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arXiv 2025年

作者： De Carufel, Jean-Lou Maheshwari, Anil Odak, Saeed Roy, Bodhayan Smid, Michiel Vicuna, Marc School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada School of Computer Science Carleton University Ottawa Canada Department of Mathematics Indian Institute of Technology Kharagpur India

The interestingness score of a directed path Π = e1, e2, e3,. . ., e in an edge-weighted directed graph G is defined as score(Π):= Pi=1 w(ei) · log (i + 1), where w(ei) is the weight of the edge ei. We consider two optimization problems that arise in the analysis of Mapper graphs, which is a powerful tool in topological data analysis. In the IP problem, the objective is to find a collection P of edge-disjoint paths in G with the maximum total interestingness score. For k ∈ N, the k-IP problem is a variant of the IP problem with the extra constraint that each path in P must have exactly k edges. Kalyanaraman, Kamruzzaman, and Krishnamoorthy (Journal of Computational Geometry, 2019) claim that both IP and k-IP (for k ≥ 3) are NP-complete. We point out some inaccuracies in their proofs. Furthermore, we show that both problems are NP-hard in directed acyclic graphs. © 2025, CC BY.

关键词： Directed graphs

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QMDS:QKD-based multi-index data secure sharing solution on blockchain

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Journal of Information Security and Applications 2025年 91卷

作者： Zhu, Dexin Zhao, Zilong Zhao, Jian Song, Lijun Zheng, Jun School of Cyberspace Science and Technology Beijing Institute of Technology China College of Computer Science and Technology Changchun University China Jilin Engineering Laboratory for Quantum Information Technology Changchun Institute Of Technology China

Aiming at the privacy leakage of user data in cloud storage, secure search of encrypted data in cloud storage has become a research hotspot. Most of the current schemes suffer from the problems of assuming secure key distribution channels and considering only one-to-one search mode. In this paper, we propose QMDS, QKD-based(Quantum key distribution-based) multi-index data secure sharing solution on blockchain. To ensure secure key sharing among multiple data owners and data users, we use an actual quantum key distribution network to generate theoretically absolutely secure symmetric quantum keys and encrypt files using quantum keys to achieve secure file storage. To achieve secure sharing of parameters among users, we use zero-knowledge to prove the legal identity of users’ keys. Combined with the Merkle tree, we propose the Merkle XOR tree(MXT). The multi-index structure containing high-priority keywords is extracted in MXT to achieve one-to-many secure retrieval. Meanwhile, we use smart contracts to reliably search and achieve a search time complexity of O(1). Based on the Decisional Diffie-Hellman(DDH) difficult problem assumption, it is proved that the scheme can guarantee the security of keyword index and trapdoor. The correctness and efficiency of the scheme are proved by a large number of actual data experiments. © 2025 Elsevier Ltd

关键词： Sensitive data

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Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions 1st

Navigating the Complexities of Municipal Waste Management: E...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Minu, M.S. Samal, Tushar Bisani, Khushi Tewari, Aishwarya Department of Computer Science and Engineering with Specialisation in Big Data Analytics SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031823824

The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emphasizing the significance of accurate cost prediction for sustainable decision-making. Urbanization, driven by population growth and rising standards of living, intensifies waste generation, while shifts in consumption patterns further complicate waste management efforts. Environmental concerns and regulatory pressures mandate innovative waste reduction strategies. Accurate cost prediction is paramount for budgetary planning and resource allocation, enabling proactive measures to optimize operational efficiency and mitigate financial risks. Emerging methodologies, including data analytics and predictive modeling, offer promising avenues for enhancing cost prediction accuracy by capturing the multifaceted drivers of cost variability. By leveraging these methodologies, decision-makers can forecast municipal waste management costs with precision, informing strategic initiatives to foster sustainable waste management practices in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Leveraging Hybrid Ensemble Framework for Diabetes Prediction: Integrating Random Forest, ANN, ETC, and Stack-CNN

Leveraging Hybrid Ensemble Framework for Diabetes Prediction...

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Disruptive Technologies (ICDT), International Conference on

作者： Adnan Naseem Nitin Tyagi Manas Kumar Mishra Department of Computer Science & Engineering G.L. Bajaj Institute of Technology & Management Greater Noida Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Mellitus (DM), or diabetes, is a dominant universal medical affair dilemma harming a growing number of people around the world and putting them at severe risk. Diabetes is a long-lasting affliction that leads to a high annual mortality rate. Diagnosing diabetes initially is crucial to limit its growth and reduce the risk of serious kidney disease and eye disease. This paper recommends using an ensemble hybrid framework for a high-accuracy DM prediction. In this paper, a hybrid ensemble framework blends ML and DL the machine and deep learning structures with an ensemble learning-based stacking model to improve diabetes prediction. The Diabetes Dataset from the Frankfurt Hospital Germany (DDFH-G) trains the hybrid model. The conduct of the recommended model was assessed based on numerous parameters like accuracy, precision, specificity, sensitivity, ROC/AUC, F-score, and Matthews Correlation Coefficient. The recommended hybrid framework with stack-CNN achieves excellent performance with a 99.24% accuracy rate in testing the Diabetes Dataset from Frankfurt Hospital (DDFH-G). The results show that the recommended hybrid framework outperformed previous studies on independent machine and deep learning structures in the earliest diabetes screening.

