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Profit-based uncertainty estimation with application to credit scoring

European Journal of Operational Research 2025年第2期325卷 303-316页

作者： Xu, Yong Kou, Gang Ergu, Daji College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu610225 China College of Electronic and Information Southwest Minzu University Chengdu610225 China Key Laboratory of Electronic Information Engineering Southwest Minzu University Chengdu610225 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Xiangjiang Laboratory Changsha410205 China

Credit scoring is pivotal in financial risk management and has attracted significant research interest. While existing studies primarily concentrate on enhancing model predictive power and economic value, they often overlook the crucial aspect of predictive uncertainty, especially in the context of deep neural networks applied to credit scoring. This study addresses uncertainty estimation in credit scoring and evaluates three widely used uncertainty methods across various credit datasets. Additionally, guided by the maximum profit criterion, we propose two profit-based uncertainty metrics to assess profit uncertainties stemming from predictive uncertainty, specifically targeting class-dependent and instance-dependent cost scenarios. Subsequently, we develop a classification system with a rejection mechanism based on these metrics. Our approach aims to improve model profitability and reduce predictive uncertainty, specifically regarding model profit. Empirical results across several benchmark credit datasets indicate that our proposed framework outperforms existing methods in terms of increasing model profit in different credit-scoring scenarios. Furthermore, sensitivity analyses of varying cost parameter settings highlight the robustness of our framework. © 2025 The Authors

关键词： Decision support systems Uncertainty estimation Performance measurement Deep learning Credit scoring

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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An Efficient Soil Image Classification Framework Using Transfer Learning Models for Intelligent Geotechnical Applications 2nd

An Efficient Soil Image Classification Framework Using Trans...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Hemdan, Ezz El-Din Al-Atroush, M.E. Structure and Materials Research Lab Prince Sultan University Riyadh Saudi Arabia Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt Department of Engineering Management College of Engineering Prince Sultan University P.O. Box 66833 Riyadh Saudi Arabia

ISBN: (纸本)9789819787111

Recently, the application of deep learning approaches has significantly contributed to the success of soil investigations, particularly in the categorization of soil images. This, in turn, facilitates informed decision-making in geotechnical engineering projects. This paper introduces an efficient soil classification method utilizing transfer learning models based on Convolutional Neural Networks (CNNs) to categorize soil images. Various pre-trained networks, including Alexnet, Vgg16, Googlenet, Resnet50, Mobilenetv2, and Squeezenet, are employed in this study. The highest accuracy, reaching 90.32%, is achieved by Googlenet, while Alexnet follows closely with an accuracy of 89.24%. Conversely, Squeezenet yields the lowest accuracy at 79.56%. This proposed framework has the potential for integration into Internet of Things (IoT) systems, leading to the development of an innovative, portable IoT-based device for soil type recognition at remote geo-sites, catering to the needs of geotechnical engineers. The results obtained highlight that the Googlenet-based framework outperforms others, offering a superior performance in soil classification. This framework serves as a valuable tool for implementing an intelligent soil image classification system, supporting real-time decision-making in geotechnical engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Complex Singular Spectrum Analysis Leveraging Adaptive Taper Windows for Enhancing Mode Reconstruction From Multivariate Signals

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IEEE Signal Processing Letters 2025年 32卷 1820-1824页

作者： Gu, Jialiang Hung, Kevin Ling, Bingo Wing-Kuen Chow, Daniel Hung-Kay Zhou, Yang Hong Kong Metropolitan University Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Hong Kong Tsientang Institute for Advanced Study Center for the Integrated Circuits and Artificial Intelligence Hangzhou310024 China The Education University of Hong Kong Department of Health and Physical Education Faculty of Liberal Arts and Social Sciences Hong Kong Hong Kong Guangdong Ocean University School of Electronic and Information Engineering Zhanjiang524088 China

In this letter, a generic extension of complex singular spectrum analysis (CSSA), referred to as GC-SSA, is proposed to enhance mode reconstruction from multivariate signals. This is achieved by introducing adaptive taper windows for CSSA. Specifically, we formulate an optimization problem related to window design for specific multivariate signals, and then employ an iterative algorithm to optimize the coefficients of the taper windows. GC-SSA using optimized taper windows can decompose multivariate signals and perfectly reconstruct time-varying modes that have maximally concentrated energy. Numerical simulations were demonstrated to validate the effectiveness of the proposed method in mode reconstruction compared to other multivariate signal processing methods. © 1994-2012 IEEE.

关键词： Image reconstruction

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AI-enhanced cognitive behavioral therapy with novel ontological and pattern-based thought segmentation

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Vidanage, Kaneeka Maduranga, M.W.P. Department of Computer Science Faculty of Computing General Sir John Kotelawala Defense University Kandawala Road 10390 Dehiwala-Mount Lavinia Sri Lanka Department of Electrical and Electronic Engineering University of Sri Jayewardenepura Nugegoda 10500 Sri Lanka

Use of Artificial Intelligence (AI) has contributed to improving the quality standards of the healthcare solutions provided for the patients. Therefore, healthcare informatics has been recognized as a very demanding discipline which is on constant evolution. Thus, the utilization of AI advancements to uplift the treatment strategies are mainly confined to provide solutions linked with treating physical ailments. Application of AI to improve psychotherapeutic treatments are very naïve. However, psychological well-being is also a vital requirement ensuring the physical well-being of an individual. Cognitive Behavioral Therapy (CBT) is recognized as the world's most acceptable psychotherapeutic treatment tool. Consequently, with the involvement of the psychological consultants and referring the CBT manuals, specialized ontological structure has been orchestrated to assess the human “thoughts” as per the CBT guidelines. The designed ontological structure has been accompanied with a specialized knowledge elicitation algorithm designed to reason the knowledge constructs embedded in the ontology against the stakeholder`s input. Operational effectiveness of this entire pipeline has been assessed via both quantitative and qualitative measurements under the observation of the consultants. The entire prototype designed yielded an overall accuracy of 82%. This entire pipeline compiled can be considered as a novel contribution enriching the expansion of the application of AI into the psychotherapeutic domain as well. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： AI CBT Ontology Psychotherapy RegEX

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E-mode p-channel GaN/AlGaN HFETs with κ-Ga2O3 as gate oxide

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Optical and Quantum electronics 2025年第5期57卷 1-15页

作者： Ma, Wenchao Wang, Wei Wang, Jin Wang, Rui Zhi, Ting Parkhomenko, Irina N. Komarov, Fadei F. Chen, Dunjun Zhang, Rong Xue, Junjun Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty of Radiophysics and Computer Sciences Belarusian State University Minsk220045 Belarus A.N. Sevchenko Institute of Applied Physical Problems of Belarusian State University Minsk220045 Belarus Key Laboratory of Advanced Photonic and Electronic Materials School of Electronic Science and Engineering Nanjing University Nanjing210023 China

In this article, a κ-Ga2O3 cap layer is introduced as gate dielectric to achieve E-mode GaN p-channel heterostructure FETs (p-HFETs). Due to the high spontaneous polarization of κ-Ga2O3, 2DEG that up to 1.51 × 1014 cm− 2 are induced at the κ-Ga2O3/GaN interface. Based on device simulations, the threshold voltage (VTH) can reach a high value of -2.42 V and maintain negative even when the thickness of the GaN channel (tch) is increased to 50 nm. By interconnecting base and gate to form a double-gate (DG) structure, the control of 2DEG and 2DHG can be realized. The results show that p-HFETs with DG structure not only exhibit a threefold increase in the on-current (ION) while maintaining the E-mode operation, reaching 24.87 mA/mm with VTH of -1.25 V, but also reduce the gate leakage current at forward bias. Furthermore, the utilization of κ-Ga2O3 as the gate dielectric results in an enhancement of the gate breakdown voltage to -35.8 V. The proposed DG p-HFETs represent a promising approach to achieving high-performance enhancement mode p-channel GaN devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gallium nitride

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Personalized Governance Strategy for Patient Data in a Digital One Health Surveillance System

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Procedia computer Science 2025年 254卷 20-29页

作者： Edoghogho Olaye Imonikosaye Omafovbe Williams O. Aigbe Daniel Obuh Department of Computer Engineering Faculty of Engineering University of Benin Edo State Nigeria Fraunhofer-Institute for Electronic Nano Systems (ENAS) Chemnitz Germany.

The integration of personal devices in health surveillance has introduced significant patient data risks. In this research, we developed a Patients’ Personal Data Sovereignty System (PPDSS) to intelligently mask the patient's personal and sensitive data that should not be part of the data for analysis, ensuring these sensitive records do not find its way into machine learning models or to the Local Storage of the Capture Device. The approach presented in this paper is to ensure a high level of privacy and confidentiality for patients’ private health information (such as name, address, age and phone number) from the process of data collection, transmission, and storage to data analysis. The PPDSS is an android application built using new Flutter-based Cross Platform Technology which allows us to target other devices in future with same code base is designed to handle the data masking and elimination of Personal Identifiable Patients Data captured using the device camera before sending to the Machine Learning Models as texts. Records were captured from paper records using the camera on a smartphone installed with PPDSS. The image captured by PPDSS was obfuscated and then converted to text using AI-powered optical character recognition (OCR). The result is a personalized governance strategy of patient data, which ensures personal data privacy, confidentiality, and ethical use while maximizing the benefits of data-driven insights. The paper contributes to data governance by proposing a way of solving the problems at the point of data collection, rather than after the data have been collected.

关键词： digital sovereignty data privacy machine learning data obfuscation digital health surveillance healthcare records

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Forgery-Aware Adaptive Learning With Vision Transformer for Generalized Face Forgery Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4116-4129页

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China Laboratory School of Electrical and Electronic Engineering Jurong West 639798 Singapore Sun Yat-sen University Guangdong Key Laboratory of Information Security School of Computer Science and Engineering Guangzhou510006 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China China-Singapore International Joint Research Institute Guangzhou China

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. To this end, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm for generalized face forgery detection, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. © 1991-2012 IEEE.

关键词： Forgery Faces Adaptation models Transformers Feature extraction Adaptive learning computer vision Face recognition Deepfakes Visualization

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Fully Distributed Model-Free Adaptive Sliding Mode Control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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