Risultati della ricerca - split learning
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Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities
Pubblicazione 2025-01-01Soggetti: “...Split federated learning...”The rise of smart cities has leveraged the power of Internet of Things devices to transform urban services. A key element of this transformation is the widespread deployment of IoT devices for data collection, which feeds into machine learning algorithms to improve city services. However, the centra...
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution
Pubblicazione 2025-01-01Soggetti: TestoSplit Learning (SL) is an emerging decentralized paradigm that enables numerous participants, to train a deep neural network without disclosing sensitive information, such as patient data, in fields such as healthcare. In healthcare, SL enables distributed training across a variety of medical device...
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Collaborative Split Learning-Based Dynamic Bandwidth Allocation for 6G-Grade TDM-PON Systems
Pubblicazione 2025-07-01Soggetti: TestoDynamic Bandwidth Allocation (DBA) techniques enable Time Division Multiplexing Passive Optical Network (TDM-PON) systems to efficiently manage upstream bandwidth by allowing the centralized Optical Line Terminal (OLT) to coordinate resource allocation among distributed Optical Network Units (ONUs)....
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Self-Supervised Neural Networks for Precoding in MIMO Rate Splitting Multiple Access Systems
Pubblicazione 2025-01-01“...The use of machine learning (ML) tools to address the challenges posed in next generation of wireless communication systems has been gaining significant momentum. ...”The use of machine learning (ML) tools to address the challenges posed in next generation of wireless communication systems has been gaining significant momentum. In this paper, we investigate the use of self-supervised data-driven schemes for precoder optimization in the downlink of a Multiple-Inpu...
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Integrating Genetic Programming with Dimensional Analysis and Data-Splitting for Heat Transfer Estimation of Nanofluids
Pubblicazione 2025-12-01Soggetti: TestoThe use of nanofluids is expected to enhance heat transfer owing to their unique thermophysical properties when used as heat media. Although there have been many reports on the construction of equations for estimating heat transfer in nanofluids, the selection of the variables used and simplificatio...
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Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization
Pubblicazione 2025-06-01Soggetti: “...photoelectrochemical water splitting...”The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on brid...
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Integrating bibliometrics and deep learning to analyze emerging trends and predict future directions in photoelectrochemical water splitting for hydrogen production: A Scopus-database driven study
Pubblicazione 2025-12-01Soggetti: TestoHydrogen production is gaining significant attention due to its high energy content, zero emissions, and availability of renewable sources. Photoelectrochemical (PEC) water splitting has gained significant attention recently because it utilizes solar energy for green and renewable hydrogen productio...
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Edge-Deployed Band-Split Rotary Position Encoding Transformer for Ultra-Low-Signal-to-Noise-Ratio Unmanned Aerial Vehicle Speech Enhancement
Pubblicazione 2025-05-01“...While existing deep learning methods face limitations in dynamic UAV noise suppression under such constraints, including insufficient harmonic modeling and high computational complexity, the proposed Edge-BS-RoFormer distinctively synergizes a band-split strategy for fine-grained spectral processing, a dual-dimension Rotary Position Encoding (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>RoPE</mi></semantics></math></inline-formula>) mechanism for superior joint time–frequency modeling, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>FlashAttention</mi></semantics></math></inline-formula> to optimize computational efficiency, pivotal for its lightweight nature and robust ultra-low-SNR performance. ...”Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (...
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Machine learning‐accelerated computational screening of CrNiCu ternary alloy as superior cocatalyst for photocatalytic hydrogen evolution
Pubblicazione 2025-06-01Soggetti: TestoAbstract The development of cost‐effective noble‐metal‐free cocatalysts with exceptional hydrogen evolution reaction (HER) activity is critical for advancing scalable and sustainable photocatalytic hydrogen production. Although platinum (Pt) remains a benchmark HER catalyst, its scarcity and high co...
