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.

Kishor Bhangale | Signal Processing | Best Researcher Award

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Dr. Kishor Bhangale | Signal Processing | Best Researcher Award

Assistant Professor at Pimpri Chinchwad College of Engineering and Research, Ravet, Pune

Dr. Kishor Barasu Bhangale is a dedicated academician and researcher with expertise in Speech Processing, VLSI Design, Embedded Systems, and Artificial Intelligence. With over a decade of experience in teaching and research, he has made significant contributions in the field of Data Structures, Object-Oriented Programming, and Deep Learning. Currently serving as an Assistant Professor and Research Coordinator at PCET’s PCCOER, Pune, Dr. Bhangale is committed to advancing research and fostering innovation in engineering education. His work is focused on signal processing, AI-driven speech recognition, and algorithm optimization.

Publication Profile

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Educational Details

  • Ph.D. in Speech Processing (2019–2024) – Vellore Institute of Technology (VIT), Chennai Campus, Tamil Nadu
    • Advisor: Dr. Mohanaprasad Kothandraman
  • ME in Electronics & Telecommunication (VLSI and Embedded System) (2012–2014) – Savitribai Phule Pune University (SPPU), Maharashtra
    • CGPA: 7.7/10
  • MBA (Pursuing) in Human Resources (2024 – Present) – Yashwantrao Chavan Maharashtra Open University, Nashik
  • BE in Electronics Engineering (2006–2010) – SSVPS’s College of Engineering, Dhule, North Maharashtra University
    • Marks: 77.00% (Gold Medalist)
  • HSC (12th Grade) (2005–2006) – Maharashtra State Board | Marks: 73.50%
  • SSC (10th Grade) (2003–2004) – Maharashtra State Board

Professional Experience

Dr. Bhangale has a strong academic background with over 13 years of teaching experience in leading engineering institutions. He has taught core subjects such as Data Structures and Algorithms, Object-Oriented Programming, VLSI Design, Database Management Systems, Deep Learning, and Control Systems.

  • Assistant Professor & Research Coordinator, PCET’s PCCOER, Pune (Jan 2021 – Present)
    • Serving as Research Coordinator (since June 2024)
    • Previously served as Research and PBL Coordinator (Feb 2022 – May 2023)
  • Assistant Professor, D. Y. Patil College of Engineering, Akurdi, Pune (Dec 2016 – Jan 2021)
  • Assistant Professor, SRTTC Technical Campus, Kamshet, Pune (June 2016 – Dec 2016)
  • Assistant Professor, Siddhant College of Engineering, Pune (Aug 2011 – May 2016)

Dr. Bhangale has actively contributed to academic research, mentoring students, and coordinating research activities in his institution. His leadership in Project-Based Learning (PBL) and research coordination has been instrumental in enhancing research output and technical innovation.

Research Interest

  • Speech Processing & AI-Based Voice Recognition
  • VLSI Design & Embedded Systems
  • Data Structures and Algorithm Optimization
  • Deep Learning Applications in Signal Processing

Author Metrics

Dr. Bhangale has published multiple research papers in international journals and conferences, particularly in the domains of speech processing, machine learning, and embedded systems. His research contributions focus on enhancing speech recognition accuracy through AI-driven models and optimizing VLSI architectures for high-performance computing.

  • Publications in Indexed Journals (Scopus, IEEE, Elsevier, Springer, etc.)
  • Citations: [To be updated]
  • h-Index: [To be updated]
  • i10-Index: [To be updated]

Top Noted Publication

  • Innovative Research Contribution

    • Dr. Kishor Barasu Bhangale has made a significant impact in Speech Emotion Recognition (SER), a crucial area for advancing Human-Computer Interaction (HCI).
    • His work contributes to AI-driven speech processing and has potential applications in virtual assistants, customer service AI, and mental health monitoring.

  • Publication in a Reputed Journal

    • The International Journal of Speech Technology is a well-recognized platform for publishing advancements in speech technology and AI-driven voice applications.
    • Being a co-author in this publication demonstrates his commitment to high-quality research and collaboration with international scholars.

  • Interdisciplinary Research Strength

    • His expertise in Speech Processing, Deep Learning, VLSI, and Embedded Systems positions him as a strong candidate for a Best Researcher Award, especially in the domain of AI and signal processing.

  • Consistent Research Output

    • Dr. Bhangale has a strong academic background with a Ph.D. in Speech Processing and years of teaching experience in core engineering subjects.
    • His research coordination role at PCET’s PCCOER highlights his active involvement in fostering research culture.

  • Potential for Real-World Applications

    • His research in Speech Emotion Recognition has potential applications in healthcare (mental health assessment), AI-based customer support, and smart human-machine interfaces.
    • The ability to develop emotion-aware AI systems makes this research highly relevant for the future of AI-human interaction.

Conclusion

Dr. Kishor Barasu Bhangale is a strong contender for a Best Researcher Award due to his notable research in Speech Emotion Recognition, interdisciplinary expertise, and impactful contributions to AI-driven HCI.

