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Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure from the stated environment can affect our heart and lungs very badly. The particle pollution exposure creates a variety of problems including nonfatal heart attacks, premature deaths in people with lung or heart disease, asthma, difficulty in breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture the diverse dynamics of ambient particulate matter and then applied the Chi-square feature selection method to acquire the most relevant features. We also optimized parameters of robust machine learning algorithms to further improve the prediction performance such as Decision Tree, SVM with Linear and Regression, Naïve Bayes (NB), Random Forest (RF), Ensemble Classifier, K-Nearest Neighbor, and XGBoost for classification. The classification results with and without feature selection methods yielded the highest detection performance with random forest, and GBM yielded 100% of accuracy and AUC. The results revealed that the proposed methodology is more robust to provide an efficient system that will detect the particulate matters automatically and will help the individuals to improve their lifestyle and comfort. The concerned department can monitor the individual's healthcare services and reduce the mortality risk [ABSTRACT FROM AUTHOR]

Precision Nutrition makes use of personal information about individuals to produce nutritional recommendations that have more utility than general population level recommendations. In many cases, being able to predict current status is a necessary first step in offering tailored nutritional advice. The objective of this study is to predict plasma vitamin C using machine learning. The NHANES dataset was used to predict plasma vitamin C in a cohort of 2952 American adults using regression algorithms and clustering in a way that a hypothetical health application might. Variables were selected based on a known or hypothesized relationship with plasma vitamin C, and variables that are expensive or difficult to obtain were excluded in order to more closely replicate the situation of a real health application. The best performance was seen with the XGBoost regressor, with random forest performing almost identically. Clustering was also investigated as a means of improving regression accuracy by splitting the data up into smaller yet more homogeneous groups, however, this was not successful. The low R-squared scores obtained by the models are likely to be due to the low resolution of the NHANES data, particularly the dietary data. This emphasizes the need for high-quality data sets in Precision Nutrition research. [ABSTRACT FROM AUTHOR]

In today's world, people need sleeker devices with better functionality and longer battery life. This can be achieved by integrating more components onto smaller chips, resulting in a shift to low-geometry chip design. However, power dissipation due to dynamic and static currents is more prominent in all ICs, resulting in an increase in overall power consumption. Estimating power dissipation early will provide more accurate usage of power pads/strips and help floor plan engineers do power planning efficiently. As you provide more details about your design characteristics, the estimation of power will be accurate. The major focus of this work is to give an alternative solution to predict the power dissipation of integrated circuits using a machine learning approach in both pre and post layout. The proposed work uses supervision models and algorithms like Linear regression, KNN, SVM, and RF for power prediction and a comparative study is made between power estimates made using ML algorithms and by the Cadence EDA tool for a particular technology for various bench circuits. The average error is less than 4% when we compare the estimated power using ML and by using the Cadence EDA tool and shows that for estimation of power in integrated circuits, Random Forest is a well-suited algorithm with an error percentage varying from 2 to 4. [ABSTRACT FROM AUTHOR]

Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms. We investigated the actual effects of missing data for regression by analyzing its impact in several publicly available databases implementing popular algorithms like Decision Tree, Random Forests, Adaboost, K-Nearest Neighbors, Support Vector Machines, and Neural Networks. Our goal is to offer a systematic view of how missing data may affect regression results. After exhaustive simulation analyzing eight public data-sets from UCI and KEEL (Abalone, Arfoil, Bike, California, Compactiv, Mortage, Wankara and Wine), we concluded that the effect of missing data may be significant. The results obtained showed that K-Nearest Neighbors works better than others in the regression of data that has missing data. [ABSTRACT FROM AUTHOR]

Studies reported that playing video games with harmful content can lead to adverse effects on players. Therefore, understanding the harmful content can help reduce these adverse effects. This study is the first to examine the potential of interpretable machine learning (ML) models for explaining the harmful content in video games that may potentially cause adverse effects on players based on game rating predictions. First, the study presents a performance analysis of the supervised ML models for game rating predictions. Secondly, using an interpretability analysis, this study explains the potentially harmful content. The results show that the ensemble Random Forest model robustly predicted game ratings. Then, the interpretable ML model successfully exposed and explained several harmful contents, including Blood, Fantasy Violence, Strong Language, and Blood and Gore. This revealed that the depiction of blood, the depiction of the mutilation of body parts, violent actions of human or non-human characters, and the frequent use of profanity might potentially be associated with adverse effects on players. The findings suggest the strength of interpretable ML models in explaining harmful content. The knowledge gained can be used to develop effective regulations for controlling identified video game content and potential adverse effects. [ABSTRACT FROM AUTHOR]

A stroke is a medical disorder in which blood arteries in the brain rupture, causing brain damage. Symptoms may appear when the brain's blood supply and other nutrients are cut off. According to the World Health Organization, Stroke is the leading cause of death and disability globally. Early recognition of the multiple warning signs of a stroke helps reduce the severity of the stroke. The paper presents a modified version of the Genetic Folding algorithm to predict stroke based on symptoms. Considerable Machine Learning models, including Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, and the proposed Minimal Genetic Folding, were compared to forecast the probability of having a stroke in the brain using a variety of physiological characteristics. The proposed minimal Genetic Folding approach has been developed using the open-access Stroke Prediction dataset using minimal kernel operators. The datasets generated and/or analyzed during the current study are available in the Kaggle repository. With an accuracy of 83.2%, the proposed minimal Genetic Folding approach outperformed Logistic Regression by 4.2%, Naïve Bayes by 1.2%, Decision Tree by 17.2%, and Support Vector Machine by 83.2%. The area under the curve of the proposed model is much more significant than earlier research by 7%, demonstrating that this model is more dependable and was the top-performing algorithm. [ABSTRACT FROM AUTHOR]

Disruptive events within a country can have global repercussions, creating a need for the anticipation and planning of these events. Crystal Cube (CC) is a novel approach to forecasting disruptive political events at least one month into the future. The system uses a recurrent neural network and a novel measure of event similarity between past and current events. We also introduce the innovative Thermometer of Irregular Leadership Change (ILC). We present an evaluation of CC in predicting ILC for 167 countries and show promising results in forecasting events one to twelve months in advance. We compare CC results with results using a random forest as well as previous work. [ABSTRACT FROM AUTHOR]

The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modeling using historical data. Estimation of power for CMOS VLSI circuit using various circuit attributes is proposed using passive machine learning-based technique. The proposed method uses supervised learning method, which provides a fast and accurate estimation of power without affecting the accuracy of the system. Power estimation using random forest algorithm is relatively new. Accurate estimation of power of CMOS VLSI circuits is estimated by using random forest model which is optimized and tuned by using multiobjective NSGA-II algorithm. It is inferred from the experimental results testing error varies from 1.4% to 6.8% and in terms of and Mean Square Error is 1.46e-06 in random forest method when compared to BPNN. Statistical estimation like coefficient of determination (R) and Root Mean Square Error (RMSE) are done and it is proven that random Forest is best choice for power estimation of CMOS VLSI circuits with high coefficient of determination of 0.99938, and low RMSE of 0.000116. [ABSTRACT FROM AUTHOR]