Abdul Wahid | Computer Science | Research Excellence Award

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Dr. Abdul Wahid | Computer Science | Research Excellence Award

IIIT Dharwad, Karnataka | India

Dr. Abdul Wahid is an Assistant Professor at the Indian Institute of Information Technology Dharwad, specializing in Data Science, Machine Learning, Artificial Intelligence, and Multi-agent Systems. He earned his Ph.D. from the Indian Institute of Technology (ISM) Dhanbad and has held postdoctoral and research positions across leading European institutions, including the University of Galway and TΓ©lΓ©com Paris. Dr. Wahid has authored over 15 peer-reviewed publications in high-impact journals and conferences, alongside a European patent in AI-based fraud detection. His research emphasizes intelligent systems for sustainable energy, agriculture, and cybersecurity, with notable international collaborations. Recognized with the IEEE Best Student Paper Award, he actively contributes as an organizer, editor, and speaker. His work advances real-world AI applications, promoting sustainable development and secure digital ecosystems globally.

Citation Metrics (Scopus)

300
200
100
50

Citations
287

Documents
32

h-index
9

🟦 Citations Β Β  πŸŸ₯ Documents Β Β  🟩 h-index


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Featured Publications

An Outlier Detection Algorithm based on KNN-kernel Density Estimation
– IJCNN 2020, July 2020 Β· Conference Paper

NaNOD: A natural neighbour-based outlier detection algorithm
– Neural Computing and Applications, June 2020 Β· Journal Article

ODRA: an outlier detection algorithm based on relevant attribute analysis method
– Cluster Computing, June 2020 Β· Journal Article

Khawaja Iftekhar Rashid | Computer Science | Research Excellence Award

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Dr. Khawaja Iftekhar Rashid | Computer Science | Research Excellence Award

Xiamen University | China

Dr. Khawaja Iftekhar Rashid is an emerging researcher in artificial intelligence, machine learning, and computer vision, with a strong specialization in semantic image segmentation for urban scenes, autonomous driving, and medical imaging. His work focuses on advanced deep learning models, including attention mechanisms, GANs, vision transformers, graph neural networks, and semi-/few-shot learning frameworks. He has published in high-impact, peer-reviewed journals such as Neurocomputing, Engineering Applications of Artificial Intelligence, and Expert Systems with Applications, reflecting both theoretical innovation and real-world applicability. His research profile demonstrates growing scholarly impact with 46 citations, an h-index of 5, and an i10-index of 1.

Citation Metrics (Scopus)

50

40

30

20

10

0

Citations 45

Documents 8

h-index
5

Citations
Documents
h-index


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Featured Publications

Rebwar Khalid | Artificial Intelligence | Editorial Board Member

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Dr. Rebwar Khalid | Artificial Intelligence | Editorial Board Member

Erbil Polytechnic University | Iraq

Dr. Rebwar Khalid Hamad is an emerging researcher in artificial intelligence with a strong focus on nature-inspired algorithms, metaheuristics, and data-driven optimization systems. His work advances cutting-edge computational models such as the Krill Herd, FOX, Gravitational Search, and GOOSE algorithms, contributing significantly to optimization theory and its real-world engineering and healthcare applications. He has developed impactful frameworks for intelligent problem-solving, integrated AI-based search techniques, and enhanced algorithmic performance through systematic reviews and novel implementations. His publications in high-impact journals highlight his ability to bridge theoretical AI mechanisms with advanced data management and practical optimization challenges. Beyond research, he contributes to academic development through teaching, student supervision, and the design of data management systems. His scholarly portfolio demonstrates strong analytical capabilities, innovation in metaheuristic modeling, and a commitment to advancing the fields of artificial intelligence, data science, and computational optimization.

Profile:Β  Google Scholar

Featured Publications

Hamad, R. K., & Rashid, T. A. (2023). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond. Evolving Systems.

Hamad, R. K., & Rashid, T. A. (2023). Current studies and applications of Krill Herd and Gravitational Search Algorithms in healthcare. Artificial Intelligence Review, 56(Suppl 1), 1243–1277.