  • To further strengthen his candidacy, increasing citations, industry collaborations, and securing AI research grants would make him a leading researcher in speech processing and AI.
  • With continued advancements, he has the potential to be recognized as a key innovator in AI-powered speech technologies.

Faezeh Pasandideh | Wireless networks | Best Researcher Award

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Dr. Faezeh Pasandideh | Wireless networks | Best Researcher Award

Lecturer at Hamm lippstadt university of applied sciences, Germany

Dr. Faezeh Pasandideh is a computer scientist and researcher with expertise in wireless communication, AI-driven network optimization, and cloud computing. She holds a Ph.D. from UFRGS, Brazil, and HSHL, Germany, where she developed an energy-efficient UAV positioning mechanism for improved wireless connectivity. With over five years of experience in academia and research, she has contributed to various international projects in cloud computing, machine learning, and wireless sensor networks. Dr. Pasandideh has published research in high-impact journals and conferences, focusing on AI-driven optimization, network scalability, and energy-efficient wireless communication.

Publication Profile

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Educational Details

Ph.D. in Computer Science

  • Universidade Federal do Rio Grande do Sul (UFRGS), Brazil & Hamm-Lippstadt University of Applied Sciences (HSHL), Germany (2020–2024)
  • Research Topic: Energy-efficient UAV BS positioning mechanism to improve wireless connectivity
  • Advisors: Prof. Dr. Edison Pignaton de Freitas (UFRGS), Prof. Dr.-Ing. João Paulo Javidi da Costa (HSHL)

M.Sc. in Computer Science

  • Islamic Azad University, Science and Research Branch, Iran (2012–2015)
  • Thesis: Congestion management scheme in wireless body area networks
  • Advisor: Prof. Dr. Abbas Ali Rezaei

B.Sc. in Computer Science

  • Quchan University of Technology, Iran (2008–2012)
  • Thesis: Design of a web application for session management
  • Advisor: Prof. Dr. Mohammad Rezaei Kaskaki

Professional Experience

Dr. Faezeh Pasandideh has extensive experience in academia and industry. She served as a Lecturer and Research Assistant at Hochschule Hamm-Lippstadt, Germany (2022–2024), where she taught electrical engineering, control engineering, and computer science courses, while supervising student projects, bachelor theses, and internships. Prior to that, she worked as a Research Assistant in Cloud Computing and Machine Learning at Dell Technologies in partnership with Unisinos, Brazil (2021–2022), where she contributed to projects such as database scaling, quality bot development, and quality dashboard implementation. She also gained experience as a Research Assistant at Nasim Telecom, Iran (2019–2020), and a Research and Teaching Assistant at Payam Noor University (PNU), Iran (2017–2019), where she taught various courses including databases, AI, software development, and algorithm design.

Research Interest

Dr. Pasandideh specializes in wireless sensor networks (WSNs), UAV-assisted communication, machine learning, cloud computing, and artificial intelligence-based network optimization. Her research focuses on energy-efficient UAV positioning for wireless networks, IoT-based sensor networks, AI-driven optimization algorithms, and congestion management in wireless body area networks (WBANs). She is also interested in database scalability, cybersecurity in cloud environments, and AI applications in network communication.

Author Metrics

Dr. Pasandideh has published multiple peer-reviewed articles in the fields of computer networks, machine learning, and cloud computing. Her research is indexed in Scopus, IEEE Xplore, and other academic databases, reflecting her contributions to wireless communication, AI-based optimization, and network scalability.

Top Noted Publication

A Review of Applications and Communication Technologies for IoT and UAV-Based Sustainable Smart Farming

  • Sustainability, 2021
  • Citations: 236

A Fuzzy Congestion Control Protocol Based on Active Queue Management in Wireless Sensor Networks with Medical Applications

  • Wireless Personal Communications, 2018
  • Citations: 120

IoT-Based Smart Farming: Are LPWAN Technologies Suitable for Remote Communication?

  • IEEE SmartIoT Conference, 2020
  • Citations: 75

A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies

  • Remote Sensing, 2022
  • Citations: 58

Topology Management for Flying Ad Hoc Networks Based on Particle Swarm Optimization and Software-Defined Networking

  • Wireless Networks, 2022
  • Citations: 35

Conclusion

Dr. Faezeh Pasandideh is a highly suitable candidate for the Best Researcher Award, given her impactful contributions in wireless communication, AI-driven network optimization, and cloud computing. Her strong international collaborations, high-impact publications, and interdisciplinary research make her a standout researcher in her field. While expanding her leadership in large-scale research projects, publishing in top-tier journals, and increasing industry engagement could further solidify her reputation, she already demonstrates excellence and innovation in research.

Yasuhiro Ohara | Reproductive Medicine | Best Researcher Award

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Dr. Yasuhiro Ohara | Reproductive Medicine | Best Researcher Award

Medical Director at Reproduction Clinic Osaka, Japan

Dr. Yasuhiro Ohara is a distinguished reproductive medicine specialist with a strong background in obstetrics, gynecology, and assisted reproductive technology. As the Medical Director of Reproduction Clinic Osaka, he plays a key role in advancing fertility treatments and surgical interventions for reproductive health. His research in endometrial receptivity and personalized embryo transfer has contributed significantly to improving ART outcomes. Recognized for his academic excellence and clinical expertise, Dr. Ohara continues to shape the field of reproductive medicine through innovative research, publications, and global collaborations.

