Sumaira Manzoor | Chemistry | Editorial Board Member

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Ms. Sumaira Manzoor | Chemistry | Editorial Board Member

Student | Institute of chemical sciences, Bahauddin zakariya university, Multan | Pakistan

Sumaira Manzoor is an accomplished researcher specializing in robotics, computer vision, and artificial intelligence, with a focus on developing advanced frameworks for autonomous systems and intelligent robots. Her work encompasses the design and deployment of edge-based vision models, human-following robots, and real-time inference systems, emphasizing efficiency and practical applicability in dynamic environments. She has made significant contributions to single and multi-object tracking, face mask recognition, semantic environment modeling, and robotic perception, leveraging both traditional machine vision and modern deep learning techniques. Manzoor has also explored ontology-based knowledge representation for cognitive robotic systems, providing comprehensive surveys and frameworks to enhance robot decision-making and interaction capabilities. Her research includes performance evaluations of cutting-edge detection models such as YOLOv3 and YOLOv4 in real-world applications, and she has developed innovative semantic SLAM and autonomous navigation frameworks aimed at high-level interaction and planning in complex environments. Throughout her career, she has published extensively in high-impact journals and presented her findings at leading international conferences, including Sensors, IEEE Access, Applied Sciences, and the International Conference on Control, Automation, and Systems (ICCAS). Her collaborative work with multidisciplinary teams has advanced the integration of AI-driven perception and reasoning in robotic systems, addressing practical challenges in mobile robotics and human-robot interaction. By bridging theoretical research and applied robotics, Manzoor’s contributions support the development of intelligent systems capable of autonomous decision-making, environment understanding, and adaptive interaction, positioning her as a leading figure in the field of AI-enabled robotics and computer vision. Her research not only advances technical knowledge but also has broad implications for real-world applications, including service robots, surveillance, healthcare, and autonomous navigation technologies.

Profile: ORCID

Featured Publications

  1. Manzoor, S., An, Y.-C., In, G.-G., Zhang, Y., Kim, S., & Kuc, T.-Y. (2023). SPT: Single pedestrian tracking framework with re-identification-based learning using the Siamese model. Sensors, 23(10), 4906. https://doi.org/10.3390/s23104906

  2. Manzoor, S., Kim, E.-J., Joo, S.-H., Bae, S.-H., In, G.-G., Joo, K.-J., Choi, J.-H., & Kuc, T.-Y. (2022). Edge deployment framework of GuardBot for optimized face mask recognition with real-time inference using deep learning. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3190538

  3. Manzoor, S., Joo, S.-H., Rocha, Y. G., Bae, S.-H., Kim, E.-J., Joo, K.-J., & Kuc, T.-Y. (2021). Ontology-based knowledge representation in robotic systems: A survey oriented toward applications. Applied Sciences, 11(10), 4324. https://doi.org/10.3390/app11104324

  4. Manzoor, S., Joo, S.-H., Rocha, Y. G., Lee, H.-U., & Kuc, T.-Y. (2019). A novel semantic SLAM framework for humanlike high-level interaction and planning in global environment. CEUR Workshop Proceedings.

  5. Manzoor, S., Joo, S.-H., & Kuc, T.-Y. (2019). Comparison of object recognition approaches using traditional machine vision and modern deep learning techniques for mobile robot. International Conference on Control, Automation and Systems. https://doi.org/10.23919/ICCAS47443.2019.8971680

Sumaira Manzoor’s work advances intelligent robotics and AI-driven perception, enabling autonomous systems to interact safely and efficiently with complex environments. Her research bridges cutting-edge computer vision and real-world applications, driving innovation in robotics, healthcare, surveillance, and autonomous navigation while contributing to global technological progress.

