Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award

Ms. Saleha Kamal | Pattern Recognition Awards | Best Researcher Award 

Ms. Saleha Kamal, Air University, Pakistan

Saleha Kamal is an accomplished AI and Computer Vision professional based in Rawalpindi, Pakistan, with expertise in image processing, silhouette detection, segmentation, and feature classification. She is currently pursuing an MS in Computer Science at Air University, Islamabad, Pakistan (2023-2025). Saleha’s research focuses on human interaction analysis and the development of advanced algorithms for computer vision tasks. Her work has been published in esteemed international conferences, including IEEE ICECT 2024 and IEEE ICET 2024, showcasing her innovative contributions to multi-feature descriptors and composite feature-based classifiers for human interaction recognition.

Professional Profile:

GOOGLE SCHOLAR

Suitability of Saleha Kamal for the Best Researcher Award

Saleha Kamal demonstrates exceptional potential and achievements in AI, machine learning, and computer vision research, making her a compelling candidate for the Best Researcher Award. Her dedication to advancing knowledge in human interaction recognition, along with her technical and academic accomplishments, positions her as a rising star in the research community.

Education 🎓

  • MS in Computer Science (2023 – 2025)
    Air University, Islamabad, Pakistan

Work and Research Experience 💼

  • Research Experience
    • Co-authored research papers published in international conferences:
      • “Multi-Feature Descriptors for Human Interaction Recognition in Outdoor Environments” – IEEE ICECT, 2024.
      • “A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier” – IEEE ICET, 2024.

Achievements and Certifications 🏆

  • Published research in prestigious IEEE conferences.
  • Certifications:
    • Advanced Computer Vision with TensorFlow – Coursera, 2023.
    • Machine Learning Specialization – Coursera, 2023.

Publication Top Notes:

A Novel Human Interaction Recognition via Composite Features and Max Entropy Classifier

CITED:8

Dr. Nicoletta Bianchini | Analysis Awards | Women Researcher Award

Dr. Nicoletta Bianchini | Analysis Awards | Women Researcher Award 

Dr. Nicoletta Bianchini, University of West London, United Kingdom

Nicoletta Bianchini is a dedicated PhD candidate in Civil Engineering, specializing in Seismic Engineering, with a robust background as an architect and bridge engineer. She holds a PhD from the University of Minho in Portugal, where her research focused on the seismic response of masonry cross vaults, utilizing shaking table tests and numerical analysis. Her academic journey includes an M.Sc. in Structural Analysis of Historic Constructions from the same institution, complemented by her previous studies in Building Engineering – Architecture at the University of Genoa, Italy.

Professional Profile:

ORCID

Summary of Suitability for Women Researcher Award

Overview: Dr. Nicoletta Bianchini is an accomplished researcher and engineer in the field of civil engineering, with a specific focus on seismic engineering and the preservation of historical structures. Her extensive educational background and diverse research experience make her a suitable candidate for the Research for Women Researcher Award.

Education

  1. University of Minho, Guimarães, Portugal
    • PhD Candidate in Seismic Engineering (Historic Masonry Structure group)
      • Dates: January 2019 – July 2023
      • Thesis: Evaluation of the seismic response of masonry cross vaults through shaking table tests and numerical analysis.
      • Advisors: Prof. Paulo Lourenço and Dr. Nuno Mendes
    • M.Sc. in Structural Analysis of Historic Constructions
      • Dates: September 2017 – July 2018
      • Final Grade: 19/20
      • Integrated Project: Carmo Convent in Lisbon: in situ inspection, structural analysis, and retrofitting.
      • Thesis: Conserving the Bagan (Myanmar) built heritage: Structural assessment of the Loka-Hteik-Pan Temple.
      • Advisor: Dr. Nuno Mendes
  2. Sapienza University, Rome, Italy
    • Structural Design from Empirical Tradition
      • Date: June 2017
      • Lectures by: Prof. T. Boothby (Pennsylvania State University)
  3. University of Genoa, Genoa, Italy
    • M.Sc. in Building Engineering – Architecture
      • Dates: September 2011 – March 2016
      • Thesis: From observed damage to vulnerability curves for masonry buildings: the case of L’Aquila 2009 earthquake.
      • Advisors: Prof. Sergio Lagomarsino, Prof. Serena Cattari, Dr. Daria Ottonelli

