Ahlem Aboud | Computer Science | Research Excellence Award

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Dr. Ahlem Aboud | Computer Science | Research Excellence Award

Université de Picardie Jules Verne | France

Dr. Ahlem Aboud is a researcher in Artificial Intelligence and bio-inspired optimization, with a strong focus on dynamic and multi-objective optimization problems. Her research integrates population-based metaheuristics such as Particle Swarm Optimization, Crow Search Algorithm, and Whale Optimization Algorithm with machine learning and deep learning frameworks. According to Scopus, she has published 5 indexed research articles, received 59 citations, and holds an h-index of 4. Her work is published in high-impact venues including Applied Soft Computing, Applied Sciences, and IEEE flagship conferences. She has contributed novel dynamic Pareto bi-level optimization strategies applied to feature selection, big data fusion, and intelligent decision systems. Her research advances adaptive optimization for complex, evolving environments and interdisciplinary AI applications.

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View Scopus Profile  View Google Scholar Profile  View ORCID Profile  View LinkedIn Profile

Featured Publications


DPb-MOPSO: a dynamic pareto bi-level multi-objective particle swarm optimization algorithm

– A. Aboud, N. Rokbani, R. Fdhila, et al. · Applied Soft Computing, 2022 · Cited by 36


MOPSO for dynamic feature selection problem based big data fusion

– A. Aboud, R. Fdhila, A.M. Alimi · IEEE SMC, 2016 · Cited by 15


A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

– A. Aboud, N. Rokbani, S. Mirjalili, A. Alimi · Applied Sciences, 2021 · Cited by 8


A novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm

– A. Aboud, R. Fdhila, A. Hussain, et al. · Authorea Preprints, 2023 · Cited by 5

Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

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Mrs. Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mrs. Gulnaz Zakariya is a researcher specializing in Machine Learning, Data Science, and Geospatial Analytics, with strong interdisciplinary contributions spanning FinTech and remote sensing. Her research focuses on the development of intelligent data-driven models for credit risk assessment, antifraud systems, and decision-support frameworks using machine learning and deep learning techniques. She has also made notable contributions to object-based image analysis and spatial change detection, particularly in the study of informal settlements using satellite imagery. Her work bridges applied artificial intelligence, geoinformatics, and financial technology, emphasizing model robustness, interpretability, and real-world deployment. She has published in peer-reviewed international journals and conferences indexed in major scholarly databases, contributing to innovation at the intersection of data science, spatial analytics, and applied AI-driven solutions.

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Featured Publications


Innovative Credit Scoring and Sales Accounting Solutions for SMEs in Kazakhstan

– Gulnaz Zakariya, Olzhas Akylbekov, Aiman Moldagulova,· FinTech, 2025


Object-based change detection of informal settlements

– P. Hofmann, G. Bekkarnayeva · JURSE 2017 (IEEE)

Nafiz Fahad | Computer Science | Best Researcher Award

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Mr. Nafiz Fahad | Computer Science | Best Researcher Award

Multimedia University | Malaysia

Mr. Nafiz Fahad is an emerging AI researcher at Multimedia University, Cyberjaya, Malaysia, recognized for his growing contributions to artificial intelligence in healthcare, computer vision, and natural language processing. His research focuses extensively on explainable AI, clinical decision support systems, and data-driven healthcare intelligence. According to Scopus, he has 19 indexed publications, 122 citations, and an h-index of 6, reflecting the influence and visibility of his scholarly work within the global research community. His scientific output spans chronic disease prediction, dementia analytics, lung disease classification, hypertension ontology development, wound-image segmentation, obesity prediction, and precision public health. These studies incorporate techniques such as deep learning, transfer learning, ensemble learning, hybrid architectures, and explainable machine learning to advance diagnostic accuracy and interpretability in medical AI systems. Beyond health-focused research, Fahad has also contributed high-impact work in fake news detection, generative AI, machine learning security, student performance prediction, agricultural disease detection, vision transformers for physics data, and federated learning enhanced with homomorphic encryption. His ongoing research extends to mental health analytics, EEG decoding models, diabetic retinopathy detection, and agentic AI solutions for healthcare innovation. Fahad’s growing academic recognition includes research awards, best paper achievements, and contributions to high-impact journals and conferences. His multidisciplinary scholarship positions him as a promising young researcher advancing applied AI at the intersection of healthcare, societal well-being, and intelligent systems.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

1. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., … (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access.

2. Mahamud, E., Fahad, N., Assaduzzaman, M., Zain, S. M., Goh, K. O. M., & Morol, M. K. (2024). An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning. Decision Analytics Journal, 12, 100499.

3. Ahmed, R., Fahad, N., Miah, M. S. U., Hossen, M. J., Morol, M. K., Mahmud, M., … (2024). A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6, 100362.

4. Fahad, N., Goh, K. M., Hossen, M. I., Shopnil, K. M. S., Mitu, I. J., Alif, M. A. H., & Tee, C. (2023). Stand up against bad intended news: An approach to detect fake news using machine learning. Emerging Science Journal, 7(4), 1247–1259.

5. Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing student’s mental health problems with the help of data mining. International Journal of Computing, 23(1), 101–108.

