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International Conference on Web Research (ICWR)

作者： Ghazaleh Kakavand Teimoory Mohammad Reza Keyvanpour Department of Computer Engineering Data Mining Laboratory Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Diabetes is a chronic metabolic disorder caused by insufficient insulin production or ineffective insulin utilization, resulting in elevated blood glucose levels. As a major global health concern, it affects millions and can lead to severe complications if undiagnosed or mismanaged. This study proposes an explainable artificial intelligence model for accurate diabetes prediction using a calibrated Random Forest classifier. To ensure reliability, comprehensive data preprocessing techniques were implemented, including regression-based imputation, Recursive Feature Elimination, outlier removal using the IQR method, and class imbalance handling with ADASYN. Hyperparameter tuning through RandomizedSearchCV and model calibration improved predictive accuracy. The final model achieved an accuracy of 88 %, precision of 87 %, recall of 87 %, and F1-score of 86 %. Beyond performance, explainability was prioritized using LIME and decision path visualizations, providing clinicians with transparent reasoning behind predictions. This research demonstrates how integrating XAI techniques with robust machine learning can enhance clinical decision support systems. It also highlights the model's potential for integration into practical web-based tools that assist early detection and improve patient outcomes in diabetes care.

关键词： Accuracy Explainable AI Predictive models Imputation Cognition Robustness Diabetes Insulin Random forests Tuning

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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ILRM: Imitation Learning-Based Resource Management for Integrated CPU–GPU Edge Systems With Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This letter focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability constraints. We propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: 1) offline Oracle generation and 2) online IL. In the offline phase, we design a fast-converging heuristic to generate near-optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize IL using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Efficient Prediction of Cardiovascular Disease via Extra Tree Feature Selection

Efficient Prediction of Cardiovascular Disease via Extra Tre...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Mina Abroodi Mohammad Reza Keyvanpour Ghazaleh Kakavand Teimoory Department of Computer Data Mining Laboratory Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

All abnormalities related to the heart can be classified as heart diseases. These diseases are also known as cardiovascular diseases. Predicting heart disease is crucial due to its status as a leading global cause of mortality. According to predictions, if this disease can be diagnosed earlier, treatment cost can be reduced, prompt initiation of care for patients and millions of lives can be saved. Utilizing machine learning and artificial intelligence for this purpose is paramount. Previous research has explored various methodologies such as KNN and SVM. However, the preprocessing phase significantly influences model performance, especially in e-health domains. This work proposes a groundbreaking method that uses the Extra Tree Classification method for feature selection, optimizing the selection of key attributes from the Cleveland Heart Disease dataset. Our method is chosen for the low computational cost and interpretability of feature selection through randomized scoring. Additionally, we employ logistic regression, renowned for its effectiveness and efficiency in classification tasks and to ensure the robustness of the model, we used the k-fold cross validation method. Our findings demonstrate a notable accuracy of 87.09 % when predicting heart disease, with a precision of 87.45% and an F-measure of 86.02%, surpassing previous benchmarks. Considering the high interpretability of models like logistic regression, this level of accuracy is particularly valuable in medical contexts where transparency and explainability are critical, making it a competitive alternative to more complex, less interpretable methods. These findings highlight the effectiveness of combining Extra Tree-based feature selection with logistic regression in improving the predictive accuracy of heart disease diagnostics. Furthermore, we address missing values and their impact on model performance, further strengthening the reliability of our approach.

关键词： Heart Logistic regression Accuracy Sensitivity Computational modeling Machine learning Feature extraction Prediction algorithms Cardiovascular diseases Medical diagnostic imaging

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An Effective Feature Selection for Type II Diabetes Prediction

An Effective Feature Selection for Type II Diabetes Predicti...

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International Conference on Web Research (ICWR)

作者： Ghazaleh Kakavand Teimoory Mohammad Reza Keyvanpour Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

Diabetes is a condition related to metabolism that arises due to various reasons. Each year, a significant portion of patients experience life-threatening complications leading to fatality. This illness is classified into three distinct forms: Type 1, Type 2, and Gestational diabetes. It is of utmost importance to predict type 2 diabetes, which arises from cellular insulin utilization deficiency or secretion disorder, as it enables the prevention of complications or delays in the onset of the disease. Predicting the occurrence of illnesses using machine learning and artificial intelligence can substantially mitigate its costs. However, due to interpretability challenges in proposed models, physicians and patients are hesitant to adopt them. Previous studies have utilized various algorithms including Naïve Bayes, SVM, KNN, and decision tree algorithms for patients’ classification. In this paper, conducted on the Pima dataset, we employed a preprocessing method utilizing the most significant features selected by the Random Forest algorithm. Additionally, for model testing, we utilized the SVM algorithm, known for its high discriminative power in binary classification tasks and its relatively good interpretability. The results indicate that the proposed model achieved an accuracy of 80.09%, outperforming other models by a 2.26% improvement. Furthermore, there were notable improvements in precision and specificity metrics with the proposed model. By utilizing these methods, web-based applications can be employed to motivate physicians and patients for diabetes prediction.

关键词： Support vector machines Measurement Machine learning algorithms Medical services Predictive models Prediction algorithms Diabetes

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Sentiment Analysis on Drug Reviews Using Ensemble Learning Method

Sentiment Analysis on Drug Reviews Using Ensemble Learning M...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Sahar Eskandarisani Mohammad Reza Keyvanpour Department of Computer Engineering Data Mining Laboratory Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Sentiment analysis, a branch of natural language processing, has gained considerable attention recently. This analytical approach is now widely used across multiple sectors, such as healthcare, finance, and customer service. Sentiment analysis in the healthcare domain can contribute to advancements in this field and help doctors to gain a deeper understanding of patient sentiments, enabling them to make more informed prescriptions. In our research, we concentrated on sentiment analysis of drug reviews. We applied ensemble learning methods, including the stacking learning method, using three base models and one meta-model. We also employed three feature extraction methods: TF-IDF, Bag of Words (BOW), and Continuous Bag of Words (CBOW). Among these methods, the stacking learning model with TF-IDF achieved the highest accuracy, reaching 89.8%.

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