Work Experience

  1. AtkinsRéalis, Epsom, England
    • Position: Structural Engineer in Bridges & Civils Department
    • Dates: September 2023 – Present
    • Responsibilities:
      • Design bearing replacement schemes.
      • Assessment of existing masonry bridges and planning their strengthening interventions, as well as conducting in-situ tests.
      • Collaboration with colleagues, stakeholders, and clients.
  2. RELUIS, Rome and Central Italy
    • Position: Structural Engineer
    • Dates: October 2016 – June 2017
    • Responsibilities:
      • Conducted post-earthquake in situ surveys of monumental and historic buildings (e.g., churches, palaces) and strategic buildings (e.g., hospitals, schools) as part of state of emergency efforts across Central Italy.
      • Assessed the condition of damaged structures and the level of risk for people and surroundings.
      • Provided instructions to the Fire Department regarding provisional structural works to ensure a minimum level of safety.
      • Surveyed listed archaeological sites and monuments in Rome (e.g., Terme Caracalla, Santa Maria in Trastevere, Santa Maria del Popolo) to assess their damage and vulnerability.

Publication top Notes:

Shake-Table Testing of a Brick Masonry Groin Vault: Overview of Blind Predictions and Postdictions and Comparison with Experimental Results

Influence of wall-to-floor connections and pounding on pre- and post-diction simulations of a masonry building aggregate tested on a shaking table

Simulation of blind pre-diction and post-diction shaking table tests on a masonry building aggregate using a continuum modelling approach

Preservation and Protection of Cultural Heritage: Vibration Monitoring and Seismic Vulnerability of the Ruins of Carmo Convent (Lisbon)

Modelling of the Dynamic Response of a Full-Scale Masonry Groin Vault: Unstrengthened and Strengthened with Textile-Reinforced Mortar (TRM)

Prof. Jie Dou | Hazard prediction | Best Researcher Award

Prof. Jie Dou | Hazard prediction | Best Researcher Award 

Prof. Jie Dou, The University of Tokyo, China

Professor Dou Jie is a distinguished scholar at the China University of Geosciences, China. With a Ph.D. from the University of Tokyo and extensive experience at institutions such as the University of Tokyo, the Public Works Research Institute, and Nagaoka University of Technology, Professor Dou has been recognized through prestigious programs like the High-level Hundred Talents Program and the Wuhan City Talent Program. He has been a competitive applicant for the Japan Society for the Promotion of Science (JSPS). His research focuses on earthquake and rainfall-triggered geohazards, spatial analysis using artificial intelligence (AI), and risk mitigation, with fieldwork spanning countries including Japan, China, Italy, Algeria, Vietnam, and Brazil. Professor Dou has authored or co-authored over 60 peer-reviewed articles and 10 book chapters, including 15 Essential Science Indicators (ESI) papers and 2 hotspot papers, with one of his papers being selected among the Top 100 Nature Scientific Reports. His work has garnered around 7,100 citations on Google Scholar. He has delivered numerous plenary and invited talks at international conferences and serves as a Topic Editor for the journal Remote Sensing and an Associate Editor for Frontiers in Earth Science. Additionally, he is on the editorial boards of several journals, including the Journal of Mountain Science, Geocarto International, and Geomatics

Professional Profile:

GOOGLE SCHOLAR

 

Summary of Suitability for Best Researcher Award: Professor Dou Jie

Dr. Dou Jie stands out as an exemplary candidate for the Best Researcher Award due to his extensive and impactful contributions to the field of geohazards, spatial analysis, and risk mitigation. Here are the key reasons for his suitability:

🌟 Achievements and Recognition:

  • Selected for the High-level Hundred Talents Program
  • Part of the Wuhan City Talent Program
  • Competitive applicant for the Japan Society for the Promotion of Science (JSPS)

🌪️ Research Interests:

  • Earthquake and rainfall-triggered geohazards
  • Spatial analysis with artificial intelligence (AI)
  • Risk mitigation strategies

📊 Global Research Impact:

  • Conducted research in Japan, China, Italy, Algeria, Vietnam, and Brazil
  • Authored/co-authored over 60 peer-reviewed articles and 10 book chapters
  • Published 15 Essential Science Indicators (ESI) and 2 hotspot papers
  • One paper selected among the Top 100 Nature Scientific Reports papers
  • Over 7,100 citations on Google Scholar

🎤 Conference Contributions:

  • Delivered several plenary and invited talks at international conferences on geohazards

📖 Editorial Roles:

  • Topic editor for the journal Remote Sensing
  • Associate Editor of Frontiers in Earth Science
  • Editorial board member for several international journals including:
    • Journal of Mountain Science
    • Geocarto International
    • Geomatics, Natural Hazards and Risk

📝 Review and Committee Work:

  • Reviewed for over 45 ISI-listed international journals
  • Steering committee member for several international academic societies and conferences, including:
    • World Landslide Forum 5
    • BIGS2021
    • BIGS2023
    • XIV IAEG 2023

Publication top Notes:

 

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

 

A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods

 

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

 

Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan

 

Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high …