Ikram Ullah | Cryptography | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Ikram Ullah
Government Polytechnic Institute Lakki Marwat, Pakistan

Ikram Ullah
Affiliation Government Polytechnic Institute Lakki Marwat
Country Pakistan
Documents 14
Subject Area Cryptography
Event Technology Scientists Awards
ORCID 0000-0002-4431-3186

Ikram Ullah is a researcher affiliated with Government Polytechnic Institute Lakki Marwat, Pakistan, whose work is situated in cryptography and secure information processing. His recent publications address substitution-box construction, elliptic-curve methods, pseudo-random number generation, and image security, reflecting an applied research focus on developing computational techniques for secure digital data.

Abstract

Ikram Ullah’s research focuses on cryptographic methods for secure digital information, with particular attention to substitution boxes, elliptic curves, pseudo-random number generation, and image encryption. His recent publications examine statistically optimized dynamic S-boxes, Montgomery-curve-based S-box generation, and elliptic-curve methods for secure image data. These studies address cryptographic design through mathematical structures and computational techniques, with emphasis on security properties, efficiency, and practical data protection. Collectively, the publications indicate a research profile connecting theoretical cryptography with application-oriented security problems, particularly where nonlinear components, randomness, and image protection are important. The documented work also illustrates collaboration across mathematical and computing research environments.[1] [2] [3]

Keywords

Cryptography; substitution boxes; S-box generation; elliptic curves; Montgomery curves; pseudo-random number generation; SHA-512; image encryption; nonlinear cryptographic components; information security.

Introduction

Modern digital systems require cryptographic mechanisms that can protect information against unauthorized access and manipulation. Substitution boxes and pseudo-random number generators are important components of many security architectures. Recent studies involving Ikram Ullah examine elliptic-curve structures and dynamic constructions to address security, nonlinearity, randomness, and efficient protection of image data.[1] [2] [3]

Research Profile

Ikram Ullah’s documented research centers on cryptography, particularly the mathematical and computational design of secure primitives. His publications address dynamic substitution boxes, Montgomery elliptic curves, pseudo-random number generation, and image encryption. The work combines algebraic structures with computational evaluation to investigate security characteristics and practical efficiency in cryptographic security applications.[1] [2] [3]

Research Contributions

The reported contributions include construction of statistically optimal dynamic S-boxes, development of a Montgomery-curve-based S-box generator, and an elliptic-curve pseudo-random number generator using SHA-512. These studies explore nonlinear components, dynamic generation, key sensitivity, and secure image-data processing. Together, they demonstrate an application-oriented approach to cryptographic primitive design and evaluation further.[1] [2] [3]

Publications

Ullah has coauthored recent studies covering dynamic S-box construction, Montgomery-curve-based substitution-box generation, and elliptic-curve pseudo-random number generation for image security. The publications appear in Physica Scripta and Security and Privacy, and collectively address mathematical constructions and computational mechanisms intended to strengthen cryptographic processes used for protecting digital information and systems.[1] [2] [3]

Research Impact

The research addresses security challenges in image encryption and cryptographic primitive generation, areas relevant to secure digital communication and data protection. Reported studies evaluate properties such as nonlinearity, randomness, key sensitivity, computational efficiency, and security performance. Their focus provides potential relevance to cryptographic systems requiring adaptable and efficient security components.[1] [2] [3]

Award Suitability

The documented publication record provides evidence of sustained research activity in cryptography and secure information processing. His recent work directly addresses technically relevant problems involving S-boxes, elliptic curves, pseudo-random generation, and image security. These contributions provide a substantive basis for considering the Innovative Research Award within the stated research domain.[1] [2] [3]

Conclusion

Ikram Ullah’s research profile reflects a focused contribution to cryptography through studies of dynamic S-boxes, elliptic-curve constructions, pseudo-random generation, and image security. The documented publications demonstrate engagement with both mathematical foundations and practical security requirements. Continued research in these areas may further develop efficient and adaptable cryptographic mechanisms for applications.[1] [2] [3]

References

  1. Aslam, C. M. A., Ullah, I., & Ishaq, M. (2026). Construction of statistically optimal dynamic S-boxes for secure image encryption. Physica Scripta, 101(35).
    https://doi.org/10.1088/1402-4896/ae9c79
  2. Ullah, I., Arif, S., Abbas, F., & Hayat, U. (2026). Efficient and Secure Montgomery Curve Based Substitution Box Generator With Optimal Nonlinearity. Security and Privacy, 9(1), e70168.
    https://doi.org/10.1002/spy2.70168
  3. Bilal, M., Ullah, I., Zhang, X., & Hayat, U. (2026). Efficient pseudo-random number generator using elliptic curves over small primes for securing image data. Physica Scripta, 101(1), 015005.
    https://doi.org/10.1088/1402-4896/ae3034

Ali Broumandnia | Cybersecurity | Innovative Research Award

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Innovative Research Award

Ali Broumandnia
Islamic Azad University, Iran

Ali Broumandnia
Affiliation Islamic Azad University
Country Iran
Scopus ID 23003455800
Documents 61
Citations 615
h-index 12
Subject Area Cybersecurity
Event Technology Scientists Awards
ORCID 0000-0001-5145-2013

Ali Broumandnia is a researcher affiliated with Islamic Azad University whose scholarly work addresses cybersecurity through digital image encryption, chaotic maps, modular arithmetic, and secure multimedia processing. His publications examine scale-invariant encryption approaches designed to address image-size constraints while strengthening cryptographic properties, providing a focused contribution to contemporary information-security research. [1] [2]

Abstract

Ali Broumandnia’s research is situated within cybersecurity, with particular emphasis on digital image encryption using chaotic and modular mathematical techniques. His scholarly work addresses limitations associated with image-size dependency and conventional cryptographic approaches by investigating scale-invariant encryption frameworks. Research involving three-dimensional modular chaotic maps explores permutation, substitution, diffusion, key-space characteristics, and statistical security measures for digital imagery. Related work extends these concepts to color images and prime-modular constructions, demonstrating a consistent research direction toward adaptable and computationally considered image-security mechanisms. The publication record supplied for this recognition profile indicates sustained engagement with encryption research and its applications to secure digital multimedia communication. [1] [2] [3]

