explainable and ethically sound AI systems that can be deployed in real-world settings. His work in medical diagnostics, including projects on Alzheimer’s and Parkinson’s disease detection using deep learning, underscores a commitment to socially impactful research. Rohan also explores Generative AI and Large Language Models, applying them to applications such as automated script evaluation, infant care, and educational feedback systems. His interest in edge AI and IoT-enabled smart devices reflects a drive to create scalable, efficient, and context-aware solutions for real-time environments. By combining transformer models, retrieval augmented generation (RAG), signal processing, and explainable AI (XAI) techniques, Rohan aims to push the boundaries of intelligent automation in human-centric domains. His interdisciplinary approach and ethical consideration make his research both innovative and future-ready.

Award and Honor🏆

Rohan Duppala has been recognized for his academic and technical excellence with several awards and honors. He received a Certificate of Honor from OpenCV University in recognition of his outstanding performance in deep learning applications and project execution. Additionally, he was awarded a unique NFT (Non-Fungible Token) honor from The Hashgraph Association, celebrating his innovation and engagement in the decentralized technology community. His publications in reputed platforms such as Scientific Reports (Nature) and MDPI further reflect the quality and impact of his research. These accolades highlight not only his technical achievements but also his ability to stand out in competitive, global academic and developer communities. His participation in specialized bootcamps, advanced workshops, and certificate programs offered by Google, AWS, Cisco, and Weights & Biases has further solidified his position as an accomplished early-career researcher poised for excellence in AI-driven innovation.

Research Skill🔬

Rohan Duppala possesses an advanced and versatile skill set tailored for research in artificial intelligence and related domains. He is proficient in Python and Java, with hands-on experience in building custom convolutional neural networks, implementing transformer models, and deploying real-time deep learning systems on edge devices like Raspberry Pi. His practical expertise spans data preprocessing, hyperparameter tuning, model evaluation, explainable AI (XAI) techniques like LIME and Saliency Maps, and fine-tuning large models like LLaMA 3 8b using LoRA. He has also worked with tools such as Hugging Face Transformers, Weights & Biases, and Streamlit for model development and deployment. Rohan is skilled in retrieval augmented generation (RAG), multimodal data processing, and prompt engineering. His ability to combine AI techniques with IoT, computer vision, and NLP enables him to develop interdisciplinary solutions. These research skills, backed by strong implementation and critical thinking abilities, make him a technically mature and innovation-ready researcher.

Conclusion💡

Rohan Duppala is a highly deserving candidate for the Best Researcher Award, owing to his exceptional drive, technical acumen, and impactful research contributions at an early stage of his academic journey. His work spans critical societal applications—from medical diagnostics using deep learning to educational tools powered by large language models—demonstrating both depth and relevance. With peer-reviewed publications in reputed journals like Scientific Reports and MDPI, and hands-on innovation in smart technologies and AI systems, he has already laid a strong foundation for a distinguished research career. Given his dedication, continuous learning, and visionary approach to solving real-world problems, Rohan holds immense potential for future leadership in the fields of artificial intelligence and intelligent healthcare systems.

Publications Top Noted✍

  • Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
    Authors: D. Rohan, G.P. Reddy, Y.V.P. Kumar, K.P. Prakash, C.P. Reddy
    Year: 2025
    Citations: 4

  • Title: A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection
    Authors: P. Reddy G., S.M.A. Kareem, Y.V.P. Kumar, P.P. Kasaraneni, M. Janapati
    Year: 2025
    Citations: 1

  • Title: Artificial intelligence-based effective detection of Parkinson’s disease using voice measurements
    Authors: G. Pradeep Reddy, D. Rohan, Y.V.P. Kumar, K.P. Prakash, M. Srikanth
    Year: 2024
    Citations: 1

Assist Prof Dr. Sina Fard Moradinia | Machine Learning | Editorial Board Member

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Assist Prof Dr. Sina Fard Moradinia | Machine Learning | Editorial Board Member

Sina Fard Moradinia at Islamic Azad University, Iran

👨‍🎓 Profiles

Scopus

Orcid

Summary

Dr. Sina Fard Moradinia is a dedicated Assistant Professor in Civil Engineering with over 20 years of experience specializing in water resources management. He has made significant contributions to both academia and industry through research, teaching, and numerous projects. Dr. Moradinia has been involved in leadership roles, including serving as the Head of the Water Resources Management Working Group and a member of various scientific councils and committees. He is also an accomplished author with expertise in project management, hydraulic structures, and innovative water management techniques.

