Kiran Ravulakollu | Machine Learning | Best Researcher Award

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Prof Dr. Kiran Ravulakollu | Machine Learning | Best Researcher Award

Dean at Woxsen University, India.

Prof. Dr. Kiran Ravulakollu is currently the Dean and Professor at the School of Technology at Woxsen University, Kamkole, Telangana, where he has played a pivotal role in establishing the school since 2021. He also serves as the Director of Research and Development at the same institution. With a strong academic background, Dr. Ravulakollu holds a Ph.D. in Computer Science from the University of Sunderland, UK, and has extensive teaching and research experience in areas such as artificial intelligence, image processing, and hybrid intelligent systems. Throughout his career, he has held various academic positions, including Assistant Dean of Research at the University of Petroleum and Energy Studies and has been instrumental in developing research ecosystems that foster collaboration and innovation. His contributions to the field are evident in his impressive publication record, with over 879 publications and numerous patents. An active participant in research advisory committees and academic councils, Dr. Ravulakollu is dedicated to advancing technology and education through strategic leadership and innovative research initiatives.

Profile:

Education:

Prof. Dr. Kiran Ravulakollu has a solid academic foundation, beginning with a Bachelor’s degree in Computer Science and Engineering from Jawaharlal Nehru Technological University, Hyderabad, India, awarded in 2004. He further advanced his expertise by earning a Postgraduate Certificate in Artificial Intelligence from City, University of London, United Kingdom, in 2006. Dr. Ravulakollu’s academic journey culminated in a Ph.D. in Computer Science from the University of Sunderland, United Kingdom, in 2012. His Ph.D. research focused on developing a computational architecture inspired by the Superior Colliculus of the mid-brain, utilizing neural networks for the efficient integration of audio and visual stimuli. Additionally, he completed a Post-Graduate Certificate in Academic Practices from the University of Petroleum and Energy Studies, Dehradun, India, in 2019, further enhancing his teaching and research credentials.

Professional Experience:

Prof. Dr. Kiran Ravulakollu has an extensive professional experience in academia and research, currently serving as the Dean and Professor at the School of Technology at Woxsen University, Kamkole, Telangana, since August 2021. In this role, he successfully established the school, significantly increasing student enrollment from 31 to 1,500, faculty and staff from 2 to 75, and generating revenue of ₹80 lakhs. He has also served as the Director of Research and Development at Woxsen University since August 2022, where he developed a robust R&D ecosystem, resulting in a remarkable rise in publications from 10 to 879, citations from 428 to over 7,400, and the acquisition of 36 patents. Prior to his current roles, Dr. Ravulakollu held various positions at the University of Petroleum and Energy Studies, Dehradun, including Assistant Dean of Research and Senior Associate Professor. His academic journey began as an Assistant Professor at Sharda University, where he contributed to curriculum development and student mentoring. His research background includes a tenure as a Research Associate at the HIS Research Group at the University of Sunderland, UK, where he focused on sensory data integration for artificial agents. Throughout his career, Dr. Ravulakollu has demonstrated exceptional leadership, strategic thinking, and a commitment to fostering research excellence in technology and engineering.

Research Interests:

Prof. Dr. Kiran Ravulakollu’s research interests lie at the intersection of artificial intelligence, computer science, and intelligent systems. His Ph.D. research focused on creating computational architectures inspired by the mid-brain’s Superior Colliculus, utilizing artificial neural networks to integrate audio and visual stimuli for enhanced localization capabilities. This foundational work has propelled his ongoing investigations into various advanced topics, including image processing methodologies, artificial agents, ambient intelligence, and hybrid intelligent systems. Additionally, Dr. Ravulakollu is keenly interested in the Internet of Things (IoT) and its applications, exploring how interconnected devices can contribute to smarter solutions in urban environments. His dedication to innovation is reflected in his ability to translate complex theoretical concepts into practical applications, fostering knowledge transfer that shapes products and enhances research outcomes. Overall, his diverse research portfolio showcases a commitment to pushing the boundaries of technology and developing cutting-edge solutions for real-world challenges.

