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

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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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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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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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Sustainable Selection of machine learning Algorithm for Gender-Bias Attenuated Prediction

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 348-358页

作者： Orbay, Raik Wikner, Evelina Volvo Car Corp Prop & Energy Strategy & Execut 97100 SE-40531 Gothenburg Sweden Chalmers Technol Univ Dept Elect Engn SE-41296 Gothenburg Sweden

Research into novel approaches like machine learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.

关键词： Pipelines Vehicles Classification algorithms machine learning algorithms Imputation Training Global Positioning System Automobiles Tensors Industries Battery electric vehicles driving style classification energy consumption feature engineering machine learning unsupervised/supervised learning

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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 1-41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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Estimating Soil Depth in Data-Sparse Regions: A machine learning Approach for Western India

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COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 2025年第7期56卷 1106-1131页

作者： Moharana, Pravash Chandra Yadav, Brijesh Nogiya, Mahaveer Meena, Roshan Lal Jangir, Abhishek Malav, Lal Chand Kumar, Sunil Sharma, Ram Prasad Meena, Ram Swaroop Naitam, Ravindra Kumar, Nirmal Vasu, Duraisamy Tiwari, Gopal Dash, Benukantha Shirale, Abhay Omprakash Adamala, Sirisha Singh, Ram Sakal Mina, Banshi Lal Biswas, Hrittick Patil, Nitin Gorakh ICAR Natl Bur Soil Survey & Land Use Planning Nagpur 440033 India ICAR Natl Bur Soil Survey & Land Use Planning Reg Ctr Udaipur India ICAR Natl Bur Soil Survey & Land Use Planning Reg Ctr New Delhi India ICAR Natl Bur Soil Survey & Land Use Planning Reg Ctr Jorhat India

Soil depth is essential for eco-hydrological modeling, carbon storage estimation, and land evaluation, yet its spatial variation is often poorly understood and rarely mapped, particularly in complex landscapes with limited sample sizes. Digital soil mapping, employing various machine learning methods, has become crucial in predicting and mapping soil attributes. This study investigates soil depth distribution in Rajasthan and Gujarat, India, using machine learning techniques. A total of 37 environmental variables were examined to identify factors influencing soil depth, utilizing data from 3206 soil profiles. Five machine learning algorithms - Random Forest (RF), Cubist, Support Vector machines (SVM), extreme gradient boosting (XGBoost), and Partial Least Squares Regression (PLSR) - were evaluated. MODIS, WorldClim, and Digital Elevation Model were used as environmental covariates. Among these, channel network base level, and valley depth were found to be the most significant factors in soil depth modeling. The RF model produced the best results, with an R2 of 0.53, RMSE of 25.19 cm, and MAE of 16.04 cm, outperforming Cubist and other models. Predicted soil depths ranged from 23 to 197 cm in Rajasthan and from 16 to 182 cm in Gujarat. The spatial pattern of soil depth closely matched expectations for the studied regions, demonstrating effective model and variable selection. However, uncertainty assessment revealed significant uncertainty in 60% of the study area due to low soil profile points. This study presents a promising approach to predict soil depth via digital mapping, aiding regional land use planning and potentially informing national and international land resource management strategies.

关键词： Digital soil mapping machine learning algorithms Rajasthan and Gujarat soil depth

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machine learning Models for Bedrock Condition Classification in Pavement Structure Evaluation: A Comparative Study

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第2期43卷 1-17页

作者： Wang, Yujing Zhao, Yanqing Fu, Guozhi Dalian Univ Technol Dept Civil Engn Dalian 116024 Peoples R China Dalian Univ Technol Dept Transportat & Logist Dalian 116024 Peoples R China Nanjing Forestry Univ Coll Civil Engn Nanjing 210037 Peoples R China