Hamad, R. K., & Rashid, T. A. (2023). A systematic study of Krill Herd and FOX algorithms. In Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB) (pp. 168–186).

Hamad, R. K., & Rashid, T. A. (2025). A systematic study of GOOSE algorithms. In Multi-objective Optimization Techniques: Variants, Hybrids, Improvements, and Applications.

Hamad, R. K. (2024). GOOSE algorithm: A powerful optimization tool for real-world engineering challenges and beyond [Computer software]. GitHub.

Kachi Anvesh | Machine Learning | Best Researcher Award

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Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

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Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Alper Mitincik | Artificial Intelligence | Best Researcher Award

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Mr. Alper Mitincik | Artificial Intelligence | Best Researcher Award

Galatasaray University | Turkey

Mr. Alper Mitincik is an accomplished software engineer and researcher with extensive expertise in Java, Python, SQL, and scalable data-driven applications. He has led significant projects, including a national cloud storage system and one of the largest Turkish-language crawling-based search engines, demonstrating exceptional skills in search engine architecture, Elasticsearch optimization, ranking algorithms, and large-scale data pipelines. Alper has published research on information retrieval and deep learning, notably β€œText-Based Image Retrieval System Using Semantic Visual Content for Re-Ranking” in Engineering Applications of Artificial Intelligence (2025), and his M.Sc. thesis focused on semantic search frameworks. Currently pursuing a Ph.D. in Computer Engineering, his research emphasizes advanced recommendation systems, transformers, and graph attention networks. With experience mentoring engineers, implementing best practices, and designing robust software architectures, Alper combines industrial impact with academic innovation. Recognized with awards such as Turkcell’s CXO Award and holding certifications in machine learning,

Profile: Google Scholar

Featured Publications

Parlak, Δ°. B., & MΔ±tΔ±ncΔ±k, A. (2022). Designing an information framework for semantic search. Avrupa Bilim ve Teknoloji Dergisi, 682–689.

Topcu, B., MΔ±tΔ±ncΔ±k, A., Erdem, M. G., & Yanikoglu, B. (2025). Text-based image retrieval system using semantic visual content for re-ranking. Engineering Applications of Artificial Intelligence, 160, 111770.

Lingxin Jin | Computer Science | Best Researcher Award

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Dr. Lingxin Jin | Computer Science | Best Researcher Award

Dr. Lingxin Jin, University of Electronic Science and Technology of China

Dr. Lingxin Jin, based in Chengdu, China, is a Ph.D. candidate in Software Engineering at the University of Electronic Science and Technology of China, where he also completed his Bachelor’s degree with a GPA of 3.8/4.0. His academic focus includes artificial intelligence, machine learning, network security, and software systems. Dr. Jin has gained international experience through an exchange program at the International Technological University in Silicon Valley and held internships involving front-end development and research on backdoor attacks against deep neural networks. His research contributions include publications in high-impact journals such as IEEE Transactions on Computers and the Journal of Circuits, Systems, and Computers, with additional submissions to ACM and IJCAI. Dr. Jin has worked on projects ranging from Linux shell simulations to public opinion analysis systems. He has received several scholarships and honors, including direct Ph.D. program recommendation, and is recognized for his promising research in AI security and adversarial attacks.

Publication Profile

Scopus

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πŸŽ“ Educational Background

Dr. Lingxin Jin pursued his academic journey in Software Engineering at the University of Electronic Science and Technology of China. He completed his Bachelor’s degree from September 2018 to June 2022, achieving an impressive GPA of 3.8/4.0. During his undergraduate studies, he built a strong foundation through comprehensive coursework in Software Engineering, Computer Networks, Operating Systems, and Artificial Intelligence. Driven by academic excellence, Dr. Jin was recommended for direct entry into the Ph.D. program, which he began in September 2022. Currently, he is a Ph.D. candidate in Software Engineering at the same university, maintaining a GPA of 3.71/4.0. His advanced studies focus on cutting-edge topics such as Information Security Fundamentals and Frontiers, Network Security Theory and Technology, Machine Learning Theory and Algorithms, and Statistical Machine Learning. This academic background highlights his commitment to research and innovation in secure intelligent systems and computational technologies.

