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Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

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 is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted. Funding: This work was funded by Eurocontrol (APOC Business Process Reengineering Big Data Study) [Grant 15-220643-AWP6.3.1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0975 .

Metal–organic frameworks (MOFs) containing open metal sites are important materials for acetylene (C2H2) adsorption. However, it is inefficient or even impossible to search suitable MOFs by molecular simulation method in nearly infinite MOFs space. Therefore, machine learning (ML) methods are adopted in the material screening and prediction of high-performance MOFs. In this paper, architecture, chemical and structural features are used to analyze the C2H2 adsorption performance of the MOFs. Different ML algorithms are applied to perform classification and regression analysis to the factors affecting material adsorption. By decision tree (DT) algorithm, it is found that only PV, GSA, and Cu-OMS are sufficient to determine the high adsorption of the MOFs. Furthermore, the influence of topology on the performance of MOFs is obtained. Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN), are introduced to analyze the quantitative structure–property relationship (QSPR) between C2H2 adsorption and the features of MOFs. The prediction of the GBDT model is found to have the highest accuracy, with R2 as 0.93 and RMSE as 11.58. In addition, the GBDT model is used for feature analysis, and the contribution of each feature to the performance is obtained, which is of great significance for the design and analysis of MOFs. The successful application of ML to MOFs screening greatly reduce the calculation time and provides important reference for the design and synthesis of new MOFs.

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements---this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude.

An accurate environment model is a fundamental issue in greenhouses control to improve the energy consumption efficiency and to increase the crop yield. With the increase of agricultural data generated by the Internet of Things (IoT), more feasible models are necessary to get full usage of such information. In this research, a Gradient Boost Decision Tree (GBDT) model based on the newly-developed Light Gradient Boosting Machine algorithm (LightGBM or LGBM) was proposed to model the internal temperature of a greenhouse. Features including climate variables, control variables and additional temporal information collected within five years were used to construct a suitable dataset to train and validate the LGBM model. An adaptive cross-validation method was developed as a novelty to improve the LGBM model performance and self-adaptive ability. For comparison of the predictive accuracy, a Back-Propagation (BP) Neural Network model and a Recurrent Neural Network (RNN) model were built under the same process. Another two GBDT algorithms, Extreme Gradient Boosting (Xgboost) and Stochastic Gradient Boosting (SGB), were also introduced to compare the predictive accuracy with LGBM model. Results suggest that the LGBM has best fitting ability for the temperature curves with RMSE value at 0.645℃, as well as the fastest training speed among all algorithms with 60 times faster than the other two neural network algorithms. The LGBM has strongly potential application prospect on both greenhouse environment prediction and real-time predictive control.

Effective on-street parking is key to reduce urban traffic and pollution in densely populated cities. Thus, researchers have focused on forecasting future occupancy values depending on factors like time, space, or weather. This approach shows high average performances, but fails in predicting congested scenarios, actually the most critical. This work proposes a data-driven parking level of service (LOS) predictor that outperforms traditional methods, solving its inherent class imbalance issue by means of Random Undersampling Boost classifiers. We trained and validated the LOS classifiers using 13 months of data collected from the smart parking system in the city of Madrid, Spain. Results display average recall values above 0.94 and 0.87 at prediction horizons up to 10 and 60 minutes respectively. We compare these results with traditional regression-based occupancy predictors showing that our classifier clearly outperforms the formers predicting the minority classes, which carry the most significant information for drivers and parking managers. We further analyze the impact on performance of temporal and spatial features, revealing mid-term temporal data as the most relevant forecasting information, and low correlations between parking behaviors in bordering neighborhoods. In the light of these results, we believe that the proposed data-driven parking LOS classification has the potential to open a novel perspective on the parking occupancy forecasting field.