Education

  • Ph.D. in Civil Engineering (Water Resources) – Iran University of Science and Technology, Tehran, Iran, 2014

💼 Professional Experience

  • Assistant Professor – Islamic Azad University, Tabriz (2002–Present)
  • Head of Water Resources Management Working Group – East Azerbaijan Province Elite Foundation
  • University Representative – Skills Training and Career Counseling Center
  • Civil Engineering Expert – Faraz Ab Consulting Engineers
  • Engineering Services Expert – Dam and Network Activities, River Engineering

🔬 Research Interests

Dr. Moradinia’s research interests focus on water resources management, hydraulic structures, and construction project optimization. His work spans advanced machine learning applications in flood risk assessment, time and cost management in dam projects, and BIM integration in civil engineering. He is also actively engaged in addressing environmental issues such as controlling dust storms in the Urmia Lake basin.

 

Publications

Economic and environmental analysis of EVs’ in urban transportation using system dynamics

  • Authors: Azarnoosh, Z., Moradinia, S.F., Golchin, B., Jani, R.
  • Journal: Sustainable Futures
  • Year: 2024

A novel approach to flood risk zonation: integrating deep learning models with APG in the Aji Chay catchment

  • Authors: Bina, A.A., Moradinia, S.F.
  • Journal: Aqua Water Infrastructure, Ecosystems and Society
  • Year: 2024

Wavelet–ANN hybrid model evaluation in seepage prediction in nonhomogeneous earthen dams

  • Authors: Fatehi-Nobarian, B., Fard Moradinia, S.
  • Journal: Water Practice and Technology
  • Year: 2024

Time and Cost Management in Water Resources Projects Utilizing the Earned Value Method

  • Authors: Hussein, A.R., Moradinia, S.F.
  • Journal: Journal of Studies in Science and Engineering
  • Year: 2024

The prediction of precipitation changes in the Aji-Chay watershed using CMIP6 models and the wavelet neural network

  • Authors: Khoramabadi, F., Moradinia, S.F.
  • Journal: Journal of Water and Climate Change
  • Year: 2024

Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

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Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

Professor at Zaporizhzhia State Medical University, Ukraine

Profiles

Scopus

Orcid

Google Scholar

📚 Summary

Prof. Igor Fedorovich Belenichev is a distinguished Full Professor and Head of the Department of Pharmacology and Medical Formulation at Zaporizhzhia State Medical University. Renowned for his innovative research in neuroprotection and pharmacology, he is a laureate of the Cabinet of Ministers of Ukraine Prize for the development and implementation of groundbreaking technologies.

Education

  • Zaporizhzhia State Medical Institute (1988): Graduated with a degree in medicine.
  • Postgraduate studies (1988), professor assistant (1991), senior teacher (1999), associate professor (2004), and full professor (2006).

💼 Professional Experience

  • Zaporizhzhia State Medical University: Head of the Department of Pharmacology and Medical Formulation since 2005.
  • Main Scientific Researcher at «Pharmatrone» (since 1993).
  • Head of the regional branch of the Association of Pharmacologists of Ukraine.
  • Co-worker of the regional group of the National Expert Centre of the Ministry of Health of Ukraine.

🔬 Research Interests

Prof. Belenichev’s research focuses on the molecular and biochemical mechanisms of ischemic brain damage and the development of effective neuroprotectors. His work explores the roles of reactive oxygen and nitrogen species, thiol-disulfide systems, pro-/anti-apoptotic proteins, estrogen receptors, and endogenous neuroprotection factors. He also investigates drugs for CNS pathologies and effective neuro- or cardioprotectors from derivatives of 1,2,4-triazole, chinazoline, and xanthine.