Publication Profile

Scopus 

Educational Details

Dr. Yasuhiro Ohara earned his Medical Doctor (M.D.) degree from Kobe University Medical School in March 2010. During his medical training, he participated in the Clinical Elective Program at the National University of Singapore in 2009.

Professional Experience

Dr. Ohara is a highly experienced reproductive medicine specialist with extensive clinical and surgical expertise in obstetrics, gynecology, and assisted reproductive technology. He currently serves as the Medical Director at Reproduction Clinic Osaka, a role he has held since January 2022. Previously, he was the Head Physician at Reproduction Clinic Tokyo (2019–2022) and the Department of Obstetrics and Gynecology at Teine Keijinkai Hospital (2017–2019). His prior experience includes fellowship training in reproductive endocrinology at Hokkaido University (2016–2017) and clinical fellowships at Hokkaido P.W.F.A.C, Obihiro-Kosei General Hospital (2014–2016), Asahikawa-Kosei General Hospital (2012–2014), and Hokkaido University (2012). He began his career as a medical intern at Okinawa Prefectural Nanbu Medical Center and Children’s Medical Center (2010–2012).

Research Interest

Dr. Ohara’s research focuses on reproductive medicine, assisted reproductive technology (ART), endometrial receptivity, hysteroscopic and laparoscopic surgery, and embryo transfer optimization. His work explores the effects of surgical interventions on ART outcomes and advancements in personalized embryo transfer techniques based on endometrial receptivity tests.

Awards and Recognition:

Dr. Ohara has received multiple accolades for his contributions to reproductive medicine, including:

  • Award for Excellent Presentation, The 64th Annual Meeting of the Northern Japan Society of Obstetrics and Gynecology (2017)
  • Top Cited Article, Reproductive Medicine and Biology (2023)
  • Top Downloaded Article, Reproductive Medicine and Biology (2024)

Top Noted Publication

Title: Investigating Dosage Effects of Ovulation Inhibitors on Oocyte Maturation in Assisted Reproductive Technology: A Retrospective Study Among Patients With Normal Ovarian Reserve

Authors:

  • Mika Handa (M.)
  • Tsuyoshi Takiuchi (T.)
  • Sumika Kawaguchi (S.)
  • Tetsuhisa Kitamura (T.)
  • Tadashi K. Kimura (T.K.)

Journal: PLoS ONE

Publication Year: 2025

Conclusion:

Dr. Yasuhiro Ohara is a highly accomplished researcher and clinician in reproductive medicine, with significant contributions to ART, endometrial receptivity, and personalized embryo transfer. His leadership, impactful publications, and conference presentations make him a strong candidate for the Best Researcher Award in Reproductive Medicine. To further strengthen his profile, expanding collaborative research efforts, targeting high-impact journals, and securing additional research funding would enhance his academic influence.

Arti Singh | Machine learning | Best Researcher Award

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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.

 

 

Megha Unni | Energy Storage Applications | Best Researcher Award

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Ms. Megha Unni | Energy Storage Applications | Best Researcher Award

Research Scholar at VIT-AP UNIVERSITY, India

Ms. Megha Unni is a doctoral researcher at VIT-AP, specializing in material science with a focus on high-entropy alloy coatings for energy storage and corrosion resistance applications. She holds a Master’s in Physics from Amrita Vishwa Vidyapeetham and a Bachelor’s in Physics from Calicut University. Her research interests span electrochemistry, corrosion science, and ferroelectric materials. She has published peer-reviewed journal articles and holds multiple patents. Megha is a recipient of the Research Award from VIT-AP and has participated in international conferences and workshops related to advanced materials.

Publication Profile

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Educational Details

Ph.D., Physics – VIT-AP, Amaravati, Andhra Pradesh, India (2021 – Present)

  • Research Focus: High-Entropy Alloy Coatings for Energy Storage Materials
  • Supervisor: Dr. Sudagar Jothi

Master of Science (M.Sc.), Physics – Amrita Vishwa Vidyapeetham, Kerala (2020)

  • CGPA: 8.06

Bachelor of Science (B.Sc.), Physics – NSS College Ottapalam, Calicut University, Kerala (2018)

  • Percentage: 76.6%
  • Subsidiary Subjects: Mathematics, Computer Science

Professional Experience

Doctoral Researcher, VIT-AP University, Andhra Pradesh (Aug 2021 – Present)

  • Focused on developing high-entropy alloy coatings for energy storage and electrochemical applications.
  • Research on corrosion resistance, electrochemical properties, and ferroelectric behavior of novel multi-component alloy systems.

