The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models

Machine learning (ML) has recently been used as an efficient surrogate to estimate different steps of performance‐based earthquake engineering (PBEE), from dynamic structural analysis to fragility and loss assessments. However, due to the varied data, models, and features in existing literature, the relative efficiency of ML models across different PBEE steps remains unclear. Additionally, the black‐box nature of advanced ML algorithms limits their ability to provide design‐oriented insights, hindering the broader application of ML in PBEE‐based design. This study provides a comprehensive comparison of the accuracy and explainability of design‐oriented ML models across different steps of PBEE using a consistent database of 621 steel moment frames with varying designs and geometry. Eight ML algorithms were used in a careful training workflow comprising feature selection, hyperparameter tuning, cross‐validation, and model inference. The sensitivity of model accuracy to representative PBEE outputs—maximum responses, median fragility, and expected annual loss—was assessed using statistical measures. In addition, the explainability of the best models for each step was examined to explore the relationship between design parameters and the corresponding PBEE output. The results show that while ML models can reasonably map design parameters to all different PBEE outputs, models accuracy was higher for drift responses, median fragilities, and component‐based loss metrics. In addition, the optimal algorithm remained the same across different PBEE steps, where support vector machines and random forests provided the highest accuracy with an average R2 of 0.93 and 0.91 over different outputs on the test set. Although the selected feature sets varied across outputs and algorithms, height, number of stories, fundamental period, and the minimum of the beams’ moment of inertia were influential for both models and notably affected different PBEE outputs.

Background: The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning [ML] models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol- Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning [SML] models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article. Methods: The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability. Results: The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters. method: In this research work the researcher considered 9 different ML models and used them as supervised as well as semi supervised model to determine the pollution level of the certain area. In this research work the researcher also selected the most relevant features and discarded the less relevant features. In case of SML algorithm, the accuracy level of the different ML algorithm has been determined and depicted in the result analysis section. The most effective ML model has been chosen for the proposed embedded system so that accuracy could be achieved at most. In case of semi supervised algorithm the feature selection is done as per the supervised algorithm. In this case the training is done same as the SML algorithm, but the testing phase is done like unsupervised machine learning algorithm where the decision parameter is predicted and ultimately matched with the previously achieved data of SML algorithm. The reliability of this approach is much more effective than simple SML algorithm. Conclusion: This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Developing predictive models for surgical outcomes in patients with degenerative cervical myelopathy: a comparison of statistical and machine learning approaches

This study aimed to apply four machine learning algorithms to develop the optimal model to predict decline in platelet count (DPC) after interventional closure in children with patent ductus arteriosus (PDA). Data from children with PDA who underwent successful transcatheter closure at the Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital from January 2016, to December 2022, were collected. The cohort data were split into training and testing sets. DPC following the intervention is defined as a percentage DPC ≥25% [(baseline platelet count-nadir platelet count)/baseline platelet count]. The extra tree algorithm was used for feature selection and four ML algorithms [random forest (RF), adaptive boosting, extreme gradient boosting, and logistic regression] were established. Moreover, SHapley Additive exPlanation (SHAP) to explain the importance of features and the ML models. This study included 330 children who underwent successful transcatheter closure of PDA, of which 113 (34.2%) experienced DPC. After 62 clinical features were considered, the extra tree algorithm selected six clinical features to build the ML models. Amongst the four ML algorithms, the RF model achieved the greatest AUC. SHAP analysis revealed that pulmonary artery systolic pressure, size of defect and weight were the top three most important clinical features in the RF model. Furthermore, clinical descriptions of two children with PDA, with accurate predictions, and explanations of the prediction results were provided. In this study, an ML model (RF) capable of predicting post-intervention DPC in children with PDA undergoing transcatheter closure was established.

Bias-reduced dental age estimation in children: Machine-learning evaluation and a novel constrained random forest adaptation of the Cameriere method.

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works & The time to reach the desired level of the model’s functionality is essential to gauge the environment training for the model training & pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live(time until 1st code modification) and time-for-modification(time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models & frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline.

Cellular networks evolve towards future generations, facing unprecedented levels of device and network programmability. At the same time, the new vision of the cyber-physical continuum will rely on diverse network architectures in extremely dense deployments. The emergence of new types of cells, like mobile and drone ones, would rely on instantly available AI/ML algorithms to provide a service within a few seconds after being powered on, avoiding long periods of data collection and training.In this work, we discuss how cell-specific characteristics, like the radio environment, can impact area-based ML models. Even though area-based models simplify the management of ML workflows considerably, there is also a need for cell-based models as these tend to provide better performance. Moreover, we show that area-based models can be part of ML workflow as they can complement cell-based ones. We finalize our work by discussing the possibility of reusing available ML models from other cells as a way of reducing the time needed for applying ML algorithms in newly deployed cells. We provide initial insights on the model re-usability and performance assessment and highlight the need for more research in this direction.In our work, we utilize the data from a test network, allowing us to explore the dynamics of real networks and provide results with increased confidence.

