Ayasha Malik | AI | Best Researcher Award

Posted on by Best Paper Award

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

scopus

scholar

orcid

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.

Arti Singh | Machine learning | Best Researcher Award

Posted on by Best Paper Award

Mrs. Arti Singh | Machine learning | Best Researcher Award

Assistant Professor at DYPIEMR, India

Mrs. Arti Singh is an accomplished academician and researcher with a robust background in Computer Science, Artificial Intelligence, and Data Science. She is currently serving as an Assistant Professor at Dr. D Y Patil Institute of Engineering Management and Research. With a passion for teaching and research, her expertise lies in machine learning, sentiment analysis, data science, and computational intelligence. Mrs. Singh has presented and published several research papers at national and international conferences. She is committed to continuous learning, having completed various industry-relevant certifications and training programs.

Publication Profile

Google Scholar

Educational Details

  • M.Tech in Computer Technology and Applications from National Institute of Technical Teachers’ Training and Research (RGPV, Bhopal) – 2016 (CGPA: 8.69)
  • B.E. in Computer Science Engineering from Sagar Institute of Research Technology and Science (RGPV, Bhopal) – 2014 (CGPA: 8.35)

Professional Experience

  • Assistant Professor in the Department of Artificial Intelligence and Data Science at Dr. D Y Patil Institute of Engineering Management and Research since July 1, 2022.
  • Lecturer in the Computer Department at Marathwada Mitra Mandal Polytechnic College.
  • Assistant Professor at Sri Sai Shail Manglam College, Singrauli (June 1, 2019, to June 30, 2021).
  • Resource Person for the B.C.A Vocational course at Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur (May 30, 2017, to May 27, 2019).

Research Interest

  • Data Science
  • Machine Learning
  • Software Engineering
  • Operating Systems
  • Quantum Artificial Intelligence
  • Pattern Recognition
  • Computational Intelligence

Top Noted Publication

An Opinion Mining for Indian Premier League Using Machine Learning Techniques

  • Authors: KP Dubey, S Agrawal
  • Conference: 2019 4th International Conference on Internet of Things: Smart Innovation, Usage, and Application
  • Pages: 25
  • Year: 2019
  • Summary: This paper presents a sentiment analysis model for social media data related to the Indian Premier League (IPL). The authors employed machine learning techniques to classify public opinions, enabling better understanding of audience engagement and predicting trends in sports sentiment.

Comparing Classification and Regression Tree and Support Vector Machine for Analyzing Sentiments for IPL

  • Author: Arti Singh
  • Journal: International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)
  • Volume: 4
  • Issue: 6
  • Pages: 172-175
  • Year: 2016
  • ISSN: 2321-8169
  • Summary: This study compares the performance of two machine learning algorithms, Classification and Regression Tree (CART) and Support Vector Machine (SVM), for sentiment analysis on IPL data. The research evaluates the accuracy and effectiveness of both approaches for sports sentiment analysis.

AI Application in Production

  • Author: Arti Singh
  • Publisher: Taylor & Francis
  • Book Title: Industry 4.0: Enabling Technologies and Applications
  • Chapter: AI Application in Production
  • Year: 2024
  • URL: Link to book
  • Summary: This book chapter explores the integration of Artificial Intelligence (AI) in manufacturing and production processes. It highlights AI-driven innovations, predictive maintenance, process optimization, and intelligent automation in modern industrial setups.

Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches

  • Authors: Arti Singh, Sneha Kanwade, Siddhant Shendge, Amoksh Layane, Kohsheen Tikoo
  • Journal: International Journal of Scientific Research in Engineering and Management (IJSREM)
  • Volume: 8
  • Pages: 1-6
  • ISSN: 2582-3930
  • Year: 2024
  • Summary: This paper addresses the growing need for automated invoice data extraction using Optical Character Recognition (OCR) technologies. It discusses the latest advancements, the challenges faced, and potential solutions to enhance accuracy in invoice processing systems.

An In-Depth Analysis of Sentiment Polarity Using Various Machine Learning Algorithms

  • Author: Arti Singh
  • Conference: 8th International Conference on ISDIA 2024
  • Volume: 1107
  • Pages: 157–167
  • Year: 2024
  • Summary: This research investigates the effectiveness of different machine learning algorithms for sentiment polarity detection. The study evaluates models such as SVM, Random Forest, and Extremely Randomized Trees to improve sentiment classification accuracy in social media data.

Comparative Study of Machine Learning Algorithms for Sentiment Polarity

  • Author: Arti Singh
  • Conference: IRF International Conference
  • Pages: 1-5
  • Year: 2017
  • Summary: The paper compares several machine learning techniques, including Naive Bayes, Decision Trees, and SVM, for sentiment polarity classification. It emphasizes the importance of selecting the appropriate algorithm for accurate sentiment detection in online text data.

