Ayasha Malik | AI | Best Researcher Award

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Ms. Ayasha Malik | AI | Best Researcher Award

Assistant Professor at GL Bajaj Institute of Management and Research, Greater Noida, India.

A committed educator and researcher, Dr. Ayasha Malik specializes in Machine Learning and Information Security. With her expertise in artificial intelligence applications, she has made significant contributions to both teaching and research. Over the years, she has mentored students, published research in esteemed journals, and collaborated on projects focusing on AI-based cybersecurity solutions.

Her work aims to bridge theoretical advancements with real-world applications, making significant strides in secure AI technologies and human-computer interaction models.

Publication Profile

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Educational Details

Dr. Ayasha Malik holds a Ph.D. in Machine Learning from the School of Computer Science and Engineering, Sharda University (2021). She completed her M.Tech. in Information Security (2019) at the Ambedkar Institute of Advanced Communication Technologies and Research, Guru Gobind Singh Indraprastha University (NSIT), securing a CGPA of 8.65. Her B.Tech. in Computer Science & Engineering (2017) was awarded by Raj Kumar Goel Institute of Technology and Management, APJ Abdul Kalam Technical University, Lucknow, with a 71.68% score.

Dr. Malik completed her HSC (2013) in the Science stream from P.C School (CBSE) with 62.20% and her SSC (2011) from P.C Institute (CBSE) with a CGPA of 8.2.

Professional Experience

Dr. Malik has a strong academic background with over four years of teaching and research experience in reputed institutions:

  • GL Bajaj Institute of Management and Research, AKTU – Assistant Professor (Feb 2025–Present)
  • IIMT College of Engineering, AKTU – Assistant Professor (August 2023–Feb 2025)
  • Delhi Technical Campus (DTC), GGSIPU – Assistant Professor (Sept 2022–Aug 2023)
  • Noida Institute of Engineering and Technology (NIET), AKTU – Assistant Professor (Jan 2021–Aug 2022)
  • IEC Group of Institutions (IECGI), AKTU – Assistant Professor (Aug 2019–Nov 2020)
  • INMANTEC Institute, AKTU – Assistant Professor (Jan 2019–July 2019)

Research Interest

Dr. Malik’s research focuses on Machine Learning, Artificial Intelligence, and Information Security, with an emphasis on:

  • Deep learning applications in cybersecurity
  • Speech and image recognition models
  • AI-driven data privacy solutions
  • Human-computer interaction in intelligent systems

She is particularly interested in developing machine learning algorithms for secure computing environments, aiming to integrate AI into cybersecurity frameworks to enhance digital protection.

Author Metrics

Dr. Malik has contributed to several international journal publications and conferences, particularly in speech emotion recognition, AI-based security systems, and machine learning for cybersecurity. Her research has been cited in prominent academic circles, reinforcing her impact in the fields of artificial intelligence and information security.

Top Noted Publication

Conference Paper: Harnessing Data Mining for Improved Hindi Isolated Speech Recognition

Authors: Ayasha Malik, Veena Parihar, Shrikant A. Mapari, Malathy Sathyamoorthy, Shilpa Saini
Publication Type: Conference Paper
Citations: 0
Abstract:
This research explores the application of data mining techniques to enhance Hindi isolated speech recognition. The study leverages machine learning models to improve accuracy and efficiency in recognizing isolated words in the Hindi language. The work focuses on feature extraction, classification, and the impact of data mining methodologies on speech processing. The findings contribute to the development of intelligent speech interfaces for Hindi language users in human-computer interaction systems.

Book Chapter: Harnessing the Power of Artificial Intelligence in Software Engineering for the Design and Optimization of Cyber-Physical Systems

Authors: Shubham Tiwari, Ayasha Malik
Publication Type: Book Chapter
Citations: Not available
Abstract:
This book chapter examines how Artificial Intelligence (AI) is revolutionizing software engineering in the design and optimization of Cyber-Physical Systems (CPS). The chapter highlights AI-driven methodologies, including machine learning, deep learning, and reinforcement learning, to enhance CPS performance and security. It discusses AI-based automation, fault detection, and predictive analytics, offering insights into next-generation smart systems that integrate computational intelligence with physical infrastructure.

Conclusion

Dr. Ayasha Malik is a highly qualified researcher with strong expertise in machine learning, AI security, and human-computer interaction. Her academic background, publications, and teaching contributions make her a strong candidate for the Best Researcher Award.