Skills:

Prof. Dr. Kiran Ravulakollu possesses a robust skill set that encompasses analytical expertise, strong research methodologies, and innovative development capabilities. His analytical skills enable him to design and implement significant projects, effectively identifying problems and conducting feasibility studies to ensure research viability. With a solid foundation in project management, he excels at coordinating research and development activities, fostering collaboration among diverse teams. Dr. Ravulakollu is adept at data analysis, utilizing advanced techniques to derive meaningful insights and inform decision-making processes. His proficiency in knowledge transfer allows him to bridge the gap between theoretical research and practical application, shaping products that meet industry demands. Additionally, his experience in developing policies and frameworks for effective administration reflects his strategic thinking ability and commitment to continuous improvement within academic and research environments. Overall, Dr. Ravulakollu’s diverse skill set positions him as a leader in his field, driving impactful research initiatives and fostering innovation.

Conclusion:

Prof. Dr. Kiran Ravulakollu’s exceptional track record in academia, research, and administration make him a highly suitable candidate for the Best Researcher Award. His ability to blend innovative research with practical applications, along with his leadership in establishing a robust R&D environment, highlights his suitability for this prestigious recognition. His career demonstrates a consistent commitment to advancing knowledge and technology in the fields of artificial intelligence, machine learning, and hybrid intelligent systems, positioning him as a leader in contemporary research.

Publication Top Noted:

SIGN LANGUAGE RECOGNITION: STATE OF THE ART

  • Authors: AK Sahoo, GS Mishra, KK Ravulakollu
  • Year: 2013
  • Cited by: 105

Improving automated latent fingerprint detection and segmentation using deep convolutional neural network

  • Authors: M Chhabra, KK Ravulakollu, M Kumar, A Sharma, A Nayyar
  • Journal: Neural Computing and Applications
  • Year: 2023
  • Cited by: 33

Naïve Bayes Classifier with LU Factorization for Recognition of Handwritten Odia Numerals

  • Authors: KKR, Pradeepta K. Sarangi, P. Ahmed
  • Journal: Indian Journal of Science and Technology
  • Year: 2014
  • Cited by: 32

HANDWRITTEN DEVNAGARI DIGIT RECOGNITION: BENCHMARKING ON NEW DATASET

  • Authors: R Kumar, KK Ravulakollu
  • Journal: Journal of Theoretical & Applied Information Technology
  • Year: 2014
  • Cited by: 27

A Hybrid Intrusion Detection System: Integrating Hybrid Feature Selection Approach with Heterogeneous Ensemble of Intelligent Classifiers

  • Authors: KKR, Amrita
  • Journal: International Journal of Network Security
  • Year: 2018
  • Cited by: 25

Fuzzy-membership based writer identification from handwritten devnagari script

  • Authors: R Kumar, KK Ravulakollu, R Bhat
  • Journal: Journal of Information Processing Systems
  • Year: 2017
  • Cited by: 23

Indian sign language recognition using skin color detection

  • Authors: AK Sahoo, KK Ravulakollu
  • Journal: International Journal of Applied Engineering Research
  • Year: 2014
  • Cited by: 19

VISION BASED INDIAN SIGN LANGUAGE CHARACTER RECOGNITION

  • Authors: AK Sahoo, KK Ravulakollu
  • Journal: Journal of Theoretical & Applied Information Technology
  • Year: 2014
  • Cited by: 18

A review on artificial intelligence in orthopaedics

  • Authors: T Hamid, M Chhabra, K Ravulakollu, P Singh, S Dalal, R Dewan
  • Conference: 2022 9th International Conference on Computing for Sustainable Global Development
  • Year: 2022
  • Cited by: 17

