Nikhil Suryawanshi | Software Development | Best Researcher Award

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Mr. Nikhil Suryawanshi | Software Development | Best Researcher Award

Principal Software Engineer at ADT, United States
Summary:

Mr. Nikhil Suryawanshi is a seasoned Principal Software Engineer with extensive experience in software development, machine learning, and data analysis. His career spans over a decade, during which he has contributed to numerous high-profile projects across industries, including technology, education, and healthcare. His commitment to quality software solutions, coupled with his passion for research, positions him as a thought leader in his field. Nikhil is also actively engaged in the academic community as a peer reviewer for several international journals, including the Cureus Springer Journal and the International Journal of Innovative Research in Engineering (IJIRE). He continues to contribute to advancements in software engineering and data science.

Professional Profile:

👩‍🎓Education:

He holds two Master’s degrees: an M.S. in Technology Management from Campbellsville University, Kentucky, USA, with a CGPA of 3.5, awarded in 2019, and an M.S. in Computer Science from San Francisco Bay University, California, USA, with a CGPA of 3.94, awarded in 2016. He completed his Bachelor of Engineering in Technology at Sinhgad Academy of Engineering, Pune, India, in 2010, graduating with a CGPA of 3.50.

🏢 Professional Experience:

Nikhil Suryawanshi has amassed over 15 years of experience in the IT industry, currently serving as a Principal Software Engineer at ADT Commercial, CA, USA, since July 2019. He leads a team of eight, providing expertise in software development, project management, and training. His technical proficiencies span across Python, .Net, AngularJS, SQL, and software testing, where he has delivered high-quality software solutions and collaborated in leadership meetings to drive corporate strategy. Previously, he worked as a Senior Software Engineer at the same company, contributing to Python-based software development and network testing.

Earlier in his career, Nikhil was an Analytics Manager at IMRB Abacus, Pune, India (2014–2015), where he led data mining and analysis projects for Unilever brands using SPSS and SQL. Before that, he was an Assistant Professor at Sandip Foundation, Nashik, India (2012–2014), teaching Database Management Systems and Computer Networks, while also guiding students on final-year projects. His industry journey began as a Software Engineer at IMRB Abacus, Mumbai, India (2011–2012), where he designed online surveys and dashboards for statistical analysis.

Research Interests:

Nikhil Suryawanshi’s research interests lie in machine learning, sentiment analysis, and data clustering techniques. He is particularly focused on predictive analytics in healthcare and enhancing diagnostic capabilities using machine learning algorithms. His recent research delves into sentiment analysis with machine learning and deep learning techniques, as well as applications of clustering methods like K-Means and Gaussian Mixture Models in healthcare data.

Author Metrics:

Number of Publications: 6

Significant contributions in fields such as machine learning, healthcare, sentiment analysis, air quality prediction, and consumer behavior.

  • Accurate Prediction of Heart Disease Using Machine Learning: 2024
  • Sentiment Analysis with Machine Learning and Deep Learning: A Survey: 2024
  • Enhancing Breast Cancer Diagnosis Through Clustering: 2023
  • Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning: 2021 
  • Air Quality Prediction in Urban Environment Using IoT Sensor Data: 2020
  • Predicting Consumer Behavior in E-Commerce Using Recommendation Systems: 2019 

Top Noted Publication:

Accurate Prediction of Heart Disease Using Machine Learning: A Case Study on the Cleveland Dataset

  • Journal: International Journal of Innovative Science and Research Technology (IJISRT)
  • Year: 2024
  • Summary: This paper presents a case study on heart disease prediction using the Cleveland dataset, comparing various machine learning models to assess their accuracy and efficacy in diagnosing heart disease.

Sentiment Analysis with Machine Learning and Deep Learning: A Survey of Techniques and Applications

  • Journal: International Journal of Science and Research Archive
  • Volume: 12
  • Issue: 2
  • Pages: 005-015
  • Year: 2024
  • Summary: The paper provides a comprehensive survey of machine learning and deep learning techniques for sentiment analysis, discussing their applications and performance in various domains such as social media, e-commerce, and customer feedback.

