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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning

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science China(Information sciences) 2025年第3期68卷 66-79页

作者： Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU School of Electronic Information and Communications Huazhong University of Science and Technology Platform Operation and Marketing Center JD Retail Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University School of Computer Science Wuhan University

In recent years, unsupervised multiplex graph representation learning(UMGRL) has received increasing research interest, which aims to learn discriminative node features from the multiplex graphs supervised by data without the guidance of labels. Although these designed UMGRL methods have obtained great success in various graph-related tasks, most existing UMGRL models still have the following issues: highly depending on complex self-supervised strategies(i.e., data augmentation,pretext tasks, and negative pairs sampling), restricted receptive fields, and only aggregating low-frequency information between nodes. In this paper, we propose a simple unsupervised multiplex graph diffusion network(UMGDN) with the aid of multi-level canonical correlation analysis to solve the above issues. Specifically, we first decouple the feature transform and propagation processes of the graph convolution layer to further improve the generalization of the learnable parameters. And then, we propose adaptive diffusion propagation to capture long-range dependency relationships between nodes, not the local neighborhood interactions. Finally, a multi-level canonical correlation analysis loss on both the feature transform and propagation processes is proposed to maximize the correlation of the same node features from multiple graphs for guiding model optimization. Compared to the existing UMGRL models, our proposed UMGDN does not need to introduce any data augmentation, negative pairs sampling techniques, complex pretext tasks, and also adaptively aggregates the optimal frequency information between nodes to generate more robust node embeddings. Extensive experiments on four popular datasets and two graph-related tasks demonstrate the effectiveness of the proposed method.

关键词： unsupervised multiplex graph representation learning graph neural networks node classification node clustering

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BeGrading: large language models for enhanced feedback in programming education

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Neural Computing and Applications 2025年第2期37卷 1027-1040页

作者： Yousef, Mina Mohamed, Kareem Medhat, Walaa Mohamed, Ensaf Hussein Khoriba, Ghada Arafa, Tamer Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

In recent years, large language models (LLMs) have gained significant traction across various domains, including education. This paper explores the application of LLMs in grading programming assignments. By leveraging data collected from existing programming assignments and their corresponding grades, we aim to develop a robust LLM-based grading system. We also incorporate augmented data representing various grading scenarios to enhance the model’s performance and ensure comprehensive coverage across all grading levels. Our approach involves training the LLM on this combined dataset to enable accurate and consistent evaluation of programming assignments. The proposed model, BeGrading, aims to reduce the grading burden on educators and provide timely and objective feedback to students. Compared to the Codestral model, our proposed model demonstrates an absolute difference rate of 19%, equivalent to ±0.95 out of 5. This is acceptable for using a small, fine-tuned model with optimized data. Additionally, the Codestral model compared to the dataset optimized score shows a difference of 15% equivalent to a margin of ±0.75 out of 5. Preliminary results demonstrate the potential of LLMs to perform grading tasks with a high degree of reliability, opening avenues for further research and practical applications in automated education systems. © The Author(s) 2024.

关键词： Digital elevation model

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq 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 Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Semi-analytic solution of the nonlinear Sharma-Tasso-Olver equation via ramadan group integral transform and accelerated adomian decomposition

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Journal of Umm Al-Qura University for Applied sciences 2025年 1-14页

作者： Ramadan, Mohamed A. Mansour, Mariam M. A. El-Shazly, Naglaa M. Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Shibin El-Koom Egypt Department of Basic Science Modern Academy of Computer Science and Management Technology Cairo Maadi Egypt

This work focuses on finding an approximate solution to the Sharma-Tasso-Olive (STO) equation. The approach combines accelerated Adomian decomposition (ADM) with the Ramadan group integral transform (RGT) to tackle these nonlinear partial differential equations. Two analytical solution forms are analyzed to evaluate the accuracy and effectiveness of the proposed method. The results demonstrate that the suggested technique yields a rapidly convergent series solution for the STO equation. The paper emphasizes that these semi-analytic methods are powerful tools applicable in engineering and physics for approximating solutions to such nonlinear equations. The resulting series can offer a closed-form solution. Ultimately, the accelerated RGT-ADM variant outperforms the traditional RGT combined with standard Adomian decomposition due to its faster convergence and the elimination of the need for complex Adomian polynomials. © The Author(s) 2025.

