Dr. Roohollah Shirani Faradonbeh | Mining Engineering | Best Researcher Award

Dr. Roohollah Shirani Faradonbeh | Mining Engineering | Best Researcher Award

Dr. Roohollah Shirani Faradonbeh, Curtin University, Australia

Dr. Roohollah Shirani Faradonbeh is an accomplished mining engineer with expertise in intelligent mining, mine electrification, and sustainable resource management. Currently, he serves as an Assistant Professor at Curtin University’s WA School of Mines, where he contributes to innovative research in digital mining technologies and advanced rock mechanics. With a PhD in Mining Engineering from the University of Adelaide, his research focuses on AI-driven predictive models for rockburst risk assessment in underground mines. Dr. Shirani has published extensively on topics like mine tailings recovery, blasting optimization, and sustainable mining practices.

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Summary of Suitability for the “Research for Best Researcher Award” – Roohollah Shirani Faradonbeh

Dr. Roohollah Shirani Faradonbeh has an outstanding academic background and extensive experience in the field of mining engineering, making him a highly suitable candidate for the Research for Best Researcher Award. His research focuses on critical areas such as rockburst phenomena in deep underground mining, which has significant implications for safety and operations in the mining industry. His development of AI-based models and novel testing methodologies, as demonstrated in his doctoral work, has opened new frontiers in intelligent mining, especially in predicting and mitigating rockburst risks.

 🎓Education

Dr. Shirani holds a PhD in Mining Engineering from the University of Adelaide, where his thesis explored AI-based models for predicting and controlling rockburst phenomena in deep underground mines. His MSc in Mining Engineering from Tarbiat Modares University focused on minimizing blast-induced ground vibrations using gene expression programming. During his BSc at the University of Kashan, he investigated methods for reducing the back-break phenomenon in Iran’s Sungun Copper Mine. His educational journey highlights his expertise in predictive modeling, experimental mechanics, and sustainable mining practices.

 💼 Experience

Dr. Shirani has held multiple academic and industry roles, including his current position as Assistant Professor at Curtin University’s WA School of Mines. He has served as an industry advisor for Fortescue Metals Group and was a research and teaching assistant at the University of Adelaide. His teaching portfolio covers advanced topics like rock excavation technology, mine automation, and slope engineering. In addition to academic contributions, Dr. Shirani has supervised numerous PhD and M.Phil. students in fields such as autonomous mining systems, rockburst early warning tools, and environmental impact assessments for deep-sea mining.

 🏅Awards and Honors

Throughout his career, Dr. Shirani has received recognition for his contributions to mining engineering and research excellence. He has been honored with multiple academic awards for his innovative work in intelligent mining systems and AI-driven rockburst models. His research on blasting operations and ground vibration prediction has garnered attention from industry and academia alike. Additionally, Dr. Shirani has played a key role in international mining conferences, where his contributions to sustainable mining and resource recovery have been highly regarded by peers and industry professionals.

🌍 Research Focus

Dr. Shirani’s research centers on cutting-edge mining technologies, focusing on areas like mine digitalization, autonomous systems, and AI-based predictive models. He is particularly interested in the electrification and decarbonization of mining operations, as well as sustainable mine rehabilitation and waste management. His work in the experimental analysis of rockburst behavior and mine tailings recovery has paved the way for advancements in mining safety and efficiency. He also explores alternative mining methods, such as deep-sea mining and asteroid mining, reflecting his forward-thinking approach to resource extraction.

 📖 Publications Top Notes

Forecasting blast-induced ground vibration developing a CART model
Cited by: 172
Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction
Cited by: 171
Prediction of the uniaxial compressive strength of sandstone using various modeling techniques
Cited by: 164
Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting
Cited by: 153
Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques
Cited by: 147

Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed, Taylor’s University Lakeside Campus, Malaysia

Danish Khan is a Ph.D. scholar specializing in Data Science at Taylor’s University, Malaysia, where he is advancing research in natural language processing (NLP). With a strong academic background in Software Engineering from Bahria University, Islamabad, Danish has built expertise in Python, machine learning, and deep learning. He has held teaching roles as a Senior Lecturer at the University of Central Punjab, Lahore, and is currently a tutor at Taylor’s University, Malaysia. His career reflects a commitment to advancing computer science education, mentoring students, and leading post-graduate councils. Danish is also a prolific researcher, contributing to various data science and sentiment analysis projects, particularly in the analysis of social media content and NLP.

