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Objective: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried blood spots, followed by thyroid-stimulating hormone (TSH) and thyroxine-binding globulin (TBG) measurements enabling detection of both CH-T and CH-C, with a positive predictive value (PPV) of 21%. A calculated T4/TBG ratio serves as an indirect measure for free T4. The aim of this study is to investigate whether machine learning techniques can help to improve the PPV of the algorithm without missing the positive cases that should have been detected with the current algorithm. Design & methods: NBS data and parameters of CH patients and false-positive referrals in the period 2007–2017 and of a healthy reference population were included in the study. A random forest model was trained and tested using a stratified split and improved using synthetic minority oversampling technique (SMOTE). NBS data of 4668 newborns were included, containing 458 CH-T and 82 CH-C patients, 2332 false-positive referrals and 1670 healthy newborns. Results: Variables determining identification of CH were (in order of importance) TSH, T4/TBG ratio, gestational age, TBG, T4 and age at NBS sampling. In a Receiver-Operating Characteristic (ROC) analysis on the test set, current sensitivity could be maintained, while increasing the PPV to 26%. Conclusions: Machine learning techniques have the potential to improve the PPV of the Dutch CH NBS. However, improved detection of currently missed cases is only possible with new, better predictors of especially CH-C and a better registration and inclusion of these cases in future models.

This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.

This article introduces a new iterative approach to explainable feature learning. During each iteration, new features are generated, first by applying arithmetic operations on the input set of features. These are then evaluated in terms of probability distribution agreements between values of samples belonging to different classes. Finally, a graph-based approach for feature selection is proposed, which allows for selecting high-quality and uncorrelated features to be used in feature generation during the next iteration. As shown by the results, the proposed method improved the accuracy of all tested classifiers, where the best accuracies were achieved using random forest. In addition, the method turned out to be insensitive to both of the input parameters, while superior performances in comparison to the state of the art were demonstrated on nine out of 15 test sets and achieving comparable results in the others. Finally, we demonstrate the explainability of the learned feature representation for knowledge discovery.

This article conducts a comparative analysis to investigate the effects of different classification algorithms and structural proximity indexes on the performance of the supervised link prediction approach to anticipating technology convergence at different forecast horizons. For this, we identify relationships between technologies of interest for different time periods and compute 10 structural proximity indexes among unconnected technologies at each period. We develop a set of classification models that identify potential convergence among unconnected technologies where each model is configured differently by a classification algorithm and a combination of the proximity indexes. We compare the performance of the classification models to investigate effective combinations of classification algorithms and proximity indexes at different forecast horizons. The empirical analysis on Wikipedia articles about artificial intelligence technology indicates that random forest outperforms others in short-term forecasting while support vector machine outperforms others in mid-term forecasting. We also identify structural proximity indexes that produce higher performance when combined with the most effective algorithm at each forecast horizon. The results of this article are expected to offer guidelines for choosing classification algorithms and indexes when applying the supervised link prediction approach in anticipating technology convergence.

As an indicator of exchanges between watersheds, rivers and coastal seas, salinity may provide valuable information about the exposure, ecological health and robustness of marine ecosystems, including especially estuaries. The temporal variations of salinity are traditionally approached with numerical models based on a physical description of hydrodynamic and hydrological processes. However, as these models require large computational resources, such an approach is, in practice, rarely considered for rapid turnaround predictions as requested by engineering and operational applications dealing with the ecological monitoring of estuaries. As an alternative efficient and rapid solution, we investigated here the potential of machine learning algorithms to mimic the non-linear complex relationships between salinity and a series of input parameters (such as tide-induced free-surface elevation, river discharges and wind velocity). Beyond regression methods, the attention was dedicated to popular machine learning approaches including MultiLayer Perceptron, Support Vector Regression and Random Forest. These algorithms were applied to six-year observations of sea surface salinity at the mouth of the Elorn estuary (bay of Brest, western Brittany, France) and compared to predictions from an advanced ecological numerical model. In spite of simple input data, machine learning algorithms reproduced the seasonal and semi-diurnal variations of sea surface salinity characterised by noticeable tide-induced modulations and low-salinity events during the winter period. Support Vector Regression provided the best estimations of surface salinity, improving especially predictions from the advanced numerical model during low-salinity events. This promotes the exploitation of machine learning algorithms as a complementary tool to process-based physical models.

The auditory brainstem response (ABR) by evoked potentials is a widespread auditory pathway assessment technique. This is largely applied due to its cost-effectiveness, practicality and ease of use. In contrast, it requires a trained professional to carry out the analysis of the results. This motivates several research efforts to increase the independence of the diagnostician. To this end, the present work shows the ability of three signal classification tools to differentiate ABR studies of normal hearing subjects from those who may have some pathology. As a starting point, the PhysioNet short term auditory evoked potentials databases are used to calculate the features later applied to construct the dataset. The features used are diverse classes of permutation entropy, fractal dimension, the Lyapunov exponent and the zero crossing rate. To ensure more accurate results, a Montecarlo simulation of one thousand trials is employed to train the classifiers and test the results. The goodness of the classification is performed upon the basis of the computation of quality parameters, such as the area under the receiver operating characteristic curve (ROC), the F1 score coefficient and the accuracy. In all the cases the methodology proposed gives high performance results that encourage the line of research of the present work.