WORD BASED STATISTICAL MACHINE TRANSLATION FROM ENGLISH TEXT TO INDIAN SIGN LANGUAGE

  • Authors: GS Mishra, AK Sahoo, KK Ravulakollu
  • Journal: ARPN Journal of Engineering & Applied Sciences
  • Year: 2017
  • Cited by: 17

State-of-the-Art: A Systematic Literature Review of Image Segmentation in Latent Fingerprint Forensics

  • Authors: KKR, Megha Chhabra, Manoj Kumar Shukla
  • Journal: Recent Patents on Computer Science
  • Year: 2019
  • Cited by: 16

Surface Corrosion Grade Classification using Convolution Neural Network

  • Authors: KKR, Sanjay Kumar Ahuja, Manoj Kumar Shukla
  • Journal: International Journal of Recent Technology and Engineering
  • Year: 2019
  • Cited by: 15

kassem Danach | Machine Learning | Excellence in Research

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Assoc Prof Dr. kassem Danach | Machine Learning | Excellence in Research

Associate Professor at Department of Information Technology and Management Systems, Faculty of Business Administration, Al Maaref University, Lebanon

Summary:

Assoc. Prof. Dr. Kassem Danach is an accomplished academic and researcher with a diverse background in computer and telecommunication engineering, business analytics, and big data. He is currently the Chairperson of the Information Technology and Management Systems Department at Al Maaref University, where he oversees academic and administrative matters, curriculum development, and faculty management. Dr. Danach completed his Ph.D. in Computer and Telecommunication Engineering at École Centrale de Lille, France, and has a postdoctoral research experience in Business Analytics from Université d’Artois, France. His research interests span operational research, business analytics, logistics, supply chain, heuristics, bioinformatics, virtual network functions, cyber-attacks, and optimization. Dr. Danach has held various academic positions at the Islamic University of Lebanon and has extensive experience in teaching, research supervision, and conference activities. He is also skilled in data science, programming, data analytics, data visualization, database administration, and online teaching. Dr. Danach is a reviewer for several prestigious conferences and scientific journals.

Profile:

Education:

Assoc. Prof. Dr. Kassem Danach has an extensive educational background. He completed a postdoctoral research program in Business Analytics at Université d’Artois, Bethune, France, from November 2020 to December 2021. He earned his Ph.D. in Computer and Telecommunication Engineering from École Centrale de Lille, France, in December 2016. Dr. Danach holds multiple Master II degrees: one in Big Data Analytics from ISAE (le CNAM Liban), expected to certify in March 2022; another in Network Engineering from the Islamic University of Lebanon, completed in 2014; and a third in Management Information Systems (MIS) from the Islamic University of Lebanon, completed in 2013. Additionally, he earned a Bachelor of Engineering in Computer and Telecommunication Engineering from the Islamic University of Lebanon in June 2008. Dr. Danach’s doctoral thesis, titled “Hyperheuristics in Logistics,” focused on the application of artificial intelligence in optimizing logistics problems through the development and comparison of various hyperheuristic frameworks.

Professional Experience:

Assoc. Prof. Dr. Kassem Danach has a rich professional background that spans academia, research, and industry. He currently serves as the Chairperson of the Information Technology and Management Systems Department at Al Maaref University since September 2023, where he leads the department in academic and administrative matters, oversees curriculum development and faculty management, and drives research initiatives and collaborations. From November 2020 to December 2021, he was a Postdoctoral Researcher at Université d’Artois, France. At the Islamic University of Lebanon, he held multiple roles, including Head of the Computer Science Department from November 2020 to 2023, Head of the Management Information System Department from November 2018 to November 2020, and full-time Lecturer at the Faculty of Economics and Business Administration from December 2014 to November 2018. He also served as the Coordinator of the Management Information System Department (Tyr branch) from October 2011 to 2014. Dr. Danach co-founded TTO-Lebanon, where he was the General Manager from June 2015 to February 2017. Earlier in his career, he worked as a Network Engineer and Developer at Danach Co – Saida from June 2008 to March 2009. Since January 2017, he has been a trainer in various fields, including data science, programming, digital marketing, and artificial intelligence, at several training centers such as CUBICWIN, Edumotion, ASD, and Global Golden/Rainbow.

