Article
Mathematics, Interdisciplinary Applications
Tooraj Karimi, Yalda Yahyazade
Summary: Risk management is a crucial aspect of project management that significantly impacts project success. This paper proposes an expert system to predict the risk level of new banking software development projects. By using rough set theory and grey system theory, the expert system accurately predicts project risks before initiation.
GREY SYSTEMS-THEORY AND APPLICATION
(2022)
Review
Computer Science, Artificial Intelligence
Wanting Ji, Yan Pang, Xiaoyun Jia, Zhongwei Wang, Feng Hou, Baoyan Song, Mingzhe Liu, Ruili Wang
Summary: Feature selection is a key method for data preprocessing in data mining tasks, aiming to select a feature subset based on evaluation criteria. Fuzzy rough set theory has been proven to be ideal for dealing with uncertain information in feature selection. This article provides a comprehensive review of fuzzy rough set theory and its applications, discussing challenges in feature selection methods.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Wenyan Song, Yue Zhu, Jianbo Zhou, Zhiyu Chen, Jiantao Zhou
Summary: This study proposes an integrated method for evaluating risks in public-private partnership projects, utilizing the strengths of GAHP, rough set theory, and cloud model theory. The approach effectively copes with vagueness and randomness in expert assessments, assisting managers in making reasonable and effective decisions in risk management.
Article
Computer Science, Information Systems
Sofian Kassaymeh, Salwani Abdullah, Mohammed Azmi Al-Betar, Mohammed Alweshah
Summary: This paper proposes a combination of the salp swarm algorithm and backpropagation neural network to solve the software fault prediction problem. The proposed method, SSA-BPNN, outperforms the conventional BPNN and state-of-the-art methods in terms of prediction accuracy on various datasets.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Construction & Building Technology
Muhammad Saiful Islam, Madhav Prasad Nepal, Martin Skitmore, Robin Drogemuller
Summary: This study introduces an integrated model combining fuzzy set theory and fuzzy Bayesian belief network for determining contingency costs in infrastructure projects. The model, demonstrated in real power plant projects in Bangladesh, shows promising results with a maximum error of 20% in predicting contingency costs. It has the potential to assist owners and contractors in setting realistic contingency costs and dynamically updating them during project execution.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Multidisciplinary Sciences
Mohit Arora, Sahil Verma, Kavita, Marcin Wozniak, Jana Shafi, Muhammad Fazal Ijaz
Summary: This paper investigates the application of ANFIS and EEBAT techniques for effort prediction in the Scrum environment. The proposed approach provides the best results in evaluation and is statistically validated.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Sciences
En Shi, Yanchen Shang, Yafeng Li, Miao Zhang
Summary: In this study, a self-adapting algorithm called BP-ANN was proposed to analyze cumulative risks to the water environment by combining tools from WWF, DEG, and USEPA. After optimization, the model had six hidden layers and showed a correlation coefficient with LM exceeding 80%. The findings suggest that the BP-ANN model is applicable for predicting cumulative risks and sensitive to factors such as the number of wastewater treatment facilities and treatment rate along the river.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Multidisciplinary Sciences
Lingzi Gu
Summary: This article introduces a Backpropagation-aided Neural Network for designing an Optimal Risk Prediction (ORP-BNN) model to pre-validate existing and new financial imbalances. The snowfall-like computing model in the risk prediction model assigns significant weights to fund allocation and restraining based on previous financial decision outcomes. The training process uses different structural modifications for successful risk mitigation.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Ghazaala Yasmin, Asit Kumar Das, Janmenjoy Nayak, S. Vimal, Soumi Dutta
Summary: Speech is a delicate medium for identifying the gender of speakers. Deep learning has provided a good research area to explore gender discrimination deficiencies in traditional machine learning techniques. The combination of automatically generated features and human-generated features can enhance gender recognition performance, especially when considering transgender individuals.
Article
Computer Science, Artificial Intelligence
Sofian Kassaymeh, Mohamad Al-Laham, Mohammed Azmi Al-Betar, Mohammed Alweshah, Salwani Abdullah, Sharif Naser Makhadmeh
Summary: Software Defect Estimation is a fundamental mechanism in software engineering, and utilizing a hybrid metaheuristic algorithm to optimize BPNN parameters can effectively reduce estimation errors and increase accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Priyanka Das, Asit Kumar Das, Janmenjoy Nayak, Danilo Pelusi, Weiping Ding
Summary: The proposed method integrates neural network and rough set theory for clustering crime reports by identifying named entities and selecting phrases to describe each report. The phrases are vectorized and clustered using a graph-based algorithm, with an adaptive resonance theory neural network used to generate clusters. This approach adapts to dynamic data environments and has been validated with various crime report datasets, demonstrating its effectiveness compared to other clustering algorithms.
Article
Engineering, Civil
Jieh-Haur Chen, Li-Ren Yang, Jui-Pin Wang, Shang- Lin, Jiun-Yao Cheng, Meng-Hsueh Lee, Chih-Lin Chen
Summary: This study explores the use of rough set theory to classify and weigh impact attributes for construction projects, developing a feasible model to assess total labor needed. After analyzing and weighting the attributes, a rough set enhanced artificial neural network yielded higher accuracy compared to a regular neural network.
CANADIAN JOURNAL OF CIVIL ENGINEERING
(2021)
Article
Mathematics, Applied
Fernando Chacon-Gomez, M. Eugenia Cornejo, Jesus Medina, Eloisa Ramirez-Poussa
Summary: The use of decision rules allows for reliable extraction of information and inference of conclusions from relational databases, but the concepts of decision algorithms need to be extended in fuzzy environments.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2024)
Article
Computer Science, Information Systems
Yetian Fan, Wenyu Yang
Summary: This paper proposes a BP algorithm with graph regularization (BPGR) to optimize the parameters and improve the generalization performance of BP neural networks. The proposed method enforces the latent features of the hidden layer to be more concentrated, enhancing the network's generalization capability. The modified graph regularization simplifies gradient calculation and better penalizes extreme weight values. Additionally, the graph regularization can be integrated with deep neural networks to further improve their generalization performance.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Tareq M. Al-shami, Wen Qing Fu, E. A. Abo-Tabl
Summary: This paper presents rough approximations based on topology, using 8 types of E-neighborhoods to construct approximations of any subset X of U, and studying properties and relationships between these approximations. It also provides some easy-to-understand examples and compares our approximations with those in published literature.