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Quantitative detection of adulteration in avocado oil using laser-induced fluorescence and machine learning models

MICROCHEMICAL JOURNAL 2025年 211卷

作者： Bavali, Ali Rahmatpanahi, Ali Chegini, Reza Mirkhovand Amirkabir Univ Technol Dept Energy Engn & Phys Tehran Polytech Tehran Iran

The increasing demand and high market value of avocado oil (AO) have heightened the risk of adulteration, necessitating the development of rapid, non-destructive, and reliable detection methods. This study explores the application of Laser-Induced Fluorescence (LIF) spectroscopy, combined with advanced machine learning techniques, for detecting and quantifying adulteration of AO with sunflower oil (SO). LIF spectra were collected using a 405 nm laser from AO samples adulterated with SO at levels ranging from 0 % to 50 % (5 % increments) and trace levels below 1 % (as low as 0.1 %). Three machine learning models-1D-Convolutional Neural Network (1D-CNN), Support Vector machine (SVM), and Extreme Gradient Boosting (XGBoost)-were employed to classify adulteration levels. Among these, the SVM model achieved the highest accuracy (99.06 %), followed closely by the 1D-CNN (98.75 %), while XGBoost demonstrated good performance with an accuracy of 88.75 %. Principal Component Analysis (PCA) revealed distinct clustering patterns corresponding to different adulteration levels, emphasizing the sensitivity of LIF spectroscopy. Additionally, the method demonstrated the ability to detect trace adulteration levels below 1 %, with a calculated minimum Limit of Detection (LOD) of 0.0288 %. These results validate the potential of LIF spectroscopy, coupled with machine learning, as a robust and efficient tool for ensuring the authenticity and quality of avocado oil, offering a portable, non-destructive solution for food quality control and fraud prevention.

关键词： Avocado oil Adulteration detection Sunflower oil machine learning algorithms Laser-induced fluorescence Difference spectra analysis Principal component analysis

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FairGuard: A Fairness Attack and Defense Framework in Federated learning

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第3期22卷 2519-2532页

作者： Sheng, Xinyi Yang, Zhengjie Bao, Wei Univ Sydney Fac Engn Sch Comp Sci Sydney NSW 2006 Australia Hong Kong Univ Sci & Technol Hong Kong Generat AI Res & Dev Ctr Hong Kong Peoples R China

Algorithmic fairness, emphasizing that machine learning algorithms should not discriminate against specific demographic groups, has recently gained increasing attention in the context of federated learning. Existing algorithmic fairness algorithms in federated learning primarily focus on mitigating inherent biases within the training data or biases introduced by non-IID data distributions. However, these methods heavily rely on the assumption of trust and cooperation among participants in federated learning, leaving a notable gap in exploring the robustness of algorithmic fairness under adversarial environments. Therefore, our study advocates the exploration of the robustness of algorithmic fairness in federated learning. We first introduce four types of fairness attacks targeting the fairness of federated learning models. Then, we propose FairGuard, an innovative defense framework designed to counter these attacks. FairGuard employs a novel dual-inference algorithm that effectively identifies malicious clients during the global aggregation phase of federated learning. Our comprehensive experiments demonstrate that the introduced fairness attacks significantly compromise the global model's fairness and cannot be effectively mitigated by existing defense mechanisms, while FairGuard exhibits remarkable effectiveness in defending against these fairness attacks.

关键词： Federated learning machine learning algorithms Servers Training data Robustness Training Data models Measurement Collaboration Threat modeling Adversarial attacks and defenses algorithmic fairness in machine learning resilient federated learning

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Adapting to the Evolution: Enhancing Intrusion Detection Through machine learning in the QUIC Protocol Era

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1929-1944页

作者： Kadi, Adam Khoukhi, Lyes Viinikka, Jouni Fabre, Pierre-Edouard Normandie Univ ENSICAEN UNICAEN CNRSGREYC F-14000 Caen France 6cure F-14200 Caen France

The advent of the QUIC protocol may herald a significant shift in the composition of online traffic in the years to come. The transport layer encryption of the QUIC protocol is one of its main evolutions, especially for metadata that was previously transmitted over TCP traffic without encryption. This new protocol has the potential to require significant alterations in future Internet traffic analysis methods and impact network intrusion detection. On the other side, machine learning has been used in several research projects to identify network intrusions, with positive outcomes. However, we must take into account new evolution of network traffic. In this paper, we propose a new approach that employs supervised machine learning algorithms to identify flows generated by bots interacting with a Web server during a DDoS attack, focusing on the challenges posed by the QUIC protocol and its implications for effective intrusion detection and cybersecurity. Our contribution in this work is divided into three main parts: 1) A guided process with model architecture for emulating and collecting traffic that depict a range of situations our system may encounter;2) an analysis module that consists on the creation of two labeled datasets, where observations represent the traffic flows detected in PCAP files. We studied the relevance of different features for these datasets, contributing to a thorough understanding of the quality of the data used;3) a real world experimention for evaluating the effectiveness of several supervised machine learning algorithms on our datasets. This experimentation allows us to determine which algorithm provides the best prediction results.

