Hana Catur Wahyuni – Technological Networks – Top Researcher Award

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Ms. Hana Catur Wahyuni   distinguished academic and researcher in the field  Technological Network.  In the past five years, she has been actively engaged in various research projects. In 2023, she conducted research on “A Blockchain Technology-Based Risk Management Model for the Beef Supply Chain According to Food Safety and Halal Standards to Achieve Food Independence in Ruminant Commodities,” funded by the Directorate General of Higher Education (Dikti). Additionally, she worked on “Strategies to Increase Productivity in Local Food Agro-Industries Based on Halal Product Assurance Systems with Integration of FMEA and Bayesian Network (BN),” supported by the Research Council of the Directorate General of Higher Education (Majelis Dikti Litbang PP).

🌐 Professional Profiles

Educations📚📚📚

Her educational background includes completing her Bachelor’s, Master’s, and Doctoral degrees in Engineering Management from the University of Islam Indonesia, Yogyakarta, and the Institut Teknologi Sepuluh Nopember (ITS) in Surabaya. She entered the University of Islam Indonesia in Yogyakarta in 1995 and graduated in 1999. Later, she pursued her Master’s degree at ITS in Surabaya from 2007 to 2009, followed by her Doctoral studies at the same institution from 2016 to 2020. Her dissertation titles were “Proposal for a standard work method for packaging brem based on work motion analysis with video recordings,” “Analysis of the sophistication level of humanware using a technometric approach at PG Candi Baru, Sidoarjo,” and “Integrated Risk Management Model for Food Safety and Halal in the Food Industry.” Throughout her academic journey, she was guided by Dr. Ir. Wahyu Purwanto MSIE, Prof. Dr. Ir. Udisubakti Ciptomulyono M.Eng.Sc, and Prof. Iwan Vanany ST., MT., Ph.D.

Research

In 2022, she submitted a proposal for the “World Class Professor Program” focusing on enhancing the quality of lecturers and students through collaborative publications between UMSIDA and IPB, funded by the Directorate General of Higher Education (Dikti). Furthermore, she researched “Strategies for Sustainable Development of Small and Medium-sized Fisheries Products Based on Integration of Blue Economy Concepts, Food Safety Standardization, and Halal,” supported by the Indonesian Institute of Sciences (LIPI) under the Mandatory Research Program (Rispro).

Her research endeavors in 2021 included implementing a Halal Assurance System for MSMEs under PCM Tanggulangin, Ka Sidoarjo, funded by the Muhammadiyah University (PP Muhammadiyah). In the same year, she analyzed risks in the Halal food supply chain to ensure food security in the New Normal era, under the auspices of Sidoarjo Muhammadiyah University.

Prior to that, in 2019 and 2018, she developed methods for analyzing food safety risks in the supply chain of fish-based food products produced by IKM as safe, healthy, and Halal alternative foods, funded by the Directorate General of Higher Education (Dikti). She also worked on developing a Green Human Resource Management Model to enhance the productivity of Micro, Small, and Medium Enterprises (MSMEs) as part of a National Strategic Research project in Indonesia (Dikti).

Her earlier research in 2016 focused on developing a technology adoption model to improve the quality of products from Small and Medium Enterprises (SMEs) in support of innovation-driven economics in Sidoarjo District, funded by the Competitive Grant Scheme for the second year (Dikti).

Experience

In the past five years, she has authored several books and obtained intellectual property rights. In 2015, she published “Quality Control in Service and Manufacturing Industries with Lean, Servqual, and Six Sigma,” totaling 175 pages, under PT Graha Ilmu, Yogyakarta. In 2017, she released “Human Capital in the Perspective of Business Productivity” consisting of 100 pages, published by NLC, Sidoarjo. Additionally, she authored “Assessment of Technology in Technology Adoption Processes” and “Analysis of Productivity (Basic Concepts and Measurement Techniques)” in 2017, both published by Umsida Press with 100 and 200 pages, respectively.

In 2018, she published “Pocket Book: Guide to Implementing GHRM in MSMEs” comprising 20 pages, also under Umsida Press. The following year, she authored “Risk Analysis in the Supply Chain (Focus of Research: Food Safety Risks)” published by Umsida Press, spanning 60 pages. In 2020, she released “Quality Control in Manufacturing and Service Industries” with 106 pages, published by Umsida Press. Her most recent book, “Technology Management in Industry,” was published in 2023, totaling 122 pages, under Umsida Press.