Keywords

  • Cybersecurity
  • Digital Image Encryption
  • Chaotic Maps
  • Modular Arithmetic
  • Scale-Invariant Encryption
  • Cryptography
  • Secure Multimedia

Introduction

Digital images require protection against unauthorized access, manipulation, and disclosure as multimedia communication expands across networked environments. Broumandnia’s research approaches this challenge through cryptographic methods based on chaotic maps and modular operations. His studies investigate scale-invariant image encryption, seeking approaches applicable across differing image dimensions while maintaining measurable security characteristics and computational practicality. [1] [2]

Research Profile

Broumandnia’s documented research profile centers on cybersecurity and image cryptography, particularly the application of three-dimensional modular chaotic maps to digital image protection. His work encompasses grayscale and color-image encryption, scale-invariant processing, permutation and diffusion mechanisms, and prime-modular techniques. These themes demonstrate a coherent specialization connecting mathematical transformations with practical multimedia security requirements. [1] [2] [3]

Research Contributions

The research contributions represented by these publications include development and evaluation of scale-invariant encryption strategies using three-dimensional modular chaotic maps. The studies consider permutation, substitution, diffusion, key-space expansion, statistical properties, and image-size flexibility. Color-image encryption extends the methodology to multidimensional visual data, while prime-modular techniques further explore cryptographic robustness and implementation characteristics. [1] [2] [3]

Publications

Selected publications associated with Broumandnia’s research include studies on scale-invariant digital color image encryption, scale-invariant digital image encryption using three-dimensional modular chaotic maps, and digital image encryption using chaotic maps with prime modular constructions. Collectively, these works reflect a continuing investigation of cryptographic architectures intended to improve flexibility, security parameters, and image-processing performance. [1] [2] [3]

Research Impact

The supplied academic metrics list 61 documents, 615 citations, and an h-index of 12, indicating a documented body of scholarly output and citation activity. Within the cited research, scale-invariant encryption addresses practical image-dimension considerations, while color-image and prime-modular studies broaden the technical scope of secure multimedia processing. [1] [2] [3]

Award Suitability

The documented research aligns with an Innovative Research Award through its sustained focus on image-security methodologies involving chaotic maps, modular arithmetic, scale invariance, and multidimensional processing. The supplied publication evidence demonstrates technically focused work addressing established challenges in digital image encryption, while the reported scholarly metrics provide additional context for evaluating the researcher’s academic profile and research activity. [1] [2] [3]

Conclusion

Ali Broumandnia’s documented scholarship presents a focused research direction in cybersecurity and digital image encryption. His publications investigate three-dimensional chaotic-map architectures, scale-invariant processing, color-image protection, and prime-modular techniques. Together with the supplied publication metrics, these works provide a structured basis for recognizing a research profile centered on cryptographic approaches for secure digital multimedia. [1] [2] [3]

References

  1. Momeni Asl, A., Broumandnia, A., & Mirabedini, S. J. (2021). Scale invariant digital color image encryption using a 3D modular chaotic map. IEEE Access, 9, 102433–102449.
    https://ieeexplore.ieee.org/document/9481114
  2. Broumandnia, A. (2020). Scale invariant digital image encryption using 3D modular chaotic map. Multimedia Tools and Applications, 79, 11327–11355.
    https://link.springer.com/article/10.1007/s11042-019-08337-y
  3. Ghazanfaripour, H., & Broumandnia, A. (2020). Designing a digital image encryption scheme using chaotic maps with prime modular. Optics and Laser Technology, 131, 106339.
    https://www.scopus.com/pages/publications/85086361488

Renuka Cheeturi | Quantum Computing | Innovative Research Award

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Innovative Research Award

Renuka Cheeturi is a researcher affiliated with the National Institute of Technology, Warangal, India, whose research interests include quantum computing and lattice-based security for cloud-assisted Internet of Things (IoT) environments. Her scholarly work includes studies of public auditing, certificateless cryptographic mechanisms, and quantum-resistant security approaches for cloud storage and IoT systems.[1][2][3]

Renuka Cheeturi
Affiliation National Institute of Technology, Warangal
Country India
Scopus ID 58651022600
Documents 6
Citations 6
h-index 2
Subject Area Quantum Computing
Event Technology Scientists Awards
ORCID 0009-0003-4533-2519

Abstract

Renuka Cheeturi is a researcher affiliated with the National Institute of Technology, Warangal, India, whose scholarly interests encompass quantum computing, cloud security, lattice-based cryptography, and public auditing for cloud-assisted Internet of Things environments. Her research includes work on trapdoor-free certificateless public auditing, lattice-based auditing mechanisms, and quantum-resistant security schemes for cloud-assisted IoT. These studies address security, privacy, auditability, and resistance to emerging computational threats in distributed cloud environments. Her publication record includes research examining cryptographic mechanisms designed to strengthen data integrity and verification while considering security requirements associated with future quantum computing capabilities.[1][2][3]

Keywords

Quantum Computing; Quantum-Resistant Cryptography; Lattice-Based Cryptography; Cloud Security; Internet of Things; Public Auditing; Data Integrity; Certificateless Cryptography; Cloud-Assisted IoT; Post-Quantum Security.

Introduction

Cloud-assisted IoT systems require mechanisms that can verify outsourced data without exposing sensitive information or imposing excessive computational requirements. Renuka Cheeturi’s research addresses this area through public auditing schemes based on lattice-related cryptographic constructions and quantum-resistant security principles. Her work considers emerging requirements for integrity verification and secure cloud-assisted IoT architectures.[1][3]

Research Profile

Cheeturi’s research profile is centered on cryptographic security for cloud storage and IoT systems, with particular relevance to lattice-based methods and quantum-resistant public auditing. Her work connects conventional cloud integrity verification with security considerations arising from quantum computing, emphasizing cryptographic constructions that can support trustworthy data management in distributed computing environments.[2][3]

Research Contributions

Her documented contributions include research on trapdoor-free lattice-based certificateless public auditing, comprehensive examination of lattice-based auditing approaches, and development of an efficient quantum-resistant public auditing scheme for cloud-assisted IoT. Collectively, these publications address authentication, auditability, data integrity, and resilience against cryptographic threats associated with future quantum-enabled computational capabilities.[1][2][3]