Conclusion:

Mrs. Arti Singh is a strong candidate for the Best Researcher Award, given her consistent research output in machine learning and applied AI domains, industry-relevant research contributions, and dedication to academic excellence. Her work bridges the gap between theory and practice, making her a valuable contributor to the field of computational intelligence and Industry 4.0 applications.

With increased focus on high-impact journals, research funding, and industry collaborations, she has the potential to emerge as a leading figure in her field. Therefore, she is highly deserving of recognition through the Best Researcher Award.

 

 

Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Posted on by Best Paper Award

Assoc. Prof. Dr. Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Associate Professor at School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, Egypt

Assoc. Prof. Dr. Yousry AbdulAzeem is a computer science researcher and educator specializing in distributed database systems, artificial intelligence, and software performance engineering. With over two decades of academic and industry experience, he has contributed to database optimization, software modeling, and AI-driven data analysis. He is currently affiliated with Misr Higher Institute for Engineering and Technology, University of Science and Technology (Zewail City, Giza, Egypt), and The Higher Institute of Engineering, El-Shorouk Academy, Cairo, Egypt.

Publication Profile

Scopus

Google Scholar

Educational Details

  • Ph.D. in Automatic Control Engineering (2014) – Mansoura University, Egypt
    Thesis: Ranking of Distributed Uncertain Database Systems
  • M.Sc. in Automatic Control Engineering (2009) – Mansoura University, Egypt
    Thesis: Performance Evaluation of UML Software Models Based on LQN
  • B.Sc. in Computers and Systems Engineering (2004) – Mansoura University, Egypt (Very Good)

Professional Experience

Dr. Yousry AbdulAzeem is an Associate Professor of Computer Science and Engineering with extensive academic and industry experience. He is currently a full-time Associate Professor at the Misr Higher Institute for Engineering and Technology, Mansoura, Egypt, and also serves as an Adjunct Associate Professor at both the University of Science and Technology (Zewail City, Giza) and The Higher Institute of Engineering (El-Shorouk Academy, Cairo, Egypt).

From 2015 to 2022, he worked as an Assistant Professor at Taibah University, Saudi Arabia, where he taught courses in network security, programming, database systems, and artificial intelligence. Before this, he held academic roles at Mansoura University and Misr Higher Institute for Computers and Commerce.

In addition to his academic career, Dr. AbdulAzeem has industry experience as a Technical Director and Executive Manager at Trust IT (2007–2009) and a Senior Programmer & Project Administrator at Aflak Masr for Information Technology (2004–2006). He also has significant experience in academic mentoring and quality assurance, having served as the Academic Mentoring Manager at both Taibah University (2017–2022) and Misr Higher Institute for Engineering and Technology (2023–present).

Research Interest

  • Distributed Database Systems
  • Software Performance Engineering
  • Artificial Intelligence & Knowledge Representation
  • Data Governance & Security
  • Cloud Computing & Distributed Systems

Top Noted Publication

“A CNN-based framework for classification of Alzheimer’s disease” – Neural Computing and Applications (2021)

  • Citations: 127
  • Developed a deep learning framework for Alzheimer’s disease classification using CNNs.

“A congestion-aware clustering and routing (CCR) protocol for mitigating congestion in WSN” – IEEE Access (2019)

  • Citations: 61
  • Proposed a congestion-aware routing protocol for wireless sensor networks.

“Human action recognition based on transfer learning approach” – IEEE Access (2021)

  • Citations: 53
  • Applied deep transfer learning for human activity recognition in videos.

“An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest X-ray images” – PeerJ Computer Science (2021)

  • Citations: 39
  • Introduced an optimized AI model for COVID-19 detection using X-ray images.

“Classification of breast cancer using a manta-ray foraging optimized transfer learning framework” – PeerJ Computer Science (2022)

  • Citations: 27
  • Developed a bio-inspired optimization technique for breast cancer classification.

Conclusion

Assoc. Prof. Dr. Yousry AbdulAzeem is a highly qualified and impactful researcher in Artificial Intelligence and Distributed Systems. His strong publication record, interdisciplinary expertise, and leadership roles make him a top contender for the Best Researcher Award.

To further strengthen his case, he could increase grant acquisitions, expand international collaborations, and focus on AI deployment in real-world applications. Given his high citation impact, research relevance, and academic leadership, he is a strong candidate deserving recognition.

 

 

 

 

Swati Jitendrakmar Patel | Artificial Intelligence | Best Researcher Award

Posted on by Best Paper Award

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.