Moreover, she has acquired intellectual property rights in the last decade. In 2017, she obtained copyright for a community service program aimed at enhancing the quality of equipment for child health services, and in 2018, she secured copyrights for designing an information system for population registration to improve the quality of population services in Gelam Village, Sidoarjo Regency. Additionally, she acquired copyright for a study on Indonesian consumer perceptions of the food safety system in the fish supply chain in 2018. Lastly, in the same year, she obtained copyright for the implementation guide of GHRM in MSMEs.

Publications📚📚

Jorge Laureano Moya Rodríguez – Neural Networks – Best Researcher Award

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Jorge Laureano Moya Rodríguez – Neural Networks

 Prof Dr.  Jorge Laureano Moya Rodríguez distinguished academic and researcher in the field Neural Network.  Jorge Laureano Moya Rodríguez is a Professor Emeritus at the Central University “Marta Abreu” de las Villas. Cuba. He received his Ph.D. in Mechanical Engineering at this university in 1994. He published over a three hundred papers in professional journals and he has authored several books in mechanical and electrical engineering. He has several international and national awards, including some from the Academy of Sciences of Cuba. He has lectured in different Universities of Spain, México, Nicaragua and Brazil. He is currently visiting professor at the Federal University of Bahia in Brazil. Dr. Moya’s research interests are Multiobjective Optimization, Logistics, Computer Aided Design, and Computer Aided Engineering.

Ele é também membro da ERASMUS MUNDUS ASSOCIATION (EMA) e da Associação Mexicana de Modelagem Numérica e Engenharia (AMMNI). Reconhecido como bolsista de produtividade em Pesquisa pelo CNPq (nível 2) e consultor ad hoc do CNPq, ele contribui como árbitro para diversas revistas científicas e instituições acadêmicas em países como Venezuela, Colômbia, Peru e Cuba. Com uma ampla lista de mais de 50 projetos de pesquisa concluídos e implementados em Cuba, ele é considerado Professor de Mérito pela Universidade Central Marta Abreu de Las Villas. Sua atuação como professor abrange cursos de pós-graduação e disciplinas de mestrado em várias universidades, incluindo a Universidade Federal do Espírito Santo (Brasil), Universidade Veracruzana (México), Universidade Técnica do Estado de Aragua (Venezuela) e Universidade Nacional de Engenharia (Nicarágua). Ele também coordenou o Mestrado em Engenharia Mecatrônica em várias universidades na Venezuela e trabalhou como professor convidado em diversas instituições no México, Peru e Espanha. Anteriormente, ele foi pesquisador do ITEGAM, professor visitante na Universidade Federal do Espírito Santo e na Universidade Federal da Bahia.

 

🌐 Professional Profiles

Educations: 📚🎓

Jorge Laureano Moya Rodríguez, known in bibliographic citations as J. L. M. Rodríguez, J. L. Moya, Jorge Moya, Jorge Laureano Moya Rodríguez, J. Moya, Jorge Rodríguez, Jorge L. Moya Rodríguez, or variations thereof, is affiliated with the University Federal da Bahia, where he works within the Program of Industrial Engineering Postgraduate Studies.

He completed a postdoctoral position in 2011 at the Universidad de Oviedo, UNIOVI, Spain, funded by the Agencia Española de Colaboración Internacional, AECI, Spain. The research was in the field of Engineering.

In 2008, he undertook a postdoctoral fellowship at the Universidad de Oviedo, UNIOVI, Spain, funded by ERASMUS MUNDUS, EM, Germany. The research focus was in Engineering.

In 2005, he conducted postdoctoral research at the Universidad Católica de Leuven, KLU, Belgium, supported by VLIR, VLIR, Belgium. The research was within the field of Engineering.

Publication

 

Changheun Hyun Oh – Medical Image Reconstruction- Best Researcher Award

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Changheun Hyun Oh – Medical Image Reconstruction

Changheun Hyun oh distinguished academic and researcher in the field Deep Learning based Medical Image Reconstruction. In terms of professional experience, he contributed to LG Electronics from 2017 to 2018. Subsequently, he joined the Neuroscience Research Institute of Gachon University in Fall 2018, where he has been actively involved in research endeavors. Throughout his educational and professional journey, he has demonstrated a commitment to advancing the field of Electrical Engineering and contributing to both academic and industrial domains.