Publications

Cheeturi’s listed research includes studies addressing lattice-based public auditing and quantum-resistant cloud-assisted IoT security. The publications cover both foundational and applied aspects of secure public auditing, including a trapdoor-free certificateless construction, a survey of lattice-based auditing schemes, and an efficient quantum-resistant approach for cloud-assisted IoT environments.[1][2][3]

Research Impact

The research contributes to the broader discussion of secure cloud storage and IoT data verification by examining cryptographic approaches suitable for environments facing evolving computational threats. Its relevance extends to public auditing, data integrity, privacy-aware verification, and post-quantum security, while the available bibliographic record provides measurable indicators of scholarly dissemination through indexed publications and citations.[1][2][3]

Award Suitability

Cheeturi’s research is relevant to an Innovative Research Award through its focus on cryptographic approaches for contemporary and emerging security challenges. Her publications address lattice-based public auditing and quantum-resistant cloud-assisted IoT security, demonstrating engagement with research topics at the intersection of cloud computing, cybersecurity, cryptography, and quantum computing.[1][3]

Conclusion

Renuka Cheeturi’s documented research addresses secure public auditing for cloud-assisted IoT and lattice-based cryptographic mechanisms, including approaches designed to address quantum-era security requirements. Her publication portfolio reflects a focused research direction involving cloud security, cryptography, data integrity, and quantum-resistant technologies, providing a scholarly basis for recognition within innovative technology research.[1][2][3]

References

  1. Cheeturi, R., et al. (n.d.). TF-LB-CLPAS: Trapdoor-Free Lattice-Based Certificateless Public Auditing Scheme for Cloud-Assisted IoT. Concurrency and Computation: Practice and Experience.
    https://doi.org/10.1002/cpe.70923
  2. Cheeturi, R., et al. (n.d.). Lattice-Based Public Auditing Schemes for Cloud Storage Security: A Comprehensive Survey. Concurrency and Computation: Practice and Experience.
    https://doi.org/10.1002/cpe.70556
  3. Cheeturi, R., et al. (n.d.). Efficient and Quantum-Resistant Public Auditing Scheme for Cloud-Assisted IoT. IEEE.
    https://ieeexplore.ieee.org/document/11410013
  4. Elsevier. (n.d.). Scopus author details: Renuka Cheeturi, Author ID 58651022600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58651022600

Thomas Monoth | Information Security | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Thomas Monoth
Mary Matha Arts and Science College, India

                  Thomas Monoth
Affiliation Mary Matha Arts and Science College
Country India
Google Scholar OezvzswAAAAJ
Documents 27
Citations 240
h-index 9
Subject Area Information Security
Event Technology Scientists Awards

Thomas Monoth is an academic researcher affiliated with Mary Matha Arts and Science College, India, whose documented research activity includes work in information security and visual cryptography. His publication record includes studies addressing secure fingerprint transmission, recursive visual cryptography, and contrast-enhanced visual cryptographic schemes, reflecting an interest in privacy-preserving information representation and secure image-based communication. [1] [2] [3]

Abstract

Thomas Monoth is an academic researcher associated with Mary Matha Arts and Science College, India, with research interests centered on information security and visual cryptography. His documented publications address tamper-resistant fingerprint transmission, recursive visual cryptography, random basis column pixel expansion, and contrast-enhanced visual cryptographic schemes. These studies examine methods for protecting sensitive visual information while supporting secure representation and transmission. His reported research record comprises 27 documents, 240 citations, and an h-index of 9. Collectively, the available publications demonstrate sustained engagement with cryptographic approaches for image security and privacy. [1] [2] [3]

Keywords

Information Security; Visual Cryptography; Image Security; Fingerprint Protection; Recursive Visual Cryptography; Pixel Expansion; Privacy-Preserving Systems; Cryptographic Schemes; Secure Transmission; Digital Security. [1] [2] [3]

Introduction

Visual cryptography provides techniques for protecting visual information by transforming secret images into share-based representations. Within information security, such methods can support privacy protection for sensitive biometric and image data. Monoth’s documented research addresses these themes through fingerprint transmission and visual cryptographic approaches designed for secure visual information handling. [1] [2]

Research Profile

Thomas Monoth’s documented research profile is associated with information security, particularly visual cryptography and secure image processing. His publication topics include fingerprint protection, recursive visual cryptography, pixel expansion, and contrast enhancement. These themes indicate a research focus on cryptographic techniques for representing, protecting, and transmitting sensitive visual information securely. [1] [2] [3]

Research Contributions

The documented contributions cover several aspects of visual cryptography, including tamperproof fingerprint transmission, recursive scheme construction, random basis column pixel expansion, and additional pixel patterns for contrast enhancement. Together, these studies address practical and technical considerations in securing visual information and improving the representation characteristics of cryptographic image schemes. [1] [2] [3]

Publications

The available publication record includes research on tamperproof fingerprint transmission using visual cryptography schemes, recursive visual cryptography using random basis column pixel expansion, and contrast-enhanced visual cryptography schemes based on additional pixel patterns. These publications collectively demonstrate continued investigation of cryptographic methods for protecting and representing visual information in security-oriented applications. [1] [2] [3]

Research Impact

The supplied research record reports 27 documents, 240 citations, and an h-index of 9, providing bibliometric indicators of scholarly dissemination. The cited publications address security challenges involving fingerprints and visual information, areas relevant to privacy-conscious digital systems. Their focus on visual cryptography contributes documented technical perspectives to information security research. [1] [2] [3]

Award Suitability

The documented alignment between Thomas Monoth’s research and information security is supported by publications addressing visual cryptography, fingerprint protection, and secure image representation. His reported publication and citation indicators further provide measurable evidence of scholarly activity. These factors provide relevant documentation for consideration under an Innovative Research Award focused on technology and security research. [1] [2] [3]

Conclusion

Thomas Monoth’s documented scholarly work centers on visual cryptography and information security, with publications addressing secure fingerprint transmission, recursive cryptographic schemes, pixel expansion, and contrast enhancement. The available record combines these research themes with reported bibliometric activity, establishing a documented academic profile relevant to research recognition in technology and security domains. [1] [2] [3]

