Chandra Sekhar Kolli | Computer Science | Best Researcher Award

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Dr. Chandra Sekhar Kolli | Computer Science | Best Researcher Award

Associate Professor at Shri Vishnu Engineering College for Women, India

Dr. Chandra Sekhar Kolli is an accomplished academic in Computer Science with extensive teaching experience across multiple prestigious institutions. With a passion for research and a commitment to advancing knowledge in the field, Dr. Kolli has made significant contributions to areas such as machine learning, data science, and cyber security.

Profile

Scopus Profile

Education 🎓

Dr. Kolli holds a Ph.D. in Computer Science from GITAM (Deemed to be University), Visakhapatnam, obtained in 2021. He completed his M.E. in Computer Science Engineering from HITS (Deemed to be University), Chennai, in 2011 with a CGPA of 7.99, and earned his MCA from Andhra University in 2008 with a score of 74%. He also completed his B.Sc. in Computer Science from Andhra University in 2005, achieving a 71% score.

Experience 🏫

Dr. Kolli has over 13 years of teaching experience, currently serving as an Associate Professor at Shri Vishnu Engineering College for Women, Bhimavaram since June 2023. Prior to this role, he held positions such as Senior Assistant Professor at Aditya College of Engineering and Technology, and Assistant Professor at Koneru Lakshmaiah Education Foundation and Madanapalle Institute of Technology & Science, where he contributed significantly to curriculum development and student training.

Research Interests 🔍

Dr. Kolli’s research focuses on deep learning, privacy-enhanced technologies, fraud detection, and machine learning applications in various domains. His work seeks to leverage advanced algorithms to solve real-world problems, particularly in data security and intelligent systems.

Awards 🏆

Dr. Kolli was honored with the Best Teacher Award for the academic year 2019-20 at KLEF (Deemed to be University), Vijayawada. Additionally, he is a WIPRO Certified Faculty, having qualified in the Wipro Talent Next Global Certification in October 2020, showcasing his dedication to professional development in education.

Publications 📚

Dr. Kolli has a substantial publication record, including 16 journal articles and 13 conference publications, all indexed in SCOPUS. Notable publications include:

  1. Deep learning-based credit card fraud detection in federated learning
    • Authors: Venkata Krishna Reddy, V., Vijaya Kumar Reddy, R., Siva Krishna Munaga, M., Maddila, S.K., Sekhar Kolli, C.
    • Journal: Expert Systems with Applications
    • Year: 2024
    • Citations: 0
  2. Classification of defective product for smart factory through deep learning method
    • Authors: Raffik, R., Misra, P.K., Kolli, C.S., Chandol, M.K., Shukla, S.K.
    • Journal: AIP Conference Proceedings
    • Year: 2024
    • Citations: 0
  3. A review on machine learning in agricultural sciences
    • Authors: Rayalu, G.M., Farouq, K.M., Kolli, C.S., Herrera, A.P., Muhammad, R.S.
    • Journal: AIP Conference Proceedings
    • Year: 2024
    • Citations: 0
  4. Privacy enhanced course recommendations through deep learning in Federated Learning environments
    • Authors: Kolli, C.S., Seelamanthula, S., Reddy V, V.K., Reddy, M.R.K., Gumpina, B.R.
    • Journal: International Journal of Information Technology (Singapore)
    • Year: 2024
    • Citations: 1
  5. Deep learning-based privacy-preserving recommendations in federated learning
    • Authors: Kolli, C.S., Krishna Reddy, V.V., Reddy, T.S., Dasari, D.B., Reddy, M.R.
    • Journal: International Journal of General Systems
    • Year: 2024
    • Citations: 2

His research has been widely cited, contributing to the academic community and enhancing knowledge in his areas of expertise.

Conclusion ✨

Dr. Chandra Sekhar Kolli continues to inspire students and colleagues alike with his commitment to teaching and research. With numerous accolades and a solid publication record, he stands out as a prominent figure in the field of Computer Science, making impactful contributions that pave the way for future advancements in technology.

Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

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Assist Prof Dr. Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

Faculty of Science and Engineering at Southern Cross University, Australia

Dr. Ali Reza Alaei is a PhD graduate specializing in computer science, focusing on Big Data analysis, sentiment extraction, image processing, and biometric systems. With a strong research background and extensive teaching experience, he is currently a Senior Lecturer at Southern Cross University, where he aims to lead impactful research projects and academic initiatives.