Coastal areas constitute the most important part of the world when considered in terms of their socio-economic and natural values. Measuring and monitoring the coastal areas accurately is an important issue for coastal management. Compared to ground-based studies, remote sensing applications enriched with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) provide significant benefits in terms of cost, time, and size of the study area. Within the scope of this study, Sentinel-2 images for five coastal areas located in Turkey with different morphological and hydrodynamic properties were classified as land and water-bodies using SVM and RF algorithms. Water-body segmentation results of the SVM and RF classification for the different band combinations of Sentinel-2 images have been compared. The reasons affecting the results of the accuracy analysis were examined in accordance with the geography of each area. Experimental results show that the utilized machine learning methods provide satisfactory results for combinations involving the NIR band in all study areas.

This paper analyzes the impacts of economic shocks on Japanese regional employment using causal forest algorithms, and our data-drive findings are compared with those estimated under the standard OLS estimation. Our results are as follows. The largest declines in employment growth during the recessionary period associated with the crisis are in industries and regions where employment grew before the crisis, and in industries that have a large share of employment in the region. These results differ from the standard OLS results, which show that the negative impact of shocks is greater in regions with higher unemployment rates. In addition, testing the robustness with respect to outliers, which are samples that could be arbitrarily removed, we show that the results obtained by causal forests are robust, but those of OLS are not.

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level. Although the use of regression models is common in forest measurement, there is a constant search for estimation procedures that provide greater accuracy. Recently, machine learning techniques have been used with satisfactory performance in measuring forests. However, methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS) and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil. Therefore, it is essential to check whether these techniques can provide gains in terms of accuracy. Thus, this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production. The data used come from continuous forest inventories carried out in stands of eucalyptus clones. The data were divided into 70% for training and 30% for validation. The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means. Besides, training was done with the hybrid algorithm (descending gradient and least squares) with the number of seasons ranging from 1 to 20. Several RFs were trained, varying the number of trees from 50 to 850 and the number of observations by five leaves to 35. Artificial neural networks and decision trees were also trained to compare the feasibility of the techniques. The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics: correlation coefficient (r), relative Bias (RB), and the relative root mean square error (RRMSE) in percentage. In general, the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values 0.98. The RF presented inferior statistics about the ANFIS for the prognosis of forest production. The Subtractive Clustering (SC) and Fuzzy-C-Means (FCM) algorithms provide accurate baseline and volume projection estimates; both techniques are good alternatives for selecting variables used in modeling forest production.

This paper provides an integrated analytical framework to investigate the demographic and behavioural factors that significantly influence public support for pedestrianisation. Pedestrianisation is often introduced by local authorities with the intention of improving air quality, the walkability of streets, road safety and opportunities for the local economy, however, issues remain regarding how accessible pedestrianised areas are for individuals who have conditions that limit their mobility. Using data from a survey, conducted during 2020 in Edinburgh (UK), public perceptions towards pedestrianisation were investigated through statistical testing and the development of random forest and ordered probit models. The random forest approach can help identify the relative importance of explanatory variables, whereas the ordered probit models can unveil the demographic and behavioural determinants of public support. To account for the potential effect of unobserved heterogeneity within respondents’ perceptions, random parameters were also considered in the ordered probit modelling framework. Initial results showed that residents are generally supportive of most issues surrounding pedestrianisation. Random parameters ordered probit modelling identified mode of travel and trip frequency as significant factors affecting key aspects of public support, such that active travellers were significantly more likely to support pedestrianisation, while those who rarely visit Edinburgh city centre were more likely to oppose pedestrianisation. Overall, a variety of independent analyses and modelling approaches suggest common influences on opinion, including behavioural patterns relating to transport modal choice and trip frequency, while disability was also found to have considerable effect on support as a fixed and random parameter. The statistical models are evaluated in terms of goodness-of-fit measures, before policy implications are discussed.

Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus has been placed on the analysis of a transition to dimensionless input quantities. The methods used in this paper are the support vector machine, two-tiered support vector machine, random forest, gradient-boosted trees and long-short term memory. The performance between different models is remarkably similar, with the support vector machine attaining a slightly better accuracy score. The similarity could indicate issues with the dataset, but further study is required to confirm this. Both the two-tiered model and long-short term memory performed below expectations. The former could be attributed to an implementation which did not allow error propagation between tiers. The latter could be attributed to high noise and low frequency of the input signals. Dimensionless models experienced an expected decrease in performance, caused by a loss of information in the conversion. However, random forest and gradient boosted trees experienced a significantly lower decrease, making them more suitable for dimensionless predictors. From the disruption detection times, it was concluded that several disruptions could be predicted at more than 600 ms in advance. A feature importance study using the random forest indicated the negative impact of high noise and missing data in the database, suggesting improvements in data preparation for future work and the potential reevaluation of some of the selected portable features due to poor performance.