References

  1. Tamperproof transmission of fingerprints using visual cryptography schemes. (2010). Procedia Computer Science. ScienceDirect.
    https://www.sciencedirect.com/science/article/pii/S1877050910003480
  2. Recursive visual cryptography using random basis column pixel expansion. (2007). IEEE.
    https://ieeexplore.ieee.org/abstract/document/4418265
  3. Contrast-enhanced visual cryptography schemes based on additional pixel patterns. (2010). IEEE.
    https://ieeexplore.ieee.org/abstract/document/5656468

Faizal Nujumudeen | Cryptography | Best Innovation Award

Published on by Technology Scientists

Best Innovation Award

Faizal Nujumudeen
Presidency University, India

Faizal Nujumudeen
Affiliation Presidency University
Country India
Scopus ID 59469139900
Documents 9
Citations 15
h-index 3
Subject Area Cryptography
Event Technology Scientists Awards
ORCID 0000-0002-5194-7806

Faizal Nujumudeen is a researcher associated with Presidency University whose scholarly work is situated primarily within cryptography and secure communication. His research includes visual cryptography, lightweight encryption, image security, and language-aware cryptographic approaches. His publications demonstrate an interest in developing computationally efficient methods for protecting visual and textual information in contemporary communication environments. [1] [2] [3]

Abstract

Faizal Nujumudeen’s research profile reflects sustained work in cryptography, with particular emphasis on lightweight visual cryptography, secure image communication, and language-aware security. His published studies address practical challenges involving computational efficiency, image sharing, secure reconstruction, and culturally adaptive communication. A 2025 Scientific Reports article presents an XOR-based colour visual cryptography approach using random shares, while a 2024 IEEE conference contribution examines secure communication through images and visual cryptography. A 2026 study introduces ALCS-26, a linguistically structured lightweight cryptographic standard for Arabic-based communication. Together, these publications demonstrate a research trajectory connecting cryptographic principles with practical information-security applications. [1] [2] [3]

Keywords

Cryptography; Visual Cryptography; Lightweight Cryptography; Image Security; Secure Communication; XOR Encryption; Colour Image Sharing; Linguistic Cryptography; Information Security; Secure Data Communication.

Introduction

Modern digital communication requires security mechanisms capable of protecting information while maintaining practical computational performance. Visual cryptography provides a method for distributing image information through shares that can be combined for reconstruction. Nujumudeen’s research addresses these requirements through studies of image-based security, lightweight cryptographic operations, and secure communication mechanisms. [1] [2]

Research Profile

The researcher’s documented publications are centered on cryptographic techniques for protecting digital information, particularly images and communication content. His work spans XOR-based visual cryptography, secure image communication, and linguistic approaches to lightweight cryptographic design. This profile connects theoretical cryptographic operations with application-oriented security challenges involving efficient sharing, reconstruction, and communication. [1] [2] [3]

Research Contributions

The documented research contributes to lightweight and application-oriented cryptography. The colour visual cryptography study develops a three-share XOR framework using randomization and non-expansive shares, while the secure communication study investigates images as components of protected communication. The ALCS-26 study extends lightweight cryptographic thinking toward linguistic structure and Arabic communication contexts. [1] [2] [3]

Publications

Nujumudeen’s documented publications include a 2025 Scientific Reports article on lightweight XOR-based visual cryptography for colour image sharing, a 2024 IEEE Global Conference for Advancement in Technology paper on secure communication using images and visual cryptography, and a 2026 Journal of King Saud University Computer and Information Sciences article introducing ALCS-26. These works represent complementary directions within cryptographic research. [1] [2] [3]

Research Impact

The research has relevance to information-security applications where computational efficiency, secure information sharing, and protection of digital content are important. The visual cryptography study reports evaluations involving reconstruction quality, randomness, and resistance to differential analysis, while the broader publication portfolio addresses secure communication and lightweight cryptographic design. These directions indicate application potential across digital security contexts. [1] [2] [3]

Award Suitability

The documented publication record provides a basis for consideration under an innovation-focused research category. His work combines established cryptographic operations with application-specific approaches to image security, secure communication, and linguistic cryptography. The research addresses practical security requirements through lightweight methods and distinct application contexts, providing relevant evidence for recognition associated with innovation in cryptographic research. [1] [2] [3]

Conclusion

Faizal Nujumudeen’s documented research focuses on cryptography and secure communication, with publications covering visual cryptography, image security, and linguistically structured lightweight encryption. The studies demonstrate an application-oriented research direction that considers security, efficiency, reconstruction, and communication requirements. These contributions provide a coherent scholarly basis for consideration within the Best Innovation Award category. [1] [2] [3]

References

  1. Nujumudeen, F., Mubarak, D. M. N., & Hussain, T. (2025). Lightweight XOR-based visual cryptography using random shares for secure colour image sharing with minimal shares. Scientific Reports, 15, 42868
    https://www.nature.com/articles/s41598-025-27142-2
  2. Nujumudeen, F., Azad, M. A., & Mubarak, M. N. (2024). A study of secure communication using images and visual cryptography. In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT).
    https://ieeexplore.ieee.org/document/10923992
  3. Nujumudeen, F., Hussain, T., Begum, S., Khanum, D. A., Nasimudeen, S., et al. (2026). ALCS-26: A linguistically structured Arabic-based lightweight cryptographic standard for secure and culturally adaptive communication. Journal of King Saud University Computer and Information Sciences, 38, 704.
    https://link.springer.com/article/10.1007/s44443-026-00917-x

Sufaid Shah | Gas Sensors | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Sufaid Shah — Shenzhen University, China

Sufaid Shah
Affiliation Shenzhen University
Country China
Scopus ID 56020598900
Documents 37
Citations 1,102
h-index 17
Subject Area Gas Sensors
Event Technology Scientists Awards
ORCID 0009-0003-9332-4185

Sufaid Shah is a researcher affiliated with Shenzhen University, China, whose scholarly work spans gas sensing, functional nanomaterials, thermoelectric materials, and biomedical material systems. His publication record includes studies of cobalt-doped zinc oxide, piezoelectric biopolymers, and rGO-MoS2/In2O3 junction networks for carbon monoxide detection. These contributions connect materials engineering, defect modulation, and sensing research. The record indicates interdisciplinary engagement across sensing and functional materials, supported by publications addressing material properties, processing strategies, and device performance. This profile supports consideration under the Innovative Research Award within the Technology Scientists Awards framework. Overall.[1] [2] [3]

Abstract

Sufaid Shah is a researcher affiliated with Shenzhen University, China, whose scholarly work spans gas sensing, functional nanomaterials, thermoelectric materials, and biomedical material systems. His publication record includes studies of cobalt-doped zinc oxide, piezoelectric biopolymers, and rGO-MoS2/In2O3 junction networks for carbon monoxide detection. These contributions connect materials engineering, defect modulation, and sensing research. The record indicates interdisciplinary engagement across sensing and functional materials, supported by publications addressing material properties, processing strategies, and device performance. This profile supports consideration under the Innovative Research Award within the Technology Scientists Awards framework. Overall.[1] [2] [3]

Keywords

Gas Sensors; Functional Nanomaterials; Thermoelectric Materials; Zinc Oxide; Piezoelectric Biopolymers; Carbon Monoxide Detection; Semiconductor Heterostructures; rGO-MoS2/In2O3; Materials Engineering; Sensor Technology.

