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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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Ihoya: knowledge management system based on community using User-Centered Design (UCD) method

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International Journal of information technology (Singapore) 2025年第4期17卷 2067-2074页

作者： Rafi, Muhammad Puspitodjati, Sulistyo Apriani, Niken Fitria Hakim, Shidiq Al Siagian, Al Hafiz Akbar Maulana Riyanto, Slamet Rodiah, Rodiah Kushadiani, Siti Kania Faculty of Computer Science and Information Technology Universitas Gunadarma West Java Depok Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency West Java Bogor Indonesia

Hoya represents a significant component of Indonesia’s biodiversity with considerable potential;however, its utilization remains suboptimal. There exists a keen interest among researchers, collectors, and horticultural enthusiasts to delve into this fascinating plant. The wide variety of Hoya species in Indonesia substantially challenges their identification. Consequently, there is a pressing need for a system that facilitates the identification of the Hoya plant and its various species. In this study, we propose developing a knowledge management system (KMS) to identify Hoya and its species. This proposal is anchored in the user-friendly nature of mobile-based applications, which offer accessibility at any time. To ensure the creation of a high-quality application that meets user satisfaction, a User Interface and User Experience (UI/UX) design process is implemented, employing the User Centered Design (UCD) methodology that concentrates on the preferences and requirements of users. Subsequently, the System Usability Scale (SUS) method is adopted to assess the design and verify its alignment with user needs. This research culminates in our developed KMS prototype, namely iHoya, encompassing mockup pages tailored to users’ preferences, requirements, and navigational flows. An evaluation of the prototype design using the SUS method, conducted through a survey of targeted users, categorizes the design as acceptable while indicating the necessity for certain enhancements. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Hoya Knowledge management system SUS UCD UI/UX

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Optimized Ensemble Model Towards Secured Industrial IoT Devices 24

Optimized Ensemble Model Towards Secured Industrial IoT Devi...

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24th International Arab Conference on information technology, ACIT 2023

作者： Injadat, MohammadNoor Faculty of Information Technology Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan

ISBN: (纸本)9798350384307

The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models. © 2023 IEEE.

关键词： Bayesian Optimization Industrial IoT Optimized Ensemble Learning

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches 14

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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14th International Conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

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Using Hash and Deep Learning for Content-Based Image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui Thanh Thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. The drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Easy data Augmentation in Sentiment Analysis of Cyberbullying 6

Easy Data Augmentation in Sentiment Analysis of Cyberbullyin...

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6th International Conference on information and Communications technology, ICOIACT 2023

作者： Wirawan, Alwan Cahyono, Hasan Dwi Winarno Universitas Sebelas Maret Faculty of Information Technology and Science Data Department of Informatics Surakarta Indonesia

ISBN: (纸本)9798350315639

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at ***/eda_svm. © 2023 IEEE.

关键词： Support vector machines

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Vietnamese Text Summarization Using Transformer Approach 6th

Vietnamese Text Summarization Using Transformer Approach

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui Thanh Tan, Phan Nguyen Trong Long, Tran Ho Phi Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Text summarization is a crucial aspect of natural language processing, finding widespread applications in various aspects of our lives. Numerous studies have delved into this task, employing diverse approaches ranging from linguistic analysis to machine learning and deep learning methodologies. However, there has been limited exploration of transformer-based models for Vietnamese text summarization. In our research, we leverage transformer models, namely, PhoBERT, BARTpho, and ViT5, to develop and refine effective summarization models. Through experimentation on the Vietnews dataset and evaluation using ROUGE metrics, our proposed method demonstrates noteworthy achievements in the realm of Vietnamese text summarization, showcasing its potential for advancing the quality of summarization tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BARTpho BERT Deep learning PhoBERT Text summarization Transformer ViT5

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Utilization of Machine Learning in Breast Cancer Detection 25

Utilization of Machine Learning in Breast Cancer Detection

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25th International Arab Conference on information technology, ACIT 2024

作者： Momani, Mutaz Yousef, Nidal Aljawazneh, Qotadeh Issa, A.T. Abuowaida, Suhaila Alazaidah, Raed Khouj, Mohammed Talat Batyha, Radwan Elsoud, Esraa Abu Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Faculty of Information Technology Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ML) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ML algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ML algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ML-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on dataset 2. In dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study. © 2024 IEEE.

关键词： Lung cancer

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A Novel Maize Futures Price Prediction Model based on EEMD-CNN-IGRU 22

A Novel Maize Futures Price Prediction Model based on EEMD-C...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Jingyang Han, Jingyu Liu, Wei Li, Ying Sun, Qiuhong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Kansai University Faculty of Business Data Science Osaka565-0823 Japan

ISBN: (纸本)9798331509712

Maize, a significant global food crop, is essential in agriculture and the economy. The price of maize futures is affected by many factors, and its data is a nonlinear, unstable, and long-term correlation, so it is difficult to predict it accurately. Accordingly, this paper presents a combined model based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Gated Recurrent Unit (IGRU). EEMD decomposes the maize price data to produce multiple Intrinsic Mode Function (IMF) components and residual sequences. Subsequently, the noisy IMF components are removed, and the remaining IMF components are reconstructed into low, medium, and high frequencies. The CNN is tasked with the extraction of eigenvalues for these components. The IGRU improves in two key respects on the original Gated Recurrent Unit (GRU) model. First, it enhances the update and reset gates. Second, it incorporates the Self-Attention (SA) module. Thereby, the model's predictive capabilities are improved. This study uses the primary maize futures trading data from the Dalian Commodity Exchange as the experimental data. A comparative analysis of the EEMD-CNN-IGRU model with seven baseline models shows that it outperforms other models in all evaluation indexes. © 2024 IEEE.

关键词： Convolutional neural networks

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A Rotating Target Detection Algorithm for Ships in SAR Images Based on LAOBB-YOLOv8n 22

A Rotating Target Detection Algorithm for Ships in SAR Image...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Huang, Min Wang, Shuaihui Luo, Shuaili Sun, Qiuhong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Kansai University Faculty of Business Data Science Osaka565-0823 Japan

ISBN: (纸本)9798331509712

Aiming to address challenges in SAR image ship detection tasks, such as the dense concentration of inshore ships, small target sizes, and large overlapping areas, this paper proposes an improved algorithm LAOBB-YOLOv8n based on YOLOv8n for detecting rotating ships in SAR images. In this model, we propose the Detect-LSCD detection head, which replaces the BatchNorm in the convolutional layers of the Detect module in YOLOv8n with GroupNorm to improve the positioning and classification capabilities of the detection head;Replace the original Spatial Pyramid Pooling-Fast (SPPF) module with the Attention-based Intrascale Feature Interaction (AIFI) module, which focuses on processing advanced image features through self-attention mechanism, thus improving the flexibility and accuracy of the model in processing complex scenes while reducing unnecessary computational consumption;Based on the original network structure, we add a small target detection layer to improve the detection ability for small targets;Use rotating bounding box instead of traditional horizontal bounding box, which can better accommodate the rotating posture of the target and reduce the overlapping areas between detection boxes, thereby improving the accuracy of inshore target detection. The comparative and ablation experiments are conducted on the RSDD-SAR dataset to verify the model's effectiveness. The experimental results show that the mAP50 of LAOBB-YOLOv8n reaches 98%, which is 1.4% higher than that of YOLOv8n. Although the number of parameters of LAOBB-YOLOv8n is larger than that of YOLOv8n, it is still very small compared with other baseline models, and the accuracy of LAOBB-YOLOv8n is much higher than other models. © 2024 IEEE.

关键词： Radar imaging

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