Pavement performance evaluation based on modulus is crucial for controlling the overall performance of pavements and decisions making throughout the pavement's life cycle. Falling weight deflectometer (FWD) tests are commonly employed to collect deflection data, which is subsequently back-calculated to get each layer's modulus. However, existing studies lack a complete framework for incorporating the bedrock condition in the back-calculation process. Here, an integrated process of pavement performance evaluation utilizing FWD tests is proposed, and the focus is on the classification of bedrock condition by modern classification algorithms (BPNN, MLP, SVM, and RF) to determine the presence or absence of bedrock and its depth range. The implementation of classification process allows for the inclusion of bedrock influence in the back-calculation process, thereby improving the accuracy of modulus results. Results from the four classification algorithms reveals that RF is the most suitable for classifying bedrock depth, exhibiting superior overall performance. The proposed integrated back-calculation process enables a comprehensive and objective evaluation of pavement structural performance, providing a valuable framework for informed decisions making.

关键词： Bedrock condition Classification machine learning algorithms Falling weight deflectometer Dynamic response Performance evaluation

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Privacy-Preserving machine learning (PPML) Inference for Clinically Actionable Models

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IEEE ACCESS 2025年 13卷 37431-37456页

作者： Balaban, Baris Magara, Seyma Selcan Yilgor, Caglar Yucekul, Altug Obeid, Ibrahim Pizones, Javier Kleinstueck, Frank Perez-Grueso, Francisco Javier Sanchez Pellise, Ferran Alanay, Ahmet Savas, Erkay Bagci, Cetin Sezerman, Osman Ugur European Spine Study Group, European Spine Study Acibadem Mehmet Ali Aydinlar Univ Inst Hlth Sci Dept Biostat & Bioinformat TR-34638 Istanbul Turkiye Sabanci Univ Dept Comp Sci & Engn TR-34956 Istanbul Turkiye Univ Tubingen Dept Comp Sci D-72074 Tubingen Germany Acibadem Univ Dept Orthoped & Traumatol Sch Med TR-34750 Istanbul Turkiye Elsan Jean Villar Private Hosp Clin Dos F-33520 Bordeaux France Hosp Univ La Paz Spine Surg Unit Madrid 28046 Spain Schulthess Klin Spine Ctr Div Dept Orthoped & Neurosurg CH-8008 Zurich Switzerland Hosp Univ Vall Hebron Spine Surg Unit Barcelona 08035 Spain Bilmed Comp & Software Co TR-34742 Istanbul Turkiye

machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. As algorithms have constantly been evolving with the exponential increase of computing power and vastly generated data, privacy of algorithms as well as of data becomes extremely important due to regulations and IP rights. Therefore, it is vital to address privacy and security concerns of both data and model together with other performance metrics when commercializing machine learning models. Our aim is to show that privacy-preserving machine learning inference methods can safeguard the intellectual property of models and prevent plaintext models from disclosing information about the sensitive data employed in training these ML models. Additionally, these methods protect the confidentiality of model users' sensitive patient data. We accomplish this by performing a security analysis to determine an appropriate query limit for each user, using the European Spine Study Group's (ESSG) adult spinal deformity dataset. We implement a privacy-preserving tree-based machine learning inference and run two security scenarios (scenario A and scenario B) containing four parts with progressively increasing the number of synthetic data points, which are used to enhance the accuracy of the attacker's substitute model. A target model is generated with particular operation site(s) in each scenario, and substitute models are built with nine-time threefold cross-validation using the XGBoost algorithm with the remaining sites' data to assess the security of the target model. First, we create box plots of the test sets' accuracy, sensitivity, precision, and F-score metrics to compare the substitute models' performance with the target model. Second, we compare the gain values of the target and substitute models' features. Third, we provide an in-depth analysis to check the inclusion of target model split points in substitute models with a heatmap. Finally, we compare t

关键词： Data models Computational modeling machine learning machine learning algorithms Cryptography Accuracy Analytical models Inference algorithms Homomorphic encryption Data privacy Homomorphic Encryption Privacy-Preserving machine learning XGBoost

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