关键词： Accelerated variant of Adomain Accuracy Approximate solutions Computational efficiency Convergence rate Nonlinear partial differential equations Numerical methods Polynomial calculations Ramdan group transform Semi-analytic methods Series solutions Sharma-Tasso-Olive equation

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Modelling the dynamics of information warfare: an attacker-defender scenario using Lotka-Volterra equations

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International Journal of Information technology (Singapore) 2025年第3期17卷 1407-1418页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Faculty of Radio and Computer Technology Moscow Institute of Physics & Technology Moscow 141701 Russian Federation Federal Research Center “Computer Science and Control” Russian Academy of Science Moscow 119333 Russian Federation

The dynamics of information warfare in an attacker-defender scenario pose significant challenges in today’s digital age. To address these challenges, this research models the dynamics of information warfare using modified Lotka-Volterra equations, adapted from predator-prey interactions, to simulate the interactions between attackers and defenders. The model captures the growth, interaction, and feedback loops of both populations, offering insights into their dynamics over time. By incorporating the attacker’s growth rate and the defender’s natural decline rate, and discretizing time for real-time simulation, the study reveals how attacker populations initially grow but decline as defenders enhance their countermeasures. The defender population stabilizes as their defensive capabilities improve, demonstrating the model’s effectiveness in representing the complex dynamics of information warfare. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Attacker-defender Cyber security Information warfare Lotka-Volterra equations Population dynamics

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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Memetic Algorithm for Maximizing K-coverage and K-Connectivity in Wireless Sensor Network

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 7-18页

作者： Thi, Hanh Nguyen Van Duc, Cuong Duc, Chinh Tran Minh, Hieu Ha Van, Son Nguyen Van, Quan La Faculty of Interdisciplinary Digital Technology PHENIKAA University Yen Nghia Ha Dong Hanoi12116 Viet Nam Hanoi University of Science and Technology Hanoi Viet Nam Faculty of Computer Science PHENIKAA University Yen Nghia Ha Dong Hanoi12116 Viet Nam

The rapid growth of IoT has enabled diverse applications using Wireless Sensor Networks across various fields. A significant challenge in Wireless Sensor Networks is the efficient deployment of sensors to ensure coverage and connectivity. Effective coverage allows continuous target tracking and data collection, while connectivity ensures data transmission to the base station. In this paper, we address the challenge of maximizing the number of targets satisfying K-coverage and K-connectivity, where each target is tracked by K sensors and has K transmission paths to the base station. We propose a two-phase methodology to tackle this challenge. The first phase enhances the Greedy algorithm to solve the K-coverage problem. The second phase addresses the K-connectivity problem using Memetic algorithms augmented by an efficient local search mechanism called PMA. We evaluate the algorithm on various datasets and compare it with baseline methods, including Greedy and Prim-based with the withdrawal strategy (PWS). Our results show that the proposed PMA with a robust local search outperforms alternative algorithms, with improvements exceeding 10% to 15% compared to the baseline methods. Additionally, we validate the performance of the proposed method using a real-world dataset and outline plans for further enhancements in the near future. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Consensus algorithm

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Leveraging Large Language Models to Identify the Values Behind Arguments 2nd

Leveraging Large Language Models to Identify the Values Be...

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2nd International Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： Senthilkumar, Rithik Appachi Homayounirad, Amir Siebert, Luciano Cavalcante Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031854620

Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the economy, society and politics among others, influencing the arguments they make. Identifying the values behind arguments can therefore help us find common ground in discourse and uncover the core reasons behind disagreements. Transformer-based large language models (LLMs) have exhibited remarkable performance across language generation and analysis. However, leveraging LLMs in sociotechnical systems that assist with discourse and argumentation necessitates systematically evaluating their ability to analyse and identify the values behind arguments, an under-explored research direction. Using a multi-level human value taxonomy inspired by the Schwartz Theory of Basic Human Values, we present a systematic and critical evaluation of GPT-3.5-turbo in human value identification from a dataset of multi-cultural arguments, across the zero-shot, few-shot and chain-of-thought prompting strategies, carrying forward from prior research on this task which leveraged a fine-tuned BERT model. We observe that prompting strategies exhibit performance levels close to, but still behind fine-tuning for value classification. We also detail some challenges associated with value classification with LLMs, offering potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human Values Large Language Models Prompting

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Chain of Thought Prompting in Vision-Language Model for Vision Reasoning Tasks 37th

Chain of Thought Prompting in Vision-Language Model for V...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Ou, Jianjiu Zhou, Jianlong Dong, Yifei Chen, Fang School of Computer Science Institute Faculty of Engineering and IT University of Technology Sydney Ultimo Australia

ISBN: (纸本)9789819603503

The large language model has demonstrated its ability to reason and interpret in text-to-text applications. Current Chain of Thought (CoT) research focuses on either explaining reasoning steps or improving prediction results. This paper proposes a transformer-based language model to generate explanations as the Chain of Thoughts, then fuse visual (image) modality. The proposed model allows a multimodal reasoning chain based on text and image information. In particular, we adopt an attention layer with a gated fusion mechanism to form a joint representation for image and text input. The experiment results show that using CoT as part of the input can increase answer prediction by 19% in our study. Therefore, in our modelling process, CoT prompting is not only a part of the output to generate reasoning explanations but is also used as the input of the decoder to predict answers. The proposed method outperforms GPT-3.5 (two-shot) by 14% and GPT-4 (CoT) by 4.71% with a much smaller parameter size pre-trained language model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual languages

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