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207 results on '"AlSalman, Hussain"'

8. Simple linear response model for predicting energy band alignment of two-dimensional vertical heterostructure

Author

Azadani, Javad G., Lee, Seungjun, Kim, Hyeong-Ryul, Alsalman, Hussain, Kwon, Young-Kyun, Tersoff, Jerry, and Low, Tony

Subjects
Condensed Matter - Materials Science
Abstract

The Anderson and midgap models are often used in the study of semiconductor heterojunctions, but for van der Waals (vdW) vertical heterostructures they have shown only very limited success. Using the group-IV monochalcogenide vertical heterostructures as a prototypical system, we propose a linear response model and compare the effectiveness of these models in predicting density functional theory (DFT) band alignments, band types and bandgaps. We show that the DFT band alignment is best predicted by the linear response model, which falls in between the Anderson and midgap models. Our proposed model can be characterized by an interface dipole $\alpha\times(E_{m2}-E_{m1})$, where the linear response coefficient $\alpha$ = 0 and 1 corresponds to the Anderson and midgap model respectively, and $E_{m}$ is the midgap energy of the monolayer, which can be viewed as an effective electronegativity. For group-IV monochalcogenides, we show that $\alpha$ = 0.34 best captures the DFT band alignment of the vdW heterostructure, and we discuss the viability of the linear response model considering other effects such as strains and band hybridization, and conclude with an application of the model to predict experimental band alignments.

Published
2020
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9. Advanced neural network-based model for predicting court decisions on child custody.

Author

Abrar, Mohammad, Salam, Abdu, Ullah, Faizan, Nadeem, Muhammad, AlSalman, Hussain, Mukred, Muaadh, and Amin, Farhan

Subjects
LANGUAGE models, ARTIFICIAL neural networks, LEGAL professions, LEGAL judgments, LEGAL language
Abstract

Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is evident from literature that Natural language processing (NLP) and machine learning (ML) are widely used in the prediction of court rulings. In general, the court decisions comprise several pages and require a lot of space. In addition, extracting valuable information and predicting legal decisions task is difficult. Moreover, the legal system's complexity and massive litigation make this problem more serious. Thus to solve this issue, we propose a new neural network-based model for predicting court decisions on child custody. Our proposed model efficiently performs an efficient search from a massive court decisions database and accurately identifies specific ones that especially deal with copyright claims. More specially, our proposed model performs a careful analysis of court decisions, especially on child custody, and pinpoints the plaintiff's custody request, the court's ruling, and the pivotal arguments. The working mechanism of our proposed model is performed in two phases. In the first phase, the isolation of pertinent sentences within the court ruling encapsulates the essence of the proceedings performed. In the second phase, these documents were annotated independently by using two legal professionals. In this phase, NLP and transformer-based models were employed and thus processed 3,000 annotated court rulings. We have used a massive dataset for the training and refining of our proposed model. The novelty of the proposed model is the integration of bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (Bi_LSTM). The traditional methods are primarily based on support vector machines (SVM), and logistic regression. We have performed a comparison with the state-of-the-art model. The efficient results indicate that our proposed model efficiently navigates the complex terrain of legal language and court decision structures. The efficiency of the proposed model is measured in terms of the F1 score. The achieved results show that scores range from 0.66 to 0.93 and Kappa indices from 0.57 to 0.80 across the board. The performance is achieved at times surpassing the inter-annotator agreement, underscoring the model's adeptness at extracting and understanding nuanced legal concepts. The efficient results proved the potential of the proposed neural network model, particularly those based on transformers, to effectively discern and categorize key elements within legal texts, even amidst the intricacies of judicial language and the layered complexity of appellate rulings. [ABSTRACT FROM AUTHOR]

Published
2024
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11. A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis.

