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.

 

 

 

E.Laxmi Lydia | Computer Science | Best Researcher Award

Posted on by Best Paper Award

Prof. E.Laxmi Lydia, Computer Science, Best Researcher Award

Professor at Velagapudi Ramakrishna Siddhartha Engineering College, Siddhartha Academy of Higher Education (SAHE), India

Summary:

Dr. E. Laxmi Lydia is a seasoned educator and researcher with over 20 years of experience in teaching, training, and research. Currently serving as a Professor and Dean of R&D at Vignan’s Institute of Information Technology, she has significantly contributed to the academic and research community. Dr. Lydia is recognized for her exceptional interpersonal skills, commitment to student development, and active involvement in curriculum design and institutional accreditation processes. Her extensive expertise and numerous accolades, including the Best Researcher Award for five consecutive years, highlight her dedication to advancing knowledge and fostering innovation in the field of computer science and engineering.

Professional Profile:

👩‍🎓Education:

Ph.D. in Computer Science and Engineering (Year not specified)

Master of Computer Applications (MCA) (Year not specified)

Bachelor of Science (B.Sc.) (Year not specified)

🏢 Professional Experience:

Professor & Dean of R&D Vignan’s Institute of Information Technology (A), July 2019 – Present
Dr. E. Laxmi Lydia leads the Research and Development department, overseeing various research initiatives and fostering collaborations both within and outside the institution. She plays an integral role in curriculum development and significantly contributes to the institution’s growth and academic excellence.

Associate Professor Vignan’s Institute of Information Technology (A), 2015 – 2019
In her tenure as an Associate Professor, Dr. Lydia taught both undergraduate and postgraduate courses, guided numerous research projects, and actively participated in various academic committees. Her efforts were pivotal in advancing the research capabilities and academic standards of the institution.

Associate Professor Raghu Engineering College, 2011 – 2015
At Raghu Engineering College, Dr. Lydia conducted lectures, supervised research activities, and contributed significantly to the academic community through her publications and participation in conferences. Her work helped in elevating the research profile and educational quality of the college.

Assistant Professor Ravindra and Rajendra PG College for MCA, 2003 – 2009
During her tenure as an Assistant Professor, Dr. Lydia was responsible for teaching MCA students, developing comprehensive course materials, and mentoring students. Her dedication to teaching and mentorship helped shape the careers of many students in the field of computer science and engineering.

Honors & Certifications:

  • Oracle Certified (2009)
  • Microsoft Certified Solution Developer (MCSD)
  • Outcome-Based Education (OBE) Certified
  • Engineering Education Certification
  • EPICS-Engineering Projects in Community Services Certified
  • Reviewer for JEET Scopus Journal
  • Best Researcher Award (2018, 2019, 2020, 2021, 2022) at Vignan’s Institute of Information Technology
  • Distinguished Faculty in CSE-2021 by Ambitions, an educational entity

Professional Affiliations:

  • Member of the Board of Studies (BOS) at Vignan’s Institute of Information Technology
  • Member of the International Accreditation Council of Quality Education & Research (IACQER)
  • Member of the Computer Society of India

Research Interests:

Dr. Lydia’s research interests encompass a wide range of topics within computer science and engineering, including but not limited to:

  • Artificial Intelligence and Machine Learning
  • Data Science and Big Data Analytics
  • Cybersecurity and Information Assurance
  • Software Engineering and Development
  • Internet of Things (IoT) and Smart Systems

Top Noted Publication:

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

  • Authors: M Sivaram, EL Lydia, IV Pustokhina, DA Pustokhin, M Elhoseny, GP Joshi
  • Journal: IEEE Access
  • Volume: 8
  • Pages: 120321-120330
  • Year: 2020
  • Citations: 97

Concept of Electronic Document Management System (EDMS) as an Efficient Tool for Storing Document

  • Authors: ATR Rosa, IV Pustokhina, EL Lydia, K Shankar, M Huda
  • Journal: Journal of Critical Reviews
  • Volume: 6
  • Issue: 5
  • Pages: 85-90
  • Year: 2019
  • Citations: 91

Synergic Deep Learning Model–Based Automated Detection and Classification of Brain Intracranial Hemorrhage Images in Wearable Networks

  • Author: EL Lydia
  • Journal: Personal and Ubiquitous Computing
  • Year: 2022
  • Citations: 90

Optimal Deep Learning Based Image Compression Technique for Data Transmission on Industrial Internet of Things Applications

  • Authors: B Sujitha, VS Parvathy, EL Lydia, P Rani, Z Polkowski, K Shankar
  • Journal: Transactions on Emerging Telecommunications Technologies
  • Volume: 32
  • Issue: 7
  • Article: e3976
  • Year: 2021
  • Citations: 84

Data Encryption for Internet of Things Applications Based on Catalan Objects and Two Combinatorial Structures

  • Authors: MH Saračević, SZ Adamović, VA Miškovic, M Elhoseny, ND Maček, EL Lydia
  • Journal: IEEE Transactions on Reliability
  • Volume: 70
  • Issue: 2
  • Pages: 819-830
  • Year: 2020
  • Citations: 83