Bader Alsharif | Computer Science | Best Innovation Award

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Dr. Bader Alsharif | Computer Science | Best Innovation Award

Florida Atlantic University, United States

Dr. Bader Alsharif is an accomplished PhD candidate in Computer Engineering with a strong background in teaching, technical support, and curriculum development. He has led innovative projects, including the first CISCO simulation lab in Saudi Arabia, and has managed over 300 devices, optimizing performance and security. With a focus on AI, Cybersecurity, and IoT, particularly in healthcare, Dr. Alsharif has published over 7 peer-reviewed papers. He has demonstrated leadership in both academic and technical spheres, guiding over 200 students and advocating for special needs education, ensuring their academic success. His expertise extends to training professionals, having conducted comprehensive courses for Saudi Telecom employees. Dr. Alsharif has shown a profound commitment to advancing technology and fostering inclusivity, particularly through his work with individuals with special needs. His work bridges technological innovation with social impact, positioning him as a forward-thinking leader in computer engineering and healthcare.

Professional Profile 

Education

Dr. Bader Alsharif has an extensive academic background, beginning with a Bachelor of Science in Computer Engineering from the College of Technology in Riyadh, Saudi Arabia, where he graduated in 2008. He further advanced his studies with a Master of Science in Computer Engineering from the Florida Institute of Technology, completing his degree in 2017. Currently, Dr. Alsharif is pursuing a Doctor of Computer Engineering at Florida Atlantic University in Boca Raton, USA, with an expected graduation date of 2025. His academic journey has been marked by a strong focus on integrating Artificial Intelligence (AI), Cybersecurity, and Internet of Things (IoT) technologies, particularly in healthcare applications. This multidisciplinary education has provided Dr. Alsharif with the expertise to contribute meaningfully to both research and practical innovations in these fields, bridging the gap between technology and real-world healthcare challenges.

Professional Experience

Dr. Bader Alsharif has a diverse professional background with extensive experience in both academia and technical roles. He currently serves as a Teaching Assistant at Florida Atlantic University, where he guides and evaluates over 30 students on engineering design projects and assists more than 200 students with project development and technical issues. Prior to this, Dr. Alsharif held a prominent role as a Lecturer at the Communications and Information College in Riyadh, Saudi Arabia, where he managed and maintained over 300 devices and led the installation of the first CISCO simulation lab in the country. This project, a significant innovation, involved the deployment of over 30 devices and routers. He also trained 100 employees from Saudi Telecom and designed assessments for instructors working with special needs students. Dr. Alsharif’s professional experience reflects a strong blend of technical expertise, leadership, and a commitment to education and inclusivity.

Research Interest

Dr. Bader Alsharif’s research interests lie at the intersection of Artificial Intelligence (AI), Cybersecurity, and the Internet of Things (IoT), with a particular focus on their applications in healthcare. He is deeply committed to exploring how these advanced technologies can be integrated to enhance patient outcomes and improve healthcare systems. His work aims to leverage AI algorithms to optimize medical data analysis, while also addressing critical security concerns in the rapidly growing field of IoT healthcare devices. Dr. Alsharif’s research also extends to the development of innovative solutions for securing healthcare networks and ensuring the privacy of sensitive patient information. With a strong academic foundation and several peer-reviewed publications, he is dedicated to advancing knowledge in these areas and exploring how cutting-edge technologies can be applied to solve real-world challenges in healthcare. His work demonstrates a commitment to both technological innovation and social impact, especially in the realm of health and well-being.

Award and Honor

Dr. Bader Alsharif has received numerous accolades for his contributions to academia and technology. His achievements include successfully leading the installation of the first CISCO simulation lab in Saudi Arabia, which became a groundbreaking project in the region, significantly enhancing the educational infrastructure for telecommunications. In recognition of his exceptional performance in teaching and technical support, he consistently achieved high job performance ratings, including scores no less than 99/100. Dr. Alsharif has also been honored for his commitment to inclusive education, particularly in advocating for and supporting students with special needs, ensuring their academic excellence. His research in AI, Cybersecurity, and IoT, particularly in the healthcare sector, has earned him recognition as a published researcher with over 7 peer-reviewed papers. Through his work, Dr. Alsharif has received recognition from academic institutions and industry professionals for his innovative contributions, leadership, and commitment to fostering technological advancements with social impact.

Conclusion

Bader Alsharif has demonstrated significant innovation across several key areas of AI, Cybersecurity, and IoT, particularly in healthcare. His leadership in education and advocacy for special needs individuals also reflects a deep commitment to both technological advancement and social impact. His ability to lead high-profile projects and publish extensively in relevant fields positions him as a strong candidate for the Best Innovation Award. However, expanding his research impact and involvement in larger-scale, cross-disciplinary projects could further elevate his candidacy. Overall, he has the potential to be an exceptional award recipient based on his innovative contributions and impact.

Publications Top Noted

  • Title: Deep learning technology to recognize American Sign Language alphabet
    Authors: B Alsharif, AS Altaher, A Altaher, M Ilyas, E Alalwany
    Year: 2023
    Citations: 14
  • Title: Internet of things technologies in healthcare for people with hearing impairments
    Authors: B Alsharif, M Ilyas
    Year: 2022
    Citations: 8
  • Title: Deep Learning Technology to Recognize American Sign Language Alphabet Using Multi-Focus Image Fusion Technique
    Authors: B Alsharif, M Alanazi, AS Altaher, A Altaher, M Ilyas
    Year: 2023
    Citations: 6
  • Title: Machine Learning Technology to Recognize American Sign Language Alphabet
    Authors: B Alsharif, M Alanazi, M Ilyas
    Year: 2023
    Citations: 4
  • Title: Enhancing cybersecurity in healthcare: Evaluating ensemble learning models for intrusion detection in the internet of medical things
    Authors: T Alsolami, B Alsharif, M Ilyas
    Year: 2024
    Citations: 3
  • Title: Multi-Dataset Human Activity Recognition: Leveraging Fusion for Enhanced Performance
    Authors: M Alanazi, B Alsharif, AS Altaher, A Altaher, M Ilyas
    Year: 2023
    Citations: 3
  • Title: Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet
    Authors: B Alsharif, E Alalwany, M Ilyas
    Year: 2024
    Citations: 1
  • Title: Franklin Open
    Authors: B Alsharif, E Alalwany, M Ilyas
    Year: 2024
    Citations: Not available yet

Amir Reza Rahimi | Computer | Best Researcher Award

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Dr. Amir Reza Rahimi | Computer | Best Researcher Award

PHD at University of Valencia, Spain

Dr. Amir Reza Rahimi is a Ph.D. candidate at the University of Valencia, specializing in language, literature, culture, and their applications. With extensive experience teaching English at universities, high schools, and language institutes in Iran, he is actively involved in research projects like FORTHEM and SOCIEMOVE, focusing on fostering socioemotional skills through virtual exchange. Dr. Rahimi has conducted workshops for language teachers on integrating technology into English teaching and has published extensively in prestigious journals such as Computer-Assisted Language Learning and Computers in Human Behavior Reports. His research has been presented at international conferences, and he is recognized for introducing innovative educational methodologies, earning the Best Research Award in Innovation in Data Analysis. His expertise spans psycholinguistics, CALL, MOOCs, virtual exchange, and teacher education. With a passion for advancing language learning, Dr. Rahimi continues to make significant contributions to the intersection of technology and education.

Professional Profile 

Education

Dr. Amir Reza Rahimi has an extensive academic background, beginning with a Bachelor’s degree in English Language Teaching from the University of Mohaghegh Ardabili in Iran, completed between 2014 and 2017. He then pursued a Master’s degree in English Language Teaching at Shahid Rajaee Teacher Training University in Tehran, Iran, where he conducted research on the impact of massive open online courses (MOOCs) on Iranian EFL learners’ self-regulation and motivation. Dr. Rahimi is currently a Ph.D. candidate at the University of Valencia, Spain, where he is studying language, literature, culture, and their applications. His doctoral research is focused on exploring innovative methods in language learning, particularly through virtual exchange and computer-assisted language learning (CALL). Throughout his educational journey, Dr. Rahimi has continuously demonstrated a commitment to advancing the field of language education through research, publications, and participation in international academic projects.

Professional Experience

Dr. Amir Reza Rahimi has a rich and diverse professional experience in the field of language education. He has taught English at various institutions, including universities, high schools, and language institutes in Iran, where he developed expertise in teaching English as a foreign language (EFL). His teaching career spans over several years, during which he contributed to curriculum development and language instruction. Dr. Rahimi is currently involved in the FORTHEM Research Project and the SOCIEMOVE project, where he serves as a mentor researcher and focuses on developing socioemotional skills through virtual exchange. Additionally, he has conducted workshops for language teachers, helping them incorporate technology into their teaching practices. His research, which bridges the gap between language learning and technology, has led to numerous publications in high-impact journals. Dr. Rahimi’s professional experience reflects his dedication to enhancing language education through innovative methodologies and research-driven approaches.