The aim of this investigation was to evaluate the ability to predict the roughened surface area of soft polymeric materials using the machine learning method. Random forest and extremely random forest machine learning methods have been applied to predict roughened monolithic butadiene styrene rubber surface area based on the grain diameter of the abrasive paper. It was found that there is a very strong negative correlation between surface roughness and abrasive grain diameter (the Pearson correlation coefficient is equal to - 0.83 and the Spearman correlation coefficient is equal to - 0.91). The statistics of Shapiro-Wilk criteria confirmed that independently of the diameter of abrasive grain, the lengths of rubber surface profiles are normally distributed. It was determined that random forest and extremely random forest algorithms are suitable tools for predicting rubber surface area in dependence on abrasive grain diameter. The forest structure in which results obtained best coincidence with the observed (R2 = 0.95): for the random forest model the number of trees is equal to 20, the leaf size is equal to 4, at the subset of the feature (abrasive paper grain diameter) equal to 1. In the case of an extremely random forest, the number of trees is equal to 350, the size of the subset of the features is equal to 1, and the leaf size is 2. The experimental and generated results agree well. The relative approximation error does not exceed 0.079 %.

Nowadays, machine learning (ML) algorithms have been widely chosen for classifying satellite images for mapping Earth's surface. Support Vector Machine (SVM) and Random Forest (RF) stand out among these algorithms with their accurate results in the literature. The aim of this study is to analyze the performances of these algorithms on land use and land cover (LULC) classification, especially wetlands which have significant ecological functions. For this purpose, Sentinel-2 satellite image, which is freely provided by European Space Agency (ESA), was used to monitor not only the open surface water body but also around Marmara Lake. The performance evaluation was made with the increasing number of the training dataset. 3 different training datasets having 10, 15, and 20 areas of interest (AOI) per class, respectively were used for the classification of the satellite images acquired in 2015 and 2020. The most accurate results were obtained from the classification with RF algorithm and 20 AOIs. According to obtained results, the change detection analysis of Marmara Lake was investigated for possible reasons. Whereas the water body and wetland have decreased more than 50% between 2015 and 2020, crop sites have increased approximately 50%.

In order to promote the modernisation process of rural roads and improve road capacity, the problem of bottleneck sections of rural roads needs to be solved urgently. The phenomenon of wide-road-and-narrow-bridge sections is particularly prominent in rural roads. Based on this, this paper analyses the degree of influence of roadway one-way lane width, bridge deck oneway lane width, motorised vehicles to non-motorised vehicles ratio, and road-bridge connection dimension on the capacity of the wide-road-and-narrow-bridge section based on the combination of VISSIM simulation and random forest algorithm. The result of the coefficient of determination (R2) of the random forest-based capacity prediction model shows that the random forest fits the data very well; the degree of influence on the capacity is in descending order of the bridge deck one-way lane width, motorised vehicles to non-motorised vehicles ratio, roadway one-way lane width and the road-bridge connection dimension. The model can, on the one hand, provide a reference for improving the capacity of bottleneck sections of rural roads; on the other hand, it can provide decision value for the order of measures to be taken when rural roads are rebuilt and expanded, according to the order of importance.

Classification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia's online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%.

The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%. &nbsp, The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%.

One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate. &nbsp, One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate.

International audience; Gradient methods subject images to a series of operations to enhance some characteristics and facilitate image analysis, usually the contours of large objects. We argue that a gradient must show other characteristics, such as minor components and large uniform regions, particularly for the image segmentation task where subjective concepts such as region coherence and similarity are hard to interpret from the pixel information. This work extends the formalism of a previously proposed graph-based image gradient method that uses edge-weighted graphs aggregated with Random Forest (RF) to create descriptive gradients. We aim to explore more extensive input image areas and make changes driven by the RF mechanics. We evaluated the proposals on the edge and segmentation tasks, analyzing the gradient characteristics that most impacted the final segmentation. The experiments indicated that sharp thick contours are crucial, whereas fuzzy maps yielded the worst results even when created from deep methods with more precise edge maps. Also, we analyzed how uniform regions and small details impacted the final segmentation. Statistical analysis on the segmentation task demonstrated that the gradients created by the proposed are significantly better than most of the best edge maps methods and validated our original choices of attributes.

Background:The COVID-19 pandemic has had a global impact. Knowing the variables that affect the increase in infection is crucial for public health decision-making. Mobility and socio-demographic conditions of the population are important factors in the transmission of the SARS-CoV-2. The objective of this study is to analyze the relationship between people mobility, social determinants of health and COVID-19 cases using a Random Forest (RF) method. Methods:The COVID-19 cases were analyzed in the Maule Region, Chile. Spearman rank was performed to analyze the total mobility index for each municipality. RF regression was used to create a model between COVID-19 infections, mobility index and sociodemographic variables. P-value Results: Total mobility was highly correlated with new COVID-19 cases, adjusted for total population, in each municipality (ρ: 0.52-0.92). An upward trend is observed for mobility and COVID-19 cases for the 30 municipalities analyzed. For the RF model, COVID-19 active cases, total mobility, and external mobility are obtained as VIM. The most relevant demographic variables were overcrowding, density and area of municipality. The R-Squared was 0.68 for the performed RF model. Conclusions: Artificial Intelligence methodologies are increasingly used for their excellent performance. RF Regression offers a clear solution for the design of predictor variables on the number of new cases per week. Mobility is a powerful predictor variable for the number of COVID-19 new cases.