πŸ’Ό Professional Experience

Dr. Lingxin Jin has gained diverse and valuable professional experience that complements his academic pursuits in software engineering and artificial intelligence. In July 2019, he participated in an exchange program at the International Technological University in Silicon Valley, where he engaged in programming robot motion manipulation using Raspberry Pi and Arduino, as well as composing songs using MATLABβ€”demonstrating his multidisciplinary creativity. From January to June 2021, Dr. Jin interned at Xi’an Deta Information Technology Co., where he focused on front-end development and contributed to building an opinion analysis system. This role honed his skills in UI/UX and real-time data interpretation. He later served as a Software Engineer Intern at Sichuan Meiliankai Science and Technology Co. from September 2021 to June 2022, where he conducted advanced research on backdoor attacks against deep neural networks. These experiences collectively reflect Dr. Jin’s technical versatility and growing expertise in cybersecurity and intelligent systems.

πŸ… Additional Experience and Awards

Dr. Lingxin Jin has consistently demonstrated academic excellence throughout his educational journey, earning multiple awards and recognitions. During his undergraduate studies from 2018 to 2022 at the University of Electronic Science and Technology of China, he was honored with first and second-class scholarships for outstanding academic performance. His dedication and scholarly achievements earned him a prestigious recommendation for direct admission into the Ph.D. program, a distinction reserved for top-performing students. As a postgraduate student from 2022 onward, Dr. Jin continued to excel, receiving scholarships for new students as well as second-class scholarships for academic distinction. These accolades not only highlight his strong academic capabilities but also reflect his commitment to advancing in the field of software engineering and artificial intelligence. Dr. Jin’s consistent recognition at both undergraduate and postgraduate levels underscores his potential as a future leader in cutting-edge technological research and innovation.

🧠 Research Focus

Dr. Lingxin Jin’s research primarily focuses on adversarial machine learning, with a particular emphasis on Trojan attacks and security vulnerabilities in deep neural networks (DNNs). His scholarly work explores the life-cycle threats faced by DNNs, covering both attack strategies and defensive countermeasures. His publication in ACM Computing Surveys titled “Trojan Attacks and Countermeasures on Deep Neural Networks from Life-Cycle Perspective” provides a comprehensive overview of attack surfaces throughout a model’s development and deployment phases. Additionally, his work in IEEE Transactions on Computers, “Highly Evasive Targeted Bit-Trojan on Deep Neural Networks”, introduces novel methods of crafting stealthy, highly targeted Trojans that evade standard detection techniques. Through these contributions, Dr. Jin is advancing the field of AI security, focusing on the resilience and trustworthiness of neural networks in critical applications. His research is vital for developing robust defense frameworks and ensuring safe deployment of AI systems in real-world scenarios.

Publication Top Notes

  • πŸ“„ 2024: β€œHighly Evasive Targeted Bit‑Trojan on Deep Neural Networks” (IEEE Trans. on Computers) – DOI:10.1109/TC.2024.3416705; introduces stealthy bit-level Trojans; cited 2 times

  • πŸ“„ 2023: β€œIterative Training Attack: A Black‑Box Adversarial Attack via Perturbation Generative Network” (J. of Circuits, Systems and Computers) – DOI:10.1142/S0218126623503140; black-box generative adversarial method;

  • πŸ“„ 2023: β€œA Survey of Trojan Attacks and Defense to Neural Networks” (under review at ACM Computing Surveys); comprehensive lifecycle review of Trojan threats

  • πŸ“„ 2024: β€œData Poisoning‑based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks” (submitted to IJCAI ’25); extends backdoor threats to spiking neural models

 

Arshad Muhammad | Machine Learning | Best Researcher Award

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Mr. Arshad Muhammad | Machine Learning | Best Researcher Award

Mr. Arshad Muhammad, Chongqing University, China

A goal-oriented and multi-skilled IT professional with extensive experience in managing IT infrastructure, software implementations, system administration, and research. Currently pursuing a PhD at Chongqing University, China, Mr. Arshad has previously worked as a Research Assistant and Lecturer at various institutions, including Muhammad Nawaz Sharif University and Chenab College. He holds multiple degrees in Computer Science and Information Technology. His research interests include machine learning, intrusion detection systems, and medical imaging. He has published in top journals, contributing to fields such as IoMT security and healthcare networks. πŸŒπŸ“Š