Enhancing Breast Cancer Diagnosis Through Clustering: A Study of K-Means, Agglomerative, and Gaussian Mixture Models

  • Journal: International Journal of Innovative Science and Research Technology (IJISRT)
  • Year: 2023
  • Summary: This research explores clustering algorithms like K-Means, Agglomerative, and Gaussian Mixture Models to enhance the accuracy of breast cancer diagnosis, emphasizing the role of unsupervised learning in medical diagnostics.

Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning

  • Journal: International Journal of Innovative Science and Research Technology (IJISRT)
  • Year: 2021
  • Summary: The study investigates the application of machine learning on data collected from wearable devices to predict mental health outcomes, focusing on stress, anxiety, and other health indicators.

Air Quality Prediction in Urban Environment Using IoT Sensor Data

  • Journal: International Journal of Innovative Science and Research Technology (IJISRT)
  • Year: 2020
  • Summary: The paper discusses air quality prediction using IoT sensor data in urban areas, applying machine learning models to predict air pollution levels and analyze environmental impacts.

Predicting Consumer Behavior in E-Commerce Using Recommendation Systems

  • Journal: International Journal of Innovative Science and Research Technology
  • Volume: 4
  • Issue: 9
  • Year: 2019
  • Summary: The study focuses on predicting consumer behavior in e-commerce platforms using recommendation systems, highlighting the effectiveness of machine learning in improving customer engagement and personalization.

Conclusion:

Mr. Nikhil Suryawanshi’s strengths in software development, machine learning, and healthcare analytics, combined with his extensive professional experience, make him a solid candidate for the Best Researcher Award. His technical expertise and contributions to the IT and academic communities are commendable. However, expanding his research impact through more collaborative efforts and publishing in top-tier journals could further enhance his candidacy for such an award. Overall, Nikhil is a deserving candidate, particularly given his innovative work in healthcare and predictive analytics.

 

 

 

Maksym Lupei | Predictive Analytics | Best Paper Award

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Dr. Maksym Lupei | Predictive Analytics | Best Paper Award

Doctorate at The Pennsylvania State University, United States

Summary:

Dr. Maksym Lupei is a dedicated researcher and academic with extensive expertise in machine learning and computational genomics. Holding a Ph.D. in Information Technologies and Machine Learning from Uzhhorod National University, he has contributed significantly to the fields of text mining and artificial intelligence. Currently a Postdoctoral Researcher at The Pennsylvania State University, Dr. Lupei is recognized for his innovative work in developing large language models and optimizing computational frameworks for biomedical data analysis. His career reflects a blend of academic excellence and practical industry experience, underpinned by a strong commitment to advancing technology and data science.

Professional Profile:

👩‍🎓Education:

Ph.D. in Information Technologies and Machine Learning (2021)

  • Institution: Uzhhorod National University
  • Dissertation: Determining the Eligibility of Candidates for a Vacancy Using Artificial Neural Networks

MSc in Applied Mathematics (2013)

  • Institution: Uzhhorod National University
  • Thesis: .NET Websites Optimization

BSc in Applied Mathematics (2012)

  • Institution: Uzhhorod National University
  • Thesis: Modern Methods of Prediction

🏢 Professional Experience:

Dr. Maksym Lupei is currently a Postdoctoral Researcher in Computer Science & Engineering at The Pennsylvania State University (since August 2023), where he leads projects involving large language models for biomedical data fact-checking, computational genomics, and metagenomics. His work includes developing open-source text mining services and optimizing GPU infrastructure. Previously, he served as a Senior Research Fellow at the V. M. Glushkov Institute of Cybernetics of the National Academy of Science of Ukraine, specializing in machine learning and text mining (January 2022 – August 2023). At Uzhhorod National University, Dr. Lupei was an Assistant Professor in the Department of Informative and Operating Systems (December 2019 – January 2023), teaching programming languages and machine learning courses.

Dr. Lupei’s industry experience includes roles as an Engineering Manager at TIBCO Jaspersoft (March 2014 – February 2021), where he led cross-functional teams and established Agile/Scrum processes, and as a Full Stack Developer at JustAnswer (March 2011 – February 2014) and Swan Software Solutions (August 2010 – March 2011).

Research Interests

Dr. Lupei’s research interests encompass machine learning, large language models, computational genomics, metagenomics, and explainable artificial intelligence. His work focuses on processing large information arrays using mathematical methods, particularly in text mining.

Top Noted Publication:

 

 

ShivaDutt Jangampeta | Machine Learning | Industry Innovation Research Award

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Mr. ShivaDutt Jangampeta, Machine Learning, Industry Innovation Research Award

ShivaDutt Jangampeta at JP Morgan Chase, United States

Mr. ShivaDutt Jangampeta appears to be a highly experienced professional with a strong background in cybersecurity, specifically in Security Information and Event Management (SIEM). However, determining his suitability for the “Research for Community Impact Award” requires an assessment of how his work impacts the community.

Relevant Criteria for the Research for Community Impact Award:

Community Impact: The award typically recognizes research that has a significant positive impact on the community, either through direct application or through contributions to knowledge that benefit the community.

Innovation and Advancement: Contributions to advancing the field and introducing innovative solutions or methodologies.

Collaboration and Dissemination: Engagement with stakeholders and dissemination of research findings to a broader audience.

Evaluation of Mr. Jangampeta’s Suitability:

Strengths:

Professional Experience:

  • Extensive experience in managing and optimizing SIEM systems.
  • Leadership roles in cybersecurity teams at major organizations like JPMorgan Chase and PEPSICO.
  • Development and implementation of advanced cybersecurity measures that could indirectly benefit community security by protecting sensitive data and reducing vulnerabilities.

Research Contributions:

  • Authored several publications related to data security, SIEM, and compliance, indicating a contribution to academic knowledge.
  • Research topics like anomaly detection in SIEM and the role of data security in compliance suggest a focus on enhancing cybersecurity frameworks, which can have broad implications for community safety and trust in digital systems.

Innovative Solutions:

  • Implementation of advanced technologies and automation in cybersecurity, contributing to more resilient and efficient security infrastructures.

Areas to Consider:

Direct Community Impact:

  • While Mr. Jangampeta’s work in cybersecurity is crucial, the direct impact on the community might be less apparent compared to other fields such as public health or environmental science.
  • It would be beneficial to highlight specific examples or case studies where his work directly protected community members or contributed to public safety.

Engagement and Dissemination:

  • The extent of his engagement with community stakeholders and efforts to disseminate his research findings to a broader, non-technical audience would strengthen his case for this award.

Conclusion:

Mr. Jangampeta’s extensive experience and contributions to the field of cybersecurity are impressive and certainly valuable. His work indirectly impacts the community by enhancing the security of digital infrastructures, which is increasingly important in today’s interconnected world. To bolster his application for the Research for Community Impact Award, it would be advantageous to emphasize any direct community benefits resulting from his cybersecurity initiatives and any outreach efforts he has made to educate or involve the community in his work.

If his research and professional activities can be framed to clearly demonstrate significant, tangible benefits to the community, Mr. Jangampeta could be a suitable candidate for the award.

 

Ahmed Elhenawy | Drug Design | Best Researcher Award

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Prof. Ahmed Elhenawy, Drug Design, Best Researcher Award

Doctorate at Al-Azhar University, Egypt

Summary:

Prof. Ahmed El-Henawy is a distinguished academic and researcher in the field of Chemistry, specializing in organic chemistry and pharmaceutical sciences. He holds a Ph.D. in Organic Chemistry with a focus on amino acids and proteins from Al-Azhar University in Cairo, Egypt. With over a decade of experience in academia, Prof. El-Henawy has served as a lecturer, assistant lecturer, and demonstrator at Al-Azhar University, where he has taught a wide range of undergraduate and postgraduate courses in organic chemistry.

Throughout his career, Prof. El-Henawy has made significant contributions to the field of peptide synthesis, particularly in the design and synthesis of novel peptide derivatives with antimicrobial and anticancer properties. His research interests also extend to the development of small molecule drugs, biochemical mechanisms of drug action, and spectroscopic analysis techniques.