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Summary of Suitability for the Research for Best Researcher Award

Danish Javed presents a strong candidacy for the Research for Best Researcher Award based on his significant academic and research contributions, particularly in the fields of data science, machine learning, and natural language processing (NLP). His ongoing PhD in Data Science from Taylor’s University demonstrates his deep commitment to advancing research, especially in topics like sentiment analysis, deep learning, and Twitter bot detection. Danish has published research in Scopus-indexed conferences and Q1/Q4 journals, which highlights the academic impact of his work.

🎓 Education 

Danish Khan has consistently pursued excellence in academia, currently working toward his Ph.D. in Data Science at Taylor’s University, Lakeside Campus, Malaysia. His doctoral research focuses on natural language processing (NLP), specifically exploring frameworks for sentiment analysis, bot detection, and text analytics. Prior to his Ph.D., Danish earned his M.S. in Software Engineering from Bahria University, Islamabad, Pakistan, where he delved into the intricacies of machine learning, image processing, and artificial intelligence. His academic foundation also includes a B.S. in Software Engineering from Bahria University, during which he developed strong programming skills in Python, Java, and C++, equipping him with the tools to tackle complex computational problems. His academic journey reflects his deep interest in understanding data structures and algorithms, making him proficient in implementing advanced analytics and programming solutions.

💼 Experience 

Danish Khan has a broad range of teaching and leadership experience, with over five years in academia. He is currently a Tutor at Taylor’s University, Malaysia, where he conducts tutorials in data science, supervises student projects, and plays a key role in shaping the post-graduate student experience. He previously served as the President of the Post-Graduate Student Council, organizing events and representing student perspectives in university meetings. Prior to his current role, Danish was a Senior Lecturer in the Faculty of Information Technology at the University of Central Punjab, Lahore, where he taught computer science and software engineering courses, supervised final-year projects, and contributed to extracurricular activities such as organizing sporting events. Additionally, Danish has experience as a QA Analyst at Orbit Institute of Technology in Lahore, where he maintained the quality standards in the Software Engineering department.

🏅Awards and Honors

Danish Khan has received notable recognition throughout his academic and professional career. His published research in data science and natural language processing has been featured in prominent Scopus-indexed conferences and reputed journals. As a Ph.D. scholar, he has been honored with several merit-based scholarships for academic excellence. Danish was also recognized for his leadership efforts while serving as President of the Post-Graduate Student Council at Taylor’s University, where he played a pivotal role in advocating for post-graduate student welfare. During his tenure at the University of Central Punjab, Lahore, he earned commendations for his outstanding contributions to teaching and student mentorship. Additionally, his development of an Android application, “Forex Profit Gain,” has garnered attention, earning placement in the Google Play Store. These accolades reflect his deep commitment to both academic rigor and innovative problem-solving in the field of data science.

🌐 Research Focus 

Danish Khan’s research is centered on data science, particularly natural language processing (NLP), machine learning, and sentiment analysis. His Ph.D. work at Taylor’s University, Malaysia, focuses on advanced techniques in deep learning to analyze and classify text-based data. His key areas of research include social media analytics, Twitter bot detection, and sentiment analysis of public opinion during crises such as the COVID-19 pandemic. Danish has contributed to frameworks that improve sentiment analysis by leveraging oversampling techniques and random minority oversampling, which enhance the accuracy of sentiment classification in user-generated content. His research also extends to explainable artificial intelligence (AI), where he has designed models for transparent and interpretable detection of bots on social media platforms. Danish’s academic pursuit aims to contribute practical, data-driven insights to solve real-world problems using cutting-edge AI and NLP technologies.

📖 publications Top Notes

“Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification.”
Citation count: 25
“Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis.”
Citation count: 6
“Football Analytics for Goal Prediction to Assess Player Performance.”
Citation count: 4
“Explainable Twitter Bot Detection Model for Limited Features.”
Citation count: 2
“Explainable Machine Learning Based Model for Heart Disease Prediction.”
“Analyzing the Efficacy of Bot Detection Methods on Twitter/X.”

Leandro Conte | Environmental Science | Best Researcher Award

Prof Dr Leandro Conte | Environmental Science | Best Researcher Award

Prof.Dr.leandro conte, Instituto de Desarrollo Tecnológico para la Industria Química (INTEC). CONICET, CCT-Santa Fe, Argentina, Argentina

Dr. Leandro Oscar Conte is a prominent environmental engineer and researcher, currently serving as an Associate Researcher at the Instituto de Desarrollo Tecnológico para la Industria Química (INTEC), CONICET-CCT Santa Fe, Argentina. He also holds an Associate Professor position at the Facultad de Ingeniería y Ciencias Hídricas (FICH), UNL. Dr. Conte’s expertise lies in Advanced Oxidation Processes for the degradation of Persistent Organic Pollutants. Since joining INTEC in 2016 as an Assistant Researcher, he has made significant contributions to water and soil remediation technologies. His research has been widely recognized, with 25 articles published and two book chapters. He has secured several high-profile research grants, including a Marie Curie Fellowship at Universidad Complutense de Madrid (UCM), Spain. Dr. Conte has established himself as a leader in his field, promoting sustainable environmental practices.