Research Interest

Dr. Amir Reza Rahimi’s research interests primarily focus on the intersection of language education, technology, and learner motivation. His work explores various aspects of computer-assisted language learning (CALL), particularly how digital tools and virtual exchanges can enhance language learning experiences. Dr. Rahimi is deeply interested in the role of massive open online courses (MOOCs) and the development of self-regulation and motivation in online language learners. He also delves into psycholinguistics, exploring how emotional and psychological factors influence language acquisition. His research further investigates the impact of socioemotional skills on language learners, especially through virtual exchange programs like SOCIEMOVE. Additionally, he examines theory development in education, with a particular emphasis on innovative research designs, such as bisymmetric approaches. Dr. Rahimi’s work aims to bridge the gap between technology and language teaching, contributing to the advancement of both educational theory and practice in the digital age.

Award and Honor

Dr. Amir Reza Rahimi has received several prestigious awards and honors for his outstanding contributions to language education and research. Notably, he won the Best Research Award in Innovation in Data Analysis from ScienceFather for introducing a new research design to the field of education, specifically a bisymmetric research design. This recognition highlights his innovative approach to research methodology, particularly in the context of computer-assisted language learning (CALL). Dr. Rahimi’s research has also earned him multiple publications in top-tier journals such as Computer-Assisted Language Learning, Computers in Human Behavior Reports, and Education and Information Technologies, where his work on language learning, virtual exchange, and online motivation has gained significant academic attention. His accomplishments have been further acknowledged through his active participation in international conferences, including the TESOL International Convention and the World CALL Conference. Dr. Rahimi’s honors reflect his commitment to advancing language education through technology and innovation.

Conclusion

Amir Reza Rahimi is a highly accomplished researcher whose contributions to CALL, psycholinguistics, and educational technology make him a strong contender for the Best Researcher Award. His innovative approaches, impactful publications, and leadership in international projects are commendable. To further solidify his candidacy, increased interdisciplinary collaboration, a focus on societal impact, and broader dissemination of his work are recommended. Overall, his profile aligns well with the criteria for excellence in research, making him a suitable nominee for this award.

Publications Top Noted

  • The role of university teachers’ 21st-century digital competence in their attitudes toward ICT integration in higher education: Extending the theory of planned behavior
    Authors: AR Rahimi, D Tafazoli
    Year: 2022
    Citation: The JALT CALL Journal, 18(2), 1832-4215
  • Unifying EFL learners’ online self‑regulation and online motivational self‑system in MOOCs: A structural equation modeling approach
    Authors: AR Rahimi, Z Cheraghi
    Year: 2022
    Citation: Journal of Computers in Education, 9(4)
  • EFL learners’ attitudes toward the usability of LMOOCs: A qualitative content analysis
    Authors: AR Rahimi, D Tafazoli
    Year: 2022
    Citation: The Qualitative Report, 27(1), 158-173
  • The role of EFL learners’ L2 self-identities, and authenticity gap on their intention to continue LMOOCs: Insights from an exploratory partial least approach
    Author: AR Rahimi
    Year: 2023
    Citation: Computer Assisted Language Learning, 1-32
  • Online motivational self-system in MOOC: A qualitative study
    Author: AR Rahimi
    Year: 2021
    Citation: From emotion to knowledge: emerging ecosystems in language learning, 79-86
  • Beyond digital competence and language teaching skills: The bi-level factors associated with EFL teachers’ 21st-century digital competence to cultivate 21st-century digital skills
    Author: AR Rahimi
    Year: 2024
    Citation: Education and Information Technologies, 29(8), 9061-9089
  • A bi-phenomenon analysis to escalate higher educators’ competence in developing university students’ information literacy (HECDUSIL): The role of language lecturers’ conceptual …
    Author: AR Rahimi
    Year: 2024
    Citation: Education and Information Technologies, 29(6), 7195-7222
  • The role of twenty-first century digital competence in shaping pre-service teacher language teachers’ twenty-first century digital skills: the Partial Least Square Modeling …
    Authors: AR Rahimi, Z Mosalli
    Year: 2024
    Citation: Journal of Computers in Education
  • A tri-phenomenon perspective to mitigate MOOCs’ high dropout rates: the role of technical, pedagogical, and contextual factors on language learners’ L2 motivational selves, and …
    Author: AR Rahimi
    Year: 2024
    Citation: Smart Learning Environments, 11(1), 11
  • Determinants of Online Platform Diffusion during COVID-19: Insights from EFL Teachers’ Perspectives
    Authors: AR Rahimi, S Atefi Boroujeni
    Year: 2022
    Citation: Journal of Foreign Language Teaching and Translation Studies, 7(4), 111-136
  • The role of ChatGPT readiness in shaping language teachers’ language teaching innovation and meeting accountability: A bisymmetric approach
    Authors: AR Rahimi, A Sevilla-Pavón
    Year: 2024
    Citation: Computers and Education: Artificial Intelligence, 7, 100258
  • Exploring the direct and indirect effects of EFL learners’ online motivational self-system on their online language learning acceptance: the new roles of current L2 self and …
    Authors: AR Rahimi, Z Mosalli
    Year: 2024
    Citation: Asian-Pacific Journal of Second and Foreign Language Education, 9(1), 49

