Vimala Roselin | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

Vimala roselin
Kristu Jayanti University, India

Vimala roselin
Affiliation Kristu Jayanti University
Country India
Google Scholar ID 6UYBA_UAAAAJ&hl
Documents 5
Citations 1
h-index 1
Subject Area Computer Science
Event International Academic Excellence Awards

Vimala roselin is a researcher associated with Kristu Jayanti University, India, whose documented publication record is focused on computer science and applied artificial intelligence. The supplied scholarly record includes research on generative adversarial networks, machine learning, deep learning, neural-network equalisation, clustering, human freedom indices, and big-data security. The available profile information records five documents, one citation, and an h-index of 1. [1]

Abstract

The research profile presents an interdisciplinary application of computational intelligence to practical problems. Recent work includes synthetic image generation for crop disease classification, automated waste classification, neural-network-based system equalisation, and improved clustering methods. The record also includes earlier work concerning secure sensitive-data sharing on big-data platforms. Collectively, these publications indicate engagement with machine learning, deep learning, computer vision, data security, and computational modelling. [2] [3]

Keywords

Computer science; machine learning; deep learning; generative adversarial networks; image classification; waste classification; neural networks; clustering; big data; data security.

Introduction

The supplied publication record spans 2015 to 2026 and demonstrates an evolving interest in computational methods. Recent publications emphasize artificial intelligence and machine learning applications, while the earlier record addresses information security and big-data environments. The combination illustrates the broad application of computer science techniques to classification, optimisation, infrastructure, and data-management problems. [4]

Research Profile

The stated subject area is Computer Science. The supplied profile metrics identify five documents, one citation, and an h-index of 1. Because bibliometric indicators vary according to database coverage and indexing dates, these figures represent the supplied snapshot rather than a permanent measure of research activity. The Google Scholar record provides the associated publication list used for this article. [1]

Research Contributions

  • Application of generative adversarial networks to synthetic image generation for crop disease classification. [2]
  • Development of automated waste classification approaches using machine learning and deep learning. [3]
  • Investigation of neural-network-based equalisation within narrowband electric-grid systems. [4]
  • Use of clustering and computational methods for comparative analysis and data-oriented applications. [5]

Publications

Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks (2025) examines synthetic image generation for crop disease classification using generative adversarial networks. [2]

Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid (2026) addresses computational approaches to system equalisation and appears in the International Journal of Critical Infrastructures. [4]

A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques (2025) focuses on automated classification using machine learning and deep learning techniques. [3]

Comparative Analysis of Improved K-Means Clustering for Human Freedom Index (2024) applies an improved clustering approach to comparative analysis of the Human Freedom Index. [5]

Secure sensitive data sharing on a big data platform (2015) concerns secure information sharing within big-data environments. [6]

Research Impact

The supplied profile reports one citation and an h-index of 1 across five documents. The publication topics nevertheless demonstrate a range of applied computer science problems, from agricultural image analysis and waste classification to infrastructure systems and data security. Citation counts can change as databases are updated and should therefore be interpreted with the relevant publication dates and indexing coverage. [1]

Award Suitability

The documented publication portfolio provides evidence of research activity relevant to a Research Excellence Award in Computer Science. Its themes include artificial intelligence, machine learning, deep learning, clustering, computer vision, networked infrastructure, and data security. The record may therefore be considered alongside the complete academic profile, publication evidence, and applicable criteria of the International Academic Excellence Awards.

Conclusion

vimala roselin’s supplied research record reflects activity across several areas of applied Computer Science. The publications demonstrate the use of contemporary computational techniques for image classification, waste management, infrastructure systems, clustering, and secure data sharing. The available bibliometric indicators and publication record provide a concise academic profile for consideration within the International Academic Excellence Awards framework.

