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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data

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IEEE Transactions on Image Processing 2025年 34卷 2840-2852页

作者： Zhu, Jinjing Chen, Yucheng Wang, Lin Artificial Intelligence Thrust Guangzhou China HKUST Artificial Intelligence Thrust Guangzhou China HKUST Dept. of Computer Science and Engineering SAR China Hong Kong Hong Kong

Source-free cross-modal knowledge transfer is a crucial yet challenging task, which aims to transfer knowledge from one source modality (e.g., RGB) to the target modality (e.g., depth or infrared) with no access to the task-relevant (TR) source data due to memory and privacy concerns. A recent attempt [1] leverages the paired task-irrelevant (TI) data and directly matches the features from them to eliminate the modality gap. However, it ignores a pivotal clue that the paired TI data could be utilized to effectively estimate the source data distribution and better facilitate knowledge transfer to the target modality. To this end, we propose a novel yet concise framework to unlock the potential of paired TI data for enhancing source-free cross-modal knowledge transfer. Our work is buttressed by two key technical components. Firstly, to better estimate the source data distribution, we introduce a Task-irrelevant data-Guided Modality Bridging (TGMB) module. It translates the target modality data into the source-like images based on paired TI data and the guidance of the available source model to alleviate two key gaps: 1) inter-modality gap between the paired TI data;2) intra-modality gap between TI and TR target data. We then propose a Task-irrelevant data-Guided Knowledge Transfer (TGKT) module that transfers knowledge from the source model to the target model by leveraging the paired TI data. Notably, due to the unavailability of labels for the TR target data and its less reliable prediction from the source model, our TGKT model incorporates a self-supervised pseudo-labeling approach to enable the target model to learn from its predictions. Extensive experiments show that our method achieves state-of-the-art performance on three datasets (RGB-to-depth and RGB-to-infrared). © 1992-2012 IEEE.

关键词： Knowledge transfer

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Advancing Emotional Voice Conversion: Transforming Fundamental Frequency and Mel-Cepstral Coefficients Using Cycle Consistent Adversarial Networks with Two-Step Adversarial Loss and Patch-Based Discriminators

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Human-centric Computing and Information sciences 2025年 15卷 1-18页

作者： Larraín, Pablo Díaz Patricio, Miguel A. Berlanga, Antonio Molina, José M. Engineering Team Grupo MasMovil Madrid Spain Computer Science and Engineering Department Applied Artificial Intelligence Group Universidad Carlos III de Madrid Spain

The aim of emotional voice conversion (EVC) is to alter the emotional content of spoken utterances without compromising the speaker’s identity or linguistic content. Many EVC frameworks rely on scarce parallel data recorded by actors. This paper proposes a novel framework for EVC that leverages non-parallel data through cycle consistent adversarial networks (CycleGANs). CycleGANs learn to transform input data between domains using a cycle loss that regularizes training by ensuring the reconstructed inputs match the original inputs in both domains. Despite their use in various voice conversion tasks, CycleGANs often produce audio with degraded quality, largely due to the oversmoothing of speech features. To address these issues, we devised two distinct CycleGAN-based methods within the aforementioned framework: the first method incorporates a two-step adversarial loss, while the second method enhances this by incorporating patch-based (PatchGAN) discriminators. Prior research has demonstrated that these techniques alleviate the oversmoothing of the spectrum and have shown superior capability in capturing dynamic spectral variations. In this work, we incorporate these enhancements not only to transform the spectrum, but also the fundamental frequency (F0), a speech feature that is strongly related to intonation and expression of emotion. The objective evaluation of the proposed methods shows improvements over the baseline in both Mel-cepstrum distortion and root-mean-square error, as well as in the Pearson correlation coefficient of the F0 transformation. Furthermore, subjective evaluations using the mean opinion score (MOS) and similarity MOS indicate that our model outperforms the baseline model in terms of naturalness and similarity to the target emotion. © (2025), (Korea Information Processing Society). All rights reserved.

