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

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

Sina Fard Moradinia at Islamic Azad University, Iran

๐Ÿ‘จโ€๐ŸŽ“ 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)ย โ€“ Iran University of Science and Technology, Tehran, Iran, 2014

๐Ÿ’ผ Professional Experience

  • Assistant Professorย โ€“ Islamic Azad University, Tabriz (2002โ€“Present)
  • Head of Water Resources Management Working Groupย โ€“ East Azerbaijan Province Elite Foundation
  • University Representativeย โ€“ Skills Training and Career Counseling Center
  • Civil Engineering Expertย โ€“ Faraz Ab Consulting Engineers
  • Engineering Services Expertย โ€“ Dam and Network Activities, River Engineering

๐Ÿ”ฌย 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

Waveletโ€“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

Ms. Mina Gachloo | Machine Learning | Best Researcher Award

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 Associateโ€™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.

๐Ÿ’ก 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.

ย 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

Dr. Irsa Sajjad, Machine Learning, Best Researcher Award

Doctorate at Central South University, China

Profiles

Scopus

Google Scholar

๐ŸŒ 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.

๐ŸŽ“ 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.

๐Ÿ‘ฉโ€๐Ÿซ 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.

๐Ÿ”ฌ 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.

๐Ÿ“– 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 varianceโ€“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

Mr. Nikolaos Argirusis | Machine Learning | Industry Impact Award

Mr. Nikolaos Argirusis, Machine Learning, Industry Impact Award

Nikolaos Argirusis at mat4nrg GmbH, Germany

Profiles

Scopus

Orcid

๐ŸŽ“Education:

Mr. Nikolaos Argirusis pursued his education at Ostfalia University of Applied Sciences, specializing in Energy Systems and Environmental Engineering, achieving a “Very Good” rating in his Master’s degree. Previously, he completed his Bachelor’s in Electrical and Information Technology at Ostfalia University, with a focus on Electromobility and Energy Technology. He also holds a Bachelor’s degree in Electrical Engineering from Technische Universitรคt Braunschweig, specializing in Energy Technology.

๐Ÿ’ผ Work Experience:

Currently, Nikolaos serves as a Student Assistant at CZM โ€“ TU Clausthal, where he contributes to electronics development and plasma technology experiments. He also engages in a research project for mat4nrg GmbH, focusing on prototype development and project organization. As the Co-founder and Managing Director of mat4nrg GmbH, he oversees the management, supervision, and development of research and customer projects. Additionally, Nikolaos supports his family’s business, aeras GmbH, focusing on power electronics assembly.

๐ŸŒ Skills and Languages:

He possesses advanced skills in Microsoft Office, PSpice, and LTspice, with foundational knowledge in C++ and Java programming languages. Fluent in German and Greek, Nikolaos also communicates proficiently in English.

๐ŸŽฏ Interests:

Outside of academics and professional endeavors, Nikolaos enjoys swimming, team sports, and engaging in DIY projects, reflecting his diverse interests and active lifestyle.

๐Ÿ“– Publications:

Evaluation of the effectiveness and performance of environmental impact assessment studies in Greece
  • Authors:Papamichael, I., Tsiolaki, F., Stylianou, M., Argirusis, C., Zorpas, A.A.
  • Journal:Comptes Rendus Chimie
  • Year: 2023
End-of-Life Management and Recycling on PV Solar Energy Production
  • Authors:Papamichael, I., Voukkali, I., Jeguirim, M., Argirusis, C., Zorpas, A.A.
  • Journal: Energies
  • Year: 2022
Research Progress in Metal-Organic Framework Based Nanomaterials Applied in Battery Cathodes
  • Authors: Mechili, M., Vaitsis, C., Argirusis, N., Zorpas, A.A., Argirusis, C.
  • Journal: Energies
  • Year: 2022
Research progress in transition metal oxide based bifunctional electrocatalysts for aqueous electrically rechargeable zinc-air batteries
  • Authors: Mechili, M., Vaitsis, C., Argirusis, N., Sourkouni, G., Argirusis, C.
  • Journal: Renewable and Sustainable Energy Reviews
  • Year: 2022
MOF nanomaterials for battery cathodes
  • Authors: Vaitsis, C., Mechili, M., Pandis, P.K., Argirusis, N., Sourkouni, G.
  • Year: 2022