Zouhair Lakbaibi | Chemistry | Editorial Board Member

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Prof. Dr. Zouhair Lakbaibi | Chemistry | Editorial Board Member 

Ful Professor | Laboratory of Molecular Chemistry Materials and Environment; Multidisciplinary Faculty of Nador; Mohamed First University | Morocco

Zouhair Lakbaibi is a distinguished Professor in the Chemistry Department at Mohamed First University, Multidisciplinary Faculty, Nador, Morocco, with a prolific research career focused on corrosion inhibition, green chemistry, and computational chemistry. He has contributed extensively to the understanding of the interactions between chemical compounds and metal surfaces, particularly in acidic environments, using both experimental and theoretical approaches. His work spans the study of hydrazine derivatives, perillaldehyde from essential oils, and other bioactive compounds as eco-friendly corrosion inhibitors for mild and carbon steel, combining electrochemical analyses, adsorption behavior studies, and quantum chemical modeling. In addition, Lakbaibi has investigated the degradation of synthetic dyes via Fenton reactions and studied the adsorption and removal of heavy metal ions, including cadmium and copper, employing both experimental designs and computational simulations such as Monte Carlo and factorial analysis. His research demonstrates a strong interdisciplinary focus, integrating chemistry, environmental science, and materials science, and has been published in reputable journals like Heliyon, Chemical Papers, ACS Omega, and the Journal of Failure Analysis and Prevention. His investigations also encompass tribological behaviors of metals in corrosive media, solvent effects in chemical reactions, and theoretical studies on reaction mechanisms and selectivity, including DFT and ELF approaches. Over the years, Lakbaibi has collaborated with numerous researchers across Morocco and internationally, emphasizing sustainable and environmentally friendly solutions in chemical processes. With 17 documented publications, his work highlights innovative approaches to corrosion prevention, adsorption technologies, and mechanistic studies of organic reactions, bridging theoretical and practical chemistry applications while contributing significantly to the scientific community through both experimental insights and computational modeling.

Profiles: ORCID | Scopus

Featured Publications

  1. Lakbaibi, Z., Damej, M., Molhi, A., Benmessaoud, M., Tighadouini, S., Jaafar, A., Benabbouha, T., Ansari, A., Driouich, A., & Tabyaoui, M. (2022). Evaluation of inhibitive corrosion potential of symmetrical hydrazine derivatives containing nitrophenyl moiety in 1M HCl for C38 steel: Experimental and theoretical studies. Heliyon, 8, e09087.

  2. Ansari, A., Lakbaibi, Z., Znini, M., & Manssouri, M. (2021). Evaluation of corrosion inhibition and adsorption behavior of 7-Isopropyl-4-methyl-4,5,6,7-tetrahydrobenzoisoxazole against carbon steel corrosion in 1 M HCl: Experimental and computational investigations. Analytical and Bioanalytical Chemistry Research, 8(3), 233677.

  3. Manssouri, M., Znini, M., Lakbaibi, Z., Ansari, A., & El Ouadi, Y. (2021). Experimental and computational studies of perillaldehyde isolated from Ammodaucus leucotrichus essential oil as a green corrosion inhibitor for mild steel in 1.0 M HCl. Chemical Papers, 75, 4145–4162.

  4. Jaafar, A., Ben El Ayouchia, H., Lakbaibi, Z., Regti, A., Jaafar, N., Boussaoud, A., Benallou, A., & Jodeh, S. (2021). Fenton degradation of binary synthetic dyes mixture: Experimental and DFT studies. Research Journal of Chemistry and Environment, 25, 1–12.

  5. Regti, A., Lakbaibi, Z., Ben Elayouchia, H., El Haddad, M., Laamari, M. R., Jaafar, A., Elazhary, I., & El Himri, M. (2021). Optimization and computational approach to understand the adsorption behavior of alizarine red s on the surface of fish scales. Biointerface Research in Applied Chemistry, 11(6), 14918–14934.

Zouhair Lakbaibi’s research advances sustainable chemistry by developing eco-friendly corrosion inhibitors and efficient heavy-metal remediation methods, bridging theoretical modeling and experimental studies. His work supports industry, environmental protection, and global innovation by offering practical solutions for metal preservation and pollution control.