Introduction

Sufaid Shah’s research profile reflects an interdisciplinary focus on functional materials and sensing technologies, with publications addressing thermoelectric ZnO nanostructures, piezoelectric biopolymers, and solution-processed gas-sensing junctions. These studies connect material composition, structural engineering, and device performance, illustrating research activity across technology domains relevant to advanced sensing and materials innovation.[1] [2] [3]

Research Profile

The publication record associates Sufaid Shah with research involving nanostructured materials, biomedical polymers, and semiconductor heterostructures. His work addresses relationships between material structure and functional behavior, including defect engineering, piezoelectric response, and room-temperature carbon monoxide sensing. This breadth indicates participation spanning materials science, device engineering, and sensor development.[1] [2] [3]

Research Contributions

Sufaid Shah’s contributions include research on cobalt-doped ZnO, where defect-related optical and thermoelectric properties were examined, alongside work reviewing piezoelectric biopolymers for biomedical technologies. The record includes a solution-processed rGO-MoS2/In2O3 p-n junction network designed for sensitive carbon monoxide detection at room temperature, linking materials design with sensing objectives.[1] [2] [3]

Publications

The publication record for this profile includes studies published through Scientific Reports, European Polymer Journal, and Chemical Engineering Journal. Collectively, these works address defect-engineered ZnO nanostructures, biodegradable piezoelectric materials, and solution-processed heterostructures for gas sensing. The publications demonstrate engagement with fundamental material characterization and application-oriented research questions across technology fields.[1] [2] [3]

Research Impact

The research described in these publications addresses functional materials, energy-related materials, biomedical devices, and gas sensing. Reported themes include defect engineering for thermoelectric behavior, biodegradable piezoelectric systems for biomedical applications, and intrinsic interfacial effects for carbon monoxide detection. These themes connect material investigation with functional device development. These support applications.[1] [2] [3]

Award Suitability

Based on the academic record and publications, Sufaid Shah shows research activity in innovative materials and sensing technologies. His work combines interdisciplinary materials investigation with application-oriented device concepts, particularly in gas sensing and functional nanomaterials. These contributions provide evidence for an Innovative Research Award under the Technology Scientists Awards program.[1] [2] [3]

Conclusion

Sufaid Shah’s research encompasses functional nanomaterials, thermoelectric properties, piezoelectric biopolymers, and semiconductor-based gas sensing. The publications demonstrate interdisciplinary collaboration and application-focused studies of material properties and device functionality. His profile presents a body of research relevant to contemporary materials and sensing technologies within the Technology Scientists Awards recognition framework.[1] [2] [3]

References

  1. Arif, D., Kiani, S. S., Khan, R., Abid, A. Y., Safeen, K., Alotaibi, K. M., Shah, W. H., Ali, A., Shah, S., Girma, W. M., Shah, A. U., & Safeen, A. (2026). Defect-induced optical and thermoelectric properties of cobalt doped ZnO nanostructures prepared through hydrothermal route. Scientific Reports, 16, 1726.
    https://www.nature.com/articles/s41598-025-31367-6
  2. Akram, W., Hamza, M., Iqbal, S., Shah, S., Iqbal, S., Maqsood, N., Huzaibi, H. U., Ali, W., Zhao, X., & Xu, W. (2026). Piezoelectric biopolymers for biomedical devices and applications: A review. European Polymer Journal, 257, 114962.
    https://www.sciencedirect.com/science/article/abs/pii/S0014305726004672
  3. Shah, S., Hassan, M., Akram, W., Din, S. U., Zhao, X., Ali, W., Ahmed, S., Qiao, G., & Pan, X. (2026). A solution-processed p-n junction network in rGO-MoS2/In2O3 for ultrasensitive room-temperature CO detection. Chemical Engineering Journal, 548, 181779.
    https://www.sciencedirect.com/science/article/abs/pii/S1385894726092429?via%3Dihub

Clifford Dansoh | Maritime Decarbonisation | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Clifford Dansoh — University of Brighton, United Kingdom

Clifford Dansoh
Affiliation University of Brighton
Country United Kingdom
Scopus ID 56244046600
Documents 5
Citations 18
h-index 3
Subject Area Maritime Decarbonisation
Event Technology Scientists Awards
ORCID 0000-0001-7992-4465

Clifford Dansoh is affiliated with the University of Brighton and works in the area of maritime decarbonisation. His documented research contributions address carbon-neutral pathways for maritime propulsion and wider research and innovation priorities supporting sectoral decarbonisation. His work includes collaboration within the UK clean maritime research landscape and peer-reviewed research outputs. [1][3]

Abstract

Clifford Dansoh’s documented research is situated within maritime decarbonisation, with emphasis on technological pathways, propulsion systems, alternative fuels, port infrastructure, and broader innovation requirements for reducing emissions from maritime activity. His publications contribute to discussions of carbon-neutral shipping and the research priorities needed to support transition across vessels and ports. His work includes peer-reviewed collaboration through the University of Brighton and the UK National Clean Maritime Research Hub. These contributions connect engineering research with practical decarbonisation challenges, including technology selection, infrastructure readiness, operational considerations, and future innovation. [1][3]

Keywords

Maritime Decarbonisation; Carbon Neutrality; Marine Propulsion; Alternative Fuels; Port Electrification; Renewable Energy; Maritime Sustainability; Clean Maritime Technology; Ship Engines; Port Infrastructure.