关键词： Denial-of-service attack Protocols machine learning algorithms machine learning Floods Computer crime Botnet Accuracy Mathematical models Intrusion detection DDoS QUIC HTTP/3 cybersecurity intrusion detection system

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Development and Validation of a machine learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days

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WORLD NEUROSURGERY 2025年 193卷 647-662页

作者： Qiu, Hai-yang Lu, Chang-bo Liu, Da-ming Dong, Wei-chen Han, Chao Dai, Jiao-jiao Wu, Zi-xiang Lei, Wei Zhang, Yang Air Force Med Univ Affiliated Hosp 1 Dept Orthopaed Xian Shanxi Peoples R China Air Force Med Univ Burns & Cutaneous Surg Affiliated Hosp 1 Xian Shanxi Peoples R China

BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction models (nomograms) for early URPS risk factors using machine learning methods, aiding spine surgeons in designing prevention strategies, promoting early recovery, reducing complications, and improving patient satisfaction. METHODS: Medical records of 639 patients who underwent reoperation postspinal surgery from the First Affiliated Hospital of Air Force Medical University (2018-2022) were collected, including baseline indicators, perioperative indicators, and laboratory indicators. After applying inclusion and exclusion criteria, 122 URPS and 155 nonURPS patients were identified and randomly divided into training (82 URPS and 111 non-URPS) and validation (40 URPS and 44 non-URPS) cohorts. Three machine learning methods (least absolute shrinkage and selection operator regression, Random Forest, and Support Vector machine Recursive Feature Elimination) were used to select feature variables, and their intersection was used to develop the prediction model, tested on the validation cohort. RESULTS: Six factors-implant, postoperative suction drainage, gelatin sponge, anticoagulants, antibiotics, and disease type-were identified to construct a nomogram diagnostic model. The area under the curve of this nomogram was 0.829 (95% confidence interval 0.771-0.886) in the training cohort and 0.854 (95% confidence interval 0.775-0.933) in the validation cohort. Calibration curves demonstrated satisfactory agreement between predictions and actual probabilities. The decision curve indicated clinical usefulness with a threshold between 1% and 90%. CONCLUSIONS: The established model can effectively predict URPS in patients and can assist spine surgeons in devising personalized and rational clinical prevention strategies.

关键词： Diagnosis machine learning algorithms Nomogram Prediction model Spine surgery Unplanned reoperation

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Statistical Insights Into machine learning-Based Box Models for Pregnancy Care and Maternal Mortality Reduction: A Literature Survey

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IEEE ACCESS 2024年 12卷 68184-68207页

作者： Margret, Issac Neha Rajakumar, K. Arulalan, K. V. Manikandan, S. Valentina Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India A A Child Care Clin Vellore 632014 Tamil Nadu India DRDO Bengaluru 560037 Karnataka India Univ Arad Dept Automat & Appl Informat Arad 310130 Romania

Maternal mortality is a major public health concern worldwide. It is the number of preventable deaths that occur each year due to pregnancy and childbirth. The research investigates how machine learning may be used to minimize maternal mortality. Historical data on maternal health is used to develop predictive models, early detection systems, and resource allocation techniques. machine learning helps to identify risk factors, monitor vital signs, and improve access to care. This allows for targeted interventions and better healthcare delivery. The challenges of data accessibility and model interpretation are addressed, highlighting the ethical and equitable applications of machine learning in maternal healthcare. This study emphasizes the potential of machine learning to reduce maternal mortality rates and the pressing need for its incorporation into healthcare systems worldwide.

关键词： Artificial intelligence box models machine learning algorithms maternal mortality pregnancy care

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In-Network machine learning Using Programmable Network Devices: A Survey

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2024年第2期26卷 1171-1200页

作者： Zheng, Changgang Hong, Xinpeng Ding, Damu Vargaftik, Shay Ben-Itzhak, Yaniv Zilberman, Noa Univ Oxford Dept Engn Sci Comp Infrastruct Grp Oxford OX1 2JD England VMware by Broadcom VMware Cloud Fdn Div VMware Res Grp IL-6997801 Tel Aviv Israel

machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires significant processing and often increases the load on both networks and servers. The introduction of in-network computing, enabled by programmable network devices, has allowed to run applications within the network, providing higher throughput and lower latency. Soon after, in-network machine learning solutions started to emerge, enabling machine learning functionality within the network itself. This survey introduces the concept of in-network machine learning and provides a comprehensive taxonomy. The survey provides an introduction to the technology and explains the different types of machine learning solutions built upon programmable network devices. It explores the different types of machine learning models implemented within the network, and discusses related challenges and solutions. In-network machine learning can significantly benefit cloud computing and next-generation networks, and this survey concludes with a discussion of future trends.