Author

AlSalman, Hussain, Alfakih, Taha, Al-Rakhami, Mabrook, Hassan, Mohammad Mehedi, and Alabrah, Amerah

Subjects
MACHINE learning, CONVOLUTIONAL neural networks, CANCER diagnosis, FEATURE selection, SUPPORT vector machines, DEEP learning
Abstract

Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a broad range of features from the Medical Image Analysis Society (MIAS) mammography dataset. Subsequently, a GA optimizes these features by selecting the most relevant and least redundant, aiming to overcome the typical pitfalls of high dimensionality. The selected features are then utilized to train several classifiers, including Linear and Polynomial Support Vector Machines (SVMs), K-Nearest Neighbors, Decision Trees, and Random Forests, enabling a robust evaluation of the method's effectiveness across varied learning algorithms. Our extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy, from 83.33% to 89.58%, underscoring the method's efficacy. By detailing these steps, we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Bandgap engineering of two-dimensional semiconductor materials

Author

Chaves, A., Azadani, J. G., Alsalman, Hussain, da Costa, D. R., Frisenda, R., Chaves, A. J., Song, Seung Hyun, Kim, Y. D., He, Daowei, Zhou, Jiadong, Castellanos-Gomez, A., Peeters, F. M., Liu, Zheng, Hinkle, C. L., Oh, Sang-Hyun, Ye, Peide D., Koester, Steven J., Lee, Young Hee, Avouris, Ph., Wang, Xinran, and Low, Tony

Published
2020
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27. An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal

Author

Ullah, Hadaate, primary, Bin Heyat, Md Belal, additional, AlSalman, Hussain, additional, Khan, Haider Mohammed, additional, Akhtar, Faijan, additional, Gumaei, Abdu, additional, Mehdi, Aaman, additional, Muaad, Abdullah Y., additional, Islam, Md Sajjatul, additional, Ali, Arif, additional, Bu, Yuxiang, additional, Khan, Dilpazir, additional, Pan, Taisong, additional, Gao, Min, additional, Lin, Yuan, additional, and Lai, Dakun, additional

Published
2022
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49. On Some Types of Covering-Based ℐ,T-Fuzzy Rough Sets and Their Applications

Author

Atef, Mohammed, Alcantud, José Carlos R., AlSalman, Hussain, and Gumaei, Abdu

Subjects
Article Subject, QA1-939, Mathematics
Abstract

The notions of the fuzzy β-minimal and maximal descriptions were established by Yang et al. (Yang and Hu, 2016 and 2019). Recently, Zhang et al. (Zhang et al. 2019) presented the fuzzy covering via ℐ,T-fuzzy rough set model (FCℐTFRS), and Jiang et al. (Jiang et al., in 2019) introduced the covering through variable precision ℐ,T-fuzzy rough sets (CVPℐTFRS). To generalize these models in (Jiang et al., 2019 and Zhang et al. 2019), that is, to improve the lower approximation and reduce the upper approximation, the present paper constructs eight novel models of an FCℐTFRS based on fuzzy β-minimal (maximal) descriptions. Characterizations of these models are discussed. Further, eight types of CVPℐTFRS are introduced, and we investigate the related properties. Relationships among these models are also proposed. Finally, we illustrate the above study with a numerical example that also describes its practical application.

Published
2021

50. An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data

Author

Fathi, Hanaa, AlSalman, Hussain, Gumaei, Abdu, Manhrawy, Ibrahim I. M., Hussien, Abdelazim, El-Kafrawy, Passent, Fathi, Hanaa, AlSalman, Hussain, Gumaei, Abdu, Manhrawy, Ibrahim I. M., Hussien, Abdelazim, and El-Kafrawy, Passent

Abstract

Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearsons correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV (cross-validation) to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity, F1-score, and AUC. The suggested strategy greatly decreases the number of genes required for classification, selects the most informative features, and increases classification accuracy, according to the results., Funding Agencies: King Saud University, Riyadh, Saudi Arabia [RSP-2021/244]

Published
2021
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