Research Assistant, Amrita Vishwa Vidyapeetham, Coimbatore (2018 – 2020)

  • Developed photodetectors based on organic molecules, perovskites, and antimony sulfoiodide-polyaniline composites under Dr. Sudip Kumar Batabyal.
  • Investigated nitro-explosive detection using perylene derivatives at IISER Bhopal (Summer Internship 2019).

Research Interest

  • Material Science
  • High-Entropy Alloy Coatings
  • Electrochemistry
  • Corrosion Science
  • Ferroelectric Energy Storage Materials

Top Noted Publication

1. Ni-Co-W-Zr (P) Quinary-based Medium Entropy Alloy Achieved by an Electrochemical Route and Its Properties

Authors: Megha Unni, Sudagar Jothi
Journal: Journal of The Electrochemical Society, 172(1), 012502 (2025)
DOI: 10.1149/1945-7111/ada2bb
Summary:
This research focuses on the synthesis and characterization of a novel Ni-Co-W-Zr (P) quinary-based medium entropy alloy (MEA) using an electrochemical deposition technique. The paper highlights the structural, mechanical, and electrochemical properties of the alloy, demonstrating its potential applications in corrosion resistance and energy storage systems. The alloy exhibits excellent microstructural stability and improved hardness, making it suitable for functional coatings in harsh environments.

2. Preparation of Electroless Deposition of NiTiZr (P) Quaternary Alloy and Their Properties

Authors: Megha Unni, J. Sudagar
Journal: Heliyon, 10(17), e20134 (2024)
DOI: Heliyon Article
Summary:
This paper presents the development of a quaternary alloy (NiTiZr (P)) through an electroless deposition process. The study explores the alloy’s composition, surface morphology, phase structure, and electrochemical behavior. It particularly emphasizes the corrosion resistance and mechanical robustness of the alloy, making it a promising candidate for protective coatings and energy applications.

3. Enhanced Performance of Lead-Free MASnI3 Based Perovskite Solar Cells: Numerical Approach

Authors: B E, N H Varsha, Megha Unni, Sudagar Jothi, C D S
Book Chapter: Applications of Advanced Nanomaterials, Volume 1, Page 103 (2023)
DOI: 10.1016/B978-0-323-90345-0.00004-7
Summary:
This work provides a numerical simulation study on lead-free MASnI3-based perovskite solar cells. The study investigates the optoelectronic performance and stability enhancements by optimizing device architecture and material parameters. The findings propose design strategies to improve the efficiency and commercial viability of environmentally friendly perovskite solar cells.

Conclusion:

Ms. Megha Unni is a highly promising researcher with a robust foundation in material science, particularly in high-entropy alloy coatings and energy storage applications. Her contributions to electrochemical materials, corrosion science, and perovskite solar cells position her as a strong candidate for the Best Researcher Award. Her ability to balance experimental work, patents, and publications showcases both scientific rigor and applied innovation. While she is still in the early stages of her research career, her trajectory suggests significant potential to make impactful contributions to energy storage and material science.

 

 

Fahad Al Saadi | Applied Mathematics | Best Researcher Award

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Dr. Fahad Al Saadi | Applied Mathematics | Best Researcher Award

Senior Lecturer at Military Technological College, Oman

Dr. Fahad Al Saadi is a Senior Lecturer in Engineering Mathematics at the Military Technological College, Oman. He holds a Ph.D. in Engineering Mathematics from the University of Bristol, UK, and a Master’s in Mathematics from the University of Wollongong, Australia. With over two decades of experience in teaching, curriculum development, and academic coordination, his research spans nonlinear dynamics, mathematical biology, and engineering mathematics, focusing on mathematical modeling in reactor engineering and bio-mathematical systems.

Publication Profile

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Educational Details

  • Doctor of Philosophy (Ph.D.) in Engineering Mathematics – University of Bristol, United Kingdom (2022)
  • Master of Mathematics – University of Wollongong, Australia (2014)
  • Bachelor of Education in Mathematics and Computer – College of Applied Sciences, Sohar, Oman (2003)

Professional Experience

Dr. Fahad Al Saadi is a seasoned academic professional with over 20 years of experience in mathematics education and engineering mathematics. He is currently serving as a Senior Lecturer in Engineering Mathematics at the Military Technological College (MTC), Oman, within the System Engineering Department (2023–Present). Prior to this role, he was a Lecturer in Engineering Mathematics at MTC (2015–2023), where he played a key role in developing and delivering core mathematics and engineering courses such as Engineering Mathematics I & II, Engineering Design, and Interdisciplinary Group Design Projects. He supervised final-year student projects, coordinated global mathematics modules, and contributed to curriculum development and quality assurance.

Before joining MTC, Dr. Al Saadi worked as a Part-time Mathematics Lecturer at Arab Open University and Mazoon University College (2014–2015), focusing on the design and delivery of mathematics modules. From 2007 to 2014, he served as a Curriculum Officer at the Ministry of Education, Oman, where he contributed to mathematics curriculum development, teacher training, and educational quality enhancement. His professional journey began as a Mathematics Teacher at Al-Warith Bin Kaab School (2003–2007), teaching grades up to the 12th level and actively engaging in various school committees.