With the rapid development of cloud computing, Internet of things and artificial intelligence, human–computer interaction (HCI) is playing an increasingly important role in the daily life. As an important component of HCI, hand gesture recognition (HGR) system is usually combined with edge computing server, utilizing machine learning, including neural network, decision tree, integrated learning, to achieve low latency and high reliability service. High precision HGR with low computational complexity is prerequisite for the commercialization of gesture recognition. Therefore, this paper proposed a high-precision parameter correction algorithm based on the established scattered-point model and the outlier detection scheme, and a recognition algorithm with multivariable decision tree is then presented for the dynamic hand gestures. The experimental results show that the proposed algorithms can improve the recognition accuracy and effectively reduce the running time, which is conducive to algorithm transplantation and model deployment in edge servers.

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this paper proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.

Edge computing is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, the new paradigm is faced with security and privacy challenges due to the diversity and the limited capability of edge components. In particular, data privacy is one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in edge computing, which not only gives the strong privacy guarantee, but also provides a certain amount of data utility. Firstly, we design a preservation framework to implement private random decision trees satisfying local differential privacy. Secondly, we present the concrete implementations of algorithms and the corresponding task that each participant needs to undertake. Thirdly, we analyze the key factors to influence privacy and utility, including the allocation of data and privacy budget. Fourthly, we give the improved algorithms to further increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework.

Predicting transportation mode choice is a critical component of forecasting travel demand. Recently, machine learning methods have become increasingly more popular in predicting transportation mode choice. Class association rules (CARs) have been applied to transportation mode choice, but the application of the imputed rules for prediction remains a long-standing challenge. Based on CARs, this paper proposes a new rule merging approach, called CARM, to improve predictive accuracy. In the suggested approach, first, CARs are imputed from the frequent pattern tree (FP-tree) based on the frequent pattern growth (FP-growth) algorithm. Next, the rules are pruned based on the concept of pessimistic error rate. Finally, the rules are merged to form new rules without increasing predictive error. Using the 2015 Dutch National Travel Survey, the performance of suggested model is compared with the performance of CARIG that uses the information gain statistic to generate new rules, class-based association rules (CBA), decision trees (DT) and the multinomial logit (MNL) model. In addition, the proposed model is assessed using a ten-fold cross validation test. The results show that the accuracy of the proposed model is 91.1%, which outperforms CARIG, CBA, DT and the MNL model.

The prognosis of cardiovascular and cerebrovascular events for patients suffering from hypertension is considered of a high importance in preventing any further development of cardiac diseases. Despite of the ability of current gold standard techniques in predicting vascular events risks, they still lack the required clinical efficiency. In this vein, the study proposed herein provides an investigation on the feasibility of using heart rate variability (HRV) utilized through a machine learning approach to predict hypertensive patients at higher risk of developing vascular events. Initially, HRV features were extracted from all patient's data using time-domain, frequency-domain, non-linear, and fragmentation metrics. The extraction of features was based on a 24-hour cycle analysis segmented into four time periods; namely late-night, early-morning, afternoon, and evening. Analysis of all features was performed using a one-way analysis of variance (ANOVA) test on period by period basis. Furthermore, the selection of best features was performed following a Chi-squared test for demographic and HRV features. Then, a model based on decision trees and random under-sampling boosting (RUSBOOST) was trained using demographic features, HRV features, and a combination of both features. The performance of the trained model achieved a maximum accuracy of 97.08% using the combined set of features during the afternoon time period. In addition, the precision and F1-score in predicting high risk patients reached 81.25% and 86.67%, respectively. The overall area under the curve for the model was at 0.98, suggesting a high performance in the sensitivity and specificity measures. This study paves the way towards utilizing machine learning models and heart rate variability for the prognosis of vascular events in hypertensive patient. Furthermore, it assists clinicians in decision making by providing a simple, yet effective, and continuous prediction approach when compared to other available techniques.

Fair machine learning has been focusing on the development of equitable algorithms that address discrimination. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. In this study, a novel methodology is presented to explore the tradeoff in terms of a Pareto front between accuracy and fairness. To this end, we propose a multiobjective framework that seeks to optimize both measures. The experimental framework is focused on logistiregression and decision tree classifiers since they are well-known by the machine learning community. We conclude experimentally that our method can optimize classifiers by being fairer with a small cost on the classification accuracy. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used.

Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity. As lots of people are suffering from it, access to proper treatment is necessary to control the problem. Most patients are unaware of health complexity, symptoms and risk factors before diabetes. The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with a high accuracy, in order to identify and treat diabetes patients at an early age. Our training and test dataset is an accumulation of 9483 diabetes patients’ information. The training dataset is large enough to negate overfitting and provide for highly accurate test performance. We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95.14% accuracy among all other tested machine learning classifiers. We hope our high performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.

Introduction Mammographic breast density (MBD) is a known risk factor for breast cancer and older women have higher incidence rates of breast cancer occurrence. The Breast Imaging Reporting and Data System (BI-RADS) is a commonly used MBD classification tool for mammogram reporting. However, they have limitations since there are reading inconsistencies between different radiologists with the visual assessment of breast density. Methods Digitised film-screen mammographic images were extracted from the Digital Database for Screening Mammography (DDSM). A machine learning project was developed using commercially available software with several predictive models applied to classify different amount of MBD on mammograms into different density groups. The effectiveness of different predictive models used in classifying the mammograms were tested by receiver operator characteristics (ROC) curve with comparison to the gold standard of BI-RADS classification. Results Three predictive models, Decision Tree (Tree), Support Vector Model (SVM) and k-Nearest Neighbour (kNN) showed high AUC values of 0.801, 0.805 and 0.810 respectively. High AUC values for the three predictive models indicates that the accuracy of the model is approaching that of the BI-RADS method. Discussion Our machine learning project showed to have capabilities to be potentially used in the clinical settings to help categorise mammograms into extremely dense breasts (BI-RADS Group A) from entirely fatty breasts (BI-RADS Group D). Conclusion Findings from the present study suggest that the machine learning method is potentially useful to quantify the amount of MBD in mammograms.

In this study, a high accident risk prediction model is developed to analyze traffic accident data, and identify priority intersections for improvement. A database of the traffic accidents was organized and analyzed, and an intersection accident risk prediction model based on different mechanical learning methods was created to estimate the possible high accident risk locations for traffic management departments to use in planning countermeasures to reduce accident risk. Using Bayes' theorem to identify environmental variables at intersections that affect accident risk levels, this study found that road width, speed limit and roadside markings are the significant risk factors for traffic accidents. Meanwhile, Naive Bayes, Decision tree C4.5, Bayesian Network, Multilayer perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN) were used to develop an accident risk prediction model. This model can also identify the key factors that affect the occurrence of high-risk intersections, and provide traffic management departments with a better basis for decision-making for intersection improvement. Using the same environmental characteristics as high-risk intersections for model inputs to estimate the degree of risk that may occur in the future, which can be used to prevent traffic accidents in the future. Moreover, it also can be used as a reference for future intersection design and environmental improvements.

This paper introduces the concept of context-aware attribute learning with cipher policy-attribute based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled 5.0. The concept of 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserve privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named CASE, which autonomously learns users’ contextual information exploiting edge intelligence, generates attributes, and reduces the post encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over Smart Healthcare Systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32% -33% average network delay, 33% -35% average energy consumption, and 31% -36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.

Most incidents in complex systems such as process plants are not-chance events and weak signals emerge for a long time before incidents occur. It is necessary to identify and respond to the weak signals as early as possible for preventing incidents. However, in the era of Industry 4.0, recognizing weak signals from the abundance of data is challenging. Since the terminology “weak signal” was not precisely defined, the study first proposed a formal definition of “weak signal” and discussed its characteristics. Additionally, a framework was developed to address the challenges of observing, evaluating, and responding to weak signals in complex systems. The framework first utilized Function Resonance Analysis Method (FRAM) to determine the information to be collected for observing weak signals. Then, the relevance of weak signals and the corresponding responses were evaluated by utilizing Balanced Random Forest (BRF), Weighted Random Forest (WRF) and Decision Tree (DT) classification models. The case study of a hypothetical batch process showed great potentials for applying the framework in real operations. Based on the potential weak signals that were indicated by FRAM, probabilities of temperature deviations in the process were predicted with high accuracy by the optimal BRF model. Underlying weak signals and the corresponding responses to reduce the probabilities were identified from the DT.