 

🌐 Professional Profiles

Educations: 📚🎓

He earned his Ph.D. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) during the period of Fall 2011 to Fall 2020, under the guidance of Professor HyunWook Park in the Image Computing System Lab. Prior to that, he completed his M.S. degree in Electrical Engineering at KAIST from Fall 2009 to Spring 2011, continuing his research with Professor Park. His academic journey at KAIST began with a B.S. degree in Electrical Engineering, spanning from Spring 2005 to Spring 2009.

conferences

He has actively contributed to numerous international conferences, showcasing his research in the field of Electrical Engineering and Medical Imaging. In 2011, as the first author, he presented a paper on “A Switching Circuit for the Rx Signal in the MRI System” at the International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC). Subsequently, he continued to make significant contributions to conferences such as the International Forum on Medical Imaging in Asia and the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

His research work encompasses diverse topics, including the optimization of RF birdcage coils, the development of an optimum RF shield for simultaneous MRI-PET systems, and innovations in MRI techniques like water-fat separation and diffusion-weighted imaging. He also explored applications beyond medical imaging, such as his involvement in the International Symposium on Electronic Art and the International Society for Music Information Retrieval Conference.

In 2018, he presented a paper on “ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network” at the First International Workshop on Machine Learning in Medical Image Reconstruction, held in conjunction with the International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. His research continued to advance in 2020, where he discussed “A direct MR image reconstruction from k-space via End-To-End reconstruction network using recurrent neural network (ETER-net)” at the Annual Meeting of the ISMRM.

In 2022, as the first author, he presented another breakthrough with “A deep learning based direct mapping method for EPI image reconstruction” at the Annual Meeting of the ISMRM. His active participation in these conferences reflects his dedication to pushing the boundaries of knowledge in his field and applying cutting-edge technologies to advance medical imaging techniques.

 📚Top Noted Publication

 

 

Rong Yin – Graph Neural network – Best Researcher Award

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Rong Yin – Graph Neural network

Dr. Rong yin  distinguished academic and researcher in the field Graph Neural Network. She is a highly accomplished researcher with a focus on cutting-edge areas in artificial intelligence and machine learning. Over the past five years, she has made significant contributions, publishing over ten top conference and journal papers in esteemed venues such as NeurIPS, ICML, AAAI, IJCAI, IEEE TKDE, IEEE TNNL, PR, and IEEE TC. Recognized for her expertise, she has been invited to serve as a program committee member and reviewer for prestigious conferences and journals, including ICML, NeurIPS, ICLR, AAAI, and JMLR. One of her notable contributions includes proposing an efficient unsupervised learning algorithm based on the unified randomized sketches framework, paving the way for the application of machine learning in international important fields with massive data scenarios.

Additionally, she has designed a series of approximation algorithms for large-scale tasks such as regression, classification, ranking, and distributed learning. Her theoretical analyses have yielded optimal convergence rates for large-scale unsupervised learning, regression, and distributed learning, making significant strides in machine learning theory. As the principal or core backbone of more than 20 national or provincial key projects, including the Youth/General Fund of the National Natural Science Foundation of China and the National Key R&D Program, she has demonstrated leadership in advancing research in the field.

 

Professional Profiles:

Education

She earned her Ph.D. in Computer Science from the Institute of Information Engineering at the Chinese Academy of Sciences in July 2020, under the guidance of Prof. Dan Meng. Her research during this period has significantly contributed to the field, as evidenced by her subsequent achievements. Prior to her doctoral studies, she completed her M.S. in Computer Science at Harbin Institute of Technology in China in July 2016, with Prof. Xiaohong Su as her advisor. Her academic journey commenced with a B.S. in Thermal Energy and Power Engineering from Shenyang Aerospace University, China, in July 2014, under the mentorship of Prof. Rangshu Xu. Throughout her educational trajectory, she has demonstrated a commitment to academic excellence and has seamlessly transitioned from her undergraduate studies to achieving a Ph.D. in Computer Science, showcasing a continuous pursuit of knowledge and expertise in her chosen field.

Experience

She has been an integral part of the Institute of Information Engineering at the Chinese Academy of Sciences in China, serving as an Associate Researcher since December 2023. Her journey with the institute commenced in August 2020 when she was designated as an Associate Researcher to be Appointed, a position she transitioned into officially in December 2023 and continues to hold to the present. In her capacity as an Associate Researcher, she plays a crucial role in the ongoing research activities of the institute, contributing her expertise and insights to further the objectives of the organization. Her commitment to the field and the institute is evident in her sustained role, where she actively contributes to the research endeavors of the Institute of Information Engineering.

