Three-stages concatenated Machine Learning model for SFN prediction

Current broadcast digital networks make use of either a Single Frequency Network (SFN) or a Multi Frequency Network (MFN). None of these networks topologies is ideally suited for delivery of both global and local services in an efficiently way. MFNs enable the transmission of local services but need a significant amount of frequencies and therefore large amounts of valuable spectrum. SFNs are especially suitable for global services due to mutual support of the signals from multiples transmitters operated in the same frequency. SFNs are very spectrum efficiently; however they do not support local services. In this paper a hierarchical modulation method and Time Slicing technology are proposed for providing local and global contents in a terrestrial SFN (such DVB-T) keeping all its advantages. The hierarchical modulation has already been included in the DVB-T standard and time slicing solution is backwards compatible, in the sense that receivers that have been deployed in the original system can continue receiving data without hardware modifications. The performances of these technical solutions for both global and local services are analyzed. The applicability of these concepts is shown by a signal propagation simulation and coverage estimation in real scenario of Valencia region (Spain).

Recently, great progress has been achieved in diabetes management through minimally invasive continuous glucose monitoring, which involves piercing the skin to identify changes in glucose levels in the interstitial fluid. Nevertheless, the development of accurate fully noninvasive (NI) glucose monitors remains a topic of great interest. This work examines the processing of data collected from a multisensor system, which combines millimeter-wave (mm-wave), near-infrared (NIR), and other auxiliary sensors’ technology, coupled with a random forest machine learning (ML) model for prediction. We investigated possible correlations between mm-wave, NIR, skin temperature, pressure data, and blood glucose values and deployed an ML model for prediction. The model is based on the random forest algorithm that was trained and tested on data collected from a study involving five healthy human subjects undergoing an intravenous glucose tolerance test. Our model achieves a root mean square error of 21.06 mg/dl and a mean absolute relative difference of 7.31% for glucose prediction in the collected clinical subjects dataset. In addition, 96.1% of the model’s predictions fall within the clinically acceptable zones according to the Clarke error grid analysis. The preliminary results suggest that combining multiple sensor data with ML techniques is promising for the development of NI glucose monitoring.

Due to the increasing demand of services and content, Quality of Service (QoS) has become an important aspect in digital broadcasting industry especially with the implementation of Digital Terrestrial Television (DTT) in Malaysia. Thus, the performance of DVB-T2 in Malaysia should be investigated because there are areas which are not in the coverage due to variety of terrain, where some areas received weak DVB-T2 signals. Apart from geological and topographic factors in Malaysia, the selection of DVB-T2 parameters is also an important factor in determining signal strength and coverage This research has proposed to study the performance analysis of DVB-T2 propagation for fixed reception in Malaysia's central region. The Field Strength Measurement (FSM) was set up to evaluate the QoS of DVB-T2 signal such as Field Strength (E), Modulation Error Ratio (MER), and Carrier to Noise Ratio (C/N) at specific test points across Malaysia's central region. The FSM was simulated at the designated test points using CHIRplus_BC software to evaluate the performance of Single Frequency Network (SFN) at three locations of transmitter. The DVB-T2 Modulation and Forward Error Correction (FEC) code rate parameters were simulated in order to evaluate the improvement in terms of coverage area. It can be concluded that the coverage of DTT is reduced with the increase in the distance of the measurement area to the transmitter. The finding also shows that the field strength value obtained from FSM and simulation was comparable. Furthermore, from the simulation results. it can be perceived that the coverage area across the central region of Malaysia could be improved by optimizing the modulation and FEC code rate. However, there is a trade-off between the signal coverage and channel capacity. The greater the signal coverage the less the channel capacity.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

In recent years, cardiovascular disease (CVD) has emerged as a leading cause of mortality worldwide, highlighting the urgent need for reliable predictive models to support early diagnosis and preventive care. This study conducts a comparative analysis of various machine learning models to assess CVD risk, focusing on their ability to accurately identify individuals at high risk based on clinical and demographic data. We evaluate multiple supervised learning algorithms, including logistic regression, random forests, support vector classifiers, K-nearest neighbors, gradient boosting, and AdaBoost, comparing their predictive performance in terms of accuracy, precision, recall, and F1-score. Additionally, we propose a novel hybrid model that combines Random Forest (RF) for feature selection and Deep Neural Networks (DNN) for classification, aiming to leverage the strengths of both approaches for more accurate CVD prediction. Our findings demonstrate that ensemble models such as random forests and gradient boosting achieve superior performance, with high accuracy (0.99) and balanced precision and recall values, outperforming simpler models like logistic regression and support vector classifiers. The hybrid model further enhances prediction accuracy, achieving 92.4% accuracy, 91.7% precision, 93.0% recall, and an AUC-ROC score of 96.0%. The analysis also underscores the importance of data preprocessing techniques, including normalization and handling of missing values, in optimizing model accuracy and stability. Notably, K-nearest neighbors also performed exceptionally well with a high F1-score across classes, highlighting its robustness for this task. This study provides a detailed examination of each model's strengths and limitations, including the proposed hybrid model, offering valuable insights for healthcare practitioners and data scientists in selecting effective machine learning models for CVD risk prediction. By integrating these models into healthcare systems, real-time risk prediction can be enhanced, ultimately supporting clinical decision-making and advancing personalized care in cardiovascular health. Keywords— cardiovascular disease, machine learning, risk assessment, predictive models, supervised learning, healthcare