Many clinicians remain wary of machine learning due to long-standing concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care where so many decisions are literally life and death. There has recently been an explosion of research in the field of explainable machine learning aimed at addressing these concerns. The promise of explainable machine learning is considerable, but it is important for cardiologists who may encounter these techniques in clinical decision support tools or novel research papers to have a critical understanding of both their strengths and their limitations. This paper reviews key concepts and techniques in the field of explainable machine learning as they apply to cardiology. Key concepts reviewed include interpretability versus explainability and global versus local explanations. Techniques demonstrated include permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence plots. We discuss several limitations with explainability techniques, focusing on the how the nature of explanations as approximations may omit important information about how black box models work and why they make certain predictions. We conclude by proposing a rule of thumb about when it is appropriate to use black box models with explanations, rather than interpretable models.

The substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have developed diverse models for predicting building energy consumption focused on the current building stock. However, to ensure future buildings are constructed to be more energy efficient, it is essential to consider energy efficiency at the design stage. Machine Learning (ML) algorithms are considered the most contemporary and best method for prediction, and these algorithms (such as Support Vector Machine (SVM) and Decision Tree (DT), among others) have gained much attention in the field of energy prediction. However, no study has explored the application of hyper parameter tuning and feature selection methods in developing a design stage Machine Learning (ML) energy predictive model. In this research, nine machine learning classification-based algorithms were compared for energy performance assessment at the design stage of residential buildings. Additionally, feature selection and hyper parameter tunning were implemented. The result shows that it is possible to develop a high performing ML model for building energy use prediction at the design stage. Furthermore, Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance.

Exploitability prediction has become increasingly important in cybersecurity, as the number of disclosed software vulnerabilities and exploits are soaring. Recently, machine learning and deep learning algorithms, including Support Vector Machine (SVM), Decision Tree, deep Neural Networks and their ensemble models, have achieved great success in vulnerability evaluation and exploitability prediction. However, they make a strong assumption that the data distribution is static over time and therefore fail to consider the concept drift problems due to the evolving system behaviours. In this work, we propose a novel consecutive batch learning algorithm, called Real-time Dynamic Concept Adaptive Learning (RDCAL), to deal with the concept drift and dynamic class imbalance problems existing in exploitability prediction. Specifically, we develop a Class Rectification Strategy (CRS) to handle the ‘actual drift’ in sample labels and a Balanced Window Strategy (BWS) to boost the minority class during real-time learning. Experimental results conducted on the real-world vulnerabilities collected between 1988 to 2020 show that the overall performance of classifiers, including Neural Networks, SVM, HoeffdingTree and Logistic Regression (LR), improves over 3% by adopting our proposed RDCAL algorithm. Furthermore, RDCAL achieves state-of-the-art performance on exploitability prediction compared with other concept drift algorithms.

Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.

As we all know, file and free space fragmentation negatively affect file system performance. F2FS is a file system designed for flash memory. However, it suffers from severe fragmentation due to out-of-place updates and the highly synchronous, multi-threaded writing behaviors of applications. The adaptive reserved space (ARS) scheme chooses some files instead of all files to update in the reserved space which collects file features associated with fragmentation to construct datasets and uses decision trees to pick reserved files. However, ARS processes file features according to write fds. On the one hand, fds and files are mapped many-to-many, so there is an issue in mapping reserved files according to reserved fds that a normal file is selected as a reserved file. On the other hand, the mapping of files to fds for big data traces is confusing. We propose ARST that traces the file of write fd to optimize ARS. Moreover, by selecting time-independent file features, ARST predicts whether files with little historical information are reserved to maximize the performance improvement brought by reserving space. Besides, adjustable reserved space and dynamic reservation strategies are adopted. We implement ARST on a HiKey960 development platform and a commercial smartphone with slight space and file creation time overheads. Experimental results show that ARST reduces file and free space fragmentation dramatically and improves file system performance. ARST reduces the running time of realistic workloads by up to 94.28% than F2FS with in-place updates and outperforms ARS by up to 49.06% for Wechat.

A country’s food needs mainly depend upon its agriculture resources and require reliable information related to crop health, distribution, and acreage estimation to manage and monitor resources to implement a sustainable agricultural system. Different methodologies have been used to collect this information. However, the availability of earth resource satellite data with improved spatial, spectral, and temporal resolutions, such as the European Space Agency's (ESA) Copernicus program satellites Sentinel-1 (S1) and Sentinel-2 (S2), are creating more practicability to generate crop type maps. S1 and S2, both operating in a constellation of twin satellites, carry a C-band Synthetic Aperture Radar (SAR) and Multispectral Instrument (MSI). Vertical transmit and horizontal receive (VH), and vertical transmit and vertical receive (VV) channels of S1 were used to exploit the temporal backscatter of crops present in the study area. In this research, a machine learning random forest classification algorithm is used for accurate crop type mapping through the combination of SAR (S1) and optical (S2) time-series data. Random forest classifier has produced considerable improved accuracies of crop type mapping in previous studies as it uses ensemble decision trees trained on sample data which permit the vote in favor of the most popular land use/ land cover class. The key objectives of this study are to investigate the classification accuracies for different data combinations. Three plots of data are tested (i) S1 (ii) S2 (iii) S1 & S2. A combination of SAR and optical data turn out with the best overall accuracy of 97% and a kappa coefficient of 0.97. Space-borne SAR and optical data add a new aspect of crop type mapping, which increases the classification accuracy by including valuable parameters and beating the drawbacks of each other. By comparing the results, it can be concluded that combining all-weather accessible SAR and spectrally rich optical data accomplished more accurate outcomes. It would be an imperative advance for the future endeavor to estimate crop biomass and biophysical parameters.

In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected biometric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us benefits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied. The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naive bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.

Cancer has identified a diverse condition of several various subtypes. The timely screening and course of treatment of a cancer form is now a requirement in early cancer research because it supports the medical treatment of patients. Many research teams studied the application of ML and Deep Learning methods in the field of biomedicine and bioinformatics in the classification of people with cancer across high- or low-risk categories. These techniques have therefore been used as a model for the development and treatment of cancer. As, it is important that ML instruments are capable of detecting key features from complex datasets. Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs) and decision trees (DTs). While we can understand cancer progression with the use of ML methods, an adequate validity level is needed to take these methods into consideration in clinical practice every day. In this study, the ML & DL approaches used in cancer progression modeling are reviewed. The predictions addressed are mostly linked to specific ML, input, and data samples supervision.

The ultimate objective of material science and engineering is to design a material which gives good property. As part of reaching that aim, the material science community has been working hard to build massive data sets of material characteristics so that researchers can quickly get the properties of identified materials. The development of systems that utilize machine learning techniques to derive prediction models from current materials data is a current active field of research in material science. In this work, we used a dataset which has DFT-calculated perovskite oxide energies to create machine learning models to predict the thermodynamic phase stability of perovskite oxides. Convex hull analysis was used to determine phase stability, with Ehull (energy above the convex hull) providing a direct measure of the stability. Machine learning techniques like K-nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM), Random Forest, Neural Network (NN) and AdaBoost classifiers are used. Found that Out of all these classifiers AdaBoost works better.

Background and Aims Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods We included a total of 1778 treatment-naive HCC patients who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model, and six ML models – including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index, and was validated externally using the split-sample method. Results The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into two, three, and four different risk groups (p Conclusions We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