In recent years, the development of automatic search methods based on aerial images taken by drones has been attracting attention in order to prevent secondary disasters and to perform rescue operations quickly in the search for victims of natural disasters. Although various methods exist for automatic person detection for search, they are based on the assumption that the background area of a person captured by a drone camera is a uniform ground in which only those persons who require rescue exist without any shadows or trees. In this paper, we propose a method of automatic detection of both fallen persons and person shadows, or trees on the ground. The method is combination of Ri-HOG and Ri-LBP features to search for fallen persons. These features are robust to rotation. We then employ GrabCut and brightness values to detect shadows. The effectiveness of the proposed method was verified by experiment., The 2023 International Conference on Artificial Life and Robotics (ICAROB 2023), February 9-12, 2023, on line, Oita, Japan

Communication is the basic need of humans to interact with their surroundings. BCI acts as a great tool for providing the means of basic communication to people with locked-in syndrome. For such patients, these basic things matter most and give them a sense of independence. Communication through BCI can be done using various paradigms, P300 speller is one of these paradigms in which the user is instructed to focus their attention on the desired character and able to produce negative and positive slow cortical potential changes which is then interpreted by the BCI system. Although P300 based BCI was introduced over twenty years ago, the past few years have seen a rapid increase in P300 based BCI research. In this paper, we overview the current status of P300 BCI technology and also discussed and compare different approaches using Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XgBoost) classification algorithm.

In the analysis of binary disease classification, single biomarkers might not have significant discriminating power and multiple biomarkers from a large set of biomarkers should be selected. Numerous approaches exist, but they merely work well for mean differences in biomarkers between cases and controls. Biological processes are however much more heterogeneous, and differences could also occur in other distributional characteristics (e.g. variances, skewness). Many machine learning techniques are better capable of utilizing these higher order distributional differences, sometimes at cost of explainability. In this study we propose quantile based prediction (QBP), a binary classification method that is based on the selection of multiple continuous biomarkers. QBP generates a single score using the tails of the biomarker distributions for cases and controls. This single score can then be evaluated by ROC analysis to investigate its predictive power. The performance of QBP is compared to supervised learning methods using extensive simulation studies, and two case studies: major depression disorder and trisomy. Simultaneously, the classification performance of the existing techniques in relation to each other is assessed. The key strengths of QBP are the opportunity to select relevant biomarkers and the outstanding classification performance in the case biomarkers predominantly show variance differences between cases and controls. When only shifts in means were present in the biomarkers, QBP obtained an inferior performance. Lastly, QBP proved to be unbiased in case of absence of disease relevant biomarkers and outperformed the other methods on the MDD case study. More research is needed to further optimize QBP, since it has several opportunities to improve its performance. Here we wanted to introduce the principle of QBP and show its potential., Comment: 26 pages, 5 figures

Although salinity satellites can provide high-resolution global sea surface salinity (SSS) data, the satellite data still display large errors close to the coast. In this paper, a nonlinear empirical method based on random forest is proposed to correct two Soil Moisture and Ocean Salinity (SMOS) L3 products in the tropical Indian Ocean, including SMOS BEC and SMOS CATDS data. The agreement between in-situ data and the corrected SMOS data is better than that between in-situ data and the original satellite data. The root-mean-square deviation (RMSD) of the satellite SSS data decreased from 0.366 to 0.275 and from 0.367 to 0.255 for SMOS BEC and SMOS CATDS, respectively. The effect of the correction model was better in the Arabian Sea than in the Bay of Bengal. The RMSD of corrected BEC (CATDS) SSS was reduced from 0.44 (0.48) to 0.276 (0.269), and the correlation coefficient was increased to 0.915 from 0.741(0.801) in the Arabian Sea while the correlation coefficient improved less than 0.02 in the Bay of Bengal. The cross-validation results highlight the robustness and effectiveness of the correction model. Additionally, the effects of different features on the correction model are discussed to demonstrate the vital role of geographical information in the correction of satellite SSS data. The proposed method outperformed other machine-learning methods with respect to the RMSD and correlation coefficient.

Agriculture is the back bone of India, one of the major sources for some notable population from past decade, slowly the agriculture is facing downward growth. Due to less rain fall, they are not getting proper yield, and also expected price for their crops. The farmers are getting loss or less profit. If they know the price of particular commodity, accordingly they can grow selected crop depending upon their geographical area. We can use some of the machine leaning regression algorithms to predict the price of the agricultural commodities that helps the formers to do the decision making. This research work focus on detailed study of predicting the price of the cotton using five different ML regression algorithms namely Linear Regression, Bayesian Linear Regression, Boosted Decision Tree Regression, Decision forest Regression, Poisson Regression. Metrics for evaluating the performance of ML model. The Data set contains the cotton prices of different states of India. The cotton price 2019 data set is used for this work and the prediction values are compared with 2020 data set and the predicted prices are nearing to the actual values. Compare to all other algorithms Boosted decision tree giving the good accuracy, after that Bayesian and Linear regression also performing well.

