Ammar Mansoor Mahdi Kamoona

Anomaly detection in video surveillance data is a challenging tasks due to the dynamic environment of the surveillance area. Multiple solutions have been proposed to tackle this problem ranging from using handcrafted features to end-to-end deep leaning methods. This paper proposes a model-based approach to anomaly detection for surveillance video data. This solution is based on sparsity estimation of pre-trained deep features. We aim to harvest the sparsity information of C3D deep features and use it as an additional information to differentiate the normal and anomalous event in a video. The proposed approach uses the naïve Bayes model within multiple instance learning framework. Experimental results shows that using sparsity information of C3D deep features could improve naive Bayes approach and enhance the accuracy of the decision boundary.

Low visibility hazard detection in construction sites is a crucial task for prevention of fatal accidents. Manual monitoring of construction workers to ensure they follow the safety rules (e.g., wear high-visibility vests) is a cumbersome task and practically infeasible in many applications. Therefore, an automated monitoring system is of both fundamental and practical interest. This paper proposes an intelligent solution that uses live camera images to detect workers who breach safety rules by not wearing high-visibility vests. The proposed solution is formulated in the form of an anomaly detection algorithm developed in the random finite set (RFS) framework. The proposed system is comprised of three steps: 1) applying a deep neural network to extract people in the image; 2) extracting particularly engineered features from each blob returned by the deep neural network; and 3) applying the RFS-based anomaly detection algorithm to each set of detected features. The experimental results demonstrate that in terms of F1-score, the proposed solution (as the combination of the newly engineered features and RFS-based anomaly detection algorithm) significantly outperforms various combinations of common and the state-of-the-art features and anomaly detection algorithms employed in machine vision applications.

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Accepted paper at IEEE CEC 2018. one of the highly reputed conferences in field of computational intellignece. IEEE WCCI 2018 will be held at the Windsor Convention Centre, Rio de Janeiro, Brazil. http://www.ecomp.poli.br/~wcci2018

Attended the 2017 Second Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (2nd-AIC-MITC’17) that was held in Baghdad. Publish a paper under title"A new hybrid cuckoo search algorithm for solving optimization problems". The paper will be available on IEEE explore.

Dear fourth year students, you can use the following youtube vedio link in regard how to use citation referencing manager namely, Mendeley Desktop https://www.youtube.com/watch?v=Re2Ysid5XIY you can download the software from https://www.mendeley.com/downloads

2017 Second Al-Sad1q International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITC) - 2017 IEEE sponsored (Conference Record Number#42987) paper title '' A New Hybird Cuckoo Search Algorithm for Solving Optimization Problems

Medical image enhancement has important role in computer assisted Diagnosis (CAD) system where it helps in diagnosis diseases faster and accurate. From that perspective, i would like to invite students to do a research under my supervision in this field. Please if you are interested send me an email

Receiving invitation to get a Golden Key Certificate from Golden Key international honor society/Swinburne university branch for more information about visit https://www.goldenkey.org