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete
Pubblicazione 2025-06-01“...To address the limited availability of literature on the splitting tensile strength (STS) of basalt fiber reinforced concrete (BFRC), it is necessary to create and evaluate approaches for predicting STS. ...”To address the limited availability of literature on the splitting tensile strength (STS) of basalt fiber reinforced concrete (BFRC), it is necessary to create and evaluate approaches for predicting STS. The current research examined two methods, namely least square support vector regression (LSSVR)...
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AI-generated corpus learning and EFL learners' learning of grammatical structures, lexical bundles, and willingness to write.
Pubblicazione 2025-01-01“...Pedagogical implications include the need for dynamic and engaging learning environments, while curriculum developers should consider incorporating data-driven learning approaches. ...”This research study examined the manner in which English as a Foreign Language (EFL) learners' willingness towards writing, grammatical construction, and lexical bundle acquisition were affected by language of AI-generated corpora. Eighty EFL students from China's Baotou Teachers' Col...
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AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING
Pubblicazione 2025-03-01“...Original data was preprocessed by handling missing values, normalizing features, and using feature extraction techniques. Splitting the dataset into 80% training and 20% testing, cross-validation was performed to validate outcomes on all four models. ...”Cardiovascular diseases (CD) are the common cause of death worldwide over in developed as well as underdeveloped and developing countries. Early detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. So...
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A Survey on Privacy-Preserving Machine Learning Inference
Pubblicazione 2025-07-01“...We review several approaches—from cryptographic techniques like homomorphic encryption (HE) and secure multi-party computation (MPC), to hardware solutions such as trusted execution environments (TEEs), and complementary methods including differential privacy and split learning. Each method is analyzed in terms of security, efficiency, communication overhead, and scalability. ...”This paper examines methods to secure machine learning inference (ML inference) so that sensitive data remains private and proprietary models are protected during remote processing. We review several approaches—from cryptographic techniques like homomorphic encryption (HE) and secure multi-party co...
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Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretabi...
Pubblicazione 2025-07-01“...Data were split into 80% training and 20% testing, maintaining a 63% benign and 37% malignant distribution. ...”This study evaluated machine learning (ML) models on the Wisconsin Breast Cancer Dataset (WBCD), refined to 554 unique instances after addressing 5% missing values via mean imputation, removing 15 duplicates, and normalizing features with Min–Max scaling. Data were split into 80% training and 20% te...
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch...
Pubblicazione 2025-08-01“...The retrospective cohort was randomly split into a training set and an internal validation set at an 8:2 ratio. ...”Summary: Background: Early identification and prediction of postoperative pulmonary complications (PPCs) are vital for patient management in lung cancer (LC) surgery. However, existing predictive models often lack comprehensive validation and interpretability. This study aimed to develop and valida...
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The effects of reciprocal, self–check, and command teaching styles on dance learning
Pubblicazione 2023-10-01TestoBackground and Study Aim. Implementation of dance in physical education is in conformity with the requirements of modern education which fosters lifelong exercise for health and quality of life. The study aimed to determine which of the applied teaching styles would have the most significant impact...
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Weather Classification in West Java using Ensemble Learning on Meteorological Data
Pubblicazione 2025-09-01TestoWeather classification in West Java presents several challenges, particularly related to class imbalance in the dataset and the complexity of meteorological variables. This study aims to improve classification accuracy by proposing a stacking classifier approach that combines Support Vector Machine...
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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis
Pubblicazione 2025-07-01“...The data was preprocessed, and target variables were converted into binary values for classification. The dataset was split into training and test sets in a 70-30 ratio. ...”The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms - Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting - on a dataset containing...
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest
Pubblicazione 2025-06-01“...This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. ...”Stroke is a leading cause of global death and disability. This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. A Kaggle dataset with 5110 samples (249 stroke, 4861 non-stroke) presented significant class imbalance. To addr...
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors
Pubblicazione 2025-04-01“...The results indicate the need for ensemble forecasting and evaluation beyond a static train–test split to ensure the practical utility of machine learning for DAEP forecasting across varied market dynamics. ...”Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. Hourly DAEP data from the...
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