The focal role of flow in promoting desirable outcomes in companies, such as increased employees' well-being and performance, led scholars to study flow in the context of work. However, current measurement approaches which assess flow via self-report scales after task execution are limited due to obtrusiveness and a lack of real-time support. Hence, new measurement approaches must be created to overcome these limitations. In this paper, we use cardiac features (heart rate variability; HRV) and a Random Forest classifier to distinguish high and low flow. Our results from a large-scale lab experiment with 158 participants and a field study with nine participants reveal, that with HRV features alone, flow-classifiers can be built with an accuracy of 68.5% (lab) and 70.6% (field). Our research contributes to the challenge of developing a less obtrusive, real-time measurement method of flow based on physiological features and to investigate flow from a physiological perspective. Our findings may serve as foundation for future work aiming to build physio-adaptive systems which can improve employee's performance. For instance, these systems could ensure that no notifications are forwarded to employees while the system is 'sensing' flow.

With the tremendous advancement in machine learning and deep learning, organizations are using numerous algorithms for analyzing the huge amount of data to come up with insights which contains meaningful out comes. Especially in medical health care systems, machine learning is being used widely to forecast and treat the illness at early in stage. Cervical cancer is one of such diseases which can be diagnosed if identified in early stage, but the symptom-less nature of the disease has become challenging for the researchers and practitioners to predict the disease at early stage. There are numerous factors which will prone to cervical cancer risk in women. In this study we have proposed a stacked ensemble technique which takes heterogenous base learners and a meta learner for predicting the cervical cancer from various risk factors, SMOTE has been used for data Balancing and RFE with Random Forest used for feature extraction through which we have got better accuracy than the existing methods. We have also identified top 8 features which impact the advancement of the classification model.

Text Neck Syndrome is repeated stress injury to the neck, or it is a pain encountered in the neck due to excessive usage of handheld devices for a prolonged period. In this paper, we have used the Microsoft Kinect sensor to create the dataset for normal and abnormal sitting postures by obtaining 3D skeletal coordinates of people. Features are extracted from the data collected from the Kinect sensor data based on the various parameters like Minkowski distance, Euclidean distance, Manhattan distance, etc. Then we have applied different machine learning algorithms, namely, K-nearest neighbours, Random forest, Support vector machine, Logistic and Linear Regression, to classify them. The performance of various algorithms is analysed. The best testing accuracy achieved is 92.85% by using Random Forest and Support Vector Machine algorithms.

Distributed Denial of service (DDoS) attacks is an enormous threat to today’s cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today’s network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%.

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.

Son yıllarda kalp hastalıkların hızla artışı ile birlikte bu hastalıklardan ölen insanların sayısı da hızla artmaktadır. Bu nedenle hastalıkların erken teşhisi, insan yaşam ömrünü arttırmada oldukça önemlidir. Çalışmada 2015 yılı nisan ve aralık aylarını kapsayan kalp yetmezliği hastalarına ait toplam 299 klinik hastaya ait kayıtlar açık erişimli internet sitesinden alınarak kullanılmıştır. Açık erişimli internet sitesinden elde edilen veriler veri ön işleme aşamasından geçirilerek yapay zekâ ile analiz için hazır hale getirilmiştir. Veri seti ilk olarak Rasgele Orman algoritmasının hiper parametreleri belirlenmiştir. Elde edilen hiper parametreler, ikili parçacık sürü zekâ yöntemi kullanılarak veri seti üzerinde global bir özellik seçimi gerçekleştirilmiştir. Elde edilen sonuçlar tekrar rasgele orman algoritması ve 10 kat 10 iterasyonu ile çapraz doğrulama ile eğitilerek ölüm risk durumunu gösteren en uygun model elde edilmiştir. Elde edilen modelin 100 eğitimin ortalama doğruluk oranı ise %79.66 olarak belirlenmiştir., With the rapid increase of heart diseases in recent years, the number of people who die from these diseases is also increasing rapidly. Therefore, early diagnosis of diseases is very important in increasing human life span. In the study, records of 299 clinical patients with heart failure, covering the months of April and December 2015, were taken from the open access website and used. The data obtained from the open access website were made ready for analysis with artificial intelligence by passing through the data preprocessing stage. Firstly, the data set was determined by hyper parameters of the Random Forest algorithm. Using the obtained hyper parameters, binary particle swarm intelligence method, a global feature selection was made from the data set. The obtained results were trained again with random forest algorithm and 10 times 10 iteration cross validation, and the most appropriate model showing death risk situation was obtained. The average accuracy rate of 100 trainings of the model obtained was determined as 79.66%.

In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models.