Publication Profile

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Professional & Educator πŸ’»πŸ“š

Mr. Arshad Muhammad is an experienced IT professional with a strong background in research, education, and system administration. Currently pursuing his PhD at Chongqing University, China, he has served as a Research Assistant, where he conducts literature reviews, designs research projects, and mentors undergraduates. He has also lectured at Muhammad Nawaz Sharif University and Chenab College, focusing on computer science and student development. Previously, as a Network Administrator at Al-Khair University, he managed IT infrastructure, system security, and student records. His expertise spans machine learning, data analysis, and education. πŸŒπŸ”

Academic Journey πŸŽ“πŸ’‘

Mr. Arshad Muhammad’s academic journey reflects his dedication to computer science and information technology. He began with a Secondary School Certificate in Science from the Board of Intermediate and Secondary Education, Multan. He continued his studies, earning a Higher Secondary School Certificate in Science. He then pursued a Bachelor’s degree in Computer Science from Islamia University Bahawalpur, followed by a Master’s in Computer Science (16 years) and a Master of Science in Information Technology (18 years) from Government College University Faisalabad. Currently, he is pursuing a PhD at Chongqing University, China, in the field of computer science and technology. πŸŒπŸ“š

Research Focus

Mr. Arshad Muhammad’s research primarily focuses on cybersecurity in healthcare networks and intrusion detection systems (IDS) for the Internet of Medical Things (IoMT) πŸ₯πŸ”’. His work includes developing deep reinforcement learning-based IDS to secure IoMT healthcare networks, as seen in his article “A Deep Reinforcement Learning-Based Robust Intrusion Detection System for Securing IoMT Healthcare Networks” published in Frontiers in Medicine πŸ”. He also explores anomaly detection using hybrid machine learning techniques, with a special emphasis on real-time human activity detection and smart systems like cattle management using IoT technologies πŸ„πŸ“‘. His contributions bridge machine learning, cybersecurity, and healthcare innovation. πŸŒπŸ’‘

Conclusion πŸ†

Mr. Arshad Muhammad stands out as a candidate for the Research for Best Researcher Award due to his strong academic background, significant research contributions, impressive publication record, and dedication to teaching and mentorship. His interdisciplinary expertise in machine learning, IoT, and healthcare security aligns well with the evolving demands of research in these fields. Moreover, his proactive involvement in projects and mentoring roles further solidifies his position as an impactful and influential researcher.

Publication Top Notes

  • A Deep Reinforcement Learning-Based Robust Intrusion Detection System for Securing IoMT Healthcare Networks – Frontiers in Medicine (2025) πŸ§ πŸ”’ | DOI: 10.3389/fmed.2025.1524286 πŸ“…

  • FOID: A Feature-Optimized Intrusion Detection System for Securing IoMT Healthcare Networks – 18th International Conference on Open Source Systems and Technologies (ICOSST) (2024) πŸ“ŠπŸ’» | DOI: 10.1109/icosst64562.2024.10871156 πŸ“…

  • RCLNet: An Effective Anomaly-Based Intrusion Detection System for Securing the Internet of Medical Things – Frontiers in Digital Health (2024) πŸ₯πŸ“‘ | DOI: 10.3389/fdgth.2024.1467241 πŸ“…

  • An E-Tag Based Smart Cattle Management and Diagnosis System – IEEE Xplore: 2023 IEEE 3rd International Conference on Computer Systems (ICCS) (2023) πŸ„πŸ“± | πŸ“…

  • Hybrid Machine Learning Techniques to Detect Real-Time Human Activity Using UCI Dataset – EAI Endorsed Transactions on Internet of Things (EAI.EU) (2021) πŸ§ πŸ“Š | πŸ“…

Mohammed Almulla | Artificial Intelligence | Best Researcher Award

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Prof. Mohammed Almulla | Artificial Intelligence | Best Researcher Award

Prof. Mohammed Almulla, Kuwait University, Kuwait

Prof. Mohammed Ali Almulla, a distinguished Kuwaiti computer scientist, serves as a Professor at Kuwait University. With a Ph.D. in Computer Science from McGill University, he has contributed extensively to academia, research, and administrative leadership. His expertise spans artificial intelligence, automated theorem proving, and IT consultancy. Prof. Almulla has held prominent roles, including Chairman of the Computer Science Department and Acting Vice President for Academic Support Services. Beyond academia, he has influenced national IT policies as an advisor. A dedicated educator and researcher, he actively supports academic development and technological innovation.

Publication Profile

Scopus

Google Scholar

πŸŽ“ Education

Prof. Mohammed Almulla earned his B.Sc., M.Sc., and Ph.D. in Computer Science from McGill University, Canada. His Ph.D. thesis, “Analysis of the Use of Semantic Trees in Automated Theorem Proving,” reflects his deep interest in artificial intelligence. His rigorous academic training equipped him with comprehensive expertise in programming, networking, and advanced computer science concepts. With a solid foundation in both theoretical and applied research, Prof. Almulla has contributed to academic growth and scientific discovery. His multilingual proficiency in Arabic and English further enhances his research collaborations and educational impact.

πŸ’Ό Experience

Prof. Mohammed Almulla has an illustrious career in academia and administration. Since 1995, he has progressed from Assistant Professor to Professor at Kuwait University. He served as Chairman of the Computer Science Department, securing ABET accreditation. His leadership extended to acting roles as Vice President for Academic Support Services and Planning. Prof. Almulla also contributed as an IT Consultant for Kuwait’s Council of Ministers and led the AI Policy Implementation Committee. With decades of service in education, administration, and national IT development, his expertise remains highly influential in Kuwait’s technological landscape.

πŸ† Awards and Honors

Prof. Mohammed Almulla has received numerous accolades for his academic and administrative contributions. Notably, he served as a member and coordinator of the Evaluation Committee for the H.H. Sheik Salem Al-Ali AlSabah Award for Informatics, earning recognition for his dedication to technological advancements. As a valued IT consultant and university leader, his work has significantly shaped Kuwait’s digital landscape. His participation in major university and national projects has further solidified his reputation as a pioneer in computer science and informatics.

πŸ”Ž Research Focus

Prof. Mohammed Almulla’s research interests include artificial intelligence, automated theorem proving, and decision support systems. His work explores the applications of semantic trees in AI-driven problem-solving. With a passion for advancing intelligent systems, he investigates areas like AI policy implementation and large-scale data analysis. His contributions as a reviewer for over 30 prestigious journals emphasize his influence in the field. Additionally, Prof. Almulla is committed to mentoring students and advancing AI technologies to address real-world challenges.

 

Publication Top Notes

  • πŸ“ The Effectiveness of the Project-Based Learning (PBL) Approach as a Way to Engage Students in Learning – 947 citations (2020)

  • 🌿 Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability – 104 citations (2023)

  • πŸŽ“ Constructivism Learning Theory: A Paradigm for Students’ Critical Thinking, Creativity, and Problem Solving to Affect Academic Performance in Higher Education – 94 citations (2023)

  • πŸ“– Investigating Teachers’ Perceptions of Their Own Practices to Improve Students’ Critical Thinking in Secondary Schools in Saudi Arabia – 56 citations (2018)

  • 🧠 Using Conceptual Mapping for Learning to Affect Students’ Motivation and Academic Achievement – 47 citations (2021)

  • 🏫 An Investigation of Teachers’ Perceptions of the Effects of Class Size on Teaching – 46 citations (2015)

  • πŸ“˜ The Efficacy of Employing Problem-Based Learning (PBL) Approach as a Method of Facilitating Students’ Achievement – 44 citations (2019)

  • πŸ’» Technology Acceptance Model (TAM) and E-Learning System Use for Education Sustainability – 38 citations (2021)

  • πŸ€– Investigating Influencing Factors of Learning Satisfaction in AI ChatGPT for Research: University Students Perspective – 24 citations (2024)

  • πŸ§‘β€πŸ« Using Digital Technologies for Testing Online Teaching Skills and Competencies During the COVID-19 Pandemic – 24 citations (2022)