To further solidify her standing, she can increase citations, enhance industry collaborations, and gain more international research exposure. With these improvements, she could be a leading AI researcher in cybersecurity and intelligent systems.

Mostafa Jalilifar | Medical Physics | Best Researcher Award

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Dr. Mostafa Jalilifar | Medical Physics | Best Researcher Award

Researcher at Iran University of Medical Sciences, Iran

Dr. Mostafa Jalilifar is a medical physicist with expertise in nuclear medicine, radiation therapy, and AI-driven medical imaging. He is currently completing his Ph.D. at Iran University of Medical Sciences, focusing on deep learning-based dosimetry for thyroid cancer treatments. He has taught at leading academic institutions in Iran, contributed to multiple research projects in medical physics, and actively publishes in the field. His work bridges advanced imaging techniques with artificial intelligence to improve patient outcomes in nuclear medicine.

Publication Profile

Scopus

Orcid 

Educational Details

  • Ph.D. in Medical Physics (2019–2024) – Iran University of Medical Sciences
    • Thesis: Estimation of Patient-Specific Absorbed Dose in Radioiodine Therapy of Thyroid Cancer Patients Based on Planar (2D) and SPECT (3D) Images Using Deep Learning.
  • M.Sc. in Medical Physics (2013–2016) – Jundi Shapur University of Medical Sciences, Iran
    • Thesis: Quantitative Evaluation of EEG During Amygdala Kindling to Categorize Different Stages of Kindling: A Step to Develop a Seizure Prevention Technique.
  • B.Sc. in Medical Radiation Engineering (2009–2013) – Islamic Azad University, Tehran Science and Research Branch

Professional Experience

Dr. Mostafa Jalilifar is a medical physicist specializing in nuclear medicine, radiation therapy, and medical imaging analysis. He has taught extensively at various academic institutions, including Iran University of Medical Sciences and Shahid Chamran University of Ahvaz. His teaching portfolio includes undergraduate and graduate courses such as Basic Physics, Medical Physics, and New Perspectives in Nuclear Medicine. He has also contributed to laboratory training and hands-on learning for medical students.

Dr. Jalilifar’s research focuses on dosimetry, radiotherapy optimization, deep learning applications in medical imaging, and quantitative analysis of brain activity during seizure development. His doctoral research integrates artificial intelligence with nuclear medicine imaging to enhance patient-specific dose estimations in thyroid cancer treatments.

Research Interest

  • Nuclear Medicine and Radiation Therapy
  • Dosimetry and Patient-Specific Dose Estimation
  • Medical Imaging Analysis (SPECT, PET, Planar Imaging)
  • AI and Deep Learning in Medical Imaging
  • EEG Analysis and Seizure Prediction

Top Noted Publication

Title: Quantifying Partial Volume Effect in SPECT and Planar Imaging: Optimizing Region of Interest for Activity Concentration Estimation in Different Sphere Sizes
Authors: Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekan, Kouhyar Geravand, Parham Geramifar
Journal: Nuclear Medicine Communications
Year: 2024
Citations: 1

Conclusion

Dr. Mostafa Jalilifar is a strong candidate for the Best Researcher Award. His research in AI-driven nuclear medicine imaging and patient-specific dosimetry is highly innovative and clinically relevant. To enhance his competitiveness, he should focus on increasing citation impact, securing leadership roles, and publishing in high-impact journals. With his expertise in medical physics, radiation therapy, and AI integration, he is well-positioned to make significant contributions to the field.

 

 

Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

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Ms. Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Software Engineer at Skyline Software Solutions, India

Ms. Swati Patel is an accomplished Data Analyst and Software Developer with extensive expertise in data science, machine learning, and software engineering. She has contributed significantly to academic research, publishing 8 journal papers, 2 IEEE conference papers, and 5 books on topics ranging from software security to predictive analytics. With strong analytical skills and a track record of developing efficient workflows and impactful applications, she has consistently delivered data-driven solutions for business optimization and research innovation.

Publication Profile

Google Scholar

Educational Details

Ms. Swati Patel holds a Master of Science in Advanced Computing Technologies from Birkbeck, University of London (2022-2023), where her projects included Principal Component Analysis on the Pima Indians Diabetes Dataset and predicting DDoS attacks using Darknet Time-Series data. She earned a Master’s degree in Computer Science from SES’s R. C. Patel Institute of Technology (NMU, India, 2012-2014), completing her thesis on software birthmark-based theft detection of JavaScript programs. Her Bachelor’s degree in Computer Engineering was completed at PSGVPM’s D. N. Patel College of Engineering, Shahada (NMU, India, 2008-2012), where she developed an Online Voting System using ASP.NET and SQL.

