Article
Engineering, Electrical & Electronic
Subodh Kumar, Ankit Rajpal, Neeraj Kumar Sharma, Sheetal Rajpal, Anand Nayyar, Naveen Kumar
Summary: Coronary heart disease is a leading cause of mortality worldwide. This paper proposes a robust semi-blind watermarking scheme, ROSEmark, for authenticating ECG records transmitted through networked devices. Experimental results demonstrate that ROSEmark outperforms competitors in terms of fidelity and robustness, making it suitable for real-time applications such as telemedicine.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Hidangmayum Saxena Devi, Hitesh Mohapatra
Summary: This paper aims to develop a new, robust, and efficient blind watermarking system for medical images such as CT scan, X-ray, MRI, and Ultrasound Dicom images. The proposed scheme uses three binary watermark images to hide them in the cover image using a novel GWO-optimized hybrid DWT-DCT-SVD approach. The combination of DWT and DCT is used to determine the hiding positions, while SVD transforms the DCT matrix into a singular matrix and grey wolf optimization determines the gain value for inserting message bits. The proposed method outperforms existing systems in most image processing attacks, as observed from the experimental results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Yaru Yue, Chengdong Chen, Xiaoyuan Wu, Xiaoguang Zhou
Summary: This article proposes an effective method for denoising ECG signals, which combines the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) techniques. The ECG signal is decomposed using EEMD, and then the highest frequency component is decomposed using EMD for a second time, and the high frequency components obtained from the second decomposition are decomposed and reconstructed using WP for a third time. The processed signal components are then fused to obtain the denoised ECG signal. Various evaluation metrics such as signal-to-noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalized cross correlation coefficient (R) are used to assess the noise reduction algorithm.
IET SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Yuan-Min Li, Deyun Wei, Lina Zhang
Summary: This paper proposes a robust double-encrypted watermarking algorithm based on FRFT and DCT in the invariant wavelet domain, achieving high robustness against different attacks by utilizing RIDWT and cosine transform to obtain a hybrid domain. The algorithm enhances security by double-encrypting the watermark with the Arnold transform and FRFT before applying singular value decomposition. The optimal embedding factors are obtained using multiparameter particle swarm optimization to balance invisibility and robustness.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Hardev Singh Pal, Anil Kumar, Amit Vishwakarma, Girish Kumar Singh
Summary: This work presents a novel compression algorithm combining DCT and optimized TQWT for compression of 2D ECG signals. The algorithm improves compression by increasing sparsity in the transform domain. The quantization and optimization of the algorithm are achieved using the COOT bird optimization algorithm. The obtained results show the applicability of the proposed method in telemedicine or mobile health-based monitoring systems and memory devices.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Anurag Tiwari, Divyanshu Awasthi, Vinay Kumar Srivastava
Summary: Due to the growing use of the internet, copyright protection of images faces significant difficulties. To address this issue, a scheme that combines Zigzag scanning, Schur decomposition, Discrete cosine transform, and Redundant discrete wavelet transform is proposed. The scheme also applies Henon map for secure transfer and enhanced security. Experimental analysis is conducted using ultrasound liver images of different patients, and the scheme demonstrates satisfactory performance against various attacks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Juntao Ma, Jie Chen, Gang Wu
Summary: In this paper, a blind digital watermarking technique based on DWT-DCT-SVD and optimized by genetic algorithm is proposed, which achieves high robustness and imperceptibility. A transmitter and receiver system based on IEEE 802.11a and software-defined radio (SDR) is also designed, with several optimization methods employed to improve communication reliability and rate.