  • πŸ§‘β€πŸ€β€πŸ§‘ An Investigation of Cooperative Learning in a Saudi High School: A Case Study on Teachers’ and Students’ Perceptions and Classroom Practices – 24 citations (2017)

  • πŸ— Investigating Important Elements That Affect Students’ Readiness for and Practical Use of Teaching Methods in Higher Education – 15 citations (2022)

  • πŸ“Š Developing a Validated Instrument to Measure Students’ Active Learning and Actual Use of Information and Communication Technologies for Learning in Saudi Arabia’s Higher Education – 8 citations (2022)

  • πŸ… An Investigation of Saudi Teachers’ Perceptions Towards Training in Cooperative Learning – 8 citations (2016)

  • 🌐 The Changing Educational Landscape for Sustainable Online Experiences: Implications of ChatGPT in Arab Students’ Learning Experience – 5 citations (2024)

  • πŸ“² Investigating Students’ Intention to Use M-Learning: The Mediating Role of Mobile Usefulness and Intention to Use – 5 citations (2024)

  • πŸ–₯ Using Digital Technologies for Testing Online Teaching Skills and Competencies During the COVID-19 Pandemic – 4 citations (2022)

  • πŸ‘« Students’ Perceptions of the Academic and Social Benefits of Working with Cooperative Learning – 3 citations (2016)

 

 

Abba Bashir | Machine Learning | Best Researcher Award

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Mr. Abba Bashir | Machine Learning | Best Researcher Award

Mr. Abba Bashir, Federal University Dutsin-ma, Nigeria

Abba Bashir is a civil engineer and academic dedicated to sustainable infrastructure and structural optimization. He is a lecturer at the Federal University Dutsin-ma (FUDMA), Katsina, Nigeria, specializing in structural engineering and artificial intelligence applications in construction. With over 100 citations and an h-index of 6, his research focuses on recyclability, fiber-reinforced concrete, and computational mechanics. He has authored a book on bamboo fiber-reinforced concrete and actively contributes to accreditation and curriculum development. As the AI Research Leader at FUDMA’s Faculty of Engineering, he integrates machine learning into structural design for sustainable and resilient infrastructures.

Publication Profile

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πŸŽ“ Education

Abba Bashir is currently pursuing a Master of Technology in Structural Engineering at Mewar University, India (2023–2025). He holds a Bachelor of Technology in Civil Engineering from Sharda University, India, graduating in 2017 with an 8.3/10 CGPA. His early education includes a Senior Secondary School Certificate from Nasara Academy, Kano, Nigeria (2007) and a Primary School Leaving Certificate from Maitasa Special Primary School, Kano, Nigeria (2001). His academic journey has equipped him with expertise in structural analysis, computational mechanics, and sustainable construction materials. His continuous pursuit of knowledge fuels his research in optimizing civil engineering designs through artificial intelligence and machine learning.

πŸ’Ό Experience

Abba Bashir has been a lecturer at Federal University Dutsin-ma (FUDMA) since 2020, teaching courses such as Structural Analysis, Concrete Design, and Construction Materials. He has supervised undergraduate research projects and actively contributes to curriculum development and accreditation at the university. As a practicing civil engineer since 2017, he has designed and constructed residential, commercial, and institutional structures, integrating AI-driven optimization techniques. He is a member of FUDMA’s Concrete and Steel Research Group and serves as the AI Research Leader. His expertise spans finite element modeling, numerical analysis, and sustainable building materials. He is proficient in ABAQUS, ANSYS, AutoCAD, MATLAB, and Python for structural simulations.

πŸ† Awards & Honors

Abba Bashir has been recognized for his contributions to structural engineering and AI-driven construction methodologies. He has received accolades for his research on bamboo fiber-reinforced concrete and his role in advancing sustainable materials. His academic leadership in AI applications within civil engineering has earned him university recognition. His book on bamboo fiber-reinforced concrete is a significant contribution to sustainable construction literature. As a mentor and research leader, he plays a crucial role in developing new undergraduate programs and fostering innovation in civil engineering education. His expertise in computational mechanics and recyclability research continues to influence the field.