Academic achievements

  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Scalable Kernel k-Means with Randomized Sketching: From Theory to Algorithm. In IEEE Transactions on Knowledge and Data Engineering, 2023, 35(9): 9210 – 9224. (TKDE 2023) (CCF-A, SCI-1, IF: 9.235). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Randomized Sketches for Clustering: Fast and Optimal Kernel k-Means. In Proceedings of Advances in Neural Information Processing Systems, 2022, 35: 6424-6436. (NeurIPS 2022) (CCF-A). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Distributed Nystrom Kernel Learning with Communications. In Proceedings of the 28th International Conference on Machine Learning, PMLR, 2021: 12019-12028. (ICML 2021) (CCF-A). [PDF]
  • Rong Yin, Yong Liu, Dan Meng. Distributed Randomized Sketching Kernel Learning. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022, 36(8): 8883-8891. (AAAI 2022) (CCF-A). [PDF]
  • Ruyue Liu, Rong Yin*, Yong Liu, Weiping Wang. ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, 2024. (AAAI 2024) (CCF-A, Corresponding author).
  • Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng. Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, 34(04): 6696-6703. (AAAI 2020) (CCF-A). [PDF]
  • Xueyan Wang, Jianlei Yang, Yinglin Zhao, Xiaotao Jia, Rong Yin, Xuhang Chen, Gang Qu, Weisheng Zhao. Triangle counting accelerations: From algorithm to in-memory computing architecture. IEEE Transactions on Computers, 2021, 71(10): 2462-2472. (TC 2021) (CCF-A, SCI-1, IF: 3.7). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Sketch Kernel Ridge Regression using Circulant Matrix: Algorithm and Theory. In IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3512-3524. (TNNLS 2020) (SCI-1, IF: 11.683). [PDF]
  • Ruyue Liu, Rong Yin*, Yong Liu, Weiping Wang. Unbiased and Augmentation-Free Self-Supervised Graph Representation Learning. In Pattern Recognition, 2024. (PR 2024) (SCI-1, IF: 8, Corresponding author). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Extremely sparse Johnson-Lindenstrauss transform: From theory to algorithm. In Proceedings of IEEE International Conference on Data Mining, 2020: 1376-1381. (ICDM 2020) (CCF-B). [PDF]
  • Lijing Lu, Rong Yin, Yong Liu, Weiping Wang. Hashing Based Prediction for Large-Scale Kernel Machine. In Proceedings of the International Conference on Computational Science, 2020: 496-509. (ICCS 2020). [PDF]
  • Jian Li, Yong Liu, Rong Yin, Weiping Wang. Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2880-2886. (IJCAI 2019) (CCF-A). [PDF]
  • Jian Li, Yong Liu, Rong Yin, Weiping Wang. Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2887-2893. (IJCAI 2019) (CCF-A). [PDF]

Publication

Guangli Wu – Video Summarization – Best Researcher Award

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Guangli Wu – Video Summarization

prof Dr. Guangli Wu  distinguished academic and researcher in the field  Video summarization.  The existence of software vulnerabilities will cause serious network attacks and information leakage problems. Timely and accurate detection of vulnerabilities in software has become a research focus on the security field. Most existing work only considers instruction-level features, which to some extent overlooks certain syntax and semantic information in the assembly code segments, affecting the accuracy of the detection model. In this paper, we propose a binary code vulnerability detection model based on multi-level feature fusion. The model considers both word-level features and instruction-level features. In order to solve the problem that traditional text embedding methods cannot handle polysemy, this paper uses the Embeddings from Language Models (ELMo) model to obtain dynamic word vectors containing word semantics and other information. Considering the grammatical structure in the assembly code segment, the model randomly embeds the normalized assembly code segment to represent it. Then the model uses bidirectional Gated Recurrent Unit (GRU) to extract word-level sequence features and instruction-level sequence features respectively.

Eduvation

He pursued his academic journey with a solid foundation in computer science and technology, earning a Bachelor’s degree from Shandong Technology and Business University in 2003. Building upon this, he delved into the realm of Computer Application Technology, completing his Master’s degree at Northwest Minzu University in 2007. Driven by a passion for cultural diversity and linguistic exploration, he further expanded his expertise by attaining a doctoral degree in Chinese Minority Ethnic Languages and Literature from Northwest Minzu University in 2011. This educational trajectory reflects his commitment to a multidisciplinary approach, seamlessly blending computer technology with a profound understanding of language and culture.
Professional Profiles:

RESEARCH INTEREST

⚫ Video Summarization
⚫ Temporal Language Localization in videos
⚫ Botnet Detection
⚫ Binary Code Vulnerability Detection
⚫ Video Abnormal Event Detection
FUND PROJECTS
1. Natural Science Foundation of Gansu Province (17JR5RA161, 21JR7RA570)
2. Gansu University of Political Science and Law Major Scientific Research and Innovation Projects
(GZF2020XZDA03)
3. Young Doctoral Fund Project of Higher Education Institutions in Gansu Province (2022QB-123)
4. Gansu Province Higher Education Innovation Fund Project (2017A-068)
5. University-level Innovative Research Team of Gansu University of Political Science and Law
6. Longyuan Youth Innovation and Entrepreneurship Talent Project (2022QB-123)

MAIN SCIENTIFIC PUBLICATIONS

1. Guangli Wu,ShengTao Wang,Shipeng Xu. “Feature fusion over hyperbolic graph convolution networks for
video summarization.” IET Computer Vision,2023.
2. Guangli Wu,Tongjie Xu. “Video Moment Localization Network Based on Text Multi-semantic Clues
Guidance.” Advances in Electrical and Computer Engineering,2023,23(3):85-92.
3. Guangli Wu,Huili Tang. “Binary Code Vulnerability Detection Based on Multi-Level Feature Fusion.” IEEE
Access,2023,11: 63904-63915.
4. Guangli Wu,Shanshan Song,Leiting Li. “Video Summarization Generation Model Based on Transformer
and Deep Reinforcement Learning.” in 2023 8th International Conference on Computer and
Communication Systems (ICCCS). IEEE, 2023: 916-921.
5. Guangli Wu,Shengtao Wang,Liping Liu. “Fast Video Summary Generation Based On Low Rank Tensor
Decomposition.” IEEE Access,2021,9:127917-127926.
6. Guangli Wu,Zhenzhou Guo,Mianzhao Wang,Leiting Li and Chengxiang Wang. “Video Abnormal Event
Detection Based on CNN and Multiple Instance Learning.” in twelfth international conference on signal
processing systems. SPIE,2021:134-139.
7. Guangli Wu,Zhenzhou Guo,Leiting Li and Chengxiang Wang. “Video Abnormal Event Detection Based on
CNN and LSTM.” in 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP).
IEEE,2020: 334-338.
8. Guangli WU,Leiting LI,Zhenzhou GUO,Chengxiang WANG and Yanpeng, YAO. “Video summarization
Based on ListNet Scoring Mechanism.” in 2020 5th International Conference on Computer and
Communication Systems (ICCCS). IEEE,2020: 281-285.
9. Guangli WU,Liping LIU,Chen Zhang and Dengtai TAN. “Video Abnormal Event Detection Based on ELM.”
in 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).IEEE,2019: 367-371

 

Tumlumbe Juliana Chengula – Computer Vision -Best Researcher Award

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Tumlumbe Juliana Chengula  – Computer Vision

Tumlumbe Juliana Chengula  a distinguished academic and researcher in the field of Computer Vision. He possesses proficiency in several programming languages, with a focus on Python. His expertise extends to utilizing various tools such as Tableau, QGIS, PyTorch, and Tensorflow, showcasing a well-rounded skill set in data science and machine learning. Additionally, he has earned certifications in Data Science Tools, SQL for Data Science, and Machine Learning with Python, all from IBM. Furthermore, he has completed the “Using Python for Research” certification from Harvard University, underscoring his commitment to continuous learning and staying at the forefront of relevant technologies in the field. These skills and honors collectively highlight his comprehensive knowledge and dedication to the dynamic and evolving realm of data science.

Eduvation

His master’s studies at Amirkabir University of Technology (AUT) in Tehran, Iran, from September 2018 to October 2021, he specialized in Electrical Engineering with a focus on Control. During this period, he maintained a GPA of 3.5/4, and his final project earned a perfect score of 4/4. Prior to his master’s degree, he completed his Bachelor’s in Power Electrical Engineering at Yazd University, Iran, from September 2014 to August 2018, achieving a GPA of 3.1/4.