Article
Engineering, Biomedical
Kahlessenane Fares, Amine Khaldi, Kafi Redouane, Euschi Salah
Summary: In this work, two blind watermarking approaches are proposed for the protection of exchanged medical images in telemedicine. These approaches involve combinations of DCT and Schur decomposition, as well as DWT and Schur decomposition, resulting in high-quality watermarked images with strong resistance to conventional attacks. The experimental results demonstrate the effectiveness of these methods in safeguarding patient information and ensuring the confidentiality of personal data.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Water Resources
Miao He, Shao-fei Wu, Chuan-xiong Kang, Xian Xu, Xiao-feng Liu, Ming Tang, Bin-bin Huang
Summary: The development of sequence decomposition techniques has greatly facilitated the use of decomposition-based prediction models in hydrological forecasting. However, the current overall decomposition (OD) sampling technique used in these models has some limitations. This study proposes and evaluates novel sampling techniques and applies them to predict monthly runoff in Poyang Lake, China. The results show that the models developed using the improved sampling techniques have superior performance.
APPLIED WATER SCIENCE
(2022)
Article
Automation & Control Systems
Hassan Ashraf, Asim Waris, Syed Omer Gilani, Muhammad Umair Tariq, Hani Alquhayz
Summary: Empirical Mode Decomposition (EMD) is a signal decomposition technique for denoising nonlinear and nonstationary signals. Different thresholding techniques like Interval Thresholding (IT), Iterative Interval Thresholding (IIT), and Clear Iterative Interval Thresholding (CIIT), along with operators such as SOFT, HARD, and Smoothly Clipped Absolute Deviation (SCAD), have been explored for improving the performance of EMD-based EMG denoising techniques.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Khouloud Zouaidia, Mohamed Saber Rais, Salim Ghanemi
Summary: The objective of this study is to build a reliable model for the efficient prediction of wind speed and air temperature changes in the next 12 hours. A hybrid strategy was proposed, which combines the optimized variational mode decomposition (OVMD) algorithm with the discrete wavelet transform (DWT) technique for data preprocessing. The Adaptive-Multiplicative-LSTM (Adaptive-mLSTM) model is used to independently predict the denoised sub-sequences, and the Attention-based-Adaptive mLSTM model is applied for the final prediction.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Shahid A. Malik, Shabir A. Parah, Hanan Aljuaid, Bilal A. Malik
Summary: This research article presents a hybrid strategy that combines an adaptive iterative filtering method and a fast discrete lifting-based wavelet transform to remove power-line noise and baseline wander from an ECG signal. The proposed strategy improves the signal quality and preserves the vital components accurately. Its efficacy is established through empirical calculations and comparisons with existing methods using the MIT-BIH arrhythmia database.
Article
Computer Science, Information Systems
Divyanshu Awasthi, Vinay Kumar Srivastava
Summary: In this study, a dual image watermarking algorithm is proposed to protect data against copyright violations. The algorithm utilizes various image processing techniques and optimizes the scaling factor using firefly optimization technique. The algorithm demonstrates high robustness and imperceptibility according to the evaluation results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Abdulhakeem O. Mohammed, Haval I. Hussein, Ramadhan J. Mstafa, Adnan M. Abdulazeez
Summary: With the rise of the Internet of Things and smart devices, it has become easier to access and distribute digital data. However, these advancements have also made it easier for intruders to violate copyright protection, commit identity theft, and compromise privacy. To address these issues, image watermarking, specifically a blind watermarking approach based on joint Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) for RGB color images, is proposed in this paper. The approach demonstrates enhanced watermark invisibility and robustness, meeting the main requirements of image watermarking.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics
H. K. Nigam, H. M. Srivastava
Summary: This paper investigates the use of nonlinear diffusion for denoising audio signals by applying it to wavelet coefficients obtained from different filters. The results show a significant improvement in denoising compared to wavelet shrinkage.
Article
Computer Science, Artificial Intelligence
Veena Mayya, Sowmya S. Kamath, Uma Kulkarni, Divyalakshmi Kaiyoor Surya, U. Rajendra Acharya
Summary: Chronic ocular diseases (COD) can lead to severe vision impairment or blindness. Early detection of COD is crucial to prevent vision impairment, and convolutional neural networks (CNNs) combined with preprocessing techniques have shown promise in accurately detecting COD in eye fundus images.