Research Interests:

Assoc. Prof. Dr. Kassem Danach’s research interests encompass a diverse array of fields. He is particularly focused on Operational Research, Business Analytics, Scheduling and Routing Problems, and Logistics and Supply Chain Management. His work delves into the development and application of various heuristic, meta-heuristic, math-heuristic, and hyper-heuristic methods. Dr. Danach is also interested in the areas of Bioinformatics and Virtual Network Functions. His research extends to cybersecurity, with an emphasis on cyber-attacks and optimization. This multidisciplinary approach allows him to address complex problems in both theoretical and practical aspects of these fields.

Skills:

Assoc. Prof. Dr. Kassem Danach possesses a comprehensive skill set across multiple domains. His operational research soft skills include proficiency with Solver (Excel Add-ins), Hyflex (Java Platform), GIIHH (Java Platform), and CPLEX (under JAVA). He is adept in programming languages such as VB .Net, C# .Net, ADO .Net, ASP.Net, Labview, Python, Advanced C, Advanced JAVA, JSP, Assembly, Prolog, and Matlab. In the realm of data analytics, Dr. Danach is skilled in R language, Keras Python, TensorFlow Python, Orange Data Mining, RapidMiner, Weka, and SPSS. His data visualization capabilities extend to Microsoft Power BI, Tableau, Matplotlib Python, and Google Analytics. As a database administrator, he has expertise in Microsoft SQL Server and MySQL. Additionally, Dr. Danach is proficient in online teaching tools such as Microsoft Teams, Zoom, Edmodo, OBS, Blogger, and MindMap. This diverse array of skills underpins his multifaceted approach to research, teaching, and professional practice in the field of Computer Science and Information Technology.

Reviewing and Editorial Work:

Dr. Danach is a respected reviewer for several prestigious conferences and scientific journals, including IEEE Intelligent Transportation Systems Transactions, the International Journal of Services and Standards (IJSS), LCIS 2017-2018, ICTO 2017-2018-2023-2024, ICCTA 2018-2019-2023-2024, and the Oriental Journal of Computer Science and Technology.

Awards & Honors:

Assoc. Prof. Dr. Kassem Danach has been recognized for his outstanding contributions to the field of Computer Science and Information Technology with several awards and honors. His achievements include multiple best paper awards at international conferences, highlighting the significant impact of his research. Dr. Danach has also been honored with prestigious grants and fellowships that have supported his innovative projects and academic pursuits. Additionally, he has received accolades for his exemplary teaching and mentorship, reflecting his dedication to fostering academic excellence and guiding the next generation of scholars and professionals. These honors underscore Dr. Danach’s commitment to advancing knowledge and making substantial contributions to his field.

Publications:

Zexiao Liang | Machine Learning | Best Researcher Award

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Dr. Zexiao Liang | Machine Learning | Best Researcher Award

Doctorate at School of Integrated Circuits, Guangdong University of Technology, China

Summary:

Dr. Zexiao Liang is a postdoctoral assistant researcher at the School of Integrated Circuits, Guangdong University of Technology. He completed his Bachelor’s, Master’s, and Ph.D. degrees in Automation from Guangdong University of Technology between 2012 and 2022. Dr. Liang has extensive experience in machine learning, with a focus on multi-information fusion algorithms and domain-specific applications. He has published multiple SCI papers as the lead author and holds several invention patents. Dr. Liang is also skilled in guiding students, contributing to the publication of additional SCI papers and conference papers.

Profile:

Education:

Dr. Zexiao Liang completed his Bachelor’s degree in Automation from Guangdong University of Technology in 2016. He continued his studies at the same institution, earning a Master’s degree in Automation in 2019. Dr. Liang then pursued a Ph.D. in Automation at Guangdong University of Technology, which he completed in 2022. Throughout his academic journey, Dr. Liang consistently demonstrated exceptional academic performance, securing top ranks in his major and achieving multiple academic milestones, including the publication of several papers and the acquisition of invention patents.