Profile 

Scopus profile

Education 🎓

Dr. Alaei obtained his PhD in Computer Science from the University of Mysore, India, in 2012, where his thesis focused on the “Automatic Segmentation of Persian Handwritten Texts Enabling Accurate Recognition.” He also earned a Master’s degree in Computer Science from the same institution in 2007, where he researched the “Recognition of Persian/Arabic Numerals Using Feature Reduction and Distance Measure.”

Experience 🧑‍🏫

With over 20 years of academic experience, Dr. Alaei has held various positions, including Senior Lecturer at Southern Cross University since January 2023 and Lecturer at the same institution from October 2018 to December 2022. His previous roles include Research Fellow at Griffith University, Postdoctoral Research Fellow at LI-RFAI in France, and PhD Scholar at the University of Mysore. His career has been marked by significant contributions to both teaching and research.

Research Interests 🔍

Dr. Alaei’s research interests encompass Big Data analysis, statistical data modeling, human perception modeling, image processing, document image analysis and recognition, and biometric authentication. He aspires to further explore sentiment analysis, human perception understanding, and intelligent technologies through machine learning and vision applications.

Awards 🏆

Dr. Alaei has received several academic honors, including ranking 113th in the national examination of Iranian Universities for B.Sc. entrance and achieving the second rank in his M.Sc. program. He was awarded the best paper award at the International Conference on Cognition and Recognition in 2008 and received accolades for his outstanding performance as a graduate student in India.

Publications 📚

Dr. Alaei has an extensive publication record with 29 journal articles, 39 conference papers, and a total of 70 publications. Some notable peer-reviewed articles include:

  1. Document Image Quality Assessment: A Survey – ACM Computing Survey, 2024. Cited by: 2432.
  2. Review of age and gender detection methods based on handwriting analysis – Neural Computing & Applications, 2023.
  3. Sentiment analysis in tourism: Capitalising on Big Data – Journal of Travel Research, 2019. Cited by: 564.
  4. Revisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data – Journal of Travel Research, 2022.

Conclusion ✅

Dr. Ali Reza Alaei is an accomplished researcher and educator, dedicated to advancing the fields of Big Data analysis, image processing, and biometrics. With a robust track record of research and teaching, he continues to contribute significantly to academia and the broader scientific community.

Alex Mirugwe | Computer Science | Young Scientist Award

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Mr. Alex Mirugwe | Computer Science | Young Scientist Award

Data Scientist at Makerere University, School of Public Health, Uganda

Alex Mirugwe is a highly skilled Data Scientist with over 4 years of experience, specializing in applying machine learning and AI to healthcare challenges, particularly in HIV, cancer, and tuberculosis diagnostics. He has a proven track record of developing data-driven solutions that improve patient outcomes in resource-constrained settings. His research has been published in several peer-reviewed journals, and he is proficient in a wide range of data science tools and methodologies. Alex also contributes to academia as an Assistant Lecturer and is involved in curriculum development and student mentoring in computer science.

Profile:

Strengths for the Award:

  1. Specialized Expertise in Healthcare Data Science: Alex Mirugwe has developed machine learning models and AI tools to solve critical health challenges, such as HIV patient care and cervical cancer detection. His work is not only technically sound but has made tangible impacts on healthcare delivery in resource-constrained environments.
  2. Research Contributions and Publications: Alex has authored multiple peer-reviewed journal articles on healthcare applications of AI, including sentiment analysis of public health data, tuberculosis detection, and cancer screening. These publications demonstrate his commitment to advancing the application of AI in public health and data science.
  3. Experience in Machine Learning and AI: His technical expertise spans a range of relevant tools and techniques, including deep learning, transfer learning, and predictive modeling, which are crucial for impactful healthcare interventions. His experience in both teaching and research also ensures that his knowledge is applied and shared within the academic community.
  4. Proven Success in Real-World Applications: Alex’s work on reducing HIV patient data duplication, predicting HIV patient outcomes, and improving cervical cancer screening speaks to his practical problem-solving skills in high-stakes environments. The use of AI to improve healthcare decision-making is well-aligned with global trends toward technology-driven health solutions.
  5. Cross-Disciplinary and Global Approach: Alex’s education, spanning institutions in Uganda and South Africa, and his research interests in global health issues, reflect his broad outlook. His involvement with international collaborators highlights his ability to bridge different disciplines and apply his knowledge across borders.