Professional Profiles:

Employment Experience
As a Graduate Research Assistant at South Carolina State University since August 2022, she has been actively engaged in the collection, recording, and analysis of transportation data, utilizing proficient tools such as Python, Tableau, PowerBI, and QGIS. Her research focus involves the application of cutting-edge technologies, including Machine Learning, Deep Learning, and Artificial Intelligence, to address challenges within the transportation industry.
Over the course of her tenure, she has showcased her contributions by delivering six impactful presentations on her research in Machine Learning and Artificial Intelligence at seven distinguished transportation conferences. Furthermore, her commitment to scholarly dissemination is evident through the submission and acceptance of two peer-reviewed articles, which are slated for presentation at the prestigious 2024 Annual Transportation Research Board conference. These accomplishments underscore her dedication to advancing knowledge and providing innovative solutions to enhance the efficiency and effectiveness of the transportation sector.
Research Project Highlights
She has made notable contributions to the field of transportation through her research endeavors, addressing critical issues with cutting-edge technologies. One of her significant projects involves enhancing road safety through Ensemble Learning, specifically in detecting driver anomalies using vehicle inbuilt cameras. In another study, she employed Topic Modeling and Categorical Correlations to unveil patterns associated with autonomous vehicle disengagements, shedding light on crucial aspects of autonomous driving systems.
Furthermore, she delved into the realm of quantum computing to improve classification performance in traffic sign recognition, utilizing an optimized hybrid classical-quantum approach. Additionally, her research extends to the realm of sustainable urban mobility, where she has applied Explainable Artificial Intelligence to predict bike-sharing station capacity. These diverse projects showcase her proficiency in utilizing advanced technologies and methodologies to address multifaceted challenges within the transportation sector.
Publication

Improving road safety with ensemble learning: Detecting driver anomalies using vehicle inbuilt cameras

Machine Learning with Applications
2023-12 | Journal article
DOI: 10.1016/j.mlwa.2023.100510
CONTRIBUTORS: Tumlumbe Juliana Chengula; Judith Mwakalonge; Gurcan Comert; Saidi Siuhi

Harshavardhan Awari – Machine Learning-Healthcare

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Dr.  Harshavardhan Awari – Leading Researcher in Machine Learning-Healthcare

Dr.  Harshavardhan Awari a distinguished academic and researcher in the field of Machine Learning-Healthcare. His academic prowess traces back to his S.S.C., where he attained an impressive 82% in 1997. The culmination of his educational journey is marked by his Ph.D. research work, titled “A New Framework for Brain Tumor Detection and Classification in MRI Images.”

Eduvation

He earned his Ph.D. in Computer Science and Engineering from J.N.T. University Hyderabad on February 3, 2021. Prior to that, he completed his M.Tech in Computer Science from J.N.T. University, Hyderabad, in 2008, achieving a commendable 72%. His academic journey also includes a B.Tech in Computer Science from Ramappa Engineering College, where he graduated with a 62% from J.N.T. University in 2004. Additionally, he holds a Diploma in Computer Science from S.E.S.S.N. Murthy College, securing 74.86% from S.B.T.E.T. in 2000.

Professional Profiles:

RESEARCH ACTIVITIES

He has built a distinguished career in academia. From 2004 to 2006, he served as an Assistant Professor in the Computer Science Department at Dr. V. R. K. College of Engineering & Technology, Jagtial, gaining valuable teaching experience. Subsequently, he spent six years, from 2006 to 2012, as an Associate Professor in the Computer Science department at Vaageswari College of Engineering, Karimnagar. Further enriching his academic portfolio, he dedicated nine years, spanning from 2012 to 2021, to the position of Assistant Professor in the Computer Science Department at SR University, Warangal. During his tenure, SR University achieved noteworthy accreditations, including NAAC ‘A’ Grade, NBA Tier-1, NIRF (2020) ranking 120th, and ARIIA-2020 1st rank. Since 2021, he has been contributing his expertise as a Senior Assistant Professor at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad. Notably, the institution holds an NAAC ‘A++’ Grade, NBA accreditation for five years, and a NIRF (2020) ranking between 6 to 25. Throughout his career, he has consistently demonstrated dedication to education and academic excellence.

Achievements:

  1. Primary Evaluator for the Hackthon organized by the Ministry of Education’s Innovation Cell and AICTE,2021.
  2. Editorial Member for the Blue Eyes Intelligence Engineering and Sciences Publication
  3. Editorial Member for the Lattice Science Publication
  4. Reviewer for the Imaging Science Journal
  5. Reviewer for the ICCINS-2022
  6. Reviewer for the The 4th International Conference on Machine Learning and Intelligent Systems
  7. Session Chair for the ICACECS2021 International Conference on Advances in Computer Engineering and Communication Systems(13.08.2021)
  8. Technical Committee member for the ICACECS2021 International Conference on Advances in Computer Engineering and Communication Systems
  9. Reviewer for the ICACECS2021 International Conference on Advances in Computer Engineering and Communication Systems
Awards:

He has garnered notable recognition and accolades throughout his career. In the academic year 2014-2015, he was honored as the Best eLearning Solutions and Two-Way HD Delivery Mechanism (ELSDM) Coordinator at SR Engineering College. His prowess in conceptual design was evident as he clinched the First Prize for Best Overall Conceptual Design in the “IUCEE EPICS Design Thinking Course” spanning from August 2017 to January 2018. Acknowledging his instrumental role, SWAYAM NPTEL presented him with a Certification of Appreciation for serving as the Single Point of Contact (SPOC) for the SWAYAM NPTEL Local Chapter.