关键词： Deep learning Accuracy Sensitivity Hospitals Stacking Mortality Predictive models Diabetes Random forests Testing

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ABIDS-VEM: leveraging an equilibrium optimizer and data ramification in association with ensemble learning for anomaly-based intrusion detection system

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The Journal of Supercomputing 2025年第7期81卷 1-35页

作者： Verma, Priyanka O’Shea, Donna Newe, Thomas Mehta, Nakul Bharot, Nitesh Breslin, John G. School of Computer Science University of Galway Galway Ireland Department of Computer Science Munster Technological University Cork Ireland Digital Engineering University of Limerick Castletroy Limerick Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy Limerick Ireland Data Science Institute University of Galway Galway Ireland

The convergence of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) within the Industry 4.0 paradigm leverages software-defined networking, multi-cloud architectures, and edge/fog computing to enhance industrial processes. However, this digital transformation introduces significant cybersecurity and privacy vulnerabilities within the complex, data-intensive IoT/IIoT ecosystems. To mitigate these risks, this research proposes a novel Anomaly-based Intrusion Detection System using Voting-based Ensemble Model (ABIDS-VEM) in Industry 4.0 environments. The VEM architecture synergistically combines multiple machine learning algorithms and gradient boosting frameworks, including CatBoost (CB), XGBoost (XGB), LightGBM (LGBM), Logistic Regression (LR), and Random Forest (RF), to enhance the precision and computational efficiency of intrusion detection systems (IDS) in IoT/IIoT contexts. The proposed framework incorporates a data ramification process, in which the data is divided into multiple parts, feature selection process which is optimized through the Equilibrium Optimizer (EO) algorithm, and outlier detection utilizing the Isolation Forest (IF) method. Comprehensive empirical evaluations were conducted using three benchmark datasets: XIIoTID, NSL-KDD, and UNSW-NB15, to validate the efficacy of the proposed system. The model achieves high accuracy across datasets: 98.1476% for XIIoT-ID, an impressive accuracy of 98.9671% for NSL-KDD, and 94.1327% for UNSW-NB15 dataset. These experimental results demonstrate the potential of this approach to significantly enhance the resilience of critical industrial systems and data against evolving cyber threats, thereby supporting the continued evolution of Industry 4.0 technologies and bolstering the security posture of IoT/IIoT ecosystems. This research contributes to the ongoing efforts to secure the rapidly expanding digital industrial landscape, offering a robust solution for detecting and mitigating sophistic

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Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 690-699页

作者： Xie, Tianyu Sun, Yue Yang, Hongxu Li, Shuo Song, Jinhong Yang, Qimin Chen, Hao Wu, Mingxiang Tan, Tao Macao Polytechnic University Faculty of Applied Sciences 999078 China Eindhoven University of Technology EindhovenAZ 5612 Netherlands Case Western Reserve University Department of Computer and Data Science Department of Biomedical Engineering ClevelandOH44106 United States Jiangsu JITRI Sioux Technologies Company Ltd. Suzhou215000 China University of Twente Department of Biomechanical Engineering EnschedeNB 7522 Netherlands Shenzhen People's Hospital Department of Radiology Shenzhen518020 China

This article introduces an enhanced teacher-student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher-student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated Shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by Shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment. © 2024 IEEE.

关键词： Students

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A Predictive Approach For Assessing Medication Responsiveness in Breast Cancer Through Analysis of Gene Data