GFRP bars reinforced in submerged or moist seawater and ocean concrete is subjected to highly alkaline conditions. While investigating the durability of GFRP bars in alkaline environment, the effect of surrounding temperature and conditioning duration on tensile strength retention (TSR) of GFRP bars is well investigated with laboratory aging of GFRP bars. However, the role of variable bar size and volume fraction of fiber have been poorly investigated. Additionally, various structural codes recommend the use of an additional environmental reduction factor to accurately reflect the long-term performance of GFRP bars in harsh environments. This study presents the development of Random Forest (RF) regression model to predict the TSR of laboratory conditioned bars in alkaline environment based on a reliable database comprising 772 tested specimens. RF model was optimized, trained, and validated using variety of statistical checks available in the literature. The developed RF model was used for the sensitivity and parametric analysis. Moreover, the formulated RF model was used for studying the long-term performance of GFRP rebars in the alkaline concrete environment. The sensitivity analysis exhibited that temperature and pH are among the most influential attributes in TSR, followed by volume fraction of fibers, duration of conditioning, and diameter of the bars, respectively. The bars with larger diameter and high-volume fraction of fibers are less susceptible to degradation in contrast to the small diameter bars and relatively low fiber's volume fraction. Also, the long-term performance revealed that the existing recommendations by various codes regarding environmental reduction factors are conservative and therefore needs revision accordingly.

International audience; Machine learning based anomaly and attack detection is a topic under the spotlights for more than one decade. It is raising a significant research effort worldwide. The reasons of this interest lie in the ability of machine learning to introduce a large part of autonomy in the attacks detection process compared to the classical signature based approach, and then reduces the management cost of networks for NetOps, provides rapid and efficient results, and is prone to detect 0d attacks. This paper confirms this efficiency showing that using the simple Random Forest (RF) algorithms together with features selection allows detection ratio greater than 99%. However, despite a large set of attempts on the training stage for improving this detection ratio, RF never detects 100% of attacks. It especially always misses some attacks in the traffic, especially under-represented ones in the training dataset. False Negatives are certainly the most dramatic events for network security. In addition, it makes adversarial learning very easy to perform. This paper then presents a controversial study on machine learning based attack detection (using the significantly illustrative RF example), and its trustworthiness limits. This paper is then trying to break the current dogma that machine learning is THE solution for future cyber-security systems.

Type 1 diabetes (T1D) disease is considered one of the most prevalent chronic diseases in the world, it causes a high level of glucose in the human blood. Despite the seriousness of this disease, T1D may affect people and their condition develops to an advanced stage without feeling it, which makes it difficult to control the disease. Early prediction of the presence of this disease may significantly reduce its risks. There are many attempts to overcome this disease, some of them are heading towards biological solutions and others towards bioinformatic solutions. Several studies have used a single feature selection method with a machine learning (ML) model to predict or classify T1D. In this paper, ML techniques were used for classification, such as Naive Bayes (NB), support vector machine (SVM), and random forest (RF) using a T1D gene expression dataset that has a multiclass to classify the genes associated with this disease. The proposed model can identify the genes related to T1D with high efficiency, which helps a lot in predicting a person carrying the disease before symptoms appear. The highest accuracy of 89.1% was obtained when applying SVM with chi2 as the feature selection method.

Diabetes is the most common disease all over the world and it must be detected early to receive proper treatment, which can prevent the condition from becoming more severe. Automated detection plays an essential role in diabetes early diagnosis. Over the last few decades, many complicated machine learning algorithms and data analysis approaches have been applied for diabetes prediction. To determine the best model for early-stage diabetes prediction, ten different machine learning classifiers have been used in this study. These models were evaluated in terms of accuracy, precision, specificity, recall, F1-score, negative predictive value (NPV), false positive rate (FPR), rate of misclassification, and receiver operating characteristics (ROC) curve. The experimental findings indicated that all of the models performed well. Gradient Boosting (GB), with 97.2 percent accuracy, is observed to show the best performance on the early-stage diabetes risk prediction dataset. Random forest (RF) and Adaboost performed similarly to the GB; however, RF and Adaboost's precision was not as good as the GB precision (GB’s).

With the widespread use of unmanned aerial vehicles (UAVs) in life, the real-time recognition of UAVs has become an important issue. The authors of this paper mainly studied the application of the random forest (RF) algorithm in the outdoor real-time recognition of UAVs. Mel-Frequency Cepstral Coefficient (MFCC) features were extracted from sound signals firstly, and then the RF method was combined with weighted voting to obtain the improved random forest (IRF) method to identify UAV sounds and environmental sounds. An experimental analysis was conducted. The modeling time of the IRF method increased by 9.52% compared with the RF method, and the recognition rate of the IRF method decreased with the increase of the distance from the microphone; however, the recognition rate of the IRF method was always higher than that of the RF method, and the recognition rate of the IRF method for the mixed samples was always higher than 90%. When the distance was 10 m, the IRF method still had a recognition rate of 91.29%. The experimental results verify the effectiveness of the IRF method for the outdoor real-time recognition of UAVs and its practical application feasibility.