APPLIED INTELLIGENCE
(2023)
Article
Neurosciences
Sengul Dogan, Mehmet Baygin, Burak Tasci, Hui Wen Loh, Prabal D. Barua, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: An automated Alzheimer's disease (AD) detection model using EEG signals and a novel directed graph for feature extraction achieved high accuracy in classifying AD patients and healthy controls, paving the way for further development of brain connectome-inspired models.
COGNITIVE NEURODYNAMICS
(2023)
Article
Health Policy & Services
Emrah Aydemir, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Prabal Datta Barua, Subrata Chakraborty, Oliver Faust, N. Arunkumar, Feyzi Kaysi, U. Rajendra Acharya
Summary: Accurate classification of mental performance is crucial in brain-computer interfaces. This study presents an artificial intelligence model to quantify the clarity of thought during mental arithmetic tasks, achieving high accuracy in classification. The results suggest that it is possible to determine mental performance using artificial intelligence.
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
(2023)
Article
Biology
Ali Abbasian Ardakani, Afshin Mohammadi, Mohammad Mirza-Aghazadeh-Attari, U. Rajendra Acharya
Summary: Breast cancer is a major global health burden and early detection is crucial for better clinical outcomes. Ultrasonography plays a vital role in managing breast lesions and the development of computer-aided diagnosis systems enhances its importance. In order to develop reliable CAD systems, diverse data from different populations and centers are needed to consider variations in breast cancer pathology. The current database includes ultrasound images and radiologist-defined masks of histologically confirmed benign and malignant lesions, which can aid in the development of robust CAD systems.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Multidisciplinary Sciences
Hosseinali Khalili, Maziyar Rismani, Mohammad Ali Nematollahi, Mohammad Sadegh Masoudi, Arefeh Asadollahi, Reza Taheri, Hossein Pourmontaseri, Adib Valibeygi, Mohamad Roshanzamir, Roohallah Alizadehsani, Amin Niakan, Aref Andishgar, Sheikh Mohammed Shariful Islam, U. Rajendra Acharya
Summary: Predicting treatment outcomes in traumatic brain injury (TBI) patients using machine learning algorithms was investigated in this study. Demographic features, laboratory data, imaging indices, and clinical features were evaluated using data from 3347 TBI patients in Iran. The motor component of the Glasgow coma scale, pupils condition, and cisterns condition were found to be reliable features for predicting in-hospital mortality, while age replaced cisterns condition for long-term survival prediction. The random forest algorithm was the best model for short-term mortality prediction, while the generalized linear model algorithm showed the best performance in predicting long-term survival.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Electrical & Electronic
Kenan Erdem, Mehmet Ali Kobat, Mehmet Nail Bilen, Yunus Balik, Sevim Alkan, Feyzanur Cavlak, Ahmet Kursad Poyraz, Prabal Datta Barua, Ilknur Tuncer, Sengul Dogan, Mehmet Baygin, Mehmet Erten, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: COVID-19, COPD, HF, and pneumonia can cause acute respiratory deterioration, and timely and accurate diagnosis is crucial. To address the issue of time consumption and bias in diagnosis, a computationally efficient deep feature engineering model, named Hybrid-Patch-Alex, was developed for automated diagnosis of COVID-19, COPD, and HF. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets using kNN and SVM classifiers.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Biophysics
Smith K. Khare, Varun Bajaj, U. Rajendra Acharya
Summary: This study presents the SchizoNET model, which combines the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN) for the automatic detection of schizophrenia. The model captures the instantaneous information of EEG signals in the time-frequency domain using MH-TFD and converts the time-frequency amplitude into two-dimensional plots for input to the CNN model, achieving accurate detection of schizophrenia.