References

  1. Google Scholar. (n.d.). Vimala Roselin — publication profile.
    https://scholar.google.com/citations?hl=en&user=6UYBA_UAAAAJ&view_op=list_works&sortby=title
  2. Roselin, J. V., et al. (2025). Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks. International Workshop on AI & ML-Frontiers in Cross Disciplinary Applications.
    Google Scholar publication record
  3. Roselin, J. V., et al. (2025). A Framework for Automated Waste Classification System using Machine Learning and Deep Learning Techniques. 5th International Conference on Expert Clouds and Applications (ICOECA).
    Google Scholar publication record
  4. Renjith, E. J., Roselin, J. V., Ramyadevi, R., Prema, R., & Priscila, S. S. (2026). Extrapolation against categorisation for neural network-based system equalisation in a narrowband electric grid. International Journal of Critical Infrastructures, 22(4), 351–376.
    Google Scholar publication record
  5. Ilyas, F. M., Priscila, S. S., Sheela, K., Vimala Roselin, J., Sona, K. V., & Prema, R. (2024). Comparative Analysis of Improved K-Means Clustering for Human Freedom Index. International Conference on Advancements in Smart Computing and Information.
    Google Scholar publication record
  6. Roselin, V. (2015). Secure sensitive data sharing on a big data platform. Tsinghua Science and Technology.
    Google Scholar publication record

SATEESH GORIKAPUDI | Computer Science | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

SATEESH GORIKAPUDI
Koneru Lakshmaiah Education Foundation, India

SATEESH GORIKAPUDI
Affiliation Koneru Lakshmaiah Education Foundation
Country India
Scopus ID 58249169300
Documents 13
Citations 68
h-index 4
Subject Area Computer Science
Event International Academic Excellence Awards
ORCID 0000-0002-9280-9581

SATEESH GORIKAPUDI is a computer science researcher affiliated with Koneru Lakshmaiah Education Foundation, India. The supplied research record includes publications addressing anomaly detection, machine learning, image processing, fuzzy logic, clustering, energy-efficient Internet of Things (IoT) communication, and optimization. The available bibliographic information records 13 documents, 68 citations, and an h-index of 4 in the stated Scopus profile data. [1]

Abstract

The research profile of SATEESH GORIKAPUDI reflects work across applied computer science and intelligent computational methods. The documented publications examine practical problems using machine learning, fuzzy logic, clustering, optimization, image processing, and IoT networking. Recent work includes anomaly detection in road traffic analysis and counterfeit currency detection using ensemble machine learning and image processing methods. Earlier studies address disease diagnosis, energy-efficient IoT routing, and optimized clustering. [2] [3]

Keywords

Machine learning; anomaly detection; image processing; fuzzy logic; clustering; optimization; Internet of Things; computer science; disease diagnosis; traffic analysis.

Introduction

The research record spans several computational applications in which data-driven techniques are used to identify patterns, classify information, optimize systems, or improve decision-support processes. The publication portfolio from 2023 to 2025 indicates continuing engagement with contemporary computational problems, including IoT communication, medical diagnosis, image-based recognition, and road-traffic analysis. [4]

Research Profile

The stated subject area is Computer Science. The supplied Scopus information lists 13 documents, 68 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the indexed research record and should be interpreted in relation to publication year, field, document type, and database coverage. [1]

Research Contributions

  • Road-traffic research addressing anomaly detection and computational analysis. [2]
  • Machine-learning and image-processing approaches for counterfeit currency detection. [3]
  • Fuzzy logic and machine learning applied to early disease diagnosis. [4]
  • Optimization and clustering methods for energy-efficient IoT routing and network applications. [5]

Publications

A Comprehensive Review of Anomaly Detection in Road Traffic Analysis (2025), International Journal of Computing and Digital Systems. [2]

Detection of counterfeit currency using ensemble machine learning models and image processing methods (2025), AIP Conference Proceedings. [3]

Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis (2024), International Journal of Advanced Computer Science and Applications. [4]

An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks (2023), 2023 IEEE International Conference on Contemporary Computing and Communications. [5]

A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT (2023), Concurrency and Computation: Practice and Experience. [6]

Research Impact

The supplied bibliometric record reports 68 citations and an h-index of 4 across 13 documents. The publication portfolio also demonstrates application-oriented research across multiple computational contexts. Citation indicators are database-dependent and can change as new publications and citations are indexed. [1]

Award Suitability

The documented research themes provide a substantive basis for consideration under a Research Excellence Award in the Computer Science category. The portfolio contains peer-reviewed journal and conference publications covering machine learning, intelligent systems, optimization, IoT, image processing, and anomaly detection. This assessment is based on the supplied publication and bibliometric information rather than an independent evaluation of the complete academic record.