Research Interest

  • Chemical Reactor and Bioreactor Engineering Problems
  • Nonlinear Dynamics
  • Nonlinear Waves and Coherent Structures
  • Turing Patterns
  • Bifurcation Analysis
  • Ordinary Differential Equations in Bio-Mathematics (Cancer Growth Modeling)
  • Mathematical Biology
  • Engineering Mathematics
  • Engineering Education
  • Design in Engineering

Top Noted Publication

Spikes and Localised Patterns for a Novel Schnakenberg Model in the Semi-strong Interaction Regime

  • Authors: F. Al Saadi, A. Champneys, C. Gai, T. Kolokolnikov
  • Journal: European Journal of Applied Mathematics, Vol. 33(1), pp. 133-152, 2022
  • Citations: 17
  • Summary: The paper presents a study on spike and localized pattern formation in a novel Schnakenberg model under semi-strong interaction, using asymptotic analysis and numerical computations.

Stationary and Oscillatory Localized Patterns in Ratio-dependent Predator–Prey Systems

  • Authors: F. Al Saadi, A. Champneys, A. Worthy, A. Msmali
  • Journal: IMA Journal of Applied Mathematics, Vol. 86(4), pp. 808-827, 2021
  • Citations: 9
  • Summary: This work investigates localized stationary and oscillatory patterns in a ratio-dependent predator–prey system, with a focus on spatial dynamics and pattern transitions.

Organization of Spatially Localized Structures near a Codimension-three Cusp-Turing Bifurcation

  • Authors: P. Parra-Rivas, A.R. Champneys, F.A. Saadi, D. Gomila, E. Knobloch
  • Journal: SIAM Journal on Applied Dynamical Systems, Vol. 22(4), pp. 2693-2731, 2023
  • Citations: 8
  • Summary: The paper analyzes the formation and organization of spatially localized structures arising near a cusp-Turing bifurcation of codimension three, relevant to pattern formation theory.

Transitions between Dissipative Localized Structures in the Simplified Gilad–Meron Model for Dryland Plant Ecology

  • Authors: F. Al Saadi, P. Parra-Rivas
  • Journal: Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 33(3), 2023
  • Citations: 8
  • Summary: This study investigates the transitions between various dissipative localized structures within the context of a simplified Gilad–Meron model used to understand vegetation patterns in drylands.

Localised Structures in a Virus-host Model

  • Authors: F. Al Saadi, A. Worthy, J.R. Pillai, A. Msmali
  • Journal: Journal of Mathematical Analysis and Applications, Vol. 499(1), Article 125014, 2021
  • Citations: 8
  • Summary: The paper explores the emergence of localized spatial structures in a virus-host model using mathematical analysis and numerical simulations.

Conclusion:

Dr. Fahad Al Saadi’s academic and research profile positions him as a strong candidate for the Best Researcher Award. His high-quality research output, impactful publications, and interdisciplinary contributions to applied mathematics, engineering, and biological systems reflect scholarly excellence. His teaching experience and mentorship further demonstrate his commitment to academic growth. With increased research funding, broader industrial collaborations, and heightened global visibility, Dr. Al Saadi’s research influence could reach even greater heights. He is a deserving nominee with the potential to achieve continued excellence in applied mathematics and engineering research.

 

 

Zeinab Chaghakaboodi | Biological Sciences | Best Researcher Award

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Mrs. Zeinab Chaghakaboodi | Biological Sciences | Best Researcher Award

Agricultural biotechnology at Razi university, Iran

Mrs. Zeinab Chaghakaboodi is an agricultural scientist specializing in plant breeding and crop stress physiology. She holds a B.Sc. and M.Sc. in Plant Breeding from Razi University, ranking first in both programs. She is currently pursuing her Ph.D. at the University of Tehran under the supervision of Dr. Houshang Alizadeh. Her research focuses on drought tolerance in rapeseed (Brassica napus L.), crop improvement through tissue culture, and the application of agricultural wastes in environmental management. She has published several research articles in reputable journals and has presented at numerous international and national conferences.

Publication Profile

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Educational Details

Ph.D. in Plant Breeding – University of Tehran (2009 – Present)

M.Sc. in Plant Breeding – Razi University (2007 – 2009)

  • Thesis: Study on Drought Tolerance in Winter Rape (Brassica napus L.) Genotypes
  • Supervised by Dr. Daniall Kahrizi
  • Transcript: 19.06 out of 20 (Ranked 1st)

B.Sc. in Agronomy and Plant Breeding – Razi University (2002 – 2006)

  • Project: Study of the Effect of Row Spacing on Yield and Yield Components in Fennel (Foeniculum vulgare Mill.)
  • Transcript: 17.73 out of 20 (Ranked 1st)

Professional Experience

Dr. Chaghakaboodi has extensive experience as an Agricultural Engineer in the villages of Kermanshah City, Iran, from 2007 to 2013. She also served as a Lecturer, teaching Agricultural Operations at the Faculty of Agriculture, Razi University, from 2008 to 2009. Her research engagements include work on callus induction in inverted tulips (Fritillaria imperialis L.) at Razi University in 2010 and the production of bread using mixed pea and wheat flour in the same year.