Sarra Jebri | Computer Science | Best Researcher Award

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Dr. Sarra Jebri | Computer Science | Best Researcher Award

Assistant professor of National Engineering School of Gabes, Tunisia

Sarra Jebri is an accomplished researcher and educator with a robust background in telecommunications. She earned her PhD from Ecole Nationale d’Ingénieurs de Tunis, specializing in IoT security and privacy, and has a comprehensive educational foundation that includes a National Diploma of Engineering and a Master’s in Instrumentation and Communication. Her professional experience includes significant teaching roles at the National School of Engineers in Gabès, reflecting her expertise in the field. Jebri’s research focuses on critical areas such as security, mutual authentication, and IoT, with several influential publications presented at international conferences. 🌟🌿🔬

Professional profile

Education📚

Sarra Jebri holds a PhD in Telecommunications from Ecole Nationale d’Ingénieurs de Tunis (ENIT) with a focus on Internet of Things (IoT) security and privacy. Her academic journey includes a National Diploma of Engineering in Communications and Networks from Ecole Nationale d’Ingénieurs de Gabès (ENIG), where she studied IPTV services, and a Master’s degree in Instrumentation and Communication from the Faculty of Sciences of Sfax, focusing on information system design. She also has a Bachelor’s Degree in Mathematics. Her diverse educational background underscores her strong foundation in telecommunications and related fields, preparing her well for research excellence.

Professional Experience🏛️

Sarra Jebri has extensive teaching experience in telecommunications, having served as a Contractual Teacher at the National School of Engineers in Gabès from 2020 to 2024 and as a Vacant Teacher from 2014 to 2019. This teaching experience, combined with her practical knowledge in the field, demonstrates her capability in both academic and applied research environments.

Research Interest🌐

Sarra Jebri’s research focuses on security, mutual authentication, privacy, and IoT. Her recent publications include notable works such as “Local Learning-based Collaborative Authentication In Edge-Fog Network” and “Light Automatic Authentication of Data Transmission in 6G/IoT Healthcare System,” presented at prestigious international conferences. Her research contributions are recognized through multiple conference papers, indicating a strong engagement with current challenges in her field.

Certifications🏆

Jebri has obtained several certifications, including Cisco and Huawei networking and security qualifications, and has strong programming skills in C, C++, Java, and Matlab. Her certifications reflect her ongoing commitment to professional development and her ability to apply theoretical knowledge in practical scenarios.

Publications top noted📜

Sayyed Ahmed | Computer Science | Best Scholar Award

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Mr. Sayyed Ahmed | Computer Science | Best Scholar Award

Assistant Professor of  Aligarh Muslim University, India

Dr. Sayyed Usman Ahmed is a dedicated academic and researcher in the field of computer engineering, specializing in artificial intelligence and legal reasoning. He has been recognized for his contributions with awards such as the Best Paper Award (2022-23) and the Visvesvaraya Part-Time PhD Fellowship (2018-19). His teaching and research continue to inspire and shape the next generation of engineers and technologists.

Publication profile

Education

Dr. Sayyed Usman Ahmed holds a Ph.D. in Computer Engineering from Aligarh Muslim University (AMU), India, where he conducted research on “Decision Intelligence in Augmentation of Legal Reasoning” under the supervision of Prof. Nesar Ahmad. His thesis was submitted on March 6, 2024. He earned his M.Tech in Computer Engineering from Rajasthan Technical University (2012-2014) with a thesis on evaluating the efficiency and effectiveness of code reading techniques, supervised by Dr. Rajendra Purohit. He completed his B.Tech in Computer Engineering from AMU (2003-2007), with a project on fingerprint detection systems under the guidance of Prof. M. Sarosh Umar and Prof. Syed Atiqur Rahman.