Professional Profile:

Google Scholar Profile

👩‍🎓Education & Qualification:

Ph.D. in Organic Chemistry:

  • Completed at Al-Azhar University, Cairo, Egypt.
  • Thesis Title: “Synthesis and Study of New Amino Acid Derivatives of Expected Biological Activities”.
  • Specialization: Amino Acids & Proteins.
  • Year of Completion: 2005-2008.

Master of Science (M.Sc.) in Organic Chemistry:

  • Earned at Al-Azhar University, Cairo, Egypt.
  • Thesis Title: “Synthesis and Study of Some Amino Acids Derivatives”.
  • Specialization: Amino Acids & Proteins.
  • Year of Completion: 2002-2005.

One-year Graduate Courses for M.Sc. Degree:

  • Partial fulfillment of the requirements for the M.Sc. degree.
  • Chemistry Department, Faculty of Science, Al-Azhar University, Cairo, Egypt.
  • Year: 2001.

Bachelor of Science (B.Sc.) in Special Chemistry:

  • Obtained from Al-Azhar University, Cairo, Egypt.
  • Year of Completion: 2000.

Professional Experience:  

Prof. Ahmed El-Henawy boasts a rich and extensive professional experience in the field of Chemistry, spanning over several roles and institutions. Since August 2008, he has served as a Lecturer in the Chemistry Department at Al-Azhar University, Cairo, Egypt, where he has been actively involved in teaching undergraduate and postgraduate courses, supervising theses, and contributing to curriculum development. Prior to this, from September 2005 to August 2008, he held the position of Assistant Lecturer at the same institution, where he further honed his teaching and academic skills. Prof. El-Henawy began his academic career as a Demonstrator at Al-Azhar University in September 2002, gaining valuable experience in laboratory instruction and student supervision. In addition to his roles at Al-Azhar University, he has also served as an Assistant Professor at Albaha University, Albaha, KSA, from October 2012 to the present, and as a Professor at Al-Azhar University, Cairo, Egypt, from February 2020 to the present. Throughout his career, Prof. El-Henawy has demonstrated a commitment to excellence in education, research, and academic leadership.

Research Interest:

Peptide Synthesis: Prof. El-Henawy explores the synthesis of novel dipeptide, tripeptide, tetrapeptide, and pentapeptide derivatives with potential antimicrobial activity. This research area involves designing and synthesizing peptide analogs to investigate their bioactivity against microbial pathogens.

Anticancer Agents: He is interested in the synthesis and evaluation of new analogs, such as 2-amino-1,3,4-thiadiazol derivatives, as potential anticancer agents. This research aims to develop novel compounds with enhanced anticancer activity and reduced toxicity profiles.

Small Molecule Drug Design: Prof. El-Henawy is involved in designing and synthesizing small molecule compounds with therapeutic potential. This includes the development of sulfonamide derivatives and other organic molecules with antimicrobial properties.

Biochemical Mechanisms: He investigates the biochemical mechanisms underlying the activity of synthesized compounds, focusing on their interaction with biological targets and elucidating their mode of action. This research contributes to understanding the molecular basis of drug efficacy and toxicity.

Spectroscopic Analysis: Prof. El-Henawy utilizes various spectroscopic techniques, such as UV spectroscopy, FTIR spectroscopy, and NMR spectroscopy, for structural elucidation and characterization of synthesized compounds. These analytical methods provide insights into the chemical structure and properties of organic molecules.

Publication Top Noted:

Title: Synthesis and characterization of some arylhydrazone ligand and its metal complexes and their potential application as flame retardant and antimicrobial additives

  • Authors: H Abd El-Wahab, M Abd El-Fattah, AH Ahmed, AA Elhenawy, NA Alian
  • Journal: Journal of Organometallic Chemistry
  • Volume: 791
  • Pages: 99-106
  • Year: 2015
  • Citations: 46

Title: Nano-amino acid cellulose derivatives: Eco-synthesis, characterization, and antimicrobial properties

  • Authors: M Hasanin, A El-Henawy, WH Eisa, H El-Saied, M Sameeh
  • Journal: International Journal of Biological Macromolecules
  • Volume: 132
  • Pages: 963-969
  • Year: 2019
  • Citations: 45