Conclusion

SATEESH GORIKAPUDI’s documented research profile represents an application-focused body of work in Computer Science. Publications from 2023–2025 demonstrate engagement with machine learning, optimization, clustering, IoT networks, image processing, medical diagnosis, and anomaly detection. The supplied Scopus indicators and publication record provide a concise basis for academic recognition within the International Academic Excellence Awards framework.

References

  1. Elsevier. (n.d.). Scopus author details: SATEESH GORIKAPUDI, Author ID 58249169300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58249169300
  2. Gorikapudi, S. (2025). A Comprehensive Review of Anomaly Detection in Road Traffic Analysis. International Journal of Computing and Digital Systems. DOI: 10.12785/ijcds/1571111482
  3. Gorikapudi, S. (2025). Detection of counterfeit currency using ensemble machine learning models and image processing methods. AIP Conference Proceedings. DOI: 10.1063/5.0296454
  4. Gorikapudi, S. (2024). Fuzzy Logic-Driven Machine Learning Algorithms for Improved Early Disease Diagnosis. International Journal of Advanced Computer Science and Applications. DOI: 10.14569/IJACSA.2024.0151111
  5. Gorikapudi, S. (2023). An Optimized Clustering Model for Energy-Efficient Routing in IoT Networks. 2023 IEEE International Conference on Contemporary Computing and Communications. DOI: 10.1109/inc457730.2023.10263199
  6. Gorikapudi, S. (2023). A novel clustering model via optimized fuzzy C-means algorithm and sandpiper optimization with cycle crossover process in IoT. Concurrency and Computation: Practice and Experience. DOI: 10.1002/cpe.7776

Vijay Srinivas Tida | Computer Science | Excellence in Research

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Dr. Vijay Srinivas Tida | Computer Science | Excellence in Research

Dr. Vijay Srinivas Tida, College of St Benedict and St John’s university, United States

Dr. Vijay Srinivas Tida is a dedicated researcher and academician currently serving as a Tenure-track Assistant Professor at the College of St. Benedict and St. John’s University, Minnesota. With a strong foundation in Electronics, Computer Engineering, and Deep Learning, he has developed a notable reputation in the fields of differential privacy, federated learning, and FPGA hardware acceleration. His Ph.D. dissertation at the University of Louisiana at Lafayette explored optimizing transpose convolution operations—a critical component in CNNs. Dr. Tida’s academic journey has taken him through top institutions including Illinois Institute of Technology and Koneru Lakshmaiah University, consistently achieving high academic honors. He has actively contributed to privacy-preserving machine learning for healthcare and has authored several journal articles and conference papers. Passionate about teaching, he also mentors students in deep learning and hardware systems, making him a valuable contributor to modern computer science education.

Profile

Google Scholar

Suitability for Research for Excellence in Research Award: Vijay Srinivas Tida

Vijay Srinivas Tida stands out as a highly deserving candidate for the Research for Excellence in Research Award due to his exceptional contributions in the fields of deep learning optimization, differential privacy, federated learning, and hardware accelerator design. His academic journey reflects consistent excellence, culminating in a Ph.D. in Computer Engineering with a remarkable GPA of 3.9/4.0 from the University of Louisiana at Lafayette. Complemented by a strong foundation in Electrical and Computer Engineering from Illinois Institute of Technology and Electronics and Communication Engineering from Koneru Lakshmaiah University, his educational background is solid and highly relevant.

Throughout his academic and professional career, Vijay has demonstrated a commitment to pioneering research, particularly focusing on the optimization of deep convolutional neural networks, privacy-preserving machine learning models, and hardware security. His doctoral dissertation on optimizing transpose convolution operations and his multiple research projects emphasize innovative approaches that enhance the efficiency and security of AI models, which are critical in today’s technology-driven healthcare and security domains.