πŸ”¬ Research Focus

Abba Bashir’s research integrates artificial intelligence, machine learning, and optimization algorithms into structural engineering. His work focuses on fiber-reinforced concrete, recyclability, and sustainability in construction materials. He has extensive experience in finite element modeling using ABAQUS and ANSYS, with a strong emphasis on computational mechanics. His studies explore mechanical properties and durability of cementitious materials with micro/nano reinforcements. He also investigates the optimization of structural designs to reduce environmental impact and enhance resilience. His multidisciplinary research combines AI, numerical modeling, and advanced construction materials to create sustainable and cost-effective infrastructure solutions.

 

Publication Top Notes

1️⃣ Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste | Cited by: 50 | πŸ“… 2022

2️⃣ An overview of streamflow prediction using random forest algorithm | Cited by: 19 | πŸ“… 2022 πŸŒŠπŸ€–

3️⃣ Analysis of Bamboo fibre reinforced beam | Cited by: 17 | πŸ“… 2018 πŸŽπŸ—οΈ

4️⃣ Antioxidant, hypolipidemic and angiotensin converting enzyme inhibitory effects of flavonoid-rich fraction of Hyphaene thebaica (Doum Palm) fruits on fat-fed obese Wistar rats | Cited by: 16 | πŸ“… 2019 πŸ₯πŸ§ͺ

5️⃣ Assessment of Water Quality Changes at Two Locations of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI) | Cited by: 15 | πŸ“… 2015 πŸš°πŸ“Š

6️⃣ High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm | Cited by: 14 | πŸ“… 2023 πŸ—οΈπŸ€–

7️⃣ Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques | Cited by: 8 | πŸ“… 2022 🌊🧠

8️⃣ Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates | Cited by: 5 | πŸ“… 2018 β™»οΈπŸ—οΈ

9️⃣ Machine Learning: A Way to Smart Environment | Cited by: 1 | πŸ“… 2021 πŸ€–πŸŒ±

πŸ”Ÿ A new strategy using intelligent hybrid learning for prediction of water binder ratio of concrete with rice husk ash as a supplementary cementitious material | πŸ“… 2025 πŸ—οΈπŸ“Š

 

 

 

 

 

Ravinesh Chand | Robotics | Best Researcher Award

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Mr. Ravinesh Chand | Robotics | Best Researcher Award

Lecturer at Fiji National University, Fiji

Mr. Ravinesh Chand is a dedicated Lecturer in Mathematics with over 21 years of teaching experience in Fiji’s academic and secondary education sectors. He has taught students from diverse backgrounds and is highly skilled in delivering engaging lessons, fostering an interactive learning environment, and conducting research in applied mathematics. Currently pursuing a PhD in Mathematics at the University of the South Pacific (USP), his expertise spans digital signature schemes, mathematical problem-solving, and curriculum development. He has held leadership roles as Head of Department at multiple institutions, contributing to academic excellence. Passionate about student success, he emphasizes innovative teaching methods and collaborative learning. As a researcher, he is committed to generating new knowledge in mathematics. His contributions extend beyond teaching, as he actively mentors students and supports extracurricular activities like soccer coaching. His work is marked by precision, analytical thinking, and a commitment to excellence.

Publication Profile

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Education πŸŽ“πŸ“š

Mr. Ravinesh Chand is pursuing a PhD in Mathematics at the University of the South Pacific (USP), focusing on advanced mathematical research. He holds a Master of Science in Mathematics from USP, where he completed a thesis on “Digital Signature Scheme Over Lattices” under the supervision of Dr. M.G.M. Khan and Dr. Maheswara Rao Valluri. He earned a Postgraduate Diploma in Applied Mathematics from USP in 2013, further strengthening his expertise in mathematical modeling and cryptographic systems. His academic foundation was laid with a Bachelor of Education in Mathematics from USP in 2001, equipping him with strong pedagogical skills and mathematical proficiency. Throughout his educational journey, he has demonstrated a commitment to academic excellence, research, and innovative problem-solving. His education has been instrumental in shaping his career as a lecturer and researcher, allowing him to contribute meaningfully to the field of applied mathematics and mathematical education.