Areas for Improvement:

  1. More Diverse Research Focus: While Alex has concentrated on significant healthcare issues, expanding his research beyond HIV, cancer, and tuberculosis may enhance his portfolio. Including more work in diverse fields, such as environmental health or genomics, would add breadth to his achievements.
  2. Leadership in Research Projects: Alex has demonstrated technical prowess and teaching capabilities, but more emphasis on leadership roles in large-scale research projects or interdisciplinary initiatives could elevate his profile. Leading a significant multi-institutional study or directing larger research teams may help solidify his standing.
  3. Policy and Implementation Impact: Though Alex has made practical contributions, more evidence of his work leading to large-scale policy changes or national-level healthcare implementations could further strengthen his application. This would demonstrate how his AI models or algorithms scale to influence public health strategies at a systemic level.
  4. International Research Collaborations: Although his work is impactful within Uganda, expanding collaborations with more international research institutes or global health organizations could further enhance his visibility and contribution to global health initiatives.

 

Education:

Alex Mirugwe holds an MSc in Data Science from the University of Cape Town, South Africa, completed in 2021, where he conducted research on automated bird detection using machine learning. His academic performance was strong, with a GPA of 74.52%. Prior to this, he earned a BSc in Computer Engineering from Makerere University, Uganda, in 2019, graduating with a CGPA of 4.18/5.0. His undergraduate dissertation focused on developing a low-cost wireless TV audio transceiver, reflecting his early interest in applying engineering principles to real-world problems. His educational background combines technical proficiency in computer science with a strong emphasis on data science and machine learning applications.

Experience:

Alex Mirugwe is a highly skilled data scientist with over four years of experience applying machine learning and AI to healthcare challenges, particularly in diagnosing HIV, cancer, and tuberculosis. He has successfully developed predictive models to improve patient care and outcomes in resource-limited settings, such as creating algorithms for cervical cancer screening and reducing HIV patient data duplication. His work spans both practical implementation and academic research, with multiple publications on AI-driven health interventions. In addition to his research, Alex is an experienced educator, teaching data science and machine learning courses at the university level.

Research Focus:

Alex Mirugwe’s research focuses on leveraging data science and machine learning to address critical healthcare challenges, particularly in resource-constrained settings. His work encompasses developing predictive models for patient care in HIV treatment, enhancing cervical cancer screening accuracy through AI algorithms, and analyzing public sentiment during health crises, such as the Ebola outbreak. Additionally, he explores various applications of AI in public health, including improving tuberculosis detection and reducing data duplication in electronic medical records. Overall, his research aims to harness advanced data analytics to improve patient outcomes and inform public health strategies, making significant contributions to the field of healthcare data science.

Publications Top Notes:

  • Automating Bird Detection Based on Webcam Captured Images Using Deep Learning
    • Authors: A. Mirugwe, J. Nyirenda, E. Dufourq
    • Year: 2022
    • Citations: Not specified in the provided information.
  • Restaurant Tipping Linear Regression Model
    • Author: A. Mirugwe
    • Year: 2020
    • Citations: Not specified in the provided information.
    • Link: SSRNPaper
  • Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers
    • Authors: A. Mirugwe, C. Ashaba, A. Namale, E. Akello, E. Bichetero, E. Kansiime, J. Nyirenda
    • Year: 2024
    • Citations: Not specified in the provided information.
    • Journal: Life, 14(6), 708.
  • Adoption of Artificial Intelligence in the Ugandan Health Sector: A Review of Literature
    • Author: A. Mirugwe
    • Year: 2024
    • Citations: Not specified in the provided information.
    • Link: Available at SSRN 4735326.

Conclusion:

Alex Mirugwe presents an impressive and well-rounded portfolio, with extensive experience in applying machine learning and AI to tackle critical healthcare challenges. His achievements, particularly in HIV care and cancer screening, demonstrate his ability to leverage data science for real-world health outcomes. While he has a strong research and technical background, focusing on leadership, broadening his research scope, and contributing to systemic policy changes could bolster his case further. He is a strong candidate for the Best Researcher Award, especially within the domain of AI-driven healthcare solutions in resource-constrained settings.