Experience

Dr. Ahmed has extensive experience in academia and industry. He is currently an Assistant Professor at AMU, teaching courses in software engineering, data structures, information security, and programming labs. He has also served as a Deputy Head of the Information Technology department at Jodhpur Institute of Engineering and Technology, where he contributed significantly to teaching, course development, and departmental administration. In the industry, he has worked as an Application Software Engineer at Computer Science Corporation, focusing on software maintenance, bug fixes, and enhancements. Additionally, he has served in various capacities at the Computer Centre of AMU, including roles as a Programmer and Technical Consultant.

Research focus

Dr. Ahmed’s research interests encompass artificial intelligence, machine learning, natural language processing, and decision intelligence. He has published extensively in journals and conferences, focusing on areas such as sentiment analysis, depression detection from social media posts, rumor-free social networks, and news article summarization. His recent research includes a framework for legal case brief generation using natural language processing and smart contract generation through NLP and blockchain.

Publication top notes

1. Ahmad, T., Ahamad, M., Ahmed, S. U., Ahmad, N. (2022) Short question-answers
assessment using lexical and semantic similarity based features, Journal of Discrete
Mathematical Sciences and Cryptography, 25:7, 2057-2067, DOI:
10.1080/09720529.2022.2133245 [ESCI & Scopus]

2. Ahmed, S. U., Ahmad, T., Ahmad, N. (2022). Sentiment Analysis Techniques for
Depression Detection from Micro-Blogging Social Media Post. NueroQuantology
DOI: 10.14704/NQ.2022.20.12.NQ77265 [Scopus]

3. Ahmad, T., Ahmed, S. U., Ali, S. O., & Khan, R. (2020). Beginning with exploring the
way for rumor free social networks. Journal of Statistics and Management Systems, 23(2),
231-238. https://doi.org/10.1080/09720510.2020.1724623 [Web of Science]

4. Ahmad, T., Ahmed, S. U., Ahmad, N., Aziz, A., Mukul, L. (2020). News Article
Summarization: Analysis and Experiments on Basic Extractive Algorithms. International
Journal of Grid and Distributed Computing, 13(2), 2366 – 2379. [Web of Science]

5. Ahmed, S. U. (2018). Monitoring Unscheduled Leaves using IVR. Global Journal of
Computer Science and Technology, 18(1), 7–9. [Peer-reviewed]

6. Ahmed, S. U., & Purohit, R. (2014). Evaluating Efficiency and Effectiveness of Code
Reading Technique with an Emphasis on Enhancing Software Quality. International
Journal of Computer Applications, 2, 32-36. [Peer-reviewed]

7. Ahmed, S. U., Azmi, M. A., Badgujar, C., (2014). How to design and test safety critical
software systems. International Journal of Advances in Computer Science and Technology,
3(1), 19-22. [Peer-reviewed]

8. Ahmed, S. U., Sahare, S. A., & Ahmed, A. (2013). Automatic test case generation using
collaboration UML diagrams. World Journal of Science and Technology. 2, [Peerreviewed]

9. Ahmed, S. U., & Azmi, M. A. (2013). A Novel Model Based Testing (MBT) approach for
Automatic Test Case Generation. International Journal of Advanced Research in
Computer Science, 4(11), 81-83. [Peer-reviewed]

Journal Publications (Under Review)
1. Ahmed, S. U., Ahmed, N., Ahmad, T. (2023) A Rhetorical Role Relatedness (RRR)
framework for Legal Case Brief Generation Natural Language Processing Journal
(Elsevier, Submitted)

Meiyan Liang | Computer Science | Best Researcher Award

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🌟Assoc Prof Dr. Meiyan Liang, Computer Science, Best Researcher Award 🏆

  •  Associate Professor at Shanxi University, China

Meiyan Liang, PhD, is an accomplished researcher in the field of Instrument Science and Technology, with a focus on Deep Learning and Medical Image Processing. Currently affiliated with the School of Physics and Electronic Engineering at Shanxi University in China, Dr. Liang completed her PhD at the Opto-Electronic College, Beijing Institute of Technology. She has made significant contributions to the development of innovative technologies for the identification and classification of various medical conditions, particularly in cancer diagnosis. Her work spans both theoretical and experimental domains, with a particular emphasis on leveraging neural networks and terahertz imaging techniques. Dr. Liang’s expertise is recognized through numerous awards, patents, and a prolific publication record in prestigious journals.

