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Ensemble Machine Learning-Based Approach to Predict Cervical Cancer with Hyperparameter Tuning and Model Explainability

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Biomedical Materials and Devices 2025年 1-28页

作者： Uddin, Khandaker Mohammad Mohi Bhuiyan, Md. Tofael Ahmed Saad, Mirza Nadim Islam, Ashfaqul Islam, Md. Manowarul Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka India

Cervical cancer remains the top killer of women at a young age in the world, 85% of cases are detected in low-income countries. Preventive measures and therapeutic response are enhanced if potential hazards are identified early. This research belongs to this field by introducing an end-to-end prediction model based on individual medical records and early screening data thus emphasizing the discovery of meaningful predictors. In order to overcome issues with feature selection and class imbalances, our study creates an ensemble prediction framework that blends Random Forest and Logistic Regression techniques. In addition to achieving an astounding accuracy of 99.75%, the model guarantees transparency in its decision-making processes by utilizing sophisticated machine learning algorithms in conjunction with interpretability tools like SHAP and LIME, which is essential for applications in healthcare. The creation of an extensive ensemble method that combines several machine learning classifiers, advanced feature selection techniques for locating important predictive factors, and interpretability techniques to help healthcare professionals better understand complex predictions are some of the research’s main investments. By offering accurate and comprehensible risk assessments, this novel method has the potential to revolutionize clinical decision-making and enhance early cancer of the cervical cavity identification. This research promotes the development of more proactive and individualized cancer screening methods by fusing cutting-edge computational technology with medical diagnostics, improving health outcomes everywhere. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cervical cancer Explainable machine learning Hybrid model introduction Hyperparameter tuning feature selection Machine learning Stacking ensemble technique

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Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

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Multimedia Tools and Applications 2025年第13期84卷 11475-11501页

作者： Alharbi, Meshal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease condition or not is to be developed. The paper employs a diverse array of models within the heart disease prediction system, each serving a specific purpose to enhance accuracy and efficiency. Data pre-processing techniques, including normalization, standardization, and missing value removal, ensure the quality and consistency of the dataset. Feature extraction methods such as mean, median, standard deviations, and higher-order statistical features contribute to extracting informative features crucial for prediction. The Multi-Objective Forest Particle Swarm Optimization (MOFPSO) algorithm (hybrid version of Forest Optimization (FO) and Particle Swarm Optimization (PSO)) is introduced for efficient feature selection, balancing predictive accuracy and model complexity. Finally, the prediction model, Naïve Bayes trained with Meliorated Ant Colony Optimization algorithm (NB-MACO), is implemented for its simplicity and effectiveness in handling medical datasets. This integration fine-tunes the Naïve Bayes classifier’s hyperparameters, optimizing its performance and resulting in an accuracy of 91.974%. The collective utilization of these models and techniques ensures the development of a robust and accurate heart disease prediction system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Building LOD representation for 3D urban scenes

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 16-32页

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. © 2025

关键词： Three dimensional computer graphics

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Enhancing cyber threat detection with an improved artificial neural network model

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Data science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

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Ensuring safety of vehicular cyber physical systems using machine learning and MQTT

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 165-183页

作者： Bagga, Neha Kalra, Sheetal Kaur, Parminder Department of Computer Science Guru Nanak Dev University Punjab Amritsar India School of Computer Science Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering G.N.D.U. Regional Campus Punjab Jalandhar India

One of the pressing concerns for emerging nations is maintenance of roads, including identification and repair of pavement distress. Previous research has focused on pothole detection and lane identification, with the distress details being shared with drivers via database or an Android application. However, this approach is battery-intensive for sensors in smart vehicles and requires a regular internet connection. To address these issues, we have proposed a model trained using Python and TensorFlow to identify road distress and steep curves with an accuracy of 85.2% and 83.1%, respectively. The simulation uses Geocoder to capture the geographical coordinates of the distress, and the collected data is transferred to other CPS devices in cars using MQTT which outperforms databases and Android applications in terms of efficiency, sensor load, and internet connectivity. Drivers receive alerts within 10 seconds, allowing them to make informed decisions which helps prevent accidents and fatalities on the road. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Highway accidents

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Domain Adaptation for Label Distribution Learning

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IEEE Transactions on Big Data 2025年第3期11卷 1221-1234页

作者： Wu, Haitao Li, Weiwei Jia, Xiuyi Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Label distribution learning (LDL) suffers from the dilemma of insufficient target data in real-world applications, while domain adaptation (DA) seems to be able to provide a solution. However, most existing methods of DA, assuming that the instances can correspond to the explicit class information, are devoted only to classification but not to LDL. We argue that indiscriminately applying such DA methods might cause performance degradation in LDL tasks. In this paper, we propose LDL-DA, a novel algorithm dedicated to supervised domain adaptation for label distribution learning, which jointly learns a shared encoding representation from two aspects: 1) contrastive alignment of scarce supervised target data, and 2) minimizing the distance between prototypes of the same label combination. Experiments show that LDL-DA outperforms existing DA methods adapted to LDL, and provides early positive results in DA for LDL. To the best of our knowledge, this paper is the first research on DA for LDL. © 2015 IEEE.

关键词： Contrastive Learning

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Code context-based reviewer recommendation

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Frontiers of computer science 2025年第1期19卷 97-108页

作者： Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Faculty of Science University of AmsterdamAmsterdam WB 1018Netherlands

Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关键词： code reviewer recommendation code context pull request

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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

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Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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Existence and uniqueness of mean field equilibrium in continuous bandit game

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science China(Information sciences) 2025年第3期68卷 395-396页

作者： Xiong WANG Yuqing LI Riheng JIA School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University Wuhan University Shenzhen Research Institute School of Computer Science and Technology Zhejiang Normal University

Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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