A Predictive Approach For Assessing Medication Responsivenes...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Kimmi Gupta Jagendra Singh Harender Pratap Singh Manoj Kumar Singh Nitin Gupta Basudeo Singh Roohani School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science & Engineering IMS Engineering College Ghaziabad India Department of CSE S. D. College of Engineering and Technology Muzaffarnagar India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Breast cancer remains one of the leading causes of cancer-related deaths worldwide, with treatment responses varying wildly among patients. The inability to predict how an individual patient will respond to a given particular medication is an essential area in optimising treatment plans and better outcomes in such deadly diseases. This research aims to address the challenge of predicting medication responsiveness in breast cancer, analysing genomic data by exploiting publicly available datasets from both The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The datasets are comprised of gene expression profiles, somatic mutations, and clinical data such as patient demographics, tumour subtypes, and treatment outcomes. Preprocessing involved cleaning and normalisation of gene expression values, imputation to address missing data, and selection of essential genes that were most relevant for drug response using feature selection techniques. Its dimensionality was reduced using Principal Component Analysis (PCA). Four machine learning models, including artificial neural networks (ANN), Convolutional Neural Networks (CNN), recurrent neural networks with long short-term memory (RNN-LSTM), and autoencoders, are used in this research. All models were trained, validated, and tested against accuracy, precision, recall, F1 score, and AUC-ROC in that the highest performance was achieved at 98.89% accuracy using the CNN model. The findings point towards the fact that deep learning models, especially CNN, open promising avenues for personalised breast cancer treatment. The findings, therefore, hold significant importance for the application of AI in precision medicine, potentially enabling more accurately targeted therapies, minimising side effects, and ultimately improving patient outcomes in oncology.

关键词： Accuracy Recurrent neural networks Precision medicine Genomics Oncology Breast cancer Convolutional neural networks Gene expression Bioinformatics Principal component analysis

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Capsule DenseNet++: Enhanced autism detection framework with deep learning and reinforcement learning-based lifestyle recommendation

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computers in Biology and Medicine 2025年 190卷 110038-110038页

作者： Alutaibi, Ahmed Ibrahim Sharma, Sunil Kumar Khan, Ahmad Raza Department of Computer Engineering College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Information Technology Department College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia

Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming increasingly prevalent worldwide, with an increase in the number of young children diagnosed with ASD in Saudi Arabia. Timely identification and customized interventions are essential for enhancing developmental outcomes. However, existing diagnostic approaches are subjective, limiting the cost-effectiveness of their utilization and the uniformity of their outcomes across different communities. In light of these concerns, this study presents a two-phase deep learning framework for autism detection with lifestyle advice using reinforcement learning. In the first phase, the proposed framework utilizes advanced multiscale statistical techniques for feature extraction, such as measures of central tendencies, variability indices, and percentiles, incorporated with the CosmoNest Optimizer, which is a hybrid of the African Vultures Optimization Algorithm and Butterfly Optimization Algorithm. For accurate ASD identification, these optimized features were classified using Capsule DenseNet++, an advanced deep learning model that increases feature representation efficiency and interpretability. In the second stage, we implement a personalized lifestyle recommendation system using the Proximal Policy Optimization (PPO) algorithm, a reinforcement learning algorithm. In the PPO approach, lifestyle decisions are sequential actions aimed at optimizing interventions, therapies, or daily activities for a given person. The PPO system dynamically learns and adapts recommendations over time to improve its effectiveness. The framework was developed in Python and tested on two datasets: autism screening data and ASD screening data for toddlers in Saudi Arabia. The performance of the detection model was recorded in terms of accuracy (99.2 % and 99.3 %, respectively), precision (98.5 % and 98.7 %, respectively), sensitivity (98.7 % and 98.9 %, resp

关键词： Deep reinforcement learning

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Task Offloading and Multidimensional Resource Allocation for Semantic-Assisted High-Speed Railway with Multi-access MEC 3rd

Task Offloading and Multidimensional Resource Allocation for...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Qu, Jiaming Guo, Qichang Yuan, Jiabin Zhang, Wei Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China China Datang Corporation Science and Technology General Research Institute Ltd. Beijing China

ISBN: (纸本)9789819624676

In high-speed railway communication scenarios, most of the computing tasks generated by trains are compute-intensive and delay-sensitive. Multi-access mobile edge computing (MEC) technology is very promising in resolving the contradiction between this situation and the limited local resources. Traditionally, MEC servers are deployed at base stations along the railway. We introduce dynamic MEC servers assisted by unmanned aerial vehicles (UAVs) additionally to deal with excessive server loads and the real-time nature of trains and tasks. Moreover, to complete real-time tasks, trains usually need abundant resources to transmit a large amount of raw data to MECs. However, limited local resources cannot meet the needs. Therefore, we propose a semantic-assisted communication model, introducing semantic encoding, decoding, extraction and feature aggregation to reduce resource overhead and computing delay while ensuring the accuracy of semantic understanding. Next, we conduct research on multi-dimensional resource allocation with the goal of minimizing delays and maximizing cache resource utilization by presenting our problem formulation. We propose a scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enable MEC agents to collaborate in offloading tasks and allocate resources. Lastly, we conduct simulation experiments and compare with two schemes: centralized resource management and random offloading resource allocation, verifying that the proposed scheme can achieve higher resource utilization and QoS satisfaction rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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