Experience πŸ“–πŸ‘¨β€πŸ«

Mr. Ravinesh Chand has served as a Lecturer in Mathematics at Fiji National University since 2015. Previously, he held leadership roles as Head of the Mathematics and Physics Department at Dudley High School (2014-2015), Suva Grammar School (2013-2014), and Assemblies of God High School (2007-2013). His teaching career began at Waidina Secondary School (2002-2006) as an Acting Head of Department and Mathematics & Physics teacher. Throughout his career, he has led curriculum planning, student engagement, and performance evaluations while mentoring both students and teachers. As a leader, he ensured high-quality teaching standards, facilitated staff development, and implemented intervention strategies for student success. He has also promoted extracurricular activities, including coaching school soccer teams. His extensive experience in academia and secondary education showcases his expertise in mathematical instruction, research, and academic leadership, significantly impacting student learning and institutional development.

Awards and Honors πŸ†πŸŽ–οΈ

Mr. Ravinesh Chand has been recognized for his outstanding contributions to mathematics education and research. His commitment to academic excellence earned him accolades from Fiji’s leading educational institutions. As Head of Department, he received multiple commendations for his leadership in improving student performance in mathematics and physics. His research on digital signature schemes and applied mathematics has been acknowledged in academic circles, leading to invitations to present his work at conferences. His dedication to mentorship and student development has also been honored through appreciation awards from schools and tertiary institutions. In addition, his extracurricular involvement as a soccer coach has been recognized for fostering teamwork and discipline among students. His excellence in curriculum development, innovative teaching methods, and mathematical research continues to be celebrated, reinforcing his reputation as a distinguished educator and scholar in Fiji’s academic community.

Research Focus πŸ”¬πŸ“Š

Mr. Ravinesh Chand’s research primarily focuses on applied mathematics, digital signature schemes, cryptography, and mathematical modeling. His Master’s thesis explored “Digital Signature Scheme Over Lattices,” contributing to secure cryptographic protocols. His work involves analyzing the efficiency of mathematical structures in cybersecurity, aiming to enhance encryption techniques. His doctoral research further delves into computational mathematics and algorithmic problem-solving. His academic interests extend to mathematical pedagogy, exploring innovative methods to enhance student learning. His research integrates theoretical mathematics with practical applications, addressing real-world challenges in data security and computational modeling. Through independent studies and collaborative projects, he seeks to advance the understanding of mathematical frameworks in digital security. His contributions to applied mathematics continue to shape the academic discourse on cryptographic systems, making a lasting impact on both research and education in mathematics.

Publication Top Notes

  • Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider – πŸ“– Heliyon 9 (1), Cited by: 17, Year: 2023
  • Switch controllers of an n-link revolute manipulator with a prismatic end-effector for landmark navigation – πŸ€– PeerJ Computer Science 8, e885, Cited by: 16, Year: 2022
  • Navigation of an n-link revolute robotic arm via hierarchal landmarks – πŸ—ΊοΈ NILES Conference, Cited by: 10, Year: 2021
  • Lyapunov-based controllers of an n-link prismatic robot arm – πŸ—οΈ IEEE Asia-Pacific Conference, Cited by: 9, Year: 2021
  • A car-like mobile manipulator with an n-link prismatic arm – πŸš— IEEE Asia-Pacific Conference, Cited by: 9, Year: 2021
  • Embedded FPGA-based motion planning and control of a dual-arm car-like robot – πŸ”§ IEEE SPEC Conference, Cited by: 8, Year: 2022
  • LbCS navigation controllers of twining Lagrangian swarm individuals – 🐝 NILES Conference, Cited by: 8, Year: 2021
  • Digital signature scheme over lattices – πŸ” IEEE Circuits Conference, Cited by: 7, Year: 2021
  • Acceleration Feedback Controller Processor Design of a Segway – πŸ›΄ IEEE SPEC Conference, Cited by: 5, Year: 2022
  • Vertically Sliding Revolute Robotic Arm intended for automated Pick-and-Place Industrial applications – 🏭 IEEE Asia-Pacific Conference, Cited by: 3, Year: 2022