Mr. Md. Asraful Sharker Nirob | Data Science | Best Researcher Award

Mr. Md. Asraful Sharker Nirob | Data Science – Researcher at Daffodil International University, Bangladesh

Md. Asraful Sharker Nirob is a highly motivated early-career researcher in computer science with a focus on machine learning, deep learning, and artificial intelligence applications. With a strong academic background and research involvement at the Health and Informatics Lab, he is driven to solve real-world problems in agriculture and healthcare using intelligent systems. His experience spans industry and academia, with a combination of technical expertise and leadership qualities that position him as a rising figure in applied AI research.

Education:

Nirob completed his Bachelor of Science in Computer Science and Engineering from a well-regarded university in Bangladesh, graduating with a CGPA of 3.70 out of 4.00. Throughout his academic journey, he maintained a high level of performance, actively engaged in research projects, and participated in student organizations. His academic background provided him with a solid foundation in software engineering, machine learning, data science, and algorithmic problem-solving.

Experience:

He is currently serving as a Research Assistant at the Health and Informatics Lab, where he works on AI-based models for disease diagnosis and predictive analysis. His responsibilities include dataset curation, preprocessing, and implementing neural network models using frameworks such as TensorFlow and PyTorch. Previously, he worked as a Junior Software Engineer, contributing to responsive web application development using React.js, JavaScript, and version control systems. He also gained administrative and communication experience as a Student Associate at the university’s career development center.

Research Interest:

Nirob’s research interests lie in machine learning 🤖, computer vision 👁️, deep learning 🧠, and natural language processing 💬. He is particularly passionate about explainable AI, neural network architectures, and hybrid deep learning models. His projects often explore the intersection of AI and practical domains like agriculture and medical imaging, with a goal to enhance classification accuracy, interpretability, and real-world impact.

Award:

Md. Asraful Sharker Nirob is a strong candidate for the Best Researcher Award due to his excellent research contributions, early career productivity, and innovative use of AI in solving domain-specific problems. His technical skills, multi-disciplinary collaborations, leadership in co-curricular activities, and strong academic record make him a well-rounded researcher. His dedication to publishing high-quality research and building accessible datasets adds further weight to his nomination.

Publications:

“XSE-TomatoNet” 🍅 – MethodsX, 2025
Introduced an explainable AI approach using EfficientNetB0; cited for improving agricultural diagnostics.

“COLD-12” 🌿 – Franklin Open, 2025
Hybrid CNN model for cotton disease detection; praised for high accuracy and innovation in multi-level features.

“Dragon Fruit Dataset” 🍓 – Data in Brief, 2023
Created a comprehensive fruit maturity grading dataset; referenced in multiple dataset-driven research works.

“Brain Tumor Classification with Explainable AI” 🧠 – ECCE Conference, 2025
Proposed a multi-scale attention fusion model; appreciated for bridging AI with medical imaging.

“Credit Card Fraud Detection” 💳 – IJRASET, 2024
Leveraged behavioral biometrics and Random Forest; cited in interdisciplinary cybersecurity studies.

“Lemon Leaf Dataset” 🍋 – Data in Brief, 2024
Shared high-quality annotated lemon disease dataset; reused in computer vision-based plant disease research.

“Sugarcane Disease Classification” 🌱 – IEEE STI Conference, 2024
Applied hybrid DL model to agriculture; useful in smart farming systems and cited in precision agri-tech work.

Conclusion:

In summary, Md. Asraful Sharker Nirob demonstrates the qualities of a dedicated and impactful young researcher. With a diverse publication portfolio, technical depth, and ongoing contributions to pressing real-world problems, he stands out as a deserving nominee for the Best Researcher Award. His strong academic background, innovation in AI applications, and leadership in student activities indicate a promising future in academia and research. 🌟

Ms. Kumi Rani - Machine Learning - Best Researcher Award

Indian Institute of Technology BHU, Varanasi - India

Professional Profiles

ORCID
Scopus

Early Academic Pursuits:

Kumi Rani's academic journey began with a passion for Computer Science and Engineering at the prestigious Indian Institute of Technology (IIT) BHU, Varanasi. During her early academic pursuits, she immersed herself in a challenging curriculum, laying the foundation for a robust understanding of computational sciences. The rigorous coursework at IIT BHU introduced her to a diverse range of subjects, including Artificial Intelligence, Neural Networks, Computer Graphics, and Mathematical Modeling.

Professional Endeavors:

Post her academic journey, Kumi Rani transitioned into the realm of academia. She served as an Assistant Professor at Sharda University, Greater Noida, where she contributed to the Computer Science and Engineering department. Subsequently, she expanded her academic footprint to include the Mathematics department at Shree DKV Science and Arts College, Jamnagar. This versatility showcased her ability to navigate and contribute to different facets of academia.

Contributions and Research Focus:

Kumi Rani's contributions in academia extend to her technical skills and her research focus. Proficient in operating systems such as Windows Vista/XP/Linux and mathematical software like Matlab R2009a, she demonstrated a command over tools crucial for computational research. Her programming expertise in C, Python, C++, and Matlab reflected her commitment to staying at the forefront of technological advancements. At the heart of her research focus lies an intersection of Machine Learning, Deep Learning, and Applied Mathematics. Her Ph.D. thesis, "Handcrafted and Deep Learning Techniques for Classification of Medical and Hyperspectral Images," underscores her commitment to addressing critical challenges in medical image analysis. By amalgamating traditional handcrafted methods with cutting-edge deep learning architectures, she aimed to elevate the precision and efficiency of medical image diagnostics. Her M.Tech thesis, "A Study of Clustering Algorithms in Fuzzy Scenario," delves into the realms of unsupervised learning and statistical data analysis. The introduction of the Kernel Intuitionistic Fuzzy c-Means algorithm reflects her innovative approach to clustering, emphasizing improved performance and robustness.

Accolades and Recognition:

Kumi Rani's academic prowess has earned her notable accolades and recognition. She secured an impressive All India Rank of 199 in the National Eligibility Test (NET) for Lectureship, a joint initiative by the Council of Scientific and Industrial Research (CSIR) and University Grants Commission (UGC). Additionally, she secured an All India Rank of 83 in the Graduate Aptitude Test in Engineering (GATE), a testament to her excellence in the field. Her pursuit of continuous learning is evident through her completion of professional development programs and courses on platforms like Coursera and Oracle Academy. This commitment to staying abreast of industry-relevant skills showcases her dedication to both personal and professional growth.

Impact and Influence:

In her professional roles, Kumi Rani has not only shared her knowledge through teaching but has also left an impact on real-world projects. Her involvement in projects at ATC Labs, including the design of a real-time broadcast communicator on the Android platform, reflects her ability to apply computational skills to practical scenarios. Her guidance on B.Tech projects further extends her influence to shaping the next generation of computational professionals.

Legacy and Future Contributions:

As Kumi Rani continues her journey, her legacy at the Indian Institute of Technology BHU, Varanasi, is marked by her early academic pursuits, versatile contributions in academia, and impactful research focus. Her commitment to education, demonstrated through the diverse courses she has taught, and her ongoing research pursuits are likely to define her future contributions. In the interdisciplinary field of computational sciences and mathematics, Kumi Rani's legacy is shaped by a dedication to excellence and a vision for the continual advancement of knowledge and application.

Notable Publications:

Classification of wireless capsule endoscopy images for bleeding using deep features fusion 2022-11-16

Cyclic learning rate based HybridSN model for hyperspectral image classification 2022-09

Automated bleeding detection in wireless capsule endoscopy images based on sparse coding 2021-08