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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Sample-based relationship for assisting diagnosis of pneumonia in medical care

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Multimedia Tools and Applications 2025年第1期84卷 267-288页

作者： Chen, Hongkang Lu, Huijuan Zhu, Wenjie Zhou, Ye Yao, Yudong Wang, Renfeng College of Information Engineering China Jiliang University Zhejiang Hangzhou China Drore Information and Technology Co. LTD Zhejiang Hangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China

Deep neural networks have succeeded in learning balanced and imbalanced data in the field of pneumonia diagnosis. However, both require separate model designs in their respective domains. The pneumonia recognition methods have poor transfer performance between the two types of image distribution data. This paper proposes BeFormer, which focuses on sample relationships. BeFormer introduces two stages, feature extraction and establishing sample relationships, to solve the problem. Specifically, in the feature extraction stage, a new backbone network called PANet based on Multi-Scale Frequency Domain Attention (FPSA) is proposed. In establishing sample relationships, Deformable Transformer is used in parallel to model the relationships between partial regions of interest among the same and different samples in the pneumonia dataset. These two modules are connected in series to form BeFormer. Without making any changes to the model, this paper conducted experiments using BeFormer on both balanced and imbalanced pneumonia datasets. As compared to traditional pneumonia recognition models, the proposed method achieved significant improvements in the two different data distributions, demonstrating outstanding transfer performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features

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International Journal of Speech technology 2024年第4期27卷 1055-1083页

作者： Khanom, Fahmida Biswas, Shuvo Uddin, Mohammad Shorif Mostafiz, Rafid Department of Mathematics American International University – Bangladesh 408/1 Kuratoli Dhaka Khilkhet1229 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh

Parkinson's disease (PD) is a progressive neurological disorder that gradually worsens over time, making early diagnosis difficult. Traditionally, diagnosis relies on a neurologist's detailed assessment of the patient's medical history and multiple scans. Recently, artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems have demonstrated superior performance by capturing complex, nonlinear patterns in clinical data. However, the opaque nature of many AI models, often referred to as "black box" systems, has raised concerns about their transparency, resulting in hesitation among clinicians to trust their outputs. To address this challenge, we propose an explainable ensemble machine learning framework, XEMLPD, designed to provide both global and local interpretability in PD diagnosis while maintaining high predictive accuracy. Our study utilized two clinical datasets, carefully curated and optimized through a two-step data preprocessing technique that handled outliers and ensured data balance, thereby reducing bias. Several ensemble machine learning (EML) models—boosting, bagging, stacking, and voting—were evaluated, with optimized features selected using techniques such as SelectedKBest, mRMR, PCA, and LDA. Among these, the stacking model combined with LDA feature optimization consistently delivered the highest accuracy. To ensure transparency, we integrated explainable AI methods—SHapley Adaptive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME)—into the stacking model. These methods were applied post-evaluation, ensuring that each prediction is accompanied by a detailed explanation. By offering both global and local interpretability, the XEMLPD framework provides clear insights into the decision-making process of the model. This transparency aids clinicians in developing better treatment strategies and enhances the overall prognosis for PD patients. Additionally, our framework serves as a valuable tool for clinical data

关键词： CAD systems Ensemble machine learning Explainable AI Feature optimization Parkinson's disease

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Frequency disentangled residual network

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Multimedia Systems 2024年第1期30卷 1-13页