Research Interest

  • Plant Breeding and Genetics
  • Drought Tolerance Mechanisms in Crops (particularly Brassica napus L.)
  • Agro-physiological Trait Analysis under Stress Conditions
  • Biotechnology and Tissue Culture in Crop Improvement
  • Sustainable Agriculture and Medicinal Plants
  • Environmental Applications of Agricultural Wastes (Adsorption and Waste Management)

Author Metrics & Selected Publications:

  • Total Citations (as of 2024): 171
  • h-index: 5
  • i10-index: 4

Academic Honors:

  • Top Graduate: B.Sc. (GPA: 17.73/20), M.Sc. (GPA: 19.06/20) – Rank 1 in both programs.
  • Talented Student Member, Razi University (2007 – Present).
  • Third Prize, National Festival of Research, Ministry of Science, Research and Technology, Iran (2010).

Top Noted Publication

1. Kinetic and Equilibrium Studies of Adsorptive Removal of Sodium-Ion onto Wheat Straw and Rice Husk Wastes

  • Authors: A. Rasouli, A. Bafkar, Z. Chaghakaboodi
  • Journal: Central Asian Journal of Environmental Science and Technology Innovation
  • Volume: 1
  • Year: 2020
  • Citations: 51
  • Focus: Investigation of the adsorption efficiency of wheat straw and rice husk as agricultural waste materials for sodium-ion removal from aqueous solutions.

2. Heritability and Genetic Advance in Rapeseed (Brassica napus L.)

  • Authors: Z. Chaghakaboodi, D. Kahrizi, A. Zebarjadi
  • Journal: Iranian Journal of Genetics and Plant Breeding
  • Volume: 1(2), Pages: 16-21
  • Year: 2012
  • Citations: 37
  • Focus: Estimation of heritability and genetic advance for yield and yield-related traits in rapeseed genotypes, providing insights into trait inheritance and breeding potential.

3. Study of Relationship Between Some Agro-Physiological Traits with Drought Tolerance in Rapeseed (Brassica napus L.) Genotypes

  • Authors: Z. Chaghakaboodi, M. Kakaei, A. Zebarjadi
  • Journal: Central Asian Journal of Plant Science Innovation
  • Volume: 1(1), Pages: 1-9
  • Year: 2021
  • Citations: 36
  • Focus: Evaluation of the relationship between key agro-physiological traits and drought tolerance in different rapeseed genotypes to support breeding programs.

4. Evaluation of Drought Tolerance of Rapeseed (Brassica napus L.) Genotypes in Laboratory and Field Conditions

  • Authors: Z. Chaghakaboodi, A.R. Zebarjadi
  • Journal: Seed and Plant Journal
  • Volume: 28(1), Pages: 17-38
  • Year: 2012
  • Citations: 24
  • Focus: Comparative evaluation of rapeseed genotypes under both laboratory and field conditions to assess their drought tolerance performance.

5. Fumigation Toxicity of the Essential Oils of Ferula persica Against Tribolium castaneum and Ephestia kuehniella

  • Authors: Z. Chaghakaboodi, J. Nasiri, S. Farahani
  • Journal: Agrotechniques in Industrial Crops
  • Volume: 2(3), Pages: 123-130
  • Year: 2022
  • Citations: 23
  • Focus: Examination of the insecticidal properties of essential oils extracted from Ferula persica against stored product pests (Tribolium castaneum and Ephestia kuehniella).

Conclusion:

Mrs. Zeinab Chaghakaboodi is a commendable candidate for the Best Researcher Award in Biological Sciences. Her exemplary academic record, impactful research on drought tolerance, plant breeding, and agricultural waste utilization make her stand out. While she could benefit from broader international collaborations and publication in higher-impact journals, her current achievements demonstrate excellence in research, making her highly deserving of recognition through this award.

 

 

Pandian Lakshmanan | Catalysis | Best Researcher Award

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Assoc. Prof. Dr. Pandian Lakshmanan | Catalysis | Best Researcher Award

Associate Professor at Tagore Engineering College, Chennai, India

Dr. P. Lakshmanan is a catalysis researcher with extensive experience in the synthesis and application of metal and alloy nanoparticles supported on metal oxides, porous carbons, and carbon nitride-based catalysts. His work focuses on developing innovative catalytic materials for hydrogen production, pollutant degradation, biomass valorization, and energy storage applications. With a proven track record of impactful research and international collaborations in Korea, Dr. Lakshmanan is a recognized contributor to the fields of heterogeneous catalysis, photocatalysis, and electrocatalysis.