[EN] The use of decision trees considerably improves the discriminating capacity of ensemble classifiers. However, this process results in the classifiers no longer being interpretable, although comprehensibility is a desired trait of decision trees. Consolidation (consolidated tree construction algorithm, CTC) was introduced to improve the discriminating capacity of decision trees, whereby a set of samples is used to build the consolidated tree without sacrificing transparency. In this work, PCTBagging is presented as a hybrid approach between bagging and a consolidated tree such that part of the comprehensibility of the consolidated tree is maintained while also improving the discriminating capacity. The consolidated tree is first developed up to a certain point and then typical bagging is performed for each sample. The part of the consolidated tree to be initially developed is configured by setting a consolidation percentage. In this work, 11 different consolidation percentages are considered for PCTBagging to effectively analyse the trade-off between comprehensibility and discriminating capacity. The results of PCTBagging are compared to those of bagging, CTC and C4.5, which serves as the base for all other algorithms. PCTBagging, with a low consolidation percentage, achieves a discriminating capacity similar to that of bagging while maintaining part of the interpretable structure of the consolidated tree. PCTBagging with a consolidation percentage of 100% offers the same comprehensibility as CTC, but achieves a significantly greater discriminating capacity. This work was funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT980-16); and by the Ministry of Economy and Competitiveness of the Spanish Government and the European Regional Development Fund -ERDF (PhysComp, TIN2017-85409-P). We would also like to thank our former undergraduate student Ander Otsoa de Alda, who participated in the implementation of the PCTBagging algorithm for the WEKA platform.

Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

Insider threats are one of the most challenging and growing security threats which the government agencies, organizations, and institutions face. In such scenarios, malicious (red) activities are performed by the authorized individuals within the company. Because of which, an insider threat has become a taxing and difficult task to identify among other attacks. Along with other monitoring parameters; email logs play a vital role in many research areas such as stalking Insider Threat involving Collaborating Traitors, Textual Analysis, and Social Media exploration. This paper presents a semi-supervised machine learning framework which embraces the pre-processing and classification techniques together for unlabeled dataset i.e. emails. Enron Corporation dataset has been used for experiments and TWOS for evaluation of the proposed framework. Initially, dataset is transformed into vector form using Term Frequency–Inverse Document Frequency (TF–IDF). Thereafter, K-Means is used to classify emails based on message content. Finally, Machine Learning algorithm Decision Tree (DT) is applied to classify the malicious activities. The proposed framework has also been tested with other algorithms such as Logistic Regression (LR), Naive Bayes (NB), KNN, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). However, Decision Tree (DT) combined with pre-processing steps has given the desired results with 99.96% Accuracy and 0.994 AUC for identification of malicious content.

Continuous integration (CI) frameworks, such as Travis CI, are growing in popularity, encouraged by market trends towards speeding up the release cycle and building higher-quality software. A key facilitator of CI is to automatically build and run tests whenever a new commit is submitted/pushed. Despite the many advantages of using CI, it is known that the CI process can take a very long time to complete. One of the core causes for such delays is the fact that some commits (e.g., cosmetic changes) unnecessarily kick off the CI process. Therefore, the main goal of this paper is to automate the process of determining which commits can be CI skipped through the use of machine learning techniques. We first extracted 23 features from historical data of ten software repositories. Second, we conduct a study on the detection of CI skip commits using machine learning, where we built a decision tree classifier. We then examine the accuracy of using the decision tree in detecting CI skip commits. Our results show that the decision tree can identify CI skip commits with an average AUC equal to 0.89. Furthermore, the top node analysis shows that the number of developers who changed the modified files, the CI-Skip rules, and commit message are the most important features to detect CI skip commits. Finally, we investigate the generalizability of identifying CI skip commits through applying cross-project validation, and our results show that the general classifier achieves an average 0.74 of AUC values.

The tire pressure state directly affects the safety and economy of the vehicle. Its monitoring is gradually becoming a necessary function for all types of vehicles. However, the existing sensor-based monitoring method is costly and without any redundancy when the sensor fails, which hinders its wide applications. This article proposed a machine learning-based framework without any additional sensors. First, the rigid tire model is introduced for feature extraction. Then, the manufacturing error in speed gear is calculated and removed by the recursive least square method. Next, the features in time- and frequency-domain of speed signals are extracted. Finally, based on the suitable signals filtered by the decision tree, the normal and pressure-loss judgment are given out by support vector machine and final synthesis. The results show that the proposed framework has 96.18% report accuracy and has the potential for further estimation of the characteristics in tires and roads.