His expertise extends to the academic and research realms, where he serves as a reviewer for esteemed journals including Inderscience, Springer Nature Scientific Reports (SNAPP), Applied Artificial Intelligence, Indian Journal of Image Processing and Recognition, and Blue Eyes Intelligence Engineering and Sciences Publication. He also actively contributes as a reviewer and session chair for various conferences, such as ICCINS-2022, ICACECS2021, ICACECS2022, ICCET – 2021, FICTA 2022, ICEAT-2021, and ICEAT-2022.

Further highlighting his commitment to academia, he served as a Program Committee (PC) member for the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022) organized by the National Institute of Technology Mizoram. He shared his insights and expertise as a resource person for the “Industry Institute Integrated Technology Transfer (I3T2)” at KGiSL Institute of Technology, Coimbatore, from March 14-19, 2022.

In recognition of his scholarly contributions, he has been appointed as a member of the Editorial Board for journals by Lattice Science Publication (LSP) and Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) for the years 2021-22 and 2022-2023. Adding to his list of achievements, he received the Best Faculty Award from the International Conference on Engineering and Advancement in Technology (ICEAT) in association with Malla Reddy Institute of Engineering and Technology, Hyderabad, on July 8, 2022.

Publication

An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

CPD Cyril, JR Beulah, N Subramani, P Mohan, A Harshavardhan, …
Concurrent Engineering 29 (4), 386-395 _ 2021

Text categorization Performance examination Using Machine Learning Algorithms

BP Yadav, S Ghate, A Harshavardhan, G Jhansi, KS Kumar, E Sudarshan
IOP Conference Series: Materials Science and Engineering 981 (2), 022044 – 2020

IoT Based Smart Solar Atmospheric Water Harvesting System

E Sudarshan, SN Korra, KMP Rajasekharaiah, S Venkatesulu, …
IOP Conference Series: Materials Science and Engineering 981 (4), 042004 – 2020

Cardiovascular disease prediction using deep learning techniques

SN Pasha, D Ramesh, S Mohmmad, A Harshavardhan
IOP conference series: materials science and engineering 981 (2), 022006 – 2020

Cardiovascular disease prediction using deep learning techniques

SN Pasha, D Ramesh, S Mohmmad, A Harshavardhan
IOP conference series: materials science and engineering 981 (2), 022006 – 2020

 

 

 

Sankar Shanmuganathan – Generative adversarial networks

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Dr. Sankar Shanmuganathan – Leading Researcher in Generative adversarial networks

Dr.  Sankar Shanmuganathan  a distinguished academic and researcher in the field of Generative adversarial networks. He is currently serving as a Professor in the Department of Computer Science and Engineering at Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. A dedicated, resourceful, and goal-driven professional educator, he is passionate about programming and has a solid commitment to academic growth. With over 20 years of experience, he is a seasoned researcher and has taught courses for both undergraduate and postgraduate students. He has successfully supervised 20 Bachelor’s theses and 10 Master’s theses, demonstrating his mentorship skills. His contributions extend to publishing articles in peer-reviewed journals and conferences, obtaining three patents, and securing grants from AICTE for organizing technical events.🌟💻🔬

Eduvation

He completed his Ph.D. in Information Technology from Hindustan University in 2018. 🎓 Prior to that, he earned a Master’s degree in Software Engineering from Periyar Maniammai College of Technology, affiliated with Anna University, Chennai, Tamil Nadu, in 2006, achieving a first-class distinction. 🏆 His academic journey began with a Bachelor’s degree in Computer Engineering from Arulmigu Kalasalingam College of Engineering, Srivilliputhur, Tamil Nadu, in 1992, where he also excelled with a first-class distinction. 🏅 This degree was affiliated with Madurai Kamaraj University, Madurai, Tamil Nadu. 🌟

Professional Profiles:

RESEARCH ACTIVITIES

🗣️He has obtained three patents from IP Australia, showcasing his innovative contributions. The first patent, dated October 27, 2021 (Patent No: 2021102955), is titled “A System and Method for Agile Meeting Dashboard.” The second patent, dated May 5, 2021 (Patent No: 2021101703), pertains to “3D Printing of Cost-Effective Human Skull Models and Skull Implants.” The third patent, dated April 7, 2021 (Patent No: 2021100286), is for “Aqua Life: A Compact Device Extracting Drinkable Water from Sea Water.”