🏆 Achievements

  • Scientific Works: Authored and co-authored 715 scientific publications.
  • Patents: Holder of 182 patents in Ukraine and the Russian Federation.
  • Theses: Supervised 3 Dr. Habs and 7 Ph.D. theses.
  • Drug Development: Contributed to the creation of drugs like Thiotriazoline, Thiocetam, and Thiodarone.
  • Awards: Token of the Bibliographical Society of America (2003), Regional Program “Zoryaniy Shlyakh” Prize (2000), and Cabinet of Ministers of Ukraine Prize (2017).

 

Publications

5+1-Heterocyclization as preparative approach for carboxy-containing triazolo[1,5-c]quinazolines with anti-inflammatory activity

  • Authors: Krasovska, Natalya; Berest, Galina; Belenichev, Igor; Severina, Hanna; Nosulenko, Inna; Voskoboinik, Oleksii; Okovytyy, Sergiy; Kovalenko, Serhii
  • Journal: European Journal of Medicinal Chemistry
  • Year: 2024

Beta-Blockers of Different Generations: Features of Influence on the Disturbances of Myocardial Energy Metabolism in Doxorubicin-Induced Chronic Heart Failure in Rats

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Biomedicines
  • Year: 2024

Characteristics of HIF-1α and HSP70 mRNA Expression, Level, and Interleukins in Experimental Chronic Generalized Periodontitis

  • Authors: Parkhomenko Daria; Igor Belenichev; Kuchkovskyi Oleh; Ryzhenko Victor
  • Journal: MicroRNA
  • Year: 2024

Comparative Analysis of the Effect of Beta Blockers of Different Generations on the Parameters of Myocardial Energy Metabolism in Experimental Doxorubicin-Induced Chronic Heart Failure

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Preprint
  • Year: 2024

Development and Optimization of Nasal Composition of a Neuroprotective Agent for Use in Neonatology after Prenatal Hypoxia

  • Authors: Igor Belenichev; Olena Aliyeva; Bogdan Burlaka; Kristina Burlaka; Oleh Kuchkovskyi; Dmytro Savchenko; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Pharmaceuticals
  • Year: 2024

Dr. Na Yi | Deep Metric Learning | Best Researcher Award

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Dr. Na Yi | Deep Metric Learning | Best Researcher Award

Doctorate at Heilongjiang University of Science and Technology, China

Profiles

Scopus

Orcid

Academic Background

Dr. Na Yi, born in June 1997 in Acheng, Harbin, is an Associate Professor and a committed member of the Communist Party of China. With a strong academic foundation in Electrical Engineering and Automation, she has quickly risen as a prominent figure in the field of Petroleum and Natural Gas Engineering.

Education

Dr. Na Yi graduated with a degree in Electrical Engineering and Automation from Northeast Petroleum University in 2019. She was subsequently recommended for a doctoral program in Petroleum and Natural Gas Engineering, during which she also studied at Southeast University, earning her doctorate in 2024.

Professional Experience

Throughout her career, Dr. Na Yi has published over 20 research papers in esteemed journals, with 10 SCI-indexed and 5 EI-indexed papers, including highly cited and hot papers. She holds 6 national patents and has participated in 5 significant scientific research projects. Her achievements have earned her more than 10 national and provincial awards.

Research Interests

Dr. Na Yi’s research interests lie in Petroleum Engineering, with a focus on sustainable energy, power systems, and technological innovation. She is an active reviewer for multiple international and Chinese academic journals and has been invited to present her research at several international and domestic conferences.