Professional Experience:

Dr. Zexiao Liang has accumulated valuable professional experience as a postdoctoral assistant researcher at the School of Integrated Circuits, Guangdong University of Technology since July 2022. His work primarily focuses on the research and design of machine learning algorithms, particularly in the integration of multi-information fusion and clustering analysis. Dr. Liang has developed algorithms to address specific domain challenges, such as predicting the effects of multi-drug interactions and conducting reliability analyses for low-quality chip images. His role also involves guiding students, which has led to the publication of numerous scientific papers and contributions to various research projects.

Research Interests:

Dr. Zexiao Liang’s research interests lie in the field of multi-information fusion machine learning algorithms, including the integration of multiple transformation domain information and the development of multi-modal, multi-view learning techniques. He is particularly interested in clustering analysis and the design and application of machine learning algorithms to address domain-specific challenges. His work encompasses algorithms for predicting the effects of multi-drug interactions and reliability analysis for low-quality chip images. Dr. Liang is dedicated to advancing machine learning methodologies and their practical applications in solving complex problems in various domains.

Skills:

Dr. Zexiao Liang specializes in advanced machine learning and data analysis techniques, focusing on the integration of multiple transformation domain information, multi-modal and multi-view learning, and clustering analysis. His expertise also includes designing algorithms to address domain-specific challenges, such as predicting multi-drug interactions and analyzing low-quality chip images for reliability. He is proficient in feature fusion strategies and parameter optimization, as well as dimensionality reduction techniques for manifold-based semi-supervised classification.

 

Publications:

 

Spectral clustering based on high‐frequency texture components for face datasets

  • Authors: Z Liang, S Guo, D Liu, J Li
  • Journal: IET Image Processing
  • Volume: 15
  • Issue: 10
  • Pages: 2240-2246
  • Year: 2021
  • Citations: 3

Optimal Mean Linear Classifier via Weighted Nuclear Norm and L2,1 Norm

  • Authors: D Zeng, Z Liang, Z Wu
  • Journal: 电子与信息学报
  • Volume: 44
  • Issue: 5
  • Pages: 1602-1609
  • Year: 2022
  • Citations: 2

An effective clustering algorithm for the low-quality image of integrated circuits via high-frequency texture components extraction

  • Authors: Z Liang, G Tan, C Sun, J Li, L Zhang, X Xiong, Y Liu
  • Journal: Electronics
  • Volume: 11
  • Issue: 4
  • Article Number: 572
  • Year: 2022
  • Citations: 2

HDGN: Heat diffusion graph network for few-shot learning

  • Authors: Q Tan, Z Wu, J Lai, Z Liang, Z Ren
  • Journal: Pattern Recognition Letters
  • Volume: 171
  • Pages: 61-68
  • Year: 2023
  • Citations: 1

2D DOA Estimation Through a Spiral Array Without the Source Number

  • Authors: J Li, J Dai, Z Liang, D Liu, S Guo, Y Liu
  • Journal: Circuits, Systems, and Signal Processing
  • Volume: 41
  • Issue: 5
  • Pages: 3011-3022
  • Year: 2022
  • Citations: 1

A fusion representation for face learning by low-rank constrain and high-frequency texture components

  • Authors: Z Liang, D Zeng, S Guo, J Li, Z Wu
  • Journal: Pattern Recognition Letters
  • Volume: 155
  • Pages: 48-53
  • Year: 2022
  • Citations: 1

Face recognition via optimal mean robust linear discriminant analysis

  • Authors: D Zeng, Z Wu, Z Ren, Z Liang, S Xie
  • Conference: 2018 Chinese Automation Congress (CAC)
  • Pages: 1504-1509
  • Year: 2018
  • Citations: 1