🎓 Education

Dr. Vijay Srinivas Tida earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette (2018–2023), under the mentorship of Dr. Sonya Hsu and Dr. Xiali Hei, graduating with an impressive GPA of 3.9/4.0. His dissertation focused on optimizing transpose convolution operations for efficient deep learning computation. Prior to this, he completed his Master’s degree in Electrical and Computer Engineering from Illinois Institute of Technology (2016–2018), working with Dr. Erdal Oruklu and maintaining a GPA of 3.8/4.0. He began his academic journey with a Bachelor of Science in Electronics and Communication Engineering from Koneru Lakshmaiah University (2011–2015), guided by Dr. Nalluri Siddaiah, achieving a perfect GPA of 4.0/4.0. His academic background reflects a blend of theoretical knowledge and practical experience in machine learning, hardware design, and optimization algorithms, which forms the core of his current research and teaching interests.

💼 Professional Experience

Dr. Tida’s professional trajectory spans across academic teaching and innovative research. He currently holds the position of Assistant Professor at the College of St. Benedict and St. John’s University, where he teaches and mentors students in computer science. Previously, he served as a Postdoctoral Research Assistant at the University of Louisiana at Lafayette (May–Aug 2023), contributing to projects in privacy-preserving AI and FPGA-based accelerators. From 2018 to 2022, he was a Graduate Teaching Assistant and Lab Instructor, where he taught courses including Computer Architecture and Computer Engineering Labs. He also held Research Assistant roles across institutions like Illinois Institute of Technology and Koneru Lakshmaiah University, engaging in high-impact projects on energy harvesting, sensor security, and neural networks. Dr. Tida’s teaching is complemented by his commitment to community outreach, where he has conducted programming workshops for high school students and offered deep learning sessions to Ph.D. candidates.

🏅 Awards and Recognition

Dr. Tida has been the recipient of numerous honors recognizing both his academic excellence and research contributions. Notably, in 2024, he received $1,750 to attend the prestigious SIGCSE Technical Symposium on Computer Science Education. He was awarded a $6,500 Summer Collaborative Research Grant and $1,000 by the Faculty Development Research Committee for conference travel. In 2023, the College of St. Benedict and St. John’s University provided him with high-performance computing resources worth $16,000. During his doctoral studies, he earned a Dissertation Completion Fellowship and secured consistent Graduate Teaching and Research Assistantships from 2018 to 2022. These accolades reflect his capabilities in leading cutting-edge projects and fostering academic excellence. His continued association with academic conferences such as HICSS and ACM further underscores his recognition within the computing research community.

🌍 Research Skill On Computer Science

Dr. Tida’s research skills encompass a dynamic combination of deep learning, optimization, hardware acceleration, and data privacy. His expertise lies in the development and optimization of Convolutional Neural Networks (CNNs), especially with transpose convolution operations—a subject central to his doctoral work. His focus on Differential Privacy and Federated Learning reflects his commitment to secure and ethical AI, particularly for healthcare data applications. He is adept at hardware-level design using Field Programmable Gate Arrays (FPGAs), enabling real-time and efficient AI computations. With a solid command over Natural Language Processing, he has also published in areas like fake news detection and spam classification using models such as BERT. Dr. Tida’s proficiency spans Python, Arduino C, and hardware descriptive languages, supported by his consistent role in mentoring and peer reviewing. His integration of theoretical algorithms with practical systems development defines his impactful presence in modern computational research.

📖 Publication Top Notes

  • Universal Spam Detection using Transfer Learning of BERT Model
    Author(s): VSTDS Hsu
    Citation: 89
    Year: 2022

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
    Author(s): SV Chilukoti, L Shan, VS Tida, AS Maida, X Hei
    Citation: 45
    Year: 2024

  • Transduction shield: A low-complexity method to detect and correct the effects of EMI injection attacks on sensors
    Author(s): Y Tu, VS Tida, Z Pan, X Hei
    Citation: 38
    Year: 2021

  • Design and Analysis of High Efficient UART on Spartran-6 and Virtex-7 Devices
    Author(s): KH Kishore, CA Kumar, TV Srinivas, GV Govardhan, CNP Kumar, …
    Citation: 20
    Year: Not specified (likely between 2015–2018 based on journal timeline)

  • A unified training process for fake news detection based on fine-tuned BERT model
    Author(s): VS Tida, S Hsu, X Hei
    Citation: 10
    Year: 2022

  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 8
    Year: 2023

  • Kernel-Segregated Transpose Convolution Operation
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 5
    Year: 2023

  • Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks
    Author(s): VS Tida, R Shah, X Hei
    Citation: 2
    Year: 2022

  • Unified Kernel-Segregated Transpose Convolution Operation
    Author(s): VS Tida, MI Hossen, L Shan, SV Chilukoti, S Hsu, X Hei
    Citation: Not listed
    Year: 2025

  • Differentially private fine-tuned NF-Net to predict GI cancer type
    Author(s): SV Chilukoti, IH Md, L Shan, VS Tida, X Hei
    Citation: Not listed
    Year: 2025

Ahona Ghosh | Computer Science | Best Researcher Award

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Ms. Ahona Ghosh | Computer Science | Best Researcher Award

👤 Ms. Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India

Ahona Ghosh is a promising researcher in the field of Computer Science and Engineering with a focus on artificial intelligence, machine learning, and rehabilitation technologies. Currently completing her Ph.D. at Maulana Abul Kalam Azad University of Technology, West Bengal, Ahona has made significant strides in the academic and research community. Her work involves a blend of deep learning, cognitive rehabilitation, and IoT-based systems for improving quality of life. With several publications in prestigious international journals and conferences, she has earned recognition for her contributions to the scientific community. Ahona has been awarded the Best Paper Award for her work on IoT-based waste management and has ranked highly in various competitions like MAKATHON’22. She is passionate about leveraging technology for social good, particularly in healthcare and rehabilitation systems.

Professional Profile

Scopus

Orcid

Google Scholar

🌟 Ms. Ahona Ghosh, Summary of Suitability

Dr. Ahona Ghosh is an outstanding candidate for the Research for Best Researcher Award, demonstrating exceptional academic accomplishments, innovative research contributions, and consistent excellence throughout her career. Her extensive academic background includes a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology, West Bengal, with her pre-submission and viva completed, reflecting her advanced expertise and dedication to her field. She has received accolades for her academic and research endeavors, such as the Best Paper Award for her IoT-based waste management system and the Academic Excellence Award from Brainware University.

Her robust portfolio of research contributions includes an impressive array of international journal articles, conference papers, patents, and book chapters. Dr. Ghosh’s work spans cutting-edge topics such as deep learning, cognitive rehabilitation, IoT applications, and fuzzy systems, addressing societal challenges like healthcare, rehabilitation, and sustainable development.

🎓 Education 

Ahona Ghosh has a strong academic background, with a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology (MAKAUT), where she is in the final stages of her thesis submission. She completed her Master of Technology (M.Tech.) in the same field at MAKAUT in 2019, with a CGPA of 8.73. Her Bachelor’s degree in Computer Science and Engineering (B.Tech.) was awarded by Techno India College of Technology in 2017, where she achieved a CGPA of 7.66. Ahona’s early education includes Higher Secondary in Science from Taki House Government Sponsored Girls High School, with an aggregate of 67.4%. She also passed the Madhyamik Pariksha (Class 10) from Duff High School for Girls with a remarkable score of 82.88%. Ahona is also certified in NTA-NET for the years 2018 and 2019.

💼  Professional Experience

Ahona Ghosh has worked extensively in academia and research, focusing on artificial intelligence, IoT, and healthcare applications. Currently, she is a Doctoral Fellow at Maulana Abul Kalam Azad University of Technology (MAKAUT). Her research includes contributions to cognitive rehabilitation using machine learning and EEG-based sensor systems. She has also been involved in various projects concerning IoT-based solutions for healthcare, such as designing smart systems for cognitive rehabilitation and enhancing data-driven rehabilitation methods. In addition, Ahona has been a part of multiple international conferences where she presented papers, co-authored patents, and contributed to the scientific community with impactful research. Her teaching experience includes mentoring undergraduate students and guiding research projects, as well as working on industry collaborations in technology development. Ahona’s expertise in both theoretical and applied aspects of Computer Science has shaped her as a versatile professional in the field.