Soil erosion by water is a main form of land degradation worldwide. The problem has been addressed, among others, in the United Nations Sustainability Goals. However, for mitigation of erosion consequences and adequate management of affected areas, reliable information on the magnitude and spatial patterns of erosion is needed. Although such need is often addressed by erosion modelling, precise erosion monitoring is necessary for the calibration and validation of erosion models and to study erosion patterns in landscapes. Conventional methods for quantification of rill erosion are based on labour-intensive field measurements. In contrast, remote sensing techniques promise fast, non-invasive, systematic and larger-scale surveying. Thus, the main objective of this study was to develop and evaluate automated and transferable methodologies for mapping the spatial extent of erosion rills from a single acquisition of remote sensing data. Data collected by an uncrewed aerial vehicle was used to deliver a highly detailed digital elevation model (DEM) of the analysed area. Rills were classified by two methods with different settings. One approach was based on a series of decision rules applied on DEM-derived geomorphological terrain attributes. The second approach utilized the random forest machine learning algorithm. The methods were tested on three agricultural fields representing different erosion patterns and vegetation covers. Our study showed that the proposed methods can ensure recognition of rills with accuracies between 80 and 90% depending on rill characteristics. In some cases, however, the methods were sensitive to very small rill incisions and to similar geometry of rills to other features. Additionally, their performance was influenced by the vegetation structure and cover. Besides these challenges, the introduced approach was capable of mapping rills fully automatically at the field scale and can, therefore, support a fast and flexible assessment of erosion magnitudes.

At present, under the condition of privacy-preserving, vertical federated learning (VFL) has played an important role in training the machine learning models in the application scenarios, such as medical prediction, fraud detection, in which the data is distributed vertically. Random forest is one of the most widely used machine learning methods in VFL, which has the advantages of strong predictive performance, availability, and the ability to parallel tasks. However, current research on privacy-preserving of vertical federated random forest is limited, and none of them can achieve the application level of security, that is, in a system where users are dynamically changing, not only privacy can be preserved, but also data integrity can be verified. Therefore, we propose a verifiable privacy-preserving scheme (VPRF) based on vertical federated random forest, in which the users are dynamic change. Firstly, we design homomorphic comparison and voting statistics algorithms based on multi-key homomorphic encryption for privacy preservation. Then, we propose a multi-client delegated computing verification algorithm to make up for the disadvantage that the above algorithms cannot verify data integrity. Finally, we used the datasets in UCI machine learning warehouse to evaluate the propose scheme. The experiment results indicate that our scheme is more efficient than the existing ones that can achieve the same security level.

Presurgical discrimination of 1p/19q codeletion status may have prognostic and diagnostic value for glioma patients for immediate personalized treatment. Artificial intelligence-based models have been proved as effective method to demonstrate computer aided diagnostic system for glioma cancer. An objective of study is to present an advanced biomedical texture descriptor to perform machine learning-assisted identification of 1p/19q codeletion status of low-grade glioma (LGG) cancer. An aim is to verify efficacy of textures, extracted using local binary pattern and derived from gray level co-occurrence matrix (GLCM). Proposed study used random forest-assisted radiomics model to analyse MRI images of 159 subjects. Four different advanced biomedical texture descriptors are proposed by experimenting different extensions of LBP method. These variants-(as variant I to IV) with 8-bit or 16-bit or 24-bit LBP codes are applied with different orientations in 5 × 5, 7 × 7 square-sized neighbourhood, which are recorded in LBP histograms. These histogram features are concatenated by GLCM-based textures including energy, correlation, contrast and homogeneity. Texture descriptors performed best with classification accuracy of 87.50% (AUC: 0.917, sensitivity: 95%, specificity: 75%, f1-score: 90.48%) using 8-bit LBP variant-I. 10-fold cross-validated accuracy of all four sets range from 65.62% to 87.50% using random forest classifier and mean-AUC range from 0.646 to 0.917.

Random forest is one of the most heated machine learning tools in a wide range of industrial scenarios. Recently, federated learning enables efficient distributed machine learning without direct revealing of private participant data. In this paper, we present a novel framework of federated random forest (RevFRF), and further emphatically discuss the participant revocation problem of federated learning based on RevFRF. Specifically, RevFRF first introduces a suite of homomorphic encryption based secure protocols to implement federated random forest (RF). The protocols cover the whole lifecycle of an RF model, including construction, prediction, and participant revocation. Then, referring to the practical application scenarios of RevFRF, the existing federated learning frameworks ignore a fact that even every participant in federated learning cannot maintain the cooperation with others forever. In company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from an off-lying company potentially leads to a significant conflict of interest. Therefore, we propose the revocable federated learning concept and illustrate how RevFRF implements participant revocation in applications.

With the rise in popularity and its open system architecture, Android has become vulnerable to malicious attacks. There are several malware detection approaches available to fortify the Android operating system from such attacks. These malware detectors classify target applications based on the patterns found in the features present in the Android applications. As the analytics data continues to grow, it negatively impacts the Android defense mechanism. A large number of irrelevant features has become the performance bottleneck of the detection mechanism. This paper presents a multi-tiered feature selection model, which can discover relevant and significant features for improving the accuracy of malware detection approaches. The proposed method applies five machine learning classification techniques to the selected feature set. This work presents the Optimal Static Feature Set (OSFS) and, Most Important Features (MIFs) discovered with each machine learning approach. Rigorous testing and analysis show that Random Forest classification achieves the highest Accuracy rate of 96.28%.

United Nations has announced the current decade as a ‘decade of action’ to deliver the 2030 agenda successfully. The 17 Sustainable Development Goals (SDG) achievement level of all the nations varies. Almost 70% of the world’s population is expected to settle in the urban region by 2050. So SDG 11, the goal of developing a resilient and sustainable city, was focused on in the study. Since the targets of each goal are interrelated to others, the progress in each goal will also reflect on the achievement scores of different goals. An attempt was made in this study to develop a model to predict the achievement status of SDG 11 based on the performance of the country’s related goals. The Random Forest machine learning algorithm was used in developing the prediction model with the KNIME analytics platform, which attained overall prediction accuracy up to 83.67%.