Introduction

Maritime decarbonisation requires coordinated advances in propulsion, fuels, vessel efficiency, ports, infrastructure, digitalisation, finance, regulation, and policy. Dansoh’s research contributes to this interdisciplinary context by examining technological routes toward lower-carbon and carbon-neutral maritime operations. His work reflects the need to evaluate technical feasibility alongside infrastructure preparedness and implementation challenges. [1][3]

Research Profile

Dansoh’s research profile is associated with maritime engineering and decarbonisation, particularly the transition of ship propulsion and supporting infrastructure. His University of Brighton research activity includes participation in clean maritime research addressing fuels, propulsion, vessel efficiency, ports, and innovation. This profile demonstrates an applied, multidisciplinary orientation toward maritime sustainability and technology development. [1][3]

Research Contributions

His documented contributions include analysis of routes toward maritime carbon neutrality and participation in research identifying priorities for decarbonising the maritime sector. These studies consider alternative fuels, engine technologies, retrofit pathways, infrastructure preparedness, vessel efficiency, port operations, digitalisation, and related innovation needs. The research therefore addresses interconnected technical dimensions of maritime transition. [1][3]

Publications

Dansoh is a co-author of peer-reviewed research addressing maritime decarbonisation. A 2025 article examines pathways toward carbon neutrality through fuel and engine technologies, retrofit considerations, economic factors, and port infrastructure. A 2024 collaborative article identifies research and innovation priorities spanning fuels, propulsion, vessel efficiency, ports, digitalisation, finance, regulation, and policy. [1][3]

Research Impact

The research contributes to the evidence base surrounding maritime decarbonisation by connecting propulsion technologies with fuels, infrastructure, ports, and implementation considerations. The identified research priorities provide a structured perspective on areas requiring continued investigation, while the carbon-neutrality roadmap discusses technology progression and infrastructure readiness relevant to future maritime systems. [1][3]

Award Suitability

The documented research record provides relevant evidence for consideration under a Best Researcher Award in a technology-focused recognition context. His peer-reviewed publications address a significant engineering challenge and demonstrate collaborative engagement with clean maritime research. The combination of maritime decarbonisation, propulsion, alternative fuels, and infrastructure research provides a coherent subject-area profile. [1][3]

Conclusion

Clifford Dansoh’s documented scholarly work focuses on maritime decarbonisation and associated technological transitions. His publications address carbon-neutral propulsion pathways and broader research priorities across fuels, vessels, ports, and infrastructure. Together, these outputs establish a focused research profile relevant to contemporary engineering efforts aimed at reducing maritime environmental impacts. [1][3]

References

  1. Tamam, M. Q. M., Dansoh, C., & Panesar, A. (2025). The roadmap to carbon neutrality for the maritime industry: An insight into various routes to decarbonise ship engines. Energy Conversion and Management: X, 27, 101184.
    https://doi.org/10.1016/j.ecmx.2025.101184
  2. Phase-Dependent Renewable Deployment for Port Electrification: A Framework for Managing the Transition from Energy-Limited to Power-Limited Operation Using Newhaven Port as a Case Study. (2026). Journal of Marine Science and Engineering, Article 1781.
    https://www.mdpi.com/2077-1312/14/19/1781
  3. Ling-Chin, J., Simpson, R., Cairns, A., Wu, D., Xie, Y., Song, D., Kashkarov, S., Molkov, V., Moutzouris, I., Wright, L., Tricoli, P., Dansoh, C., Panesar, A., Chong, K., Liu, P., Roy, D., Wang, Y., Smallbone, A., & Roskilly, A. P. (2024). Research and innovation identified to decarbonise the maritime sector. Green Energy and Sustainability, 4(1), 1–14.
    https://doi.org/10.47248/ges2404010001
  4. University of Brighton. (n.d.). Cliff Dansoh — Research profile and outputs. University of Brighton.
    https://research.brighton.ac.uk/en/persons/cliff-dansoh/

Omar El Ogri | Artificial Intelligence | Research Excellence Award

Published on by Technology Scientists

Research Excellence Award

Omar El Ogri — Sidi Mohamed Ben Abdellah University, Morocco

Omar El Ogri
Affiliation Sidi Mohamed Ben Abdellah University
Country Morocco
Scopus ID 59208342000
Documents 42
Citations 905
h-index 16
Subject Area Artificial Intelligence
Event Technology Scientists Awards
ORCID 0000-0003-4807-0641

Omar El Ogri is a researcher affiliated with Sidi Mohamed Ben Abdellah University, Morocco, whose documented work spans artificial intelligence, deep learning, image analysis, optimization, and data-driven prediction. His recent publications address solar-panel fault classification, educational prediction, and computer-assisted cancer diagnosis using computational methods and feature representations in applied research. [1] [2] [3]

Abstract

Omar El Ogri is a researcher at Sidi Mohamed Ben Abdellah University in Morocco whose work focuses on artificial intelligence and its applications in image analysis, machine learning, optimization, and predictive modeling. His documented publications address automated solar-panel fault classification, academic achievement and school-dropout prediction, and computer-assisted cancer diagnosis. The studies combine specialized mathematical representations, optimization algorithms, and deep-learning architectures to develop computational approaches for domain-specific problems. His recent research illustrates interdisciplinary applications spanning renewable-energy inspection, education, and biomedical image analysis. The supplied academic record reports 42 documents, 905 citations, and an h-index of 16 within an evolving research portfolio.

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Computer Vision
  • Image Analysis
  • Machine Learning
  • Optimization Algorithms
  • Biomedical Image Analysis
  • Predictive Analytics

Introduction

Omar El Ogri is a researcher affiliated with Sidi Mohamed Ben Abdellah University, Morocco, whose documented work spans artificial intelligence, deep learning, image analysis, optimization, and data-driven prediction. His recent publications address solar-panel fault classification, educational prediction, and computer-assisted cancer diagnosis using computational methods and feature representations in applied research. [1] [2] [3]

Research Profile

El Ogri’s research profile reflects an interdisciplinary application of artificial intelligence to image-based recognition, predictive modeling, and optimization. His reported record includes 42 documents, 905 citations, and an h-index of 16, with Artificial Intelligence identified as his subject area. These indicators provide context for assessing his research activity and visibility. [4]