Author Metrics

Scopus Profile

ORCID Profile

Dr. Liang’s research output is not only extensive but also impactful, as evidenced by her author metrics. She has consistently published in high-impact journals, demonstrating the significance of her work within the scientific community. Additionally, Dr. Liang’s patents highlight her innovative approach to problem-solving and technology development.

  • Citations: 138 citations across 136 documents
  • Documents: Authored 25 documents
  • h-index: 5

Education

Dr. Meiyan Liang obtained her PhD in Instrument Science and Technology from the Opto-Electronic College at Beijing Institute of Technology. Her doctoral research focused on the application of deep learning methodologies in medical image processing, particularly for cancer diagnosis.

Research Focus

Dr. Liang’s research primarily centers around two main areas: Deep Learning and Medical Image Processing. Within these domains, she specializes in utilizing neural networks for the interpretation and analysis of medical images, with a particular emphasis on cancer detection and classification. Her work also involves the integration of advanced imaging techniques, such as terahertz imaging, to develop novel diagnostic tools.

Professional Journey

Following her doctoral studies, Dr. Liang embarked on a professional journey that has seen her become an esteemed researcher in the field of medical imaging. She has held positions at various academic institutions, including her current role at Shanxi University. Throughout her career, Dr. Liang has secured research funding, published extensively, and obtained several patents for her innovative contributions to the field.

Honors & Awards

Dr. Liang’s outstanding contributions to her field have been recognized through numerous honors and awards. Notable accolades include being awarded the “Sanjin talent” by the government of Shanxi Province and receiving the “China Instrument & Control Society Scholarship” from the Chinese instrumentation society.

Research Timeline

Dr. Liang’s research timeline showcases her progression as a researcher and the evolution of her research interests. Starting from her doctoral studies, she has continued to expand her expertise and contribute to advancements in medical imaging technology. Her research timeline reflects a commitment to excellence and a dedication to addressing critical challenges in healthcare through innovative research.

Publications Noted & Contributions

Dr. Liang has made significant contributions to the academic community through her prolific publication record. Her research findings have been published in prestigious journals such as the IEEE Journal of Biomedical and Health Informatics, Computer Methods and Programs in Biomedicine, and The Visual Computer. These publications cover a wide range of topics, including interpretable inference, whole-slide image prediction, and pathology image restoration.

Title: Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

  • Authors: Liang, M., Wang, R., Liang, J., Zhang, T., Zhang, C.
  • Journal: IEEE Journal of Biomedical and Health Informatics, 2023, 27(12), pp. 6006–6017
  • Abstract: This paper presents a method for interpretable inference and classification of tissue types in histological colorectal cancer slides using ensembles adaptive boosting prototype tree.

Title: Multi-scale self-attention generative adversarial network for pathology image restoration

  • Authors: Liang, M., Zhang, Q., Wang, G., Liu, H., Zhang, C.
  • Journal: Visual Computer, 2023, 39(9), pp. 4305–4321
  • Abstract: This paper introduces a multi-scale self-attention generative adversarial network for pathology image restoration.
  • Citations: 1

Title: Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

  • Authors: Liang, M., Chen, Q., Li, B., Jiang, X., Zhang, C.
  • Journal: Computer Methods and Programs in Biomedicine, 2023, 229, 107268
  • Abstract: This paper proposes an interpretable classification method for pathology whole-slide images using an attention-based context-aware graph convolutional neural network.
  • Citations: 6

Title: A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy

  • Authors: Liu, H., Liu, X., Zhang, Z., Liang, M., Zhang, C.
  • Journal: Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 2022, 274, 121104
  • Abstract: This paper proposes a novel strategy for liquids discrimination based on THz reflection spectroscopy using the geometric product.
  • Citations: 1

Title: THz ISAR imaging using GPU-accelerated phase compensated back projection algorithm

  • Authors: Liang, M.-Y., Ren, Z.-Y., Li, G.-H., Zhang, C.-L., Fathy, A.E.
  • Journal: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 2022, 41(2), pp. 448–456
  • Abstract: This paper presents THz ISAR imaging using a GPU-accelerated phase-compensated back projection algorithm.
  • Citations: 3