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Firas Haddad

Imam Abdulrahman Bin Faisal University

Author-ID : 2372538

Computer Science & IT Electrical & Electronics Engineering

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Robust features extraction for general fish classification Mutasem K. Alsmadi; Mohammed Tayfour; Raed A. Alkhasawneh; Usama Badawi; Ibrahim Almarashdeh; Firas Haddad
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

Bivariate modified hotelling’s T2 charts using bootstrap data Firas Haddad; Mutasem K. Alsmadi; Usama Badawi; Tamer Farag; Raed Alkhasawneh; Ibrahim Almarashdeh; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.

Applying the big bang-big crunch metaheuristic to large-sized operational problems Yousef K. Qawqzeh; Ghaith Jaradat; Ali Al-Yousef; Anmar Abu-Hamdah; Ibrahim Almarashdeh; Mutasem Alsmadi; Mohammed Tayfour; Khalid Shaker; Firas Haddad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.

Co-Authors Ali Al-Yousef Anmar Abu-Hamdah Ghaith Jaradat Ibrahim Almarashdeh Khalid Shaker Mohammed Tayfour Mutasem Alsmadi Mutasem K. Alsmadi Raed A. Alkhasawneh Raed Alkhasawneh Tamer Farag Usama Badawi Walaa Hassan Yousef K. Qawqzeh

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