 Publications

A multi-stage low-cost false data injection attack method for power CPS

  • Authors: Yi, N., Xu, J., Chen, Y., Pan, F.
  • Journal: Zhejiang Electric Power
  • Year: 2023
A New Distributed Power Supply for Distribution Network Considering SOP Access
  • Authors: Peng, C., Xu, J., Zhao, S., Yi, N.
  • Year: 2023
Multi-stage coordinated cyber-physical topology attack method based on deep reinforcement learning
  • Authors: Yi, N., Xu, J., Chen, Y., Sun, D.
  • Journal: Electric Power Engineering Technology
  • Year: 2023
A multi-stage game model for the false data injection attack from attacker’s perspective
  • Authors: Yi, N., Wang, Q., Yan, L., Tang, Y., Xu, J.
  • Journal: Sustainable Energy, Grids and Networks
  • Year: 2021
Insulator Self-Explosion Defect Detection Based on Hierarchical Multi-Task Deep Learning
  • Authors: Xu, J., Huang, L., Yan, L., Yi, N.
  • Journal: Diangong Jishu Xuebao/Transactions of China Electrotechnical Society
  • Year: 2021

Ms. Mina Gachloo | Machine Learning | Best Researcher Award

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Ms. Mina Gachloo | Machine Learning | Best Researcher Award

Mina Gachloo at university of North Carolina Wilmington, United States

Profiles

Scopus

Orcid

Google Scholar

Education:

Ms. Mina Gachloo is set to graduate with a Master's degree in Computer Science and Information Systems from the University of North Carolina Wilmington in December 2024, boasting a GPA of 3.8. Her research at UNCW, under the guidance of Dr. Yang Song, focuses on "Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Deep Learning." Mina also holds a Bioinformatics Engineering degree from Huazhong Agricultural University in Wuhan, China, where she researched "Embedding of Cancer-Centric Entities and Knowledge Discovery." Additionally, she earned a B.Sc. in Information Technology from the University of Applied Science and Technology in Tehran, and an Associate’s degree in Information and Communication Technology from the University of Culture and Art in Tehran.

Professional Experience:

Ms. Mina has a diverse range of professional experiences. In 2023, she interned as a Data Analyst and Researcher at the UNCW Institutional Research Office, focusing on predicting the time-to-graduate for undergraduate students. As a Teaching and Research Assistant at UNCW, she conducted data cleaning, feature engineering, and implemented machine learning and deep learning models for predictive analysis. Prior to this, she worked as a Commercial Expert at Bam Khodro Company in Tehran, using SQL, Excel, and Python for data extraction, analysis, and report automation. Mina also served as a Financial Expert at Mellat Leasing Bank, facilitating loans and generating customer reports. Her career began in IT at KICCC in Tehran, specializing in Point of Sale devices and Business Intelligence.

Research Interest:

Ms. Mina's research interests include machine learning, deep learning, structure learning, data analysis, natural language processing, and computer vision. She has actively contributed to several projects, such as the construction of the Active Gene Annotation Corpus (AGAC) and the prediction of dissolved oxygen and salinity levels in the Neuse River Estuary using deep learning techniques.

Honors and Awards:

Ms. Mina's academic excellence has been recognized with multiple awards, including the Summer Research Stipend, Support for Undergraduate Research and Creativity Awards (SURCA), and CMS summer stipend at UNCW. She also received scholarships for her M.Sc. in Computer Science and Bioinformatics at Huazhong Agricultural University.

💡 Skills:

Ms. Mina is proficient in data preprocessing, machine learning, deep learning, natural language processing, and programming in Python and Java. She is skilled in using tools like Jupyter Notebook, Google Colab, Pycharm, IDLE, Microsoft Office, Pegasus, SQL, and Oracle.