Malathy N | Computer Science | Best Researcher Award

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Dr. Malathy N | Computer Science | Best Researcher Award

Associate Professor | Mepco Schlenk Engineering College | India

Dr. N. Malathy is an esteemed academician and researcher currently serving as an Associate Professor in the Department of Information Technology at Mepco Schlenk Engineering College. With over 12 years of experience in teaching and research, she has made significant contributions to the fields of Mobile Computing, IoT, Networks, Fog Computing, and Machine Learning. She holds a Ph.D. in Fog Computing from Anna University (2024) and has published 24 research papers. Her expertise extends to international certifications in AI, cloud computing, and cybersecurity, and she is an active mentor and reviewer in her domain.

Profile

Google Scholar

ORCID

Scopus

Strengths for the Awards✨

  • Research Excellence 📚

    • 24 research publications, including 5 SCI and 11 Scopus-indexed papers.
    • Notable contributions to Fog Computing, IoT, Machine Learning, and Distributed Computing.
    • Recent high-impact publications, including studies on Federated Learning, Intrusion Detection, and Green Computing.

  • Innovation & Intellectual Property 🏅

    • One granted patent and four published patents demonstrate innovative contributions to applied research.
    • Published two book chapters, contributing to academic literature.

  • Recognized Expertise & Certifications 🎓

    • International certifications from NVIDIA (Deep Learning), Infosys (Generative AI), and Checkpoint (Cybersecurity).
    • Oracle certification in PL/SQL Database Programming and EMC Cloud Infrastructure & Services certification.
    • Top 1%-5% rankings in multiple NPTEL courses, showing a commitment to continuous learning.

  • Professional Engagement & Recognition 🌍

    • Reviewer for 44 research papers, indicating active involvement in the research community.
    • Awarded Best Paper at an International Conference (ICSTA 2022).
    • Guest lectures (9) and faculty development programs (FDPs) conducted, demonstrating leadership in research dissemination.
    • Member of ISTE and CSI, indicating professional affiliation.

🎓 Education

  • Ph.D. in Fog Computing, Anna University, 2024
  • M.E. in Computer & Communication, SSN College of Engineering, Anna University, 2012
  • B.E. in Computer Science & Engineering, Francis Xavier Engineering College, Anna University, 2010

👩‍🏫 Experience

  • Associate Professor, MEPCO Schlenk Engineering College (2024–Present)
  • Assistant Professor (Sl. Grade), MEPCO Schlenk Engineering College (2023–2024)
  • Assistant Professor (Sr. Grade), MEPCO Schlenk Engineering College (2017–2023)
  • Assistant Professor, MEPCO Schlenk Engineering College (2012–2017)

🔬 Research Interests On Computer Science

Dr. Malathy’s research spans multiple cutting-edge areas, including:

  • Distributed Computing: Efficient task scheduling, multi-objective optimization for fog computing
  • IoT: Smart systems development, security research, intrusion detection
  • Fog Computing: Resource allocation, task scheduling, privacy-preserving methodologies
  • Machine Learning: Federated learning for security, optimization algorithms, AI applications

🏆 Awards & Achievements

  • Best Paper Award, ICSTA 2022, Ramco Institute of Technology
  • Top 1% & 5% Ranker in NPTEL Courses on Cloud Computing, Big Data, and HCI
  • International Certifications:
    • NVIDIA Deep Learning (2023)
    • Checkpoint Certified Security Administrator R81 (2024)
    • Generative AI Certifications (Infosys, 2024)
    • Oracle Certification in Database Programming (2019)
  • Top Performing Mentor, NPTEL (2025)

📚 Publications

Dr. Malathy has authored several impactful research papers in reputed journals:

  • Multi‐objective task scheduling in fog computing using improved gaining sharing knowledge based algorithm

    • Authors: M. Navaneetha Krishnan, R. Thiyagarajan
    • Year: 2022
    • Citations: 12

  • VAGR—Void aware in geographic routing for wireless sensor networks

    • Authors: A. Revathi, N. Malathy
    • Year: 2016
    • Citations: 8

  • Entropy‐based complex proportional assessment for efficient task scheduling in fog computing

    • Authors: N. K. Malathy, T. Revathi
    • Year: 2023
    • Citations: 7

  • Opposition‐based improved memetic algorithm for placement of concurrent Internet of Things applications in fog computing

    • Authors: N. Malathy, T. Revathi
    • Year: 2024
    • Citations: 3

  • Pedestrian safety system with crash prediction

    • Authors: M. K. V. S. Ms. N. Malathy, Dr. S. Kavi Priya
    • Year: 2022
    • Citations: 3*

  • Smart trash bin level monitoring system

    • Authors: M. S. A. Ms. N. Malathy, Miss. D. Kaviyaadharshani
    • Year: 2022
    • Citations: 3*

  • Pedwarn-enhancement of pedestrian safety using mobile application

    • Authors: N. Malathy, S. Sabarish Nandha, B. Praveen, K. Pravin Kumar
    • Year: 2020
    • Citations: 2

  • Topology control for depth adjustment using geographic routing in underwater wireless sensor networks

    • Authors: A. Revathi, N. Malathy
    • Year: 2016
    • Citations: 1

  • Energy Efficient Workflow Scheduling for Fog-enhanced IOT Based Healthcare Application

    • Authors: N. Malathy, M. Kartheeswari, M. Yogalakshmi
    • Year: Not mentioned
    • Citations: 1

📌 Conclusion

Dr. N. Malathy continues to make remarkable strides in academia, research, and technological innovation. Her expertise in Distributed Computing, IoT, Fog Computing, and Machine Learning is evident from her numerous publications, patents, and academic contributions. Through mentoring, research, and professional engagements, she actively bridges theoretical advancements with real-world applications, positioning herself as a leader in her field.