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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Post-operative Smart Healthcare Kit

Post-operative Smart Healthcare Kit

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Kanchana Devi, V. Umamaheswari, E. Gao, Xiao-Ghi Rathinavel, Nirupama School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India University of Eastern Finland Kuopio Finland

ISBN: (纸本)9789819613472

The Internet of Things is becoming an increasingly important tool for the healthcare sector. It is utilized in common healthcare settings, such as smart watches and oxygen monitors, as well as in hospitals for anything from routine checks to critical care units. One of the most crucial hospital departments is post-operative care, where patients are monitored to make sure they are getting better. Here, a constant watch is kept on the patient’s vital signs and the associated organs to detect any anomalies that might develop following the treatment. Utilizing IoT in post-operative treatment will guarantee patient contentment. This is ensured by the Post-Operative Smart Healthcare Kit (POSHK), presented in this paper, furnished with several sensors, including heartbeat, oxygen and carbon dioxide sensors, blood pressure, room, and patient temperature sensors, and more. During discharge, data is gathered and analyzed to look at possible future recovery trends. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Feasibility discussion of quantum cryptography for Internet of Things security: a literature review

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Optical and Quantum Electronics 2025年第5期57卷 1-33页

作者： Yang, Yuer Lin, Yifeng Xiao, Jiancheng Zhong, Zhihua Department of Computer Science The University of Hong Kong Hong Kong College of Cyber Security Jinan University Guangzhou China School of Economics Jinan University Guangzhou China School of Computing Tokyo Institute of Technology Tokyo Japan

As the consequences of Internet of Things (IoT) failures may be severe, research on IoT security issues is of great significance. It is a fact that the increasing number of IoT devices brings security concerns. Encryption measures to address these emerging and growing security problems have been studied. Encryption methods have been seen primarily in the field of quantum cryptography. According to this advanced modern cryptography technique, their advantages and disadvantages are discussed and analyzed. This literature review is committed to providing solutions for IoT security problems with quantum cryptography techniques when questioning their feasibility. Some possible future research directions are proposed. © The Author(s) 2025.

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MLDA-Net: Multi-Level Deep Aggregation Network for 3D Nuclei Instance Segmentation

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IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3516-3525页

作者： Hu, Bin Ye, Zhiwei Wei, Zimei Snezhko, Eduard Kovalev, Vassili Ye, Mang Hubei University of Technology School of Computer Wuhan China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan China United Institute of Informatics Problems of the National Academy of Sciences of Belarus Belarus Wuhan University School of Computer Science Wuhan China

Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation standard. However, convolutional layers are limited by their finite receptive fields and weight-sharing mechanisms. Consequently, they struggle to effectively model long-range dependencies and spatial correlations, which may lead to inadequate nuclei segmentation. Moreover, the diversity in nuclear appearance and density poses additional challenges. This work proposes a lightweight multi-layer deep aggregation network, MLDA-Net, incorporating Wide Receptive Field Attention (WRFA). This module effectively simulates the large receptive field generated by self-attention in the Swin Transformer while requiring fewer model parameters. This design implements an extended global sensory field that enhances the ability to capture a wide range of spatial information. In addition, the multiple cross-attention (MCA) module in MLDA-Net enhances the output features of different resolutions from the encoder while maintaining global effectiveness. The Multi-Path Aggregation Feature Pyramid Network (MAFPN) receives multi-scale outputs from the MCA module, generating a robust hierarchical feature pyramid for the final prediction. MLDA-Net outperforms state-of-the-art networks, including 3DU-Net, nnFormer, UNETR, SwinUNETR, and 3DUXNET, on the 3D volumetric datasets NucMM and MitoEM. It achieves average performance improvements of 4% to 7% in F1 score, MIoU, and PQ metrics, thereby establishing new benchmark results. © 2013 IEEE.

关键词： Fluorescence microscopy

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Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2194-2205页

作者： Qiu, Yansheng Jiang, Kui Yao, Hongdou Wang, Zheng Satoh, Shin'ichi Wuhan University School of Computer Science Wuhan430072 China Shenzhen518000 China National Institute of Informatics Tokyo101-8430 Japan The University of Tokyo Graduate School of Information Science and Technology Department of Information and Communication Engineering Tokyo113-8654 Japan

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC. © 2025 IEEE.

关键词： Multicarrier modulation

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Feature analysis and selection for BGP anomaly detection 28

Feature analysis and selection for BGP anomaly detection

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Hakimi, Rifqy Reed, Martin J. School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331542399

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection. © 2025 IEEE.

关键词： Adversarial machine learning

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ADMM for Energy-Efficient Computation Offloading in Marine Mobile Edge Computing Networks 20th

ADMM for Energy-Efficient Computation Offloading in Marine...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： He, Ang Lu, Zili Qin, Baolin Pan, Heng Si, Xueming Dai, Yueyue Huang, Xiaoyan Zhang, Yan Frontier Information Technology Research Institute Zhongyuan University of Technology Zhengzhou China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9789819628636

Marine mobile edge computing (MMEC) networks play a crucial role in collecting and analyzing oceanic data. However, the limited energy storage capacity and inconvenient power delivery of underwater sensor nodes (USNs) and surface sink nodes (SSNs) necessitate energy-saving operations. Moreover, the exhaustion of energy in individual USN results in a diminished lifespan of the underwater sensor networks, thereby significantly affecting the accuracy and reliability of oceanic monitoring data. In this paper, we propose a computation offloading scheme that utilizes collaborative underwater sensor data transmission via acoustic and radio frequency links. The main objective is to minimize the energy consumption of unmanned aerial vehicle (UAV)-aided MMEC networks through the joint optimization of USN clustering, SSN computation offloading decisions, and UAV trajectory planning. To address this joint problem, we design a new clustering algorithm followed by the alternating direction method of multipliers (ADMM) optimization algorithm sequentially. Numerical results show that, compared to no clustering and no computation offloading methods, the total energy consumption for the proposed method is reduced by 23.3% and 10.1%, respectively. © IFIP International Federation for Information Processing 2025.

关键词： Energy efficiency

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Prediction of Covid-19 Recovery Chances Using Supervised Machine Learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. The suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. Thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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