 Publications:

Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary
  • Authors: Mina Gachloo, Qianqian Liu, Yang Song, Guozhi Wang, Shuhao Zhang, Nathan Hall
  • Year: 2024
An overview of the active gene annotation corpus and the BioNLP OST 2019 AGAC track tasks
  • Authors: Yuxing Wang, Kaiyin Zhou, Mina Gachloo, Jingbo Xia
  • Year: 2019
A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
  • Authors: Mina Gachloo, Yuxing Wang, Jingbo Xia
  • Year: 2019
GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease
  • Authors: Kai Yin Zhou, Yu Xing Wang, Sheng Zhang, Mina Gachloo, Jin Dong Kim, Qi Luo, Kevin Bretonnel Cohen, Jing Bo Xia
  • Year: 2019
An active gene annotation corpus and its application on anti-epilepsy drug discovery
  • Authors: Yuxing Wang, Kaiyin Zhou, Jin-Dong Kim, Kevin B Cohen, Mina Gachloo, Yuxin Ren, Shanghui Nie, Xuan Qin, Panzhong Lu, Jingbo Xia
  • Year: 2019

Dr. Irsa Sajjad | Machine Learning | Best Researcher Award

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Dr. Irsa Sajjad, Machine Learning, Best Researcher Award

Doctorate at Central South University, China

Profiles

Scopus

Google Scholar

🌍 Academic Background:

Dr. Irsa Sajjad is a Research Scholar at Central South University, Changsha, China, known for her expertise in hybrid choice modeling and machine learning. Her innovative research integrates deep learning and attention mechanisms, significantly advancing methodologies and applications in the field.

🎓 Education:

Dr. Irsa’s academic background is marked by advanced studies in machine learning and choice modeling, equipping her with a comprehensive understanding of both theoretical concepts and practical applications in her field.

👩‍🏫 Professional Experience:

Dr. Irsa has actively contributed to significant research projects, including developing novel hybrid choice models and Gaussian mixture models. She has collaborated with industry partners on machine learning applications and data visualization techniques and is currently publishing a book on advanced choice modeling.

🔬 Research Interests:

Dr. Irsa’s research interests center on Hybrid Choice Models (HCM), particularly those incorporating attention mechanisms, deep learning, and latent class analysis. Her work aims to enhance the accuracy and effectiveness of choice modeling by addressing complex data structures and improving analytical insights.

📖 Publications:

Advancing Covid-19 Data Modeling: Introducing a Neutrosophic Extension of Ramous Louzada Distribution
  • Authors: Al-Aziz, S.N., Sajjad, I., Dar, J.G., El Bagoury, A.A.-A.H.
  • Journal: International Journal of Neutrosophic Science
  • Year: 2023
Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information
  • Authors: Shahzad, U., Hanif, M., Sajjad, I., Anas, M.M.
  • Journal: Scientia Iranica
  • Year: 2022
Mathematical Simulation and Numerical Computation of the Temperature Profiles in the Peripherals of Human Brain during the Tepid Sponge Treatment to Fever
  • Authors: Aijaz, M., Dar, J.G., Almanjahie, I.M., Sajjad, I.
  • Journal: Computational and Mathematical Methods in Medicine
  • Year: 2022
Imputation based mean estimators in case of missing data utilizing robust regression and variance–covariance matrices
  • Authors: Shahzad, U., Al-Noor, N.H., Hanif, M., Sajjad, I., Muhammad Anas, M.
  • Journal: Communications in Statistics: Simulation and Computation
  • Year: 2022
A new family of robust regression estimators utilizing robust regression tools and supplementary attributes
  • Authors: Sajjad, I., Hanif, M., Koyuncu, N., Shahzad, U., Al-Noor, N.H.
  • Journal: Statistics in Transition New Series
  • Year: 2021

Dr. Salomon Obahoundje | Machine Learning | Best Researcher Award

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Dr. Salomon Obahoundje, Machine Learning, Best Researcher Award

Doctorate at International Water Management Institue, Ghana  

Professional Profile

Summary:

Dr. Salomon Obahoundje is a post-doctoral research fellow specializing in digital tools for water management at the International Water Management Institute (IWMI) in Ghana. His expertise lies in assessing renewable energy resources, particularly hydropower, wind, and solar energy, and analyzing the impacts of climate change on these resources. With a strong background in physics, Dr. Obahoundje has conducted extensive research on climate and energy, focusing on West Africa. He has a keen interest in large dataset analysis, machine learning, and climate modeling to inform decision-making processes under deep uncertainty.