关键词： Surveys machine learning Tutorials machine learning algorithms Codes Switches Classification algorithms In-network computing machine learning P4 programmable data planes software defined networks

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machine learning-Based Diagnosis of Defects in 2.5-D and 3-D Interconnects

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2025年第5期15卷 1104-1116页

作者： Li, Junming Liang, Huaguo Lv, Hao Pan, Yuqi Huang, Zhengfeng Chen, Tian Lu, Yingchun Hefei Univ Technol Sch Microelect Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

Diagnosing interconnect line defects becomes increasingly challenging in advanced chiplet integration due to the immaturity of fabrication processes, reduced interconnect spacing, and increased density. In this article, a nondestructive interconnect defect diagnosis method is proposed. First, redistribution layers (RDLs) on the 2.5-D silicon interposer and 3-D through silicon via-RDL (TSV-RDL) interconnect channels are simulated by ANSYS HFSS, with open and short defects injected at various positions to analyze signal integrity;utilizing S parameters and group delay time (GDT) as feature vectors, machine learning algorithms are employed specifically for the classification and identification of defects. For defect localization, a novel high-precision scheme driven by adaptive regression and clustering is proposed, achieving highly accurate and reliable localization of defects. The results show that the algorithm can accurately identify open and short defects. In defect localization, the mean relative error (MRE) of the proposed method is less than 8%, and the maximum relative error (MaxRE) does not exceed 13%. Compared with the related algorithms, the localization accuracy is significantly improved, providing a novel perspective for the identification and localization of defects within package interconnect lines.

关键词： Integrated circuit interconnections Chiplets Through-silicon vias Packaging Manufacturing Location awareness Three-dimensional displays Signal integrity Scattering parameters machine learning algorithms Advanced packaging technologies chiplet interconnect channel defect diagnosis machine learning signal integrity

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Prediction of pulmonary embolism by an explainable machine learning approach in the real world

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SCIENTIFIC REPORTS 2025年第1期15卷 1-8页

作者： Zhou, Qiao Huang, Ruichen Xiong, Xingyu Liang, Zongan Zhang, Wei Second Mil Med Univ Changhai Hosp Dept Resp & Crit Care Med Shanghai Peoples R China Sichuan Univ Dept Resp & Crit Care Med West China Hosp Chengdu Sichuan Peoples R China

In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020. 126 features were screened and diverse MLAs were utilized to craft predictive models for PE. Area under the receiver operating characteristic curves (AUC) were used to evaluate their performance and SHapley Additive exPlanation (SHAP) values were utilized to elucidate the prediction model. Regarding the efficacy of the single model that most accurately predicted the outcome, RF demonstrated the highest efficacy in predicting outcomes, with an AUC of 0.776 (95% CI 0.774-0.778). The SHAP summary plot delineated the positive and negative effects of features attributed to the RF prediction model, including D-dimer, activated partial thromboplastin time (APTT), fibrin and fibrinogen degradation products (FFDP), platelet count, albumin, cholesterol, and sodium. Furthermore, the SHAP dependence plot illustrated the impact of individual features on the RF prediction model. Finally, the MLA based PE predicting model was designed as a web page that can be applied to the platform of clinical management. In this study, PE prediction model was successfully established and designed as a web page, facilitating the optimization of early diagnosis and timely treatment strategies to enhance PE patient outcomes.

关键词： Pulmonary embolism machine learning algorithms Prediction model SHAP value

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Optimal attack detection using an enhanced machine learning algorithm

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2025年第1期16卷 1-14页

作者： Theja, Reddy Sai Sindhu Shyam, Gopal K. Makka, Shanthi Vardhaman Coll Engn Dept CSE Hyderabad Telangana India Presidency Univ Sch CSE Cloud Comp Lab Bengaluru Karnataka India

As computer network and internet technologies advance more quickly today, the importance of network security is widely acknowledged. This research intends to introduce a new security platform for SaaS framework, which comprises two major phases: (1) Optimal Feature Selection and (2) Classification. Initially, the optimal features are selected from the data set. A novel algorithm named Accelerator updated Rider Optimisation Algorithm (AR-ROA), a modified form of ROA and Deep Belief Network (DBN) based Attack Detection System is proposed. The optimal features that are selected form AR-ROA are subjected to DBN classification process, in which the presence of attacks is determined. The proposed model outperforms other traditional models in aspects of Accuracy (95.3%), Specificity (98%), Sensitivity (86%), Precision (92%), Negative predictive value (97%), F1-score (86%), False positive ratio (2%), False negative ratio (10%), False detection ratio (10%), and Matthew's correlation coefficient (0.82%).

关键词： software-as-a-service SaaS framework security ROA optimisation DBN attack detection system feature selection classification machine learning algorithms security issues

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Unlabeled learning algorithms and operations: overview and future trends in defense sector

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第3期57卷 66-66页

作者： Oliveira, Eduardo E. Rodrigues, Marco Pereira, Joao Paulo Lopes, Antonio M. Mestric, Ivana Ilic Bjelogrlic, Sandro INEGI Inst Sci & Innovat Mech & Ind Engn Porto Portugal Univ Porto Fac Engn LAETA INEGI Rua Dr Roberto Frias P-4200465 Porto Portugal NATO Commun & Informat Agcy Oude Waalsdorperweg 61 NL-2597 AK The Hague Netherlands

In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.

关键词： machine learning algorithms machine learning operations Artificial intelligence

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