There is an increasing interest in the study of detecting psychological stress from the social media like Twitter. However, Twitter has a limitation on the number of characters used per tweet, resulting in data sparsity. Many techniques were proposed to detect stress at the tweet level, but most of them failed to leverage the text content to reduce the impact of data sparsity. In this work, two solutions are proposed to further leverage the text content for the tweet level stress detection. First, a new feature, Sarcasm_Level, is computed to indicate the sarcasm present in the tweet’s content and its influence in detecting stress. Second, a novel neighborhood tweet-based stress detection method is developed, which is a logistic regression-based approach that integrates the content of previous tweets, also known as neighborhood tweets. Experimental results reveal that the proposed model has significantly better performance in detecting stress since it incorporates the information from neighborhood tweets. Also, with the inclusion of the new feature, the proposed model outperformed other well-known machine learning models like support vector machines, random forests and logistic regression with better accuracy and F1-score.

The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required.

Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble learning method. Ensemble learning methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose of improving the stability and accuracy of machine learning algorithms. This study presented REMARS (Random Ensemble MARS), a new MARS model selection approach obtained using the Random Forest (RF) algorithm. 200 training and test data set generated via the Bagging method were analysed in the MARS analysis engine. At the end of the analysis, two different MARS model sets were created, one yielding the smallest Mean Square Error for the test data (Test MSE) and the other yielding the smallest Generalised Cross-Validation (GCV) value. The best model was estimated for both Test MSE and GCV criteria by examining the error of measurement criteria, variable importance averages, and frequencies of the knot values for each model. Eventually, a new model was obtained via the ensemble learning method, i.e., REMARS, that yields result as good as the MARS model obtained from the original data set. The MARS model, which works better in the larger data set, provides more reliable results with smaller data sets utilising the proposed method.

In the last decades, global warming has changed the temperature. It caused an increasing the wildfire in everywhere. Wildfires affect people's social lives, animal lives, and countries' economies. Therefore, new prevention and control mechanisms are required for forest fires. Artificial intelligence and neural networks(NN) have been benefited from in the management of forest fires since the 1990s. Since that time, machine learning (ML) methods have been used in environmental science in various subjects. This study aims to present a performance comparison of ML algorithms applied to predict burned area size. In this paper, different ML algorithms were used to forecast fire size based on various characteristics such as temperature, wind, humidity and precipitation, using records of 512 wildfires that took place in a national park in Northern Portugal. These algorithms are Multilayer perceptron(MLP), Linear regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Stacking methods. All algorithms have been implemented on the WEKA environment. The results showed that the SVM method has the best predictive ability among all models according to the Mean Absolute Error (MAE) metric. Son on yılda, küresel ısınma sıcaklığı değiştirdi. Orman yangınlarının her yerde artmasına neden oldu. Orman yangınları insanların sosyal yaşamlarını, hayvan yaşamlarını ve ülke ekonomilerini etkiler. Bu nedenle orman yangınları için yeni önleme ve kontrol mekanizmalarına ihtiyaç duyulmaktadır. 1990'lı yıllardan itibaren orman yangınlarının yönetiminde yapay zeka ve sinir ağlarından yararlanılmaktadır. O zamandan beri, çevre biliminde çeşitli konularda makine öğrenmesi (ML) yöntemleri kullanılmıştır. Bu çalışma, yanan alan boyutunu tahmin etmek için uygulanan ML algoritmalarının performans karşılaştırmasını sunmayı amaçlamaktadır. Bu yazıda, Kuzey Portekiz'deki bir milli parkta meydana gelen 512 orman yangınının kayıtları kullanılarak sıcaklık, rüzgar, nem ve yağış gibi çeşitli özelliklere dayalı olarak yangın boyutunu tahmin etmek için farklı ML algoritmaları kullanılmıştır. Bu algoritmalar Lineer regresyon, Destek Vektör Makineleri (SVM), Çok Katmanlı Algılayıcı, KEn Yakın Komşular (KNN), Karar Ağacı ve Yığınlama yöntemleridir. Tüm algoritmalar WEKA ortamında gerçeklenmiştir. Sonuçlar, Ortalama Mutlak Hata (MAE) metriğine göre tüm modeller arasında SVM yönteminin en iyi tahmin yeteneğine sahip olduğunu göstermiştir.

The economic boom over the recent past and the quest to further develop has made several nation states the business hubs in their regions. Along with the investments, there has been growth in the number of property sales. Social media has become convenient platform of choice for advertising property sales after the introduction of Web 2.0. This article utilizes social media platforms like Facebook to scrape data from user groups advertising properties and then using data mining techniques and approaches to determine true valuation of properties. This methodology is based on set attributes in the urban areas by looking at the property sales of the recent past within the same area. This enables investors interested in these properties and provides a fair idea of price of properties based on the key attributes associated with the respective property.