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Education:

Dr. Leandro Conte earned his degree in Environmental Engineering from the Universidad Nacional del Litoral (UNL) in 2008, where he also completed his Ph.D. in Environmental Engineering in 2015. His doctoral research focused on the development of novel advanced oxidation technologies for environmental remediation. Dr. Conte’s academic journey was marked by significant milestones, including scholarships and fellowships that allowed him to conduct cutting-edge research both nationally and internationally. He further honed his expertise through postdoctoral work at renowned institutions, including a Marie Curie Fellowship at Universidad Complutense de Madrid (UCM) in Spain. His commitment to academic excellence has earned him numerous accolades, and he has continued to advance his knowledge in the environmental engineering field through ongoing research collaborations. Dr. Conte’s educational background has formed a solid foundation for his career as a researcher and academic leader.

Experience :

Dr. Leandro Conte began his career as an Assistant Researcher at INTEC, Argentina, in 2016, where he quickly advanced to the position of Associate Researcher in 2019. His academic career also flourished with his appointment as an Associate Professor at UNL in 2018. Dr. Conte’s experience extends internationally, having served as a Guest Professor at UCM from 2021-2022, where he expanded his research on advanced oxidation processes. His involvement in over a dozen research projects, many as project director, underscores his leadership and expertise in the field. Dr. Conte has also been an evaluator for research programs and contributed as a reviewer for international journals. His broad experience and interdisciplinary collaborations have been instrumental in addressing critical environmental issues related to water and soil contamination, making him a key figure in his field.

Awards and Honors:

Dr. Leandro Conte’s distinguished career has been marked by numerous awards and recognitions. He received the prestigious Marie Curie Fellowship (GA-844209) for his research at UCM in Spain, focusing on the photo-Fenton degradation of persistent organic pollutants. His contributions to environmental engineering were further acknowledged when he achieved ANECA certification and R3 accreditation from the Spanish State Agency of Research (AEI). Additionally, Dr. Conte has been a recipient of multiple research grants, such as the highly competitive PICT funding from Argentina’s ANPCyT. His research leadership has earned him recognition within both national and international scientific communities. These accolades reflect his dedication to advancing environmental remediation technologies and his commitment to sustainable environmental practices.

Research Focus :

Dr. Leandro Conte’s research primarily revolves around Advanced Oxidation Processes (AOPs) for the degradation of persistent pollutants in water and soil. His work focuses on developing and optimizing solar photo-Fenton, ozonation, and persulfate-based reactions to address contaminants of emerging concern. Dr. Conte’s research seeks to bridge the gap between laboratory-scale experimentation and large-scale environmental applications, using pilot plant reactors to test the efficacy of AOPs in real-world conditions. His studies have led to novel insights into the degradation mechanisms of organic pollutants and have explored the synergy between various AOPs to improve treatment efficiency. His collaborations with international institutions have helped propel his research to new heights, with a particular emphasis on sustainable environmental technologies. Dr. Conte’s research is at the forefront of efforts to mitigate the impact of industrial and pharmaceutical pollutants on ecosystems.

Publications Top Notes:

  1. 🌍 “Application of Solar Photo-Fenton for Degrading Emerging Pollutants in Water”
  2. 💧 “Advanced Oxidation Techniques in Wastewater Treatment: A Comprehensive Review”
  3. 🧪 “Degradation of Persistent Organic Pollutants using Ozone and UV”
  4. ☀️ “Pilot-Scale Solar Reactors for Contaminant Removal in Real Waters”
  5. 🔬 “Comparative Study of Persulfate and Fenton Processes for Pollutant Degradation”
  6. 🌱 “Sustainable Water Treatment: Challenges in Emerging Pollutants”
  7. 🌡️ “Optimizing Solar Fenton-Like Reactions for Real Contaminated Waters”
  8. ⚙️ “Innovative Reactants for Advanced Oxidation Processes in Soil Remediation”
  9. 🏭 “Industrial Applications of Advanced Oxidation Technologies”
  10. 🧫 “Modeling the Kinetics of Ozone-Based Contaminant Degradation”