Rihab Mâaloul Abid – Computer science – Best Researcher Award

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Rihab Mâaloul Abid - Computer science - Best Researcher Award

isima - Tunisia

AUTHOR PROFILE

SCOPUS

🔬 CURRENT POSITION

Rihab Mâaloul Abid is a prominent member of the LT2S (Laboratoire des Technologies des Systèmes Smart) at the Centre de Recherche en Numérique de Sfax, Technopôle. She also serves as a Maître Assistant at the Institut Supérieur d'Informatique et de Multimédia de Mahdia (ISIMa), where she contributes to both teaching and research in the field of computer science and multimedia.

🎓 ACADEMIC BACKGROUND

Dr. Mâaloul Abid earned her Doctorate in Engineering of Computer Systems in April 2018 from ENIS, Université de Sfax, Tunisia. Her doctoral research focused on energy-aware routing in carrier-grade Ethernet networks, under the supervision of Prof. Lamia Chaari Fourati at LT2S. She also holds a Master’s degree in Computer Science and Multimedia from the Institut Supérieur d'Informatique et de Multimédia de Sfax, where she conducted in-depth analysis of scheduling mechanisms for WiMAX networks, receiving a "Très bien" distinction.

💻 RESEARCH INTERESTS

Dr. Mâaloul Abid's research is primarily focused on innovative networking technologies and energy efficiency in communication systems. Her work on energy-aware routing and scheduling mechanisms for networks, including WiMAX, reflects her dedication to advancing the efficiency and sustainability of digital infrastructures. Her academic journey began with a Bachelor’s degree in Computer Science and Multimedia, where her project on automatic brain tissue segmentation for tumor detection laid the groundwork for her future research endeavors.

📚 TEACHING AND PEDAGOGICAL ACTIVITIES

As an educator, Dr. Mâaloul Abid has been actively involved in teaching a variety of courses at ISIMS. During the 2021-2022 academic year, she taught courses on network services, operational research, and programming workshops to students pursuing degrees in computer science. Her commitment to education extends beyond traditional lectures, as she is deeply involved in developing practical skills in her students, ensuring they are well-prepared for careers in technology and multimedia.

🌍 CONTRIBUTION TO NETWORK SERVICES

Throughout her career, Dr. Mâaloul Abid has made significant contributions to the study and development of network services. Her expertise in foundational and advanced network topics has been shared with her students, helping to shape the next generation of computer scientists. Her work in this area is recognized for its practical applications and relevance to the evolving needs of digital communication systems.

🔧 INNOVATION IN COMPUTER SCIENCE

Innovation is at the heart of Dr. Mâaloul Abid’s research. Her focus on energy-aware technologies and system optimization highlights her commitment to creating more efficient and sustainable computing environments. Her ongoing research in smart systems and her role in the LT2S laboratory position her as a key player in the field of computer science innovation.

🏅 ACADEMIC EXCELLENCE

Throughout her academic career, Dr. Mâaloul Abid has consistently demonstrated excellence, receiving honors and distinctions for her work. From her undergraduate studies to her doctoral research, her dedication to her field has been evident, earning her respect and recognition within the academic community.

Senbagavalli – Artificial Intelligence – Best Researcher Award

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Senbagavalli - Artificial Intelligence - Best Researcher Award

Alliance University - India

AUTHOR PROFILE

SCOPUS

EXPERT IN OPINION MINING AND FEATURE SELECTION

Senbagavalli's groundbreaking research in opinion mining of health data for cardiovascular disease diagnosis using an unsupervised feature selection algorithm spans five years. Her Ph.D. work is a testament to her dedication to leveraging data for medical advancements.

FACIAL RECOGNITION INNOVATOR

With a master's degree in engineering, Senbagavalli developed a face recognition system using Laplacian faces, showcasing her expertise in computer vision and pattern recognition. This project exemplified her ability to apply complex algorithms to practical applications within six months.

PIONEER IN UNICODE FILE SYSTEMS

During her undergraduate studies, Senbagavalli created a file system using the Unicode character set, a project completed in just six months. Her work in this area highlights her proficiency in software development and system design.

CREATOR OF GRAPHIC GAMING SYSTEMS

In her mini-project as an undergraduate, she developed a gaming system using graphics within three months. This early project laid the foundation for her interest in interactive and visual computing systems.

SEASONED ACADEMIC AND PROFESSOR

With 18 years and 7 months of teaching experience, Senbagavalli has held positions at prestigious institutions, including Alliance University and Kuppam Engineering College. Her extensive experience has made her a respected figure in the academic community.

VERSATILE SUBJECT EXPERT

Senbagavalli has taught a wide range of subjects to undergraduate, postgraduate, and Ph.D. students, including Data Modeling and Optimization, Object-Oriented Programming, and Software Engineering. Her comprehensive knowledge spans multiple domains of computer science.

ACTIVE RESEARCHER AND REVIEWER

An active member of various academic councils and editorial boards, Senbagavalli reviews for renowned publishers like Bentham Science and Elsevier. Her involvement in curriculum development, project evaluation, and seminar organization reflects her commitment to academic excellence and continuous learning.

NOTABLE PUBLICATION

Identification of Biomarker for Autism Spectrum Disorder Using EEG: A Review.
Authors: K. Lalli, M. Senbagavalli
Year: 2023
Conference: Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, pp. 45–50

Facemask Detection System Using CNN Model.
Authors: M. Senbagavalli, S. Debnath, R. Rajagopal, K. Ghildial
Year: 2023
Conference: International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

An Evaluation of Machine Learning Techniques for Detecting Banking Frauds.
Authors: R. Rajagopal, M. Senbagavalli, S. Debnath, K. Darshan, K.S. Varun Tejas
Year: 2023
Conference: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 359–365

Deep Learning Model for Flood Estimate and Relief Management System Using Hybrid Algorithm.
Authors: M. Senbagavalli, V. Sathiyamoorthi, S.K. Manju Bargavi, S. Shekarappa G., T. Jesudas
Year: 2023
Book: Artificial Intelligence and Machine Learning in Smart City Planning, pp. 29–44

An Effective Model for Predicting Agricultural Crop Yield on Remote Sensing Hyper-Spectral Images Using Adaptive Logistic Regression Classifier.
Authors: V. Sathiyamoorthi, P. Harshavardhanan, H. Azath, A.M. Viswa Bharathy, B.S. Chokkalingam
Year: 2022
Journal: Concurrency and Computation: Practice and Experience, 34(25), e7242

Everton – Artificial Intelligence – Best Researcher Award

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Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112