Abiodun Musa Aibinu

Last updated
Abiodun Musa Aibinu
Born (1973-01-04) January 4, 1973 (age 51)
Lagos, Nigeria
CitizenshipNigerian
Alma mater
  • The Polytechnic Ibadan
  • Obafemi Awolowo University
  • Blekinge Institute of Technology
  • International Islamic University Malaysia
Occupations
  • Academic
  • engineer
OfficeVice Chancellor of Summit University Offa
PredecessorH. O. B Oloyede

Abiodun Musa Aibinu (born 4 January 1973) is a Nigerian Professor of Mechatronics from the Federal University Of Technology, Minna. [1] He is currently the Vice Chancellor of Summit University, Offa [2] [3] [4]

Contents

Early life and education

Abiodun Musa Aibinu was born in Lagos, Nigeria but he is a native of Ibadan, Oyo State. [4] He earned his National Diploma in Electrical and Electronics Engineering in 1995 rom The Polytechnic of Ibadan. He bagged his Bachelor of Science degree from Obafemi Awolowo University. [5] He received his Master of Science in Engineering at Blekinge Tekniska Hogskola in 2006. He specializes in mechanics, robotics and automation engineering. He received his PhD in Engineering in Mechanic, robotic and automation from International Islamic University of Malaysia in 2010. [5]

Chieftaincy titles

On 31 July 2023, the Olubadan of Ibadan, Mahood Olalekan Balogun, conferred the chieftaincy title of Mogaji Aikulola-Aibinuomo family to Aibinu. The investiture took place at the Olubadan Palace, Itutaliba, Ibadan, and was attended by Olubadan-in-Council members and dignitaries. The title of Mogaji represents the headship of a large family and holds significant importance in Olubadan Chieftaincy Matters. [6]

Publications

Related Research Articles

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<span class="mw-page-title-main">Neural network (machine learning)</span> Computational model used in machine learning, based on connected, hierarchical functions

In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.

An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion activity or violation is typically either reported to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision. Some researchers consider self-supervised learning a form of unsupervised learning.

<span class="mw-page-title-main">Face detection</span> Identification of human faces in images

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

<span class="mw-page-title-main">Lane departure warning system</span> Mechanism designed to warn a driver when the vehicle begins to move out of its lane

In road-transport terminology, a lane departure warning system (LDWS) is a mechanism designed to warn the driver when the vehicle begins to move out of its lane on freeways and arterial roads. These systems are designed to minimize accidents by addressing the main causes of collisions: driver error, distractions and drowsiness. In 2009 the U.S. National Highway Traffic Safety Administration (NHTSA) began studying whether to mandate lane departure warning systems and frontal collision warning systems on automobiles.

In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.

<span class="mw-page-title-main">Long short-term memory</span> Artificial recurrent neural network architecture used in deep learning

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory". The name is made in analogy with long-term memory and short-term memory and their relationship, studied by cognitive psychologists since early 20th century.

<span class="mw-page-title-main">Object detection</span> Computer technology related to computer vision and image processing

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

<span class="mw-page-title-main">Traffic-sign recognition</span> Driver assistance system to recognize traffic signs

Traffic-sign recognition (TSR) is a technology by which a vehicle is able to recognize the traffic signs put on the road e.g. "speed limit" or "children" or "turn ahead". This is part of the features collectively called ADAS. The technology is being developed by a variety of automotive suppliers to improve the safety of vehicles. It uses image processing techniques to detect the traffic signs. The detection methods can be generally divided into color based, shape based and learning based methods.

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Navlab is a series of autonomous and semi-autonomous vehicles developed by teams from The Robotics Institute at the School of Computer Science, Carnegie Mellon University. Later models were produced under a new department created specifically for the research called "The Carnegie Mellon University Navigation Laboratory". Navlab 5 notably steered itself almost all the way from Pittsburgh to San Diego.

<span class="mw-page-title-main">Deep learning</span> Branch of machine learning

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<span class="mw-page-title-main">Teleophthalmology</span>

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Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

The following is a timeline of the history of the city of Ibadan, Oyo State, Nigeria.

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.

Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models to produce solutions to problems. Soft computing was coined in the late 20th century. During this period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic is a computational paradigm that entertains the uncertainties in data by using levels of truth rather than rigid 0s and 1s in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural processes such as evolution and natural selection.

<span class="mw-page-title-main">Dominican University Ibadan</span> Private university in Oyo State, Nigeria

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References

  1. ↑ "Abiodun Musa Aibinu". IEEE . Retrieved 2023-11-09.
  2. ↑ "FUT Minna Staff Website". staff.futminna.edu.ng. Retrieved 2023-10-31.
  3. ↑ "VC urges students to embrace growth mindset, active learning". The Guardian Nigeria News - Nigeria and World News. 2023-03-30. Retrieved 2023-11-09.
  4. 1 2 "Summit University, Offa, appoints new vice-chancellor". 24 March 2022.
  5. 1 2 "Profile: Prof. Musa Abiodun Aibinu, Summit University's New VC". The Muslim Voice. 2022-02-24. Retrieved 2023-10-28.
  6. ↑ Salawu, Saheed (2023-08-04). "Olubadan installs Summit University VC, Aibinu, as Mogaji of Aikulola-Aibinuomo family". Tribune Online. Retrieved 2023-10-29.
  7. ↑ "Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles". scholar.google.com. Retrieved 2023-10-27.
  8. ↑ "Smart Pipeline Monitoring System: A Review". scholar.google.com. Retrieved 2023-10-27.
  9. ↑ "Development of an Intelligent Road Anomaly Detection System for Autonomous Vehicles". scholar.google.com. Retrieved 2023-10-27.
  10. ↑ "Advances in visual simultaneous localisation and mapping techniques for autonomous vehicles: A review". scholar.google.com. Retrieved 2023-10-27.
  11. ↑ "Overview of Satellite Communications and its Applications in Telemedicine for the underserved in Nigeria: A case study". scholar.google.com. Retrieved 2023-10-27.
  12. ↑ "Telemedicine as a panacea to medical tourism in Africa: Exploiting communication satellite technologies November 2022". scholar.google.com. Retrieved 2023-10-27.
  13. ↑ "Artificial Intelligence-Based Plant Leaf Disease Identification: A Review". scholar.google.com. Retrieved 2023-10-27.
  14. ↑ "Automatic parking management system and parking fee collection based on number plate recognition". scholar.google.com. Retrieved 2023-10-27.
  15. ↑ "An optimized routing algorithm for vehicle ad-hoc networks". scholar.google.com. Retrieved 2023-10-27.
  16. ↑ "Vascular intersection detection in retina fundus images using a new hybrid approach". scholar.google.com. Retrieved 2023-10-27.
  17. ↑ "A novel clustering based genetic algorithm for route optimization". scholar.google.com. Retrieved 2023-10-27.
  18. ↑ "New road anomaly detection and characterization algorithm for autonomous vehicles". scholar.google.com. Retrieved 2023-10-27.
  19. ↑ Adade, Richard; Aibinu, Abiodun Musa; Ekumah, Bernard; Asaana, Jerry (2021). "Unmanned Aerial Vehicle (UAV) applications in coastal zone management—A review". Environmental Monitoring and Assessment. 193 (3): 154. Bibcode:2021EMnAs.193..154A. doi:10.1007/s10661-021-08949-8. PMID   33649893 . Retrieved 2023-10-27.
  20. ↑ "Comparative analysis of basic models and artificial neural network based model for path loss prediction". scholar.google.com. Retrieved 2023-10-27.
  21. ↑ "Image processing techniques for automated road defect detection: A survey". scholar.google.com. Retrieved 2023-10-27.
  22. ↑ "Breast cancer histopathology image classification with deep convolutional neural networks". scholar.google.com. Retrieved 2023-10-27.
  23. ↑ "A proposed Fish counting algorithm, using Digital Image Processing Techniques". scholar.google.com. Retrieved 2023-10-27.
  24. ↑ "Application of neural networks in early detection and diagnosis of Parkinson's disease". scholar.google.com. Retrieved 2023-10-27.
  25. ↑ "Automatic diagnosis of diabetic retinopathy using fundus images". scholar.google.com. Retrieved 2023-10-27.
  26. ↑ "Development of solar powered irrigation system". scholar.google.com. Retrieved 2023-10-27.
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