关键词： Discriminators

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Comprehensive Study on Heart Disease Prediction and Risk Stratification using Explainable artificial intelligence Technique 1

Comprehensive Study on Heart Disease Prediction and Risk Str...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Gadde, Aruna Chintala, Sridhar Sr University Department of Computer Science & Artificial Intelligence Hasanparthy India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798331529246

artificial intelligence (AI) is becoming increasingly important in healthcare, particularly in identifying heart disease and predicting its occurrence. This article discusses how Explainable AI (XAI)-based methods are employed to assess the risks of heart disease and predict its risk stratification. The primary goal is to address the 'black-box' issue associated with older AI models by clarifying their decision-making processes. Other XAI methods discussed in this research include Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Layer-wise relevance Propagation, Partial Dependency Plots (PDP), and Gradient-Class Activation Map (Grad-CAM) method, employed for heart disease prediction and its risk stratification. Additionally, this paper discusses XAI methods that are specific to neural networks and tree-based models. Many people can better comprehend AI models using XAI, which is crucial for clinical applications. Furthermore, this paper highlights research gaps in combining XAI with Electronic Health Records (EHR) and real-time data analytics. The Dense-Net classifier method achieves an accuracy of 99.8%, recall of 98%, and F1-score of 99.8%, outperforming existing methods such as Random Forest and Extreme Boost (XG-Boost). © 2025 IEEE.

关键词： Electronic health record

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Explaining the better generalization of label distribution learning for classification

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science China(Information sciences) 2025年第5期68卷 348-361页

作者： Jing WANG Xin GENG School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Ministry of Education

Label distribution learning(LDL) has shown advantages over traditional single-label learning(SLL) in many realworld applications, but its superiority has not been theoretically understood. In this paper, we attempt to explain why LDL generalizes better than SLL. Label distribution has rich supervision information such that an LDL method can still choose the sub-optimal label from label distribution even if it neglects the optimal one. In comparison, an SLL method has no information to choose from when it fails to predict the optimal label. The better generalization of LDL can be credited to the rich information of label distribution. We further establish the label distribution margin theory to prove this explanation; inspired by the theory,we put forward a novel LDL approach called LDL-LDML. In the experiments, the LDL baselines outperform the SLL ones, and LDL-LDML achieves competitive performance against existing LDL methods, which support our explanation and theories in this paper.

关键词： label distribution learning classification generalization label distribution margin label ambiguity neural network

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Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

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Evolving Systems 2025年第2期16卷 1-17页

作者： Hassan, Esraa Saber, Abeer El-Sappagh, Shaker El-Rashidy, Nora Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt

Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient outcomes, with mammography being the primary screening method most health organizations recommend. This paper highlights the potential of deep learning (DL) in enhancing diagnostic Accuracy and speed. However, despite the promising results of DL models, the increasing complexity of breast cancer data presents a significant challenge in selecting the most relevant features. We propose a robust DL model incorporating transfer learning (TL) and grey wolf optimization (GWO) to enhance BC diagnosis. The architecture is developed by leveraging multiple deep neural networks, including ResNet and Inception, based on convolutional neural networks (CNNs). These networks are fine-tuned using a mammographic image dataset to optimize model weights and parameters for higher classification accuracy. Additionally, the Wisconsin Breast Cancer Dataset (WBCD) is utilized with GWO to refine feature selection further. Experimental results demonstrate that the proposed ensemble method improves robustness, generalization, and detection rates while minimizing false positives and negatives. Large-scale dataset evaluation yielded a precision of 0.942, sensitivity of 0.982, Accuracy of 0.965, and an area under the curve (AUC) value of 0.971. These findings suggest that the proposed framework could significantly enhance patient care and improve healthcare service organization and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Diseases

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment

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computers, Materials & Continua 2025年第4期83卷 13-64页

作者： Muhammad Arif Muhammad Rashid Department of Computer Science and Artificial Intelligence Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia Department of Computer and Network Engineering Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia

Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s ***,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models ***,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various *** factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these ***,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using *** identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software *** the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning *** the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major *** a result,this article provides valuable insights for researchers,practitioners,and *** assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.

关键词： Edge machine learning tiny machine learning model compression inference learning algorithms

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