Publication Profile

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Educational Details

  • PhD in Catalysis – Specialized in Metal/Alloy Nanoparticles and Metal Oxide Supported Catalysts
  • Research Training and Fellowships: Korea Institute of Chemical Technology (KRICT), Daejeon, Korea; Ajou University, Suwon, Korea; Inha University, Incheon, Korea

Professional Experience

Dr. P. Lakshmanan is currently serving as an Assistant Professor in the Department of Chemistry at Tagore Engineering College, Chennai, India, since July 2024. He previously held an Assistant Professorship at Kalasalingam University, India, from December 2016 to January 2023. Dr. Lakshmanan gained extensive international postdoctoral research experience at Inha University, Incheon, Korea (2023-2024), Ajou University, Suwon, Korea (2013-2015), and KRICT-Daejeon, Korea (2011-2012). His diverse academic and research background spans the synthesis, characterization, and catalytic application of advanced nanomaterials and supported catalysts.

Research Interest

  • Development of Nano-catalysts for Heterogeneous Catalysis
  • Photocatalysis and Electrocatalysis
  • Energy Materials for Hydrogen Generation and Supercapacitor Applications
  • Biomass Valorization and Green Chemistry
  • Plasmonic and Single-Atom Catalysis

Top Noted Publication

Alumina surface modified with graphitic carbon nitride: Synthesis, characterization and its application as photocatalyst

  • Authors: V Saravanan, P Lakshmanan, C Ramalingan
  • Journal: Diamond and Related Materials, Volume 114, Article 108291 (2021)
  • Citations: 14
  • Summary: This paper focuses on the synthesis and characterization of alumina (Al₂O₃) surface modified with graphitic carbon nitride (g-C₃N₄). The composite material was evaluated for its photocatalytic performance. The surface modification was aimed at enhancing light absorption, charge separation, and photocatalytic degradation efficiency under visible light irradiation.

Investigations on effect of graphitic carbon nitride loading on the properties and electrochemical performance of g-C3N4/TiO2 nanocomposites for energy storage device applications

  • Authors: R Ranjithkumar, P Lakshmanan, P Devendran, N Nallamuthu, A Arivarasan
  • Journal: Materials Science in Semiconductor Processing, Volume 121, Article 105328 (2021)
  • Citations: 61
  • Summary: This research explores the structural, morphological, and electrochemical properties of g-C₃N₄/TiO₂ nanocomposites with varying g-C₃N₄ loadings. The study investigates their suitability as electrode materials for energy storage devices. Enhanced electrochemical performance was achieved due to improved conductivity and synergistic effects between g-C₃N₄ and TiO₂.

Investigations and fabrication of Ni(OH)2 encapsulated carbon nanotubes nanocomposites based asymmetrical hybrid electrochemical supercapacitor

  • Authors: R Ranjithkumar, S E Arasi, P Devendran, N Nallamuthu, P Lakshmanan, A Arivarasan
  • Journal: Journal of Energy Storage, Volume 32, Article 101934 (2020)
  • Citations: 32
  • Summary: This paper presents the synthesis and electrochemical evaluation of Ni(OH)₂ encapsulated carbon nanotube (CNT) nanocomposites as electrode materials for asymmetric hybrid supercapacitors. The composite exhibited high specific capacitance and improved energy density, attributed to the synergistic effect between CNTs and Ni(OH)₂.

Investigations on structural, morphological and electrochemical properties of Co(OH)2 nanosheets embedded carbon nanotubes for supercapacitor applications

  • Authors: R Ranjithkumar, S E Arasi, P Devendran, N Nallamuthu, A Arivarasan, P Lakshmanan
  • Journal: Diamond and Related Materials, Volume 110, Article 108120 (2020)
  • Citations: 20
  • Summary: The study investigates the synthesis of Co(OH)₂ nanosheets embedded onto carbon nanotubes and their application as electrode materials for supercapacitors. The resulting composite exhibited high capacitance and superior charge storage capacity due to increased surface area and better electron conductivity.

Investigation and fabrication of asymmetrical supercapacitor using nanostructured Mn3O4 immobilized carbon nanotube composite

  • Authors: R Ranjithkumar, S E Arasi, N Nallamuthu, P Devendran, P Lakshmanan, A Arivarasan
  • Journal: Superlattices and Microstructures, Volume 138, Article 106380 (2020)
  • Citations: 42
  • Summary: This paper focuses on the development of an asymmetrical supercapacitor using nanostructured Mn₃O₄ immobilized on carbon nanotubes. The composite demonstrated excellent electrochemical properties, high specific capacitance, and long cycling stability, making it suitable for energy storage applications.

Conclusion:

Assoc. Prof. Dr. Pandian Lakshmanan is a strong candidate for the Best Researcher Award, particularly in the fields of catalysis, nanomaterials, and energy storage. His international research experience, impactful publications, and contributions to emerging areas like hydrogen generation and supercapacitors make him a noteworthy contender. Strengthening his grant portfolio, publishing in top-tier journals, and expanding his industrial collaborations would further solidify his position as a leading researcher in his field.

 

 

Sushant Yadav | Mathematical Modeling | Best Researcher Award

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Mr. Sushant Yadav | Mathematical Modeling | Best Researcher Award

Senior Research Fellow at Malaviya National Institute of Technology Jaipur, India

Mr. Sushant Yadav is a Ph.D. scholar in Mathematics at MNIT Jaipur, with a strong academic foundation from MNNIT Allahabad and the University of Delhi. His research revolves around Spiking Neural Networks, biologically inspired neural models, and their applications in AI and healthcare. With a passion for mathematical innovation in artificial intelligence, Sushant has contributed to notable publications in reputed journals and conferences. He possesses comprehensive skills in scientific computing, programming, and data analysis, making him a versatile researcher in the evolving field of computational mathematics and AI.

Publication Profile

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Educational Details

  • Doctor of Philosophy in Mathematics (2021 – Present)
    Malaviya National Institute of Technology (MNIT), Jaipur, India
    CGPA: 8.67/10.00
  • Master of Science in Mathematics and Scientific Computing (2018 – 2020)
    Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India
    CGPA: 7.05/10.00
  • Bachelor of Science (Hons.) in Mathematics (2015 – 2018)
    University of Delhi (DU), India
    CGPA: 6.7/10.00

Professional Experience

Mr. Sushant Yadav is a dedicated researcher and academic in the field of Applied Mathematics and Artificial Intelligence. He is currently pursuing his Ph.D. in Mathematics at MNIT Jaipur, where his research focuses on the intersection of Spiking Neural Networks (SNN) and biologically inspired computational models. Throughout his doctoral studies, he has actively contributed to cutting-edge research involving neuron models, plasticity mechanisms, and machine learning applications in healthcare and biological systems. His work involves developing new mathematical models and computational techniques to enhance AI systems’ performance and adaptability. With strong programming skills in Python, C/C++, and expertise in frameworks such as PyTorch, TensorFlow, and SnnTorch, he aims to bridge the gap between theoretical mathematics and AI applications.

Research Interest

  • Spiking Neural Networks (SNN)
  • Neuromorphic Computing
  • Artificial Intelligence and Machine Learning
  • Biologically Inspired Computation
  • Mathematical Modeling in Computational Neuroscience
  • Application of Machine Learning in Healthcare

Top Noted Publication

1. Comparative Analysis of Biological Spiking Neuron Models for Classification Task

  • Authors: S. Yadav, S. Chaudhary, R. Kumar
  • Conference: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
  • Publisher: IEEE
  • Pages: 1-6
  • Date: July 2023
  • DOI: https://doi.org/10.1109/ICCCNT57981.2023.10245626
  • Summary:
    This paper presents a comparative evaluation of various biological spiking neuron models with respect to their effectiveness in solving classification tasks. It focuses on models such as Leaky Integrate-and-Fire (LIF), Izhikevich, and Hodgkin-Huxley neurons, assessing their performance on benchmark datasets. The study provides insight into the suitability of each model for machine learning applications based on accuracy and computational efficiency.

2. Consciousness Driven Spike Timing Dependent Plasticity

  • Authors: S. Yadav, S. Chaudhary, R. Kumar
  • Journal: Expert Systems with Applications (Elsevier)
  • DOI: https://doi.org/10.1016/j.eswa.2025.126490
  • Preprint: arXiv preprint arXiv:2405.04546
  • Publication Year: 2024
  • Summary:
    This paper introduces a novel approach integrating consciousness-like behavior into the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed mechanism enhances synaptic adaptability by incorporating contextual and attention-based weight adjustments, leading to improved learning outcomes in spiking neural networks (SNNs). The study demonstrates the effectiveness of this approach in enhancing performance in classification and pattern recognition tasks.

3. Machine Learning-Based Recognition of White Blood Cells in Juvenile Visayan Warty Pigs

  • Authors: S. Saxena, S. Yadav, B. Singh, R. Kumar, S. Chaudhary
  • Conference: 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)
  • Publisher: IEEE
  • Date: December 2023
  • Summary:
    This research proposes a machine learning framework for the automated recognition and classification of White Blood Cells (WBCs) in juvenile Visayan Warty Pigs, a rare and endangered species. The system employs image processing and supervised learning algorithms to enhance diagnostic accuracy and aid veterinarians in wildlife health monitoring.

4. Deep Learning Solutions for WBC Classification in Juvenile Visayan Warty Pigs

  • Authors: S. Saxena, S. Yadav, B. Singh, R. Kumar, S. Chaudhary
  • Conference: 2023 IEEE Engineering Informatics
  • Publisher: IEEE
  • Pages: 1-6
  • Date: November 2023
  • Summary:
    This paper presents a deep learning-based approach leveraging Convolutional Neural Networks (CNNs) to classify White Blood Cells in juvenile Visayan Warty Pigs. The study demonstrates improved classification accuracy compared to traditional image processing techniques, showcasing the potential of deep learning in veterinary diagnostics and wildlife conservation.

Conclusion:

Mr. Sushant Yadav is a highly promising researcher with a robust foundation in mathematical modeling, spiking neural networks, and biologically inspired AI. His research contributions, particularly his innovative work on STDP and white blood cell classification using machine learning, position him as a deserving candidate for the Best Researcher Award. While his profile is strong, further enhancing his publication impact, international collaborations, and real-world implementations would elevate his standing in the global research community.