Research Contributions

His documented contributions include combining Krawtchouk moments with optimized deep transfer learning for solar-panel fault classification, developing Artificial Bee Colony-based models for educational prediction, and proposing Rademacher-Fourier moment representations with deep learning for cancer-image diagnosis. Together, these studies demonstrate methodological work across computer vision, optimization, classification, and predictive analytics applications. [1] [2] [3]

Publications

Selected publications illustrate the breadth of El Ogri’s research collaborations. Recent work includes a solar-panel fault classification study using Krawtchouk moments and EfficientNetB4, an educational prediction study using Artificial Bee Colony optimization, and a medical diagnosis study combining Rademacher-Fourier moments with deep learning for biomedical image analysis and recognition systems. [1] [2] [3]

Research Impact

The cited studies indicate research impact through application-oriented methods addressing renewable-energy inspection, educational analytics, and biomedical image analysis. Reported experiments include high classification and prediction performance within their respective datasets, while the publications contribute specialized feature-extraction, optimization, and machine-learning approaches. These findings support continued investigation across applied artificial intelligence domains. [1] [2] [3]

Award Suitability

Based on the supplied academic record and documented publications, the Research Excellence Award recognizes a profile centered on artificial intelligence research and applied computational methodologies. The combination of activity, citation indicators, interdisciplinary applications, and methodological studies provides evidence for considering the researcher’s contributions within the stated Technology Scientists Awards context. [1] [2] [3]

Conclusion

Omar El Ogri’s documented research demonstrates sustained engagement with artificial intelligence, machine learning, image analysis, and optimization. His recent publications address distinct application areas while introducing specialized computational techniques. The available record presents a coherent research profile combining methodological development with practical problems in energy, education, and biomedical image analysis. [1] [2] [3]

References

  1. Naouadir, I., El Ogri, O., El-Mekkaoui, J., Benslimane, M., & Hjouji, A. (2026). A deep transfer learning and optimized Krawtchouk moment-based system for fault classification in solar panels. Computers & Electrical Engineering, 138, 111324.
    https://www.sciencedirect.com/science/article/abs/pii/S0045790626003940
  2. El Yousfi Alaoui, H., Bousraraf, Z., Hjouji, A., El Ogri, O., & El-Mekkaoui, J. (2026). New regression model for academic achievement and new classification method for school dropout based on Artificial Bee Colony Algorithm. Statistics, Optimization & Information Computing, 15(5), 3401–3415.
    https://iapress.org/index.php/soic/article/view/2420
  3. El Ogri, O., El-Mekkaoui, J., & Hjouji, A. (2026). A computer-assisted medical diagnosis system for cancer diseases based on quaternion orthogonal Rademacher-Fourier moments and deep learning. Biomedical Signal Processing and Control, 112, 108744.
    https://www.sciencedirect.com/science/article/abs/pii/S1746809425012558
  4. Elsevier. (n.d.). Scopus author details: Omar El Ogri, Author ID 59208342000. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59208342000

Ondrej Kobza | Natural Language Processing | Applied Sciences Award

Published on by Technology Scientists

Applied Sciences Award

Ondrej Kobza
Czech Technical University in Prague

                Ondrej Kobza
Affiliation Czech Technical University in Prague
Country Czech Republic
Scopus ID 57274675600
Documents 5
Citations 4
h-index 1
Subject Area Natural Language Processing
Event Technology Scientists Awards
ORCID 0000-0002-0529-9860

Ondrej Kobza is a researcher at Czech Technical University in Prague, Czech Republic, whose published work addresses conversational artificial intelligence, generative language models, secure coding assistants, and dialogue systems. His research connects natural language processing with model efficiency, safety, evaluation, and practical real-world conversational applications across evolving artificial intelligence systems. [1] [2] [3]

Abstract

Ondrej Kobza is a researcher at Czech Technical University in Prague whose work focuses on natural language processing, conversational artificial intelligence, generative language models, and secure coding assistants. His publications examine dialogue management, socialbot conversations, generative model integration, conversational enhancement, and security code generation. Research introduces AlquistCoder, a coding assistant trained with synthetic data and alignment methods, and benchmarks for evaluating secure and responsible code generation. Earlier studies address Alquist 5.0 and improvements to BlenderBot 3, emphasizing dialogue quality, model efficiency, system architecture, and evaluation. These publications collectively demonstrate an applied research direction connecting language technologies with artificial intelligence systems. [1] [2] [3]

Keywords

Natural Language Processing; Artificial Intelligence; Generative AI; Conversational AI; SocialBots; Secure Coding Assistants; Large Language Models; Dialogue Systems; Synthetic Data; Model Evaluation.

Introduction

Ondrej Kobza is a researcher at Czech Technical University in Prague, Czech Republic, whose published work addresses conversational artificial intelligence, generative language models, secure coding assistants, and dialogue systems. His research connects natural language processing with model efficiency, safety, evaluation, and practical real-world conversational applications across evolving artificial intelligence systems. [1] [2] [3]

Research Profile

Kobza’s research profile centers on natural language processing and applied generative AI, with publications spanning conversational agents, language-model enhancement, and security-oriented code generation. His work includes collaborations within the Czech Technical University research environment and examines methods for improving model behavior, efficiency, evaluation, and robustness across modern language technologies today. [1] [2] [3]

Research Contributions

Kobza has contributed to research on dialogue management, generative conversational systems, and secure coding assistants. His publications describe approaches involving dialogue trees, generative models, synthetic training data, alignment techniques, benchmark development, and system optimization methods. Collectively, these contributions address both capability and responsible deployment considerations within modern language-model research today. [1] [2] [3]

Publications

Kobza’s publication record includes studies on AlquistCoder, Alquist 5.0, and enhancements to BlenderBot 3. These works address secure code generation, socialbot conversations, conversational model architecture, evaluation, and performance optimization. The publications demonstrate research interest in applying language technologies to practical systems while investigating methods for improving reliability, efficiency, and safety. [1] [2] [3]

Research Impact

The documented research provides contributions to natural language processing through publicly described methods, evaluations, and model-development practices. The AlquistCoder study introduces synthetic-data and benchmark resources for secure coding assistants, while earlier work examines conversational architectures and model improvements. Together, these studies provide technical directions for further research in language-model systems. [1] [2] [3]

Award Suitability

The documented publication record aligns with an Applied Sciences Award focused on applications within natural language processing and artificial intelligence. Kobza’s work combines methodological development with practical system evaluation, covering conversational agents and secure code generation. The evidence supports consideration of documented work through its technical scope, applied orientation, and contributions. [1] [2] [3]

Conclusion

Ondrej Kobza’s documented research reflects an interdisciplinary application of natural language processing to conversational systems, generative models, and secure coding. His publications demonstrate engagement with model development, evaluation, and applied artificial intelligence research. The record provides a basis for recognizing contributions that connect language technology research with practical computational applications. [1] [2] [3]

References

  1. Kobza, O., Černý, A., Dostál, I., Šedivý, J., Rigaki, M., Sladić, M., & Garcia, S. (2026). AlquistCoder: A synthetic data approach to training compact secure coding assistants and building security benchmarks. Computational Intelligence, 42(4), e70282.
    https://doi.org/10.1111/coin.70282
  2. Kobza, O., Herel, D., Cuhel, J., Gargiani, T., Marek, P., & Sedivy, J. (2024). Alquist 5.0: Dialogue trees meet generative models, a novel approach for enhancing SocialBot conversations. Future Internet, 16(9), 344.
    https://doi.org/10.3390/fi16090344
  3. Kobza, O., Herel, D., Cuhel, J., Gargiani, T., Pichl, J., Marek, P., Konrad, J., & Sedivy, J. (2023). Enhancements in BlenderBot 3: Expanding beyond a singular model governance and boosting generational performance. Future Internet, 15(12), 384.
    https://doi.org/10.3390/fi15120384

Zixuan Huang | Artificial Intelligence | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Zixuan Huang — Fuzhou University

Zixuan Huang
Affiliation Fuzhou University
Country China
Documents 5
Subject Area Artificial Intelligence
Event Technology Scientists Awards
ORCID 0009-0000-5508-1475

Zixuan Huang is a researcher whose documented work concerns adaptive control, event-triggered mechanisms, consensus, tracking, and constraint handling in multi-agent systems. The supplied publication record includes research on output-feedback consensus and finite-time bipartite tracking, connecting control-theoretic methods with communication-aware coordination in networked autonomous systems. [1] [2]

Abstract

Zixuan Huang’s research addresses adaptive and event-triggered control strategies for multi-agent systems, with emphasis on consensus, tracking, state constraints, and communication efficiency. Published work describes nonlinear mapping methods, state estimation, adaptive control, and dynamic event-triggering mechanisms for constrained and unconstrained systems. Huang’s studies also examine finite-time bipartite tracking under asymmetric state constraints. These contributions connect theoretical control design with communication-aware coordination problems in networked multi-agent systems. The documented research includes a 2025 article in the International Journal of Robust and Nonlinear Control and work associated with Fuzhou University, reflecting engagement with contemporary problems in intelligent control and multi-agent coordination. [1] [2]

Keywords

Multi-agent systems; adaptive control; event-triggered control; consensus control; finite-time tracking; output constraints; asymmetric state constraints; nonlinear control; state estimation; artificial intelligence.

Introduction

Multi-agent systems provide a framework for coordinating interconnected autonomous agents in engineering applications. Research in this area addresses consensus, tracking, communication constraints, and stability while considering practical limitations on states and outputs. Huang’s publications investigate adaptive and event-triggered approaches that aim to coordinate agents while respecting specified system constraints and requirements. [2]

Research Profile

Zixuan Huang’s documented research centers on control theory for multi-agent systems, particularly adaptive event-triggered consensus and finite-time tracking. The work considers output constraints, asymmetric state constraints, dead-zone inputs, state estimation, nonlinear mappings, and communication efficiency. These topics place the research within intelligent control, networked systems, and coordinated autonomous-agent applications. [1] [2]

Research Contributions

The reported contributions include a unified adaptive event-triggered output-feedback consensus framework applicable to systems with or without output constraints. Another study develops finite-time bipartite tracking control under asymmetric state constraints using nonlinear mappings, backstepping, filtering, and dynamic triggering. Together, these works address constrained control design, estimation, stability, tracking, and communication [1] [2] [3]

Publications

The supplied publication record includes a 2025 research article in the International Journal of Robust and Nonlinear Control and a study on finite-time bipartite tracking control. A related 2024 preprint presents an earlier version of the adaptive output-feedback consensus work. The publications collectively address event-triggered control, multi-agent coordination, constraints, and [1] [2] [3]

Research Impact

The documented research addresses technical challenges relevant to networked multi-agent control, including constrained outputs, asymmetric state limits, unavailable states, and communication efficiency. The published consensus study appears in a peer-reviewed control journal, while the tracking study is associated with Fuzhou University. The work provides methods and analyses for further investigation [1] [2]

Award Suitability

For recognition under a Best Researcher Award, the available record provides identifiable evidence of research activity in artificial intelligence-related control systems and multi-agent coordination. Huang is associated with Fuzhou University and has documented scholarly work addressing adaptive consensus, event-triggered mechanisms, tracking, and constraints. The supplied record supports consideration based on [1] [2] [3]

Conclusion

Zixuan Huang’s documented research focuses on adaptive and event-triggered control for multi-agent systems, combining consensus, tracking, state constraints, estimation, and communication-aware mechanisms. The supplied publications demonstrate engagement with current control problems and provide a basis for academic recognition within the stated research area. Additional bibliometric information was not supplied. [1] [2]

References

  1. Huang, Z., Chu, C., Xu, N., Zhang, L., & Zhao, N. (2025). An event-based triggered finite time bipartite tracking control for multi-agent systems with asymmetric state constraints. Information Sciences.
    https://www.sciencedirect.com/science/article/abs/pii/S0020025526010765?via%3Dihub
  2. Huang, Z., Karimi, H. R., Niu, B., Li, L., & Zhao, X. (2025). A unified adaptive event-triggered output feedback consensus for multi-agent systems with or without output constraints. International Journal of Robust and Nonlinear Control, 35(4), 1390–1405.
    https://onlinelibrary.wiley.com/doi/10.1002/rnc.7725
  3. Huang, Z., Karimi, H. R., Niu, B., Li, L., & Zhao, X. (2024). A unified adaptive event-triggered output feedback consensus for multi-agent systems with or without output constraints. Authorea [Preprint].
    https://www.authorea.com/doi/full/10.22541/au.172506025.59492691/v1