Muamar Almani Jasim -

Gender	MALE
Place of Work	Technical Engineering College/ Kirkuk
Position	Manager of IT Division
Qualification	Master
Speciality	AI, ML,DL, DB and IoT.
Email	muamar78@ntu.edu.iq
Phone	07707514147
Address	Alwasity, kirkuk, kirkuk, Iraq

Biography of Muamar Almani Jasim

# Biography of Muamar Almani Jasim

Muamar Almani Jasim is an accomplished technology professional with extensive academic credentials in computer science and information technology. He holds a Master's degree in Information Technology from South Ural State University in Russia, building upon his foundation of a Bachelor's degree in Computer Science from Alrafiden College University.

A dedicated technologist, Muamar specializes in artificial intelligence applications, with particular expertise in developing AI-powered applications and building machine learning and deep learning models. His work in the general AI space demonstrates his commitment to advancing technological solutions through intelligent systems.

Beyond his professional pursuits, Muamar maintains a balanced lifestyle with diverse personal interests. He is an avid sports enthusiast and enjoys outdoor activities, particularly hunting and walking. These pursuits reflect his appreciation for both intellectual challenges and physical engagement with the natural world.

Muamar's combination of advanced technical education, specialized expertise in artificial intelligence, and wholesome recreational interests shapes him as a well-rounded individual who bridges the worlds of cutting-edge technology and traditional pastimes.

Skills

Machine learning (75%)

Publications

Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations

Feb 4, 2025

Journal Kufa Journal of Engineerin

publisher Kufa Journal of Engineerin

DOI 10.30572/2018/KJE/160109

Issue Vol. 16 No. 1 (2025): January - 2025

Volume 16

The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings.

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Design and implementation of image based object recognition

Feb 1, 2020

Journal Periodicals of Engineering and Natural Science

publisher Periodicals of Engineering and Natural Science

DOI DOI: 10.21533/pen.v8i1.1114.g490

Issue ol. 8, No. 1, February 2020, pp.79-

Volume Vol. 8

The aim of this paper is to design and implement image based object recognition. This represents more of a challenge when speaking of advance object recognition systems. A practical example of this issue is the recognition of objects in images. This is a task that humans can perform very well, but convolutional neural network systems don't struggle to perform. AlexNet pre-trained model was used for the training the dataset because of it trouble-free architecture on very large scale dataset "Cifar-10" using R2019a Matlab. The dataset was split with the ratio of 70% for training and 30% for the testing part. This has prompted convolutional neural network to start experimenting with networks architectures as well as new algorithms to train them. This research paper presents an approach to train networks such as to improve their robustness to the recognition of object images on R2019a Matlab. This training strategy is then evaluated for designed AlexNet network architecture. The result of the study was that the training algorithm could improve robustness to different image recognition at the expense of an increase in performance for the performance of images of objects (i.e. Dog, Frog, Deer, Automobile, Airplane etc.) with high accuracy of recognition. When the advantages of different types of architectures were evaluated, it was found that accuracy of all object recognition were around 98% based on the image. It followed the findings from classical object recognition that feed-forward neural networks could perform as well their high accuracy of recognition.