Title: Naproxen based 1, 3, 4-oxadiazole derivatives as EGFR inhibitors: Design, synthesis, anticancer, and computational studies

  • Authors: MM Alam, S Nazreen, ASA Almalki, AA Elhenawy, NI Alsenani, …
  • Journal: Pharmaceuticals
  • Volume: 14
  • Issue: 9
  • Pages: 870
  • Year: 2021
  • Citations: 41

Title: Experimental and theoretical investigation for 6-Morpholinosulfonylquinoxalin-2 (1H)-one and its haydrazone derivate: Synthesis, characterization, tautomerization and …

  • Authors: DM Elsisi, A Ragab, AA Elhenawy, AA Farag, AM Ali, YA Ammar
  • Journal: Journal of Molecular Structure
  • Volume: 1247
  • Pages: 131314
  • Year: 2022
  • Citations: 36

Title: Design, synthesis and molecular docking studies of thymol based 1, 2, 3-triazole hybrids as thymidylate synthase inhibitors and apoptosis inducers against breast cancer cells

  • Authors: MM Alam, AM Malebari, N Syed, T Neamatallah, ASA Almalki, …
  • Journal: Bioorganic & Medicinal Chemistry
  • Volume: 38
  • Pages: 116136
  • Year: 2021
  • Citations: 35

Zakria Qadir | Computer Engineering

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Dr. Zakria Qadir: Leading Researcher in Computer Engineering

🎉 Congratulations Dr. Zakria Qadir on Winning the Most Reader’s Article Award! 🏆 Your dedication to research, mentorship, and collaboration with international teams is truly commendable. This award is a testament to your outstanding work and the impact it has on the broader community.

Professional Profile:

🔬 Research Focus: Enthusiastic PostDoc Research Associate at UNSW Artificial Intelligence Institute, dedicated to pushing boundaries in DIGITECH. Research spans Artificial Intelligence, Machine Learning, Wireless Communication, IoT, and Cybersecurity. Highly cited Young STEM Researcher.

🎓 Education:

  • Ph.D. in Electrical and Computer Engineering (Western Sydney University).
    • Research: Smart UAVs for disaster relief, AI, ML, IoT applications.
  • Master’s in Sustainable Environment and Energy System (METU).
    • Thesis: Neural Network-Based Prediction Algorithms for Hybrid PV-Wind System.
  • Bachelor of Science in Electronic Engineering (UET Taxila).
    • Gold Medal Award for securing First Position.

🏆 Achievements:

  • Google Scholar: Citations 1000+, H-Index 17, Total Papers 40, Cumulative Impact Factor >100+.
  • Keynote Speaker at Core A conferences.
  • Fully Funded ARC Research Discovery Scholarship for Ph.D.
  • Various scholarships and awards for academic excellence.

👨‍💻 Professional Experience:

  • Post-Doc Research Associate at UNSW, collaborating with the Department of Defence Australia.
  • Research: Drones-aided AI Algorithms for Battlefield Scenarios.
  • Research Assistant at UNSW, collaborating with Cisco, focusing on Intelligent Transportation Systems.
  • Sessional Lecturer at Victoria University, teaching Data Science, AI, Computer Science, Business Analysis, Networking.
  • Casual Lecturer at Western Sydney University (WSU) and Melbourne Institute of Technology (MIT).
  • Lecturer at National University of Technology, teaching IoT, AI, and Machine Learning.
  • Senior Research Scientist at Imam Abdulrahman Bin Faisal University.
  • Graduate Teaching Assistant at Middle East Technical University (METU).
  • Lab Engineer at National University of Science and Technology (NUST).

Publication Top Noted:

  • Towards 6G Internet of Things: Recent advances, use cases, and open challenges
  • A Hybrid Deep Learning Approach for Bottleneck Detection in IoT
  • A strong construction of S-box using Mandelbrot set an image encryption scheme
  • Resource optimization in UAV-assisted wireless networks—A comprehensive survey
  • Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment

📚 Skills:

  • Programming Languages: MATLAB, Python, C++.
  • Metaheuristic Algorithms: PSO, ACO, DGBCO, GWO.
  • Machine Learning (AI): Deep learning, Feature Extraction, CNN, FRNN, YOLO.
  • Understanding of Arduino, Raspberry Pi, Proteus, Lucid Chart, VOSViewer, LaTeX.

