Rajeev Ratna Vallabhuni | Computer Science | Young Scientist Award

Published on by NESIN Awards

Mr. Rajeev Ratna Vallabhuni | Computer Science | Young Scientist Award

Application Developer at Texans IT Services Inc., India

Rajeev Ratna Vallabhuni is an accomplished Application Developer with a rich background in computer science, technology, and engineering. He has contributed significantly to the field through several innovative patents in areas such as blockchain-based cloud applications, machine learning, and IoT security. His work spans various domains including AI/ML, image processing, and network management, with numerous research publications in international journals and conferences. With experience at Bayview Asset Management, LLC, he has a strong track record of applying cutting-edge technologies to real-world applications. His expertise in both academic and professional settings makes him a leading figure in the field of information technology and software development.

Professional Profile 

Education

Rajeev Ratna Vallabhuni holds a Master of Science in Information Technology Management from Campbellsville University, Kentucky, and a Master of Science in Computer Science Engineering from Northwestern Polytechnic University, California, USA. He also completed his Bachelor of Technology in Information and Technology at Vignan University, India. His educational foundation has equipped him with a diverse skill set, allowing him to specialize in software development, computer engineering, and cutting-edge technological innovations.

Professional Experience

Rajeev currently works as an Application Developer at Bayview Asset Management, LLC, where he plays a key role in developing and optimizing software applications. His previous professional experience includes working on various projects related to AI/ML, blockchain, and IoT security. He has contributed to numerous patents, book chapters, and international journal publications. Rajeev’s expertise spans both technical development and leadership, and his ability to integrate machine learning and deep learning techniques into practical solutions has made him a valuable asset in the tech industry.

Research Interest

Rajeev Ratna Vallabhuni’s research interests lie at the intersection of artificial intelligence, machine learning, cloud computing, and Internet of Things (IoT) technologies. His work primarily focuses on enhancing the security of IoT networks, leveraging blockchain for decentralized application architectures, and utilizing deep learning models for image and signal processing. Rajeev is also interested in exploring advanced computational methods for improving network management, resource allocation, and real-time data processing in cloud environments. His innovative research aims to develop scalable, efficient, and secure solutions for modern computing challenges, bridging the gap between theoretical algorithms and real-world applications.

Awards and Honors

Rajeev Ratna Vallabhuni has received numerous accolades for his contributions to the fields of software development, machine learning, and IoT security. Notable recognitions include multiple patents for his innovations in blockchain-based applications, AI/ML, and security systems. He has been awarded fellowships and scholarships during his academic career, showcasing his dedication to pushing the boundaries of technology. Additionally, Rajeev’s research has been published in prestigious international journals and recognized at numerous conferences, further cementing his reputation as a leading figure in his field.

Publications Top Noted

  • Smart cart shopping system with an RFID interface for human assistance
    Authors: RR Vallabhuni, S Lakshmanachari, G Avanthi, V Vijay
    Year: 2020
    Citation: 92
  • Performance analysis: D-Latch modules designed using 18nm FinFET Technology
    Authors: RR Vallabhuni, G Yamini, T Vinitha, SS Reddy
    Year: 2020
    Citation: 85
  • Disease prediction based retinal segmentation using bi-directional ConvLSTMU-Net
    Authors: BMS Rani, VR Ratna, VP Srinivasan, S Thenmalar, R Kanimozhi
    Year: 2021
    Citation: 68
  • ECG performance validation using operational transconductance amplifier with bias current
    Authors: V Vijay, CVSK Reddy, CS Pittala, RR Vallabhuni, M Saritha, M Lavanya, …
    Year: 2021
    Citation: 63
  • A Review On N-Bit Ripple-Carry Adder, Carry-Select Adder And Carry-Skip Adder
    Authors: V Vijay, M Sreevani, EM Rekha, K Moses, CS Pittala, KAS Shaik, …
    Year: 2022
    Citation: 62
  • Speech Emotion Recognition System With Librosa
    Authors: PA babu, VS Nagaraju, RR Vallabhuni
    Year: 2021
    Citation: 62
  • 6Transistor SRAM cell designed using 18nm FinFET technology
    Authors: RR Vallabhuni, P Shruthi, G Kavya, SS Chandana
    Year: 2020
    Citation: 60
  • Universal Shift Register Designed at Low Supply Voltages in 20nm FinFET Using Multiplexer
    Authors: RR Vallabhuni, J Sravana, CS Pittala, M Divya, BMS Rani, S Chikkapally, …
    Year: 2021
    Citation: 58
  • Numerical analysis of various plasmonic MIM/MDM slot waveguide structures
    Authors: CS Pittala, RR Vallabhuni, V Vijay, UR Anam, K Chaitanya
    Year: 2022
    Citation: 57
  • Design of Comparator using 18nm FinFET Technology for Analog to Digital Converters
    Authors: RR Vallabhuni, DVL Sravya, MS Shalini, GU Maheshwararao
    Year: 2020
    Citation: 55
  • High Speed Energy Efficient Multiplier Using 20nm FinFET Technology
    Authors: VR Ratna, S M, S N, V V, PC Shaker, D M, S Sadulla
    Year: 2021
    Citation: 53
  • Physically unclonable functions using two-level finite state machine
    Authors: V Vijay, K Chaitanya, CS Pittala, SS Susmitha, J Tanusha, …
    Year: 2022
    Citation: 48
  • Realization and comparative analysis of thermometer code based 4-bit encoder using 18 nm FinFET technology for analog to digital converters
    Authors: CS Pittala, V Parameswaran, M Srikanth, V Vijay, V Siva Nagaraju, …
    Year: 2021
    Citation: 45
  • Comparative validation of SRAM cells designed using 18nm FinFET for memory storing applications
    Authors: RR Vallabhuni, KC Koteswaramma, B Sadgurbabu, A Gowthamireddy
    Year: 2020
    Citation: 45

Qiao Ke | Deep Learning | Best Researcher Award

Published on by NESIN Awards

🌟Assist Prof Dr. Qiao Ke, Deep Learning, Best Researcher Award🏆

  Assistant professor at Northwestern Polytechnical University, China

Qiao Ke is an Assistant Professor at Northwestern Polytechnical University, specializing in Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and Internet of Things. Qiao holds a Ph.D. in Mathematics from Xi’an Jiao Tong University and has been actively engaged in research, contributing significantly to various areas of computational mathematics and artificial intelligence.

Author Metrics:

Ke, Qiao – Scopus Profile

Orcid Profile

Qiao Ke is affiliated with Northwestern Polytechnical University in Xi’an, China. The Scopus Author Identifier 56465532300 provides valuable metrics regarding their academic contributions.

  • Citations: Qiao Ke has received a total of 481 citations across 420 documents, indicating the impact of their research on the academic community.
  • Documents: The author has contributed to 16 documents, showcasing a consistent and substantive scholarly output.
  • h-index: With an h-index of 8, Qiao Ke has demonstrated a noteworthy level of influence in their field. The h-index is a metric that considers both the number of publications and the number of citations they receive.

These metrics reflect the academic impact and productivity of Qiao Ke, highlighting their contributions to the scholarly landscape. The provided information encourages further exploration into the specific content and context of their publications for a comprehensive understanding of their research achievements.

Education:

Qiao Ke pursued a B.S. in Mathematics from Shaanxi Normal University, an M.S. in Mathematics, and a Ph.D. in Mathematics from Xi’an Jiao Tong University. Additionally, they completed postdoctoral research in the Department of Computer Science at Northwestern Polytechnical University.

Research Focus:

Qiao Ke’s research interests span Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and the Internet of Things. Notably, their work includes innovative contributions to neural frameworks for software models, hierarchical search-based code generation, and adaptive disentangled representation learning.

Professional Journey:

Qiao Ke’s professional journey involves serving as an Assistant Professor at the School of Mathematics and Statistics, Northwestern Polytechnical University. They have also actively participated as a reviewer for several reputed journals and conferences, demonstrating their commitment to scholarly peer review.

Publications Top Noted & Contributions:

Qiao Ke has made significant contributions to the field, with publications in respected journals and conferences. Notable works include research on modular neural frameworks for software model connections, deep hierarchical search-based code generation, and adaptive disentangled representation learning.

A research paper titled “RRGcode: Deep hierarchical search-based code generation.” The paper addresses the challenges of retrieval-augmented code generation, where a retrieval model is used to select relevant code snippets from a code corpus to strengthen the generation model. The primary concern is that if the retrieval corpus contains errors or sub-optimal examples, the generation model might replicate these mistakes in the generated code.

To overcome these challenges, the authors propose RRGcode, a deep hierarchical search-based code generation framework. The key components of RRGcode are outlined as follows:

  1. Retrieval: The framework first retrieves relevant code candidates from a large code corpus. This initial retrieval step aims to gather a set of potential code snippets based on the given query.
  2. Re-ranking: A re-ranking model is introduced to fine-tune the initial retrieved code rankings. This involves a detailed semantic comparison between the code candidates and the query, ensuring that only the most relevant and accurate candidates are considered. The re-ranking process aims to mitigate the risk of replicating errors from the retrieval corpus.
  3. Generation: The re-ranked top-K codes, along with the query, serve as input for the code generation model. This final step focuses on generating high-quality and reliable code based on the refined set of code candidates.

The authors claim that RRGcode demonstrates state-of-the-art performance in code generation tasks through extensive experiments. The deep hierarchical search-based approach aims to improve the quality of generated code by addressing the limitations associated with erroneous or sub-optimal code examples present in the retrieval corpus.

1. Title: Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purposes

2. Title: Intelligent Internet of Things System for Smart Home Optimal Convection

  • Publication Date: June 2021
  • Journal: IEEE Transactions on Industrial Informatics
  • DOI: 10.1109/tii.2020.3009094
  • ISSN: 1551-3203, 1941-0050

3. Title: High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

  • Publication Date: November 28, 2020
  • Journal: Security and Communication Networks
  • DOI: 10.1155/2020/8889317
  • ISSN: 1939-0122, 1939-0114

4. Title: Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach

5. Title: Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Research Timeline:

Qiao Ke’s research journey spans from their Bachelor’s degree at Shaanxi Normal University in 2012 to their current role as an Assistant Professor at Northwestern Polytechnical University. Notable milestones include completing a Ph.D., engaging in postdoctoral research, and actively contributing to various research projects, including leadership roles in national and provincial-level foundations.

Dawei Zhang | Computer Vision and Deep Learning | Best Researcher Award

Published on by NESIN Awards
🌟Dr. Dawei Zhang, Zhejiang Normal University, China:  Computer Vision and Deep Learning🏆
Professional Profiles:

Bio Summary:

Dawei Zhang is a Ph.D. and Assistant Professor in the Department of Computer Science and Technology at Zhejiang Normal University, located in Jinhua, China. He holds expertise in computer vision, deep learning, and multimedia computing, with a focus on areas such as visual object tracking, video object segmentation, lightweight neural networks, adversarial attacks, and multi-modal information fusion.

Research Focus:

  1. Visual Object Tracking and Video Object Segmentation
  2. Light-weight Neural Networks for Mobile or Edge Computing Devices
  3. Research on Adversarial Attacks and Interpretability in Deep Learning
  4. Applications of Multi-modal Information Fusion in Vision and Language

Professional Journey:

  • Ph.D. (2017.09-2022.06) – Zhejiang Normal University, supervised by Prof. Zhonglong Zheng & Xiaoqin Zhang
  • Visiting Intern (2021.05-2021.09) – ISTBI, Fudan University, supervised by Prof. Yanwei Fu
  • B.E. (2013.09-2017.06) – Huaiyin Institute of Technology, supervised by Prof. Sen Xia

Honors & Awards:

  • 2023: 2nd “Chengtai Gonghao” Qihang Teaching Scholarship of Zhejiang Normal University
  • 2022: Talent Ambassador of Wucheng District, Jinhua City, Zhejiang Province
  • 2022: Outstanding Doctoral Dissertation Award of Zhejiang Normal University
  • 2022: Outstanding Graduate Students of Zhejiang Province
  • 2022: “Top-10 Students” of GREENTOWN Group in Zhejiang Normal University
  • 2021: National Scholarship for Postgraduate Students
  • 2018-2021: First class Academic Scholarship of Zhejiang Normal University
  • 2021: “Top-10 Academic Stars” for Graduate Students of Zhejiang Normal University
  • 2020: Academic Innovation Scholarship of Zhejiang Normal University
  • 2020: Outstanding Paper Award of National Conference of Computer Application of CCF

Publications Top Noted & Contributions:

  • Journals: Several papers in prominent journals including International Journal of Machine Learning and Cybernetics, Neurocomputing, IEEE Access, and Sensors.
  • Conferences: Contributions to conferences such as ICML, AAAI, ACM MM, and more, with papers accepted in CCF-A, CCF-B, and CCF-C category conferences.

Title: “Cross Channel Aggregation Similarity Network for Salient Object Detection”

  • Journal: International Journal of Machine Learning and Cybernetics
  • Year: 2022
  • Citations: 8

Title: “UAST: Uncertainty-Aware Siamese Tracking”

  • Conference: International Conference on Machine Learning (ICML), 2022
  • Year: 2022
  • Citations: 11

Title: “Deep Regression Tracking with Graph Attention”

  • Conference: International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2022
  • Year: 2022
  • Citations: 0

Title: “CSART: Channel and Spatial Attention-Guided Residual Learning for Real-Time Object Tracking”

  • Journal: Neurocomputing
  • Year: 2021
  • Citations: 19

Title: “Global Perception Attention Network for Fine-Grained Visual Classification”

  • Conference: International Conference on Computer Communication and Artificial Intelligence (CCAI), 2021
  • Year: 2021
  • Citations: 0

Author Metrics:

  • Total Citations: 170
  • h-index: 8
  • i10-index: 6
  • Documents: 16

Research Timeline:

  • Ongoing: Conducting research on Lightweight Siamese Networks for Efficient UAV Target Tracking (2023-2025).
  • Ongoing: Leading research on Key Algorithms of Intelligent Video Surveillance System in Smart Campus (2023-2025).
  • Ongoing: Participating in Information Asynchronous Propagation Traceability for Temporal Networks (2023-2025).
  • Ongoing: Contributing to Research on Trusted Target Tracking Based on Deep Learning in Intelligent Video Analysis (2023-2026).
  • Ongoing: Involved in Research on Visual Object Tracking Algorithms in Complex Scenarios (2022-2024).