PHYSIOLOGICAL MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Arif Metehan Yildiz, Prabal D. Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Chui Ping Ooi, Hamido Fujita, U. Rajendra Acharya
Summary: Physical violence detection using multimedia data is important for public safety and security, and research in video-based violence detection has grown rapidly in recent years. However, verbal aggression detection technologies are still limited, leading researchers to prefer computer vision models for violence detection. To address this gap, a new automatic audio violence detection model is proposed, achieving a classification accuracy of over 89% using kNN and SVM classifiers with the proposed TreePat23 model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Nursena Baygin, Emrah Aydemir, Prabal D. Barua, Mehmet Baygin, Sengul Doganm, Turker Tuncer, Ru-San Tann, U. Rajendra Acharya
Summary: We developed a machine learning model to quantify mental performance in mental arithmetic tasks, which accurately discriminated between bad counters and good counters. Our model used a novel multilevel feature extraction method and a distance-based pooling function for signal decomposition, along with feature selection and result classification algorithms. The model achieved high classification accuracies in both cross-validations, showing its effectiveness in mental arithmetic tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Taotao Lai, Yizhang Liu, Jie Chang, Lifang Wei, Zuoyong Li, Hamido Fujita
Summary: In this paper, an efficient guided sampling algorithm is proposed for multi-structure data using neighborhood consensus and residual sorting. The algorithm combines the benefits of neighborhood consensus and residual sorting to select seed data points and sample the rest of the data. Experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art sampling algorithms in three vision tasks.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Medicine, General & Internal
Aditya Tripathi, Preetham Kumar, Akshat Tulsani, Pavithra Kodiyalbail Chakrapani, Geetha Maiya, Sulatha V. Bhandary, Veena Mayya, Sameena Pathan, Raghavendra Achar, U. Rajendra Acharya
Summary: Diabetic Macular Edema (DME) is a severe ocular complication found in diabetic patients, which can cause significant vision loss. This study proposes an Artificial Intelligence (AI) driven system that accurately determines DME severity using OCT B-scan images by extracting specific biomarkers such as DRIL, HRF, and cystoids. The model demonstrates high efficacy with 93.3% accuracy in identifying images with DRIL and successful segmentation of HRF and cystoids from OCT images.
Article
Radiology, Nuclear Medicine & Medical Imaging
Ela Kaplan, Wai Yee Chan, Hasan Baki Altinsoy, Mehmet Baygin, Prabal Datta Barua, Subrata Chakraborty, Sengul Dogan, Turker Tuncer, U. Rajendra Acharya
Summary: This study proposes a novel hand-modeled feature-based learning network for detecting neurological abnormalities in magnetic resonance imaging (MRI) with reduced time complexity and high classification performance. By utilizing the Pyramid and Fixed-size Patch (PFP) structure, the model extracts multilevel and local features, selects clinically significant features using Histogram-Oriented Gradients (HOG) and Iterative Chi2 (IChi2), and performs automated classification with the k-nearest neighbors (kNN) algorithm. Experimental results demonstrate the model's high accuracy in classifying various neurological disorders such as Alzheimer's disease and brain tumors, showing potential for assisting neurologists in manual MRI brain abnormality screening.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Dalibor Cimr, Hamido Fujita, Damian Busovsky, Richard Cimler
Summary: Automated computer-aided diagnosis (CAD) is an effective method for early detection of health issues, and this study proposes a CAD system for seizure detection with optimized complexity. The results demonstrate the effectiveness of the proposed model in providing decision support in both clinical and home environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Sinan Tatli, Gulay Macin, Irem Tasci, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Turker Tuncer, Sengul Dogan, Edward J. Ciaccio, U. Rajendra Acharya
Summary: This study aims to propose a new algorithm for early diagnosis of multiple sclerosis (MS) using machine learning. The algorithm utilizes transfer learning and hybrid feature engineering, and calculates feature vectors through multiple layers of neural networks, resulting in high classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