🏅 Awards and Recognition

Ahona Ghosh has received several accolades for her academic and research achievements. She won the Best Paper Award at the IETE Eastern Zonal Seminar with ISF Congress in 2017 for her paper on “Waste Management System Based on Internet of Things (IoT)”. Her innovative contributions earned her the Academic Excellence Award from Brainware University in January 2020, based on exceptional student feedback. She also achieved 2nd place in the MAKATHON’22 competition organized by MAKAUT’s Innovation Council. Ahona’s recognition extends beyond awards, as she is a prominent figure in academic circles, having presented her research at several prestigious IEEE conferences. Her qualifications include passing the NTA-NET exams in 2018 and 2019, reinforcing her academic prowess. Ahona’s dedication to research and innovation continues to receive recognition, making her an influential presence in her field.

🌍 Research Skills On Computer Science 

Ahona Ghosh has developed a comprehensive set of research skills, particularly in the areas of Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Rehabilitation. Her expertise extends to using IoT for healthcare applications, including creating systems for rehabilitative therapy and mental health analysis. Ahona is proficient in data analysis, algorithm design, and modeling for both real-time and research-driven applications. Her experience with neural networks, sensor systems, and signal processing further enhances her ability to tackle complex problems. Ahona has contributed to developing innovative frameworks using fuzzy logic, sensor networks, and electroencephalography (EEG) in health-related projects. She excels in academic writing, having published in numerous peer-reviewed journals and international conferences. Additionally, she is well-versed in patent filing, research methodology, and project management, which are crucial in carrying out high-impact scientific work.

📖 Publication Top Notes

Scope of Sentiment Analysis On News Articles Regarding Stock Market and GDP in Struggling Economic Condition
  • Authors: S Biswas, A Ghosh, S Chakraborty, S Roy, R Bose
    Journal: International Journal of Emerging Trends in Engineering Research, 8 (7), 3594
    Citation: 30
    Year: 2020
A Detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques
  • Authors: D Mukherjee, S Chakraborty, I Sarkar, A Ghosh, S Roy
    Journal: International Journal of Advanced Trends in Computer Science and Engineering
    Citation: 26
    Year: 2020
Recognition of hand gesture image using deep convolutional neural network
  • Authors: KM Sagayam, AD Andrushia, A Ghosh, O Deperlioglu, AA Elngar
    Journal: International Journal of Image and Graphics, 22 (03), 2140008
    Citation: 22
    Year: 2022
Service aware resource management into cloudlets for data offloading towards IoT
  • Authors: D Guha Roy, B Mahato, A Ghosh, D De
    Journal: Microsystem Technologies, 1-15
    Citation: 21
    Year: 2022
Mathematical modelling for decision making of lockdown during COVID-19
  • Authors: A Ghosh, S Roy, H Mondal, S Biswas, R Bose
    Journal: Applied Intelligence
    Citation: 17
    Year: 2021
Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm: Fundamentals and Applications
  • Authors: A Ghosh, CC Ho, R Bestak
    Journal: Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks
    Citation: 12
    Year: 2020
A survey on Internet-of-Thing applications using electroencephalogram
  • Authors: D Chakraborty, A Ghosh, S Saha
    Book: Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, 21-47
    Citation: 12
    Year: 2020
Rehabilitation using neighbor-cluster based matching inducing artificial bee colony optimization
  • Authors: S Saha, A Ghosh
    Conference: 2019 IEEE 16th India Council International Conference (INDICON), 1-4
    Citation: 12
    Year: 2019
Dtnma: identifying routing attacks in delay-tolerant network
  • Authors: S Chatterjee, M Nandan, A Ghosh, S Banik
    Book: Cyber Intelligence and Information Retrieval: Proceedings of CIIR 2021, 3-15
    Citation: 11
    Year: 2022
Emotion detection using generative adversarial network
  • Authors: S Das, A Ghosh
    Book: Generative Adversarial Networks and Deep Learning, 165-182
    Citation: 10
    Year: 2023

Tariq Jamil | Machine Learning Award | Best Researcher Award

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Mr. Tariq Jamil | Machine Learning Award | Best Researcher Award

Lead multilingual education at TensorDot Solutions, Pakistan🎓

Mr. Tariq Jamil is an accomplished AI/ML engineer and data scientist with a rich background in both academia and industry. His expertise spans a wide range of AI and machine learning domains, including deep learning, natural language processing, computer vision, and large language models. With a career marked by successful project management, innovative AI applications, and educational contributions, Mr. Jamil has consistently demonstrated his capacity for leadership and technical excellence. His experience as a project leader in various AI challenges and his role in training the next generation of AI professionals highlight his commitment to advancing the field.

Professional Profile 

🎓Education

Mr. Tariq Jamil holds a Post-Graduate Diploma in Data Science with Artificial Intelligence from NED University of Engineering and Technology, Pakistan, completed between December 2021 and June 2023. His coursework included an in-depth study of Python, data visualization, machine learning, deep learning, and computer vision. His capstone project involved the development of a chatbot using deep learning, specifically fine-tuning the Falcon large language model for customer service applications. Prior to this, Mr. Jamil earned a Bachelor of Engineering in Aviation Electronics with a specialization in Aerospace Technology from the same institution, graduating in December 1991. His academic foundation in digital electronics, communications, and quality systems, combined with his later specialization in AI, has provided him with a strong interdisciplinary background, enabling him to excel in both the engineering and data science fields.

💼Work Experience

Mr. Tariq Jamil has amassed a diverse and impactful work experience that spans multiple domains in the fields of AI, machine learning, and data science. Since January 2021, he has been working as a freelance AI/ML Engineer and Data Scientist in Karachi, Pakistan. In this role, Mr. Jamil has successfully managed multiple customer projects on platforms like Upwork and Freelancer.com, as well as with private clients. His work has involved a comprehensive range of AI technologies, including computer vision, video action detection, deep learning, natural language processing, and large language models. Notably, he contributed to the success of a pioneering marketing startup, True Insights, and developed advanced AI models for video crime scene detection and sign language recognition. In addition to his freelance work, Mr. Jamil served as an AI Trainer at NED University in Karachi from October 2023 to March 2024. He designed and taught graduate-level courses in Artificial Intelligence, focusing on deep learning, LLMs, Python, and computer vision. His teaching played a pivotal role in shaping the university’s Post Graduate Diploma program in Generative AI and the “DS & AI” program, where he taught classes on foundational and advanced AI concepts. Furthermore, Mr. Jamil has been an active contributor to Omdena, a global AI community, since July 2023. As the Chapter Lead for the UAE division, he led several innovative AI projects, including the development of a voice-enabled chatbot for Abu Dhabi Open Data Intelligence and an AI application for liver cancer histopathology detection. His role involved coordinating teams of data scientists and overseeing the technical execution of these projects, which led to significant advancements in AI applications for healthcare and data analytics.

🔍Research Focus 

Mr. Tariq Jamil’s research focus lies at the intersection of artificial intelligence (AI), machine learning (ML), and their practical applications across various domains. His work encompasses deep learning, natural language processing (NLP), computer vision, and large language models (LLMs), with a particular emphasis on developing AI solutions for real-world challenges. He has applied his expertise in projects ranging from video action detection and anomaly detection to chatbot development and healthcare diagnostics, such as histopathology and cervical cytology detection.

Mr. Jamil’s research is characterized by a commitment to innovation and impact. He has led projects that harness cutting-edge AI technologies, such as Transformers, YOLO, and OpenCV, to create applications that enhance automation, accuracy, and efficiency in fields like healthcare, security, and transportation. His work in fine-tuning LLMs for specific tasks, such as customer support and legal document processing, highlights his ability to customize AI models to meet the unique needs of different industries.

🏆Awards and Honors

Mr. Tariq Jamil has been recognized for his outstanding contributions to the fields of artificial intelligence and machine learning through various awards and honors. His leadership and innovation in AI, particularly in areas such as deep learning, natural language processing, and computer vision, have earned him accolades from both academic and professional communities. He has been acknowledged for his exceptional project management skills, notably in AI challenges that addressed critical real-world problems like healthcare diagnostics, vehicle inspection, and road safety.