ObjectiveTo investigate a panel of variables using four machine learning based classifiers, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN) and logistic regression (LR) to make a diagnosis of ovarian cancer, differentiating it from benign ovarian masses.Materials and methodsA prospective observational pilot study was done between November 2021 and June 2023. Following data pre-processing to ensure compatibility with ML models, four ML algorithms, i.e., support vector machine (SVM), logistic regression (LR), random forest (RF) and artificial neural network (ANN) were tested by multimodal parameters from the datasets of 50 patients presenting with suspected epithelial ovarian cancer (Group A) or benign ovarian tumour (Group B). Statistical analysis was done using STATA version 14.0.ResultsWe found that the machine learning approach could predict malignant tumours with appreciably high accuracy similar to a few studies done so far in this field. All four ML algorithms showed high level of accuracy with a maximum AUROC of 0.92 in the RF model. Both RF and SVM had an accuracy of 85.87 and 83.05%.ConclusionThe ML algorithms can detect ovarian cancers with a high level of accuracy. Further, a large-volume prospective study on large volume data sets is required before inclusion of ML algorithms in clinical practice.

Assigning metadata of RFIs accurately plays an important role in the management of RFI documents. However, these metadata are manually entered in the RFI management system, which results in loss of time and incorrect entries. This study aims to demonstrate that metadata of RFI documents can be obtained and assigned automatically using NLP and ML algorithms. The results show that ML models perform well in automatically extracting the metadata of RFIs. The findings of this study can be used to develop an AI based RFI management system by integrating NLP and ML models into the system.

Predicting the soil swelling index (CS) is crucial for geotechnical engineer to ensure the stability of civil engineering conceptions. Recently, ML models has sparked great interest from researchers in predicting the soil swelling index. However, due to the black-box nature of ML models, their prediction capabilities are still uninterpretable. This study aims to predict the soil swelling index using ML algorithms and interpret predictions. First, it employs the prediction capability of the Gaussian process regression (GPR) algorithm and compares it to the artificial neural network (ANN) for prediction the soil swelling index. Second, the SHAP algorithm as one recent explainable artificial intelligence (XAI) models is applied to interpret the predictions of the complex GPR and ANN models. The compiled experimental database covers 362 clayey samples gathered from different sites located in Northern Algeria. The modeling involved six input features, including the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (ωn), dry density (γd), and void ratio (e) to predict the soil swelling index. The findings based on statistical metrics showed a good performance of GPR with R2 = 0.78 and of ANN with R2 = 0.79. Comparative study based on Wilcoxon signed- rank test and sign test indicated that the ANN outperform better than GPR. Based on the interpretations obtained by SHAP algorithm, it is observed that the liquid limit (LL) and plastic limit (PL) are the two main input features that influence the CS, indicating, the higher content of LL and PL increase the model's output.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment

The identification and management of Multiple Sclerosis (MS) patients who are at risk of disease progression and/or conversion to Secondary Progressive MS (SPMS) is a significant unmet clinical need. This study aimed to develop machine and deep learning (ML/DL) models to predict disability progression and/or conversion to SPMS at least 6 months before progression, determined by Expanded Disability Status Scale (EDSS) score. Three traditional ML algorithms were trained on brain parcellation volume measurements derived from T1 weighted magnetic resonance images (MRI) and convolutional neural networks (CNNs) were trained directly on MRI images. The results showed that the three ML models performed slightly better than a random classifier, with the Support Vector Classifier achieving the best results in terms of area under the receiver operating characteristic curve (AUROC) with a score of 0.62. The CNN approach yielded the most promising results, with an AUROC score of 0.75. This suggests that the ability to identify MS patients at high risk of future disability progression directly from MRI scans is a promising research direction. Further development of these algorithms could enable timely interventions, potentially preventing or delaying the onset of disability progression and/or SPMS.

Childhood anemia is a major health problem. Anemia, which is common in preschool-aged children, causes physical and mental developmental delays in this age group. It is important to address and investigate these preventable and treatable health problems using state-of-the-art methods such as artificial intelligence. Therefore, this study employs machine learning techniques, a subfield of artificial intelligence, to predict the anemia levels in children aged 0–59 months in Nigeria. To address the issue of data imbalance, which can cause problems in estimating childhood anemia levels, the SMOTE and ADASYN oversampling techniques were employed in this study. In the analyses performed with the newly obtained balanced data, it was observed that the SMOTE and ADASYN methods performed significantly better than the results obtained with imbalanced data for all ML models. When the average results of all ML algorithms used in this study in terms of accuracy, precision, recall, and F1 score metrics are compared to the oversampling methods, the most successful result in terms of all metrics was obtained with the SMOTE method.

Bitcoin (BTC) is a cryptocurrency meaning that it is a virtual asset that works on encryption systems to control production of units and verify the funds transfer. Bitcoin's operation is independent of central bank and can be used as a decentralized medium of exchange. Being an emerging domain, the blockchain systems on which these cryptocurrencies are based, are a great source of investment among enthusiasts. However, there is high volatility in the price of Bitcoin and thus it paves the way for its prediction so as to get a clear insight on its trend. Forecasting Bitcoin prices can provide valuable insights for investment portfolio management, risk assessment, and the identification of profitable trading or arbitrage opportunities. Furthermore, Bitcoin price predictions may assist firms that accept Bitcoin payments in enhancing their financial management practices by improving their revenue and cash flow forecasting. Additionally, governmental entities and regulatory bodies could leverage Bitcoin price forecasting as a tool for monitoring and regulating the cryptocurrency industry. This paper explores the effectiveness of ML models in predicting Bitcoin price by analyzing a diverse set of historical data. We evaluate several ML algorithms, and compare their performance in terms of accuracy to find out best algorithms for short term and long-term Bitcoin price prediction.