作者： Singh, Satya Rajendra Yedla, Roshan Reddy Dubey, Shiv Ram Sanodiya, Rakesh Kumar Chu, Wei-Ta Computer Science and Engineering Group Indian Institute of Information Technology Andhra Pradesh Sri India Stony Brook University Stony BrookNY United States Computer Vision and Biometrics Lab Indian Institute of Information Technology Uttar Pradesh Allahabad India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Residual networks (ResNets) have been utilized for various computer vision and image processing applications. The residual connection improves the training of the network with better gradient flow. A residual block consists of a few convolutional layers having trainable parameters, which leads to overfitting. Moreover, the present residual networks are not able to utilize the high- and low-frequency information suitably, which also challenges the generalization capability of the network. In this paper, a frequency-disentangled residual network (FDResNet) is proposed to tackle these issues. Specifically, FDResNet includes separate connections in the residual block for low- and high-frequency components, respectively. Basically, the proposed model disentangles the low- and high-frequency components to increase the generalization ability. Moreover, the computation of low- and high-frequency components using fixed filters further avoids the overfitting. The proposed model is tested on benchmark CIFAR-10/100, Caltech, and TinyImageNet datasets for image classification. The performance of the proposed model is also tested in the image retrieval framework. It is noticed that the proposed model outperforms its counterpart residual model. The effect of kernel size and standard deviation is also evaluated. The impact of the frequency disentangling is also analyzed using a saliency map. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image classification

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

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computer Systems science & engineering 2023年第5期45卷 1655-1677页

作者： G.Nallasivan V.Ramachandran Roobaea Alroobaea Jasem Almotiri Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.

关键词： Deep learning liver tumor CNN CT accuracy

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Real-time load balancing and dynamic profile management in mobile data networks

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International Journal of Vehicle information and Communication Systems 2023年第1-2期8卷 33-49页

作者： Dureja, Ajay Dureja, Aman Suman Pahwa, Payal Department of Information Technology Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology Sector-17 Rohini Delhi India Department of Computer Science & Engineering Deenbandhu Chhotu Ram University of Science and Technology Haryana Murthal India Department of Computer Science & Engineering Bhagwan Parshuram Institute of Technology Sector-17 Rohini Delhi India

As mobile data grows rapidly, carriers must intelligently manage network traffic. Due to economic realities and the physical limitations of the available spectrum, operators cannot add more network capacity. To satisfy investors, operators must maximise their service returns. The more services a subscriber receives, which costs more, the higher their expectations of acceptable network availability and quality. So it becomes very challenging for operators to provide QoS to their high-value subscribers and manage network traffic dynamically without impacting the user experience of their potential and key users. In this paper, all literature surveys are carried out on different schemes and solutions to manage mobile data networks’ profiles. This paper aims to identify an effective way of managing real-time data traffic by adjusting QoS/profile parameters dynamically so that high-value subscribers get better QoS during congestion than regular users. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Quality of service

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Many-Objective Feedback Evolutionary Algorithm for Optimizing the Software Test Suite

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SN computer science 2025年第1期6卷 1-18页

作者： Jaiswal, Updesh Kumar Prajapati, Amarjeet Department of CSE & IT Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

In automated software testing;optimization of test suites is an important and challenging problem. In the literature of test suite optimization, various single-objective and multi-objective optimization approaches have been proposed by researchers and practitioners. Even though the multi-objective optimization approaches have demonstrated effective performance in test case optimization, their performance tends to degrade when the number of objectives exceeds by more than or equal to three. To tackle this issue, recently few approaches have been proposed. Even though these approaches perform well, there is still scope for improvement. In this work, we introduce a Many-objective Differential Evolution Feedback Evolutionary Algorithm (MDEFEA) for the test case optimization problem. In this approach, we exploit the Differential Evolution (DE) algorithm and information Feedback Model (IFM) concept to optimize line coverage (LC), branch coverage (BC), mutation coverage (MC), and multi-criteria coverage (MCC) simultaneously. To assess the performance of the proposed MDEFEA, we evaluated it across 41 complex projects and compared its results with other similar optimization techniques, including NSGA-III, MOPSO, LSMaOA, and MOEA. The findings of the proposed and existing approaches indicate that MDEFEA outperforms the existing approaches in terms of the Mean Coverage Value (MCV) and Worst Coverage Value (WCV) success rates. Overall, the results demonstrate that the proposed MDEFEA is an effective method for test case optimization containing more than three criteria. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated software testing Evolutionary algorithm information feedback model Many-objective optimization Pareto front Software testing coverage Test suite optimization

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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