In addition to his technical achievements, Mr. Jamil’s dedication to education has been celebrated, with honors highlighting his impactful teaching and mentoring roles at NED University. His efforts in training the next generation of AI professionals and his involvement in pioneering AI projects have further solidified his reputation as a leading expert in his field. These recognitions serve as a testament to Mr. Jamil’s commitment to excellence and his significant contributions to advancing the frontiers of AI technology.

Conclusion

Based on Mr. Tariq Jamil’s extensive experience, leadership in AI innovation, and dedication to education, he is a strong candidate for the Best Researcher Award. His ability to apply AI solutions to real-world problems, coupled with his commitment to advancing AI education, makes him a valuable asset to the AI community. With continued focus on publishing and expanding his professional network, Mr. Jamil could further elevate his contributions to the field, making him a highly deserving recipient of this award.

📖Publications : 

  1. Publication: “Fluid Coupled Structural Analysis and Optimization of Expanded Polystyrene-Fiber-Reinforced Composite Wing of an Unmanned Aerial Vehicle”
    • Authors: Jamil, T.; Iqbal, A.; Allauddin, U.; Saleem, S.; Ikhlaq, M.
    • Year: 2024
    • Journal: Mechanics of Composite Materials
    • Citations: 0
  2. Publication: “Effect of Glass Powder on the Compressive Strength and Drying Shrinkage Behavior of OPC- and LC3-50-Based Cementitious Composites of Various Strengths”
    • Authors: Ayub, T.; Jamil, T.; Ayub, A.; Mehmood, E.; Sheikh, M.D.
    • Year: 2024
    • Journal: Advances in Materials Science and Engineering
    • Citations: 0
  3. Publication: “Mechanical and Durability Properties of High-Strength Limestone Calcined Clay Cement (LC3) Concrete Containing Waste Glass Powder”
    • Authors: Ayub, A.; Ayub, T.; Jamil, T.; Khan, A.-U.-R.
    • Year: 2023
    • Journal: Iranian Journal of Science and Technology – Transactions of Civil Engineering
    • Citations: 2
  4. Publication: “Electromechanical characterizations of PEDOT

    and its nanocomposite thin films on a cost-effective polymer substrate for microelectromechanical systems (MEMS) applications”

    • Authors: Khan, S.T.; Mehdi, M.; Jamil, T.
    • Year: 2023
    • Journal: Express Polymer Letters
    • Citations: 1
  5. Publication: “Sustainable cementitious material with ultra-high content partially calcined limestone-calcined clay”
    • Authors: Qian, X.; Ruan, Y.; Jamil, T.; Hu, S.; Liu, Y.
    • Year: 2023
    • Journal: Construction and Building Materials
    • Citations: 13
  6. Publication: “Investigating the crumpling effect in honeycomb sandwich panels under bending loads using FEA technique”
    • Authors: Saqib, N.; Jamil, T.; Zai, B.A.
    • Year: 2023
    • Journal: Aeronautical Journal
    • Citations: 0
  7. Publication: “HEAT-TRANSFER ENHANCEMENT OF A SOLAR PARABOLIC TROUGH COLLECTOR USING TURBULATORS AND NANOPARTICLES: A NUMERICAL STUDY”
    • Authors: Allauddin, U.; Ikhlaq, M.; Jamil, T.; Mustafa, H.; Azeem, M.H.
    • Year: 2023
    • Journal: Journal of Enhanced Heat Transfer
    • Citations: 1
  8. Publication: “Comparative Study on LC3-50 with OPC Concrete Using Raw Materials from Pakistan”
    • Authors: Sheikh, M.D.; Jamil, T.; Ayub, T.; Bilal, S.M.; Hu, C.
    • Year: 2023
    • Journal: Advances in Materials Science and Engineering
    • Citations: 2
  9. Publication: “Numerical simulation and validation of MWCNT-CFRP hybrid composite structure in lightweight satellite design”
    • Authors: Iqbal, S.; Jamil, T.; Murtuza Mehdi, S.
    • Year: 2023
    • Journal: Composite Structures
    • Citations: 9
  10. Publication: “Virtual Captive Model tests for Maneuvering Prediction”
    • Authors: Khan, O.U.; Mansoor, A.; Zai, B.A.; Hussain, M.; Jamil, T.
    • Year: 2022
    • Journal: Journal of Maritime Research
    • Citations: 1