🎓 Teaching Experience:

  • Lectured and supervised students at various universities.
  • Lesson planning, preparation, and research in diverse areas.

🏅 Honors and Awards:

  • Graduate Teaching Assistant Scholarship at METU.
  • Gold Medal Award for securing First Position in Bachelors.
  • Best Engineering Project Award at UET Taxila.

🏆 Funding and Recognition:

  • Awarded ARC Research Discovery Scholarship, Research Candidate Support Funding, Teaching Assistant Scholarships, Travel Grants, and Research Grants.
  • Recognition from Core A conferences and Australia’s Natural Hazard Research.

 

The paper “A Prototype of an Energy-Efficient MAGLEV Train: A Step Towards Cleaner Train Transport” focuses on the development and evaluation of a prototype Magnetic Levitation (MAGLEV) train with an emphasis on energy efficiency. Below are some key points and important content from the paper:

Abstract:

  • Focus: Development and assessment of an energy-efficient MAGLEV train prototype.
  • Goal: Contributing to cleaner and more sustainable train transportation.

Introduction:

  • Motivation: Addressing the need for environmentally friendly and energy-efficient transportation solutions.
  • Importance of MAGLEV: Highlighting the advantages of MAGLEV technology, such as reduced friction and energy consumption.

Key Features of the MAGLEV Prototype:

  • Energy Efficiency Measures: Description of features and technologies incorporated to enhance energy efficiency.
  • Magnetic Levitation System: Explanation of the MAGLEV technology used in the prototype.
  • Propulsion System: Details about the propulsion mechanism and its role in energy savings.

Performance Evaluation:

  • Energy Consumption Analysis: Quantitative assessment of energy consumption compared to traditional train systems.
  • Environmental Impact: Discussion on the potential reduction in carbon footprint and environmental benefits.

Results and Findings:

  • Energy Savings Percentage: Presentation of the achieved energy savings compared to conventional trains.
  • Operational Stability: Evaluation of the MAGLEV prototype’s stability during operations.

Conclusion:

  • Significance: Emphasizes the significance of developing energy-efficient transportation solutions.
  • Future Implications: Discusses the potential widespread adoption of MAGLEV technology for cleaner and sustainable train transport.

Impact and Citations:

  • Citation Count: Indicates the paper’s impact and recognition within the research community.
  • Reader’s Count: Reflects the broader readership and interest in the paper’s findings.

Innovation and Contribution:

  • Novelty: Highlights any novel approaches, technologies, or methodologies introduced in the MAGLEV prototype.
  • Contribution to the Field: Describes how the research contributes to advancements in cleaner and energy-efficient transportation.

This summary provides a glimpse into the essential content of the paper, focusing on its goals, methodology, findings, and impact on the field of transportation and energy efficiency.

 

 

 

 

 

Yogesh | Artificial Intelligence

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Dr. Yogesh: Leading Researcher in Artificial Intelligence

Congratulations to Dr. Yogesh on Winning the Best Researcher Award! Dr. Yogesh is a dedicated researcher known for his impactful contributions to the field of Artificial Intelligence. His commitment to research, mentorship, and collaboration with international teams has earned him this prestigious recognition.

Dr. Yogesh is a distinguished researcher in the field of Artificial Intelligence, recognized for his outstanding contributions and achievements. Currently serving as Assistant Professor-III in the Department of Computer Science and Engineering at Chitkara University, Punjab, he brings a wealth of experience and expertise to his role.

Professional Profile:

🎓 Educational Qualifications:

  • Ph.D.: Amity University Uttar Pradesh, Noida, 2021
  • M. Tech: Amity University Uttar Pradesh, Noida, 2013 (84.7%)
  • B. Tech: Magadh University, Patna, 2007 (76%)