In addition to his research achievements, he has undertaken various consultancy projects. Notably, he developed a software product for MEL Systems and Services Ltd, Chennai, involving the creation of advanced reports using Python, Django, and MongoDB. Another significant project involved the development of a software product to detect glaucoma in optical coherence tomography images for M/s Appasamy Associates R & D, Chennai, implemented in Java and Matlab.

Furthermore, he contributed to software bug fixing for General Electricals T & D Limited, Chennai, utilizing VB.NET technology. Additionally, he conducted corporate training for General Electricals T & D Limited, Chennai, imparting VB.NET platform skills to GE employees, preparing them to independently develop utility software. The funds received for training amounted to Rs. 2,00,000. Overall, his diverse expertise and accomplishments reflect his commitment to both innovation and practical application in the field.

BOOKS AUTHORED / CHAPTER CONTRIBUTED

AUTHORED BOOK on OBJECT ORIENTED PROGRAMMING Published by Laxmi PublicationsChennai,
2009
CHAPTER CONTRIBUTED LEAN SIX SIGMA: SIX SIGMA PROJECTS AND PERSONAL EXPERIENCEPublished by In-Tech Open Access Publications, Crotia, 2012

RESEARCH PAPERS PUBLISHED

  • Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram
    • Authors: J.C. Sekhar, Ch Rajyalakshmi, S. Nagaraj, S. Sankar, Rajesh Saturi, A. Harshavardhan
    • Published in: Journal of King Saud University – Computer and Information Sciences
    • Volume 35, Issue 10, December 2023
    • DOI: 10.1016/j.jksuci.2023.101848
  • Exploration of Performance of Dynamic Branch Predictors used in Mitigating Cost of Branching
    • Authors: Akash Ambashankar, Ganesh Chandrasekar, AR Charan, S Sankar
    • Published in: 2022 IEEE Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)
  • AI Enabled Educational Bot to Improve Learning Outcomes using Bag of Words Algorithm
  • Intelligent Organ Transplantation System Using Rank Search Algorithm to Serve Needy Recipients
    • Authors: S Sankar, U Shuruti, B Bhuvaneshwari
    • Published in: 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)
  • Understanding Query Intention in Search Queries of Learners in Blended Learning Environments
    • Authors: Vivekananthamoorthy Natarajan, Sankar Shanmuganathan
    • Published in: 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)
  • Sign language translator using YOLO algorithm
    • Authors: Bhavadharshini, M., Josephine Racheal, J., Kamali, M., Sankar, S.
    • Published in: Advances in Parallel Computing, 2021, 39, pp. 159-166
  • Detection of Anomalous Behaviour in Online Exam towards Automated Proctoring
    • Authors: Susithra V, Resham A, Bishruti Gope, Sankar
    • Published in: IEEE International Conference on System, Computation, Automation and Networking
    • DOI: 10.1109/ICSCAN53069.2021.9526448
  • Development of Novel Technique to Detect and Validate Pulmo Malignancy during Early Stages
    • Authors: Dhanalakshmi R, Shree Harini R, Pravallika M, S Sankar
    • Published in: International Journal of Current Research and Review, volume: 13 issue: 17, pp. 56-60, 12th September 2021
    • DOI: http://dx.doi.org/10.31782/IJCRR.2021.131711
  • Sentiment Analysis of Twitter Political Data using GRU Neural Network
    • Authors: Seenaiah Pedipina, Sankar S and R Dhanalakshmi
    • Published in: International Journal of Advanced Science and Technology 29(6), pp. 5307-5320, ISSN 2207-6360, SERSC Australia
  • Sentimental Analysis On Twitter Data Of Political Domain
    • Authors: Seenaiah Pedipina, Sankar S and R Dhanalakshmi
    • Published in: Dogo Rangsang Research Journal, UGC Care Group I Journal, Vol-10 Issue-07 No. 16 July 2020, ISSN:2347-7180
  • An Improved Framework for Sentiment Analysis for College Reviews
    • Authors: T. Sri Devi, R. Dhanalakshmi, S. Sankar
    • Published in: International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 1959-1963