Professional Experience

Swati Patel is a Data Analyst and Entrepreneur with proven expertise in designing and optimizing workflows, data visualization, and software development. She served as a Data Analyst at SSP Group PLC, UK (2023-2024), where she created interactive Power BI dashboards to analyze sales data, optimized EPOS systems for data accuracy, and reduced data processing time by 30% through SQL optimization. As an entrepreneur, she successfully led Skyline Software Solutions (2014-2022), developing over 35 applications in Java, .NET, and other technologies. She managed end-to-end project execution, providing high-quality software solutions to diverse industries.

Research Interest

Swati’s research focuses on applied machine learning, natural language processing, and predictive modeling. She is particularly interested in statistical methods for data anomaly detection, speech-based health diagnostics, and secure systems in computing environments. Her work also extends to innovative clustering techniques for theft detection in software and dimensionality reduction in large datasets.

Author Metrics

  • Publications: 2 IEEE papers, 8 journal papers, 5 books.
  • Key Topics: Data science, machine learning, NLP, secure computing systems, predictive analytics.
  • Notable Works:
    • “A Review on Statistical Analysis-Based Approaches for Data Poison Detection Using Machine Learning.”
    • “Automated Depression Assessment from Speech Signals Using Pitch and Energy Features.”
    • “Long Short-Term Memory and Gated Recurrent Unit Networks for Accurate Stock Price Prediction.”
    • Books: COVID-19 Data Analysis for the United Kingdom and Data Exploration and Machine Learning using R.

Publication Top Notes

  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Frequent Subgraph Mining
    • Authors: S.J. Patel, T.M. Pattewar
    • Conference: 2014 International Conference on Embedded Systems (ICES)
    • Pages: 63–68
    • Citations: 9
    • Year: 2014
    • Summary: This paper presents a novel method for detecting software theft in JavaScript programs using software birthmarks. The approach employs agglomerative clustering and frequent subgraph mining to identify similarities between programs, aiding in theft detection.
  • K-Means Clustering Algorithm: Implementation and Critical Analysis
    • Author: S. Patel
    • Publisher: Scholars’ Press
    • Citations: 8
    • Year: 2019
    • Summary: This publication provides an in-depth exploration of the K-means clustering algorithm, including its implementation and a critical analysis of its efficiency and limitations in clustering diverse datasets.
  • Software Birthmark Based Theft Detection of JavaScript Programs Using Agglomerative Clustering and Improved Frequent Subgraph Mining
    • Authors: S. Patel, T. Pattewar
    • Conference: 2014 International Conference on Advances in Electronics, Computers, and Communications
    • Citations: 7
    • Year: 2014
    • Summary: This paper builds upon earlier research by introducing an improved method for frequent subgraph mining, enhancing the detection accuracy of JavaScript program theft through advanced birthmark-based techniques.
  • Software Birthmark for Theft Detection of JavaScript Programs: A Survey
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJAFRC (International Journal of Advanced Foundation and Research in Computers)
    • Volume: 1, Issue 2, Pages: 29–38
    • Citations: 2
    • Year: 2014
    • Summary: This survey reviews existing methods for detecting software theft in JavaScript programs, with a focus on software birthmark techniques. It outlines challenges, solutions, and future research directions.
  • Emerging Trends in Computer Technology (NCETCT)
    • Authors: S.J. Patel, T.M. Pattewar
    • Journal: IJCA (International Journal of Computer Applications)
    • Conference Issue: NCETCT, Number 1
    • Year: 2014
    • Summary: This conference paper discusses advancements in computer technology with a focus on software security. It explores the use of software birthmarks as a means of detecting and preventing intellectual property theft in programming.

Conclusion

Ms. Swati Patel is a highly qualified and deserving candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, and real-world contributions to artificial intelligence and software engineering set her apart as an innovative thinker. To maximize her potential and visibility, she could focus on strengthening her citation impact, pursuing international collaborations, and contributing to higher-impact conferences. Overall, her track record reflects dedication, skill, and the ability to drive meaningful advancements in her field.