👩‍🎓Education:

Dr. Obahoundje holds a Ph.D. in Energy-Climate-Environment from the African Centre of Excellence on Climate Change, Biodiversity, and Sustainable Development (WASCAL/CEA-CCBAD) in Côte d’Ivoire. He completed his Master’s research program in Climate Change and Energy at the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL) in Niger. He also obtained a Bachelor’s and Master’s degree in Physics Sciences from Abomey-Calavi University in Benin.

 

Professional Experience:

Dr. Obahoundje has a diverse professional experience, including research fellowships, internships, and consultancy roles. He has served as a data scientist at the World Energy & Meteorology Council (WEMC) in the UK, where he worked on energy modeling and climate projections. He has also interned at research institutes such as the Research Institute for Sustainable Development (IRD) in Côte d’Ivoire, focusing on climate downscaling and bias correction data. Additionally, he has collaborated with international organizations like Electricté De France (EDF) and the General Direction of Climate Change in Benin on various climate and energy-related projects.

Research Interests:

Dr. Obahoundje’s research interests revolve around assessing the impacts of climate change on renewable energy resources, particularly hydropower, in West Africa. He specializes in using advanced modeling techniques, such as machine learning and climate models (CMIP5-6, CORDEX), to analyze large datasets and inform climate adaptation and mitigation strategies. His research also focuses on climate information services for the energy and agricultural sectors, aiming to enhance resilience to climate variability and extremes.

Skills:

  • Proficient in English and Malay (both speaking and writing)
  • Experienced in various software including GPS/GNSS processing tools, Microsoft Office, programming languages like MATLAB and Fortran, CAD and GIS software.
  • Capable of handling GPS/GNSS equipment and RFID tools.
  • Possesses strong communication skills, teamwork abilities, and a knack for teaching and training.
  • Demonstrates adaptability, fast learning, and strategic planning capabilities.

Publications Top Noted:

Paper Title: Predicting climate-driven changes in reservoir inflows and hydropower in Côte d’Ivoire using machine learning modeling
  • Authors: Obahoundje, S., Diedhiou, A., Akpoti, K., Ofosu, E.A., Marcel Kouame, D.G.
  • Journal: Energy
  • Volume: 302
  • Pages: 131849
  • Year: 2024
Paper Title: Implication of stratospheric aerosol geoengineering on compound precipitation and temperature extremes in Africa
  • Authors: Obahoundje, S., Nguessan-Bi, V.H., Diedhiou, A., Kravitz, B., Moore, J.C.
  • Journal: Science of the Total Environment
  • Volume: 863
  • Pages: 160806
  • Year: 2023
Paper Title: Modeling climate change impact on inflow and hydropower generation of Nangbeto dam in West Africa using multi-model CORDEX ensemble and ensemble machine learning
  • Authors: Obahoundje, S., Diedhiou, A., Dubus, L., Akpoti, K., Antwi Ofosu, E.
  • Journal: Applied Energy
  • Volume: 325
  • Pages: 119795
  • Year: 2022
Paper Title: Influence of stratospheric aerosol geoengineering on temperature mean and precipitation extremes indices in Africa
  • Authors: Obahoundje, S., N’guessan Bi, V.H., Diedhiou, A., Kravitz, B., Moore, J.C.
  • Journal: International Journal of Climate Change Strategies and Management
  • Volume: 14
  • Issue: 4
  • Pages: 399–423
  • Year: 2022
Paper Title: Analysis of hydroclimatic trends and variability and their impacts on hydropower generation in two river basins in Côte d’Ivoire (West Africa) during 1981-2017
  • Authors: Obahoundje, S., Diedhiou, A., Kouassi, K.L., Roudier, P., Kouame, D.G.M.
  • Journal: Environmental Research Communications
  • Volume: 4
  • Issue: 6
  • Pages: 065001
  • Year: 2022