As the number of users or services continues to increase, limited bandwidth resources become a huge challenge for power wireless communication networks. The communication protocols widely used in the current distribution network have unclear data meanings, less correlation between data, and inability to interoperate between terminals, which is a bottleneck restricting the development of smart distribution networks. With the development and implementation of the IEC 61850 standard, more and more power service are sent in the form of broadcast and multicast. Among them, the GOOSE (generic object oriented substation event) service is used to transmit events such as real-time trip signals that require fast delivery of message information, and real-time performance is its main feature. However, in the multicast transmission of the power service carried by the existing transmission mode, there is a serious contradiction between resource utilization and the quality of services (QoS) of users. Therefore, in order to balance the QoS with poor signal quality in SFN (Single Frequency Network) transmission mode and to solve the problem of resource allocation being limited by the worst QoS, this paper proposes a hybrid transmission mode combining PTP (point to point) and SFN transmission. In addition, in order to improve resource utilization efficiency, we design a new transmission mode selection algorithm focus on the improvement of spectrum efficiency. The optimal solution obtained through greedy algorithms is the best transmission mode scheduling strategy. The simulation results show that the MIX (mixed transmission mode) effectively reduces the impact of poor QoS users on system performance when the number of users increases. At the same time, the transmission mode selection algorithm can save system resources more effectively than the traditional selection algorithm (PTP and SFN mode).

This study presents a comparative analysis of machine learning models for predicting carbon monoxide (CO) emissions in automotive engines. Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. Among these, the Random Forest model demonstrated the highest predictive accuracy, achieving an R² score of 0.8965. Feature importance analysis identified nitrogen oxides (NOX), engine speed (RPM), and hydrocarbons (HC) as the most significant predictors of carbon monoxide emissions. Learning curve analysis provided insights into model generalization and highlighted potential limitations. The study underscores the value of data-driven approaches in optimizing engine design and controlling emissions. The findings contribute to the development of cleaner, more efficient vehicles, supporting sustainability efforts in the automotive industry. This research bridges data science and automotive engineering, offering a framework for advanced emission prediction and control that can be applied to other pollutants and engine types.

BackgroundGiven the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database.MethodsPatients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including variables that were known pre-procedurally (prior to TF-TAVR); Stage 2, including variables that were known post-procedurally; Stage 3, including length of stay (LOS) in addition to the stage 2 variables.ResultsA total of 18,793 hospitalization for TF-TAVR were analyzed. The mean and median adjusted hospitalization charges were $220,725.2 ($137,675.1) and $187,212.0 ($137,971.0–264,824.8) respectively. Random forest regression algorithm outperformed other ML algorithms at all stages with higher R2 score and lower mean absolute error (MAE), root mean squared area (RMSE) and root mean squared logarithmic error (RMSLE) (Stage 1: MAE 79,979.11, R2 0.157; Stage 2: MAE 76,200.09, R2 0.256; Stage 3: MAE 69,350.09, R2 0.453). LOS was the most important predictor of hospitalization charges.ConclusionsWe built ML algorithms that predict hospitalization charges with good accuracy in patients undergoing TF-TAVR at different stages of hospitalization and that can be used by healthcare providers to better understand the drivers of charges.

Digital terrestrial television systems (DVB-T) using single frequency networks (SFN) based on OFDM modulation schemes have been deployed during the last years. Such SFN topology make it possible to increase the number and quality of services offered due to their more efficient use of the spectrum compared to multiple frequency networks (MFN). Moreover, SFN can cut down on the number of transmitters along with their powers because of the internal gain of the network, saving overall deployment and maintenance costs, and even achieving coverage improvements or a decrease of the outage probability over larger geographical areas through the reuse of frequencies. In this work, an approach to analyze and improve the coverage of a SFN over the most populated regions with the least possible economic investment is proposed, considering several strategies to synchronize and combine the signals at the receiver side and using a hybrid algorithm, mixing up the real and binary versions of the Particle Swarm Optimization algorithm (PSO), to optimize the static delays, gains and orientations for each SFN transmitter and decide whether each sector antenna must be active or not.

Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. They show how some distinct choice of human behavior or preference influences a decision. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. The machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. These results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features.

Rainfall plays a crucial role in agricultural practices, influencing the growth of essential crops and impacting economies reliant on agriculture. In Rajasthan, India, where agriculture is predominantly rainfed and dependent on unpredictable monsoon rains, accurate rainfall forecasting is vital for effective crop management, irrigation, optimizing agricultural productivity, managing water resources, and addressing drought conditions and. This study evaluates various machine learning models for predicting rainfall in East and West Rajasthan. The models assessed include Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and K-Nearest Neighbours (KNN). These models were compared based on their accuracy measures, including Root Mean Squared Error (RMSE), Relative Root Squared Error (RRSE), and Relative Absolute Error (RAE). The results indicate that ANN and KNN perform poorly compared to SVR and RF suggesting their inadequacy for rainfall forecasting in water-scarce regions. The performance of SVR and RF are comparable as the difference is less. However SVR is identified as the most effective model for rainfall prediction in both subdivisions, as highlighted in the study. This study suggest that SVR and RF model should be includes in prediction of amount of rainfall.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation