Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market
Authors : Waref Manaseer, Hossam Faris, Ruba Abu Khurma, Mohammad Saadeh, Antonio Mora, Pedro A. Castillo, Ibrahim Aljarah
Abstract : Bankruptcy is one of the most critical financial problems that reflects the company’s failure. From a machine learning perspective, the problem of bankruptcy prediction is considered a challenging one mainly because of the highly imbalanced distribution of the classes in the datasets. Therefore, developing an efficient prediction model that is able to detect the risky situation of a company is a challenging and complex task. To tackle this problem, in this paper, we propose a hybrid approach that combines the synthetic minority oversampling technique with ensemble methods.
Keywords : Financial distress, Prediction, Ensemble learning, Financial crisis
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Automatic test data generation for Java card applications using genetic algorithm
Authors : Waref Manaseer, Saher Manaseer, Mohammad Aref Alshraideh, Nabeel Abuhashish
Abstract : The main objective of software testing is to have the highest likelihood of finding the most faults with a minimum amount of time and effort. Genetic Algorithm (GA) has been successfully used by researchers in software testing to automatically generate test data. In this paper, a GA is applied using branch coverage criterion to generate the least possible set of test data to test JSC applications.
Keywords : Software Testing, Genetic Algorithm, Java Smart Card
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Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer
Authors : Waref Manaseer, Raneem Qaddoura, Mohammad A. M. Abushariah, Mohammad Aref Alshraideh
Abstract : Image segmentation is a popular technique that is used for extracting information from images, which has also gained a lot of interest lately due to its importance in different scientific fields such as the medical field. This paper proposes a novel image segmentation technique using Expectation-Maximization (EM) clustering algorithm and Grasshopper Optimizer Algorithm (GOA). The proposed technique and the concept of image segmentation are effectively applied on dental radiography datasets that are collected from 120 patients with an age between 6 to 60 years old. To validate the proposed technique, a comparison in terms of purity and entropy measures is conducted against K-means, X-means, EM, and Farthest First algorithms.
Keywords : Image segmentation, Expectation-Maximization algorithm, Grasshopper optimization algorithm, Dental radiography, Anatomical segmentation and classification
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A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
Authors : Waref Manaseer, Mohammad Aref Alshraideh, Omar S. Al-Kadi
Abstract : Deep learning has emerged as a new area of machine learning research. It is an approach that can learn features and hierarchical representation purely from data and has been successfully applied to several fields such as images, sounds, text and motion. The techniques developed from deep learning research have already been impacting the research on Natural Language Processing (NLP). Arabic diacritics are vital components of Arabic text that remove ambiguity from words and reinforce the meaning of the text. In this paper, a Deep Belief Network (DBN) is used as a diacritizer for Arabic text. DBN is an algorithm among deep learning that has recently proved to be very effective for a variety of machine learning problems. We evaluate the use of DBNs as classifiers in automatic Arabic text diacritization
Keywords : Deep learning; Deep Belief Network; Restricted Boltzmann Machine; Natural Languageprocessing; Arabic Diacritization
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A greedy particle swarm optimization (GPSO) algorithm for testing real-world smart card applications
Authors : Waref Manaseer, Hamzeh M. Allawi, Mohammad Aref Alshraideh
Abstract : Software testing continues to be regarded as a necessary and critical step in the software development life cycle. Among the multitudes of existing techniques, particle swarm optimization (PSO) algorithm, in particular, has shown superior merits for automatically generating software test cases for its easy implementation and for relying on fewer parameters that require tuning. Hence, several state-of-the-art PSO-based algorithms have been successfully used as a test data generator. On the other hand, greedy-based algorithms, which are commonly used to solve complex and multi-step combinatorial problems, are starting to gain momentum as a solution for the complexity problem of software testing
Keywords : Software testing, Java smart card, Particle swarm optimization algorithm, Greedy algorithm, Genetic algorithm
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Thermal and mechanical properties of cement based-composite phase change material of butyl stearate/isopropyl palmitate/expanded graphite for low temperature solar thermal applications
Authors : Waref Manaseer, Awni Alkhazaleh, Mohammad Ismail, Sahar Almashaqbeh, Mohammad M Farid
Abstract : The thermal storage composite produced in this research was fabricated by integrating a novel eutectic phase change material of 80% butyl stearate (BS) and 20% isopropyl palmitate (ISOP) into supporting material of expanded graphite (EG) without leakage. 10% of eutectic mixture (EM) formed in the form-stable composite EM_EG is incorporated into the cemented test room's south wall in a laboratory-scale. The mass ratio of EM_EG is characterized by leakage test using a filter paper. The EM of BS and ISOP is successfully absorbed into the porous structure of EG, according to scanning electron microscopy (SEM) results.
Keywords : Thermal energy storage, Eutectic mixture, Butyl stearate, Isopropyl palmitate, Expanded graphite, Cement
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