The people having a perpetrating mind and the facilitation in advanced technologies cause the criminogenic activities in cyberspace, thereby creating societal problems. Darknet is an internet-based technology that builds on an encrypted network. Darknet networks can be accessed using a specific software with a specific network configuration; its content does not index by any search engines. Since its beginning, Darknet has been used for criminogenic tasks and applauded primarily for cybercrime promotion, including arms and drug dealing. Few countries have control over digital media and are ruled by a suppressive government. They have formulated strict policies for freedom fighters and journalism, using the Darknet anonymously. Also, many people use it for illegal purposes. Therefore, we have both positive and negative impacts of the darknet on human society and just cannot be discarded. However, in this paper, our prime concern emanates from the darknet network detection from the network traffic data through the deep transfer learning model. To provide a more accurate result, we transform time-based features into a three-dimensional image and then feed it into a pre-trained model for the extraction of promising features. In this study, we considered the DeepInsight method to transform the numerical features into image data. These features were then used in a proposed bi-level classification system to classify the input data into malicious activities. To identify the optimized pretrained network this paper utilized 10 pre-trained models: AlexNet, ResNet18, ResNet50, ResNet101, DenseNet, GoogLeNet, VGG16, VGG19, Inceptionv3, and SqueezeNet with three different baseline classifiers, namely support vector machine, decision tree, and random forest. In addition to malicious activity prediction, the proposed model could also predict the type of traffic. The experiment results illustrate that the VGG19 based features along with random forest can classify the traffic data with 96% of accuracy.

Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.

Blasting is a common method of breaking rock in surface mines. Although the fragmentation with proper size is the main purpose, other undesirable effects such as flyrock are inevitable. This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm, called kernel extreme learning machine (KELM), by which the flyrock distance (FRD) is predicted. Furthermore, the other three data-driven models including local weighted linear regression (LWLR), response surface methodology (RSM) and boosted regression tree (BRT) are also developed to validate the main model. A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing, burden, stemming length and powder factor data as inputs and FRD as target. Afterwards, the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools. Finally, the results verify that the proposed KELM model on account of highest correlation coefficient (R) and lowest root mean square error (RMSE) is more computationally efficient, leading to better predictive capability compared to LWLR, RSM and BRT models for all data sets.

Fuzzy parameterized fuzzy soft matrices (fpfs-matrices) which can model problems involving fuzzy objects and parameters are one of the mathematical tools used to deal with decision-making problems. To utilize soft decision-making methods via fpfs-matrices in machine learning is likely to draw much scholarly attention. In this paper, we propose Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) in order to transfer modeling success of fpfs-matrices to machine learning. We then compare FPFS-CMC with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in consideration of accuracy, precision, recall, macro-F-score, and micro-F-score performance metrics, and 15 datasets in UCI Machine Learning Repository. Besides, we compare the proposed classifier with the state-of-the-art Support Vector Machine (SVM), Decision Tree (DT), and Adaptive Boosting (AdaBoost) in terms of five performance metrics herein. Afterward, the results from the experiments are analyzed by employing the Friedman and Nemenyi tests to assess the statistical significance of the differences in performances. Both experimental and statistical results show that FPFS-CMC outperforms the others. Finally, we provide the conclusive remarks and some suggestions for further research.

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

Nowadays, Cyber security threats are a growing global problem. As technology evolves, cyber threats, including cyber-hacking threats, and cybercrime organizing groups, are on the rise. Distributed Denial of Service (DDoS) is one of the most serious attacks faced by Cloud computing. This attack aims to make cloud services unavailable to end-users by exhausting system resources, resulting in heavy losses that pose a threat to national security and information security assets, and thus making the development of defensive solutions against such attacks necessary to expand the use of Cloud computing technology. Machine learning (ML) has promising results in detecting cyber-attacks including DDoS when applied to intrusion detection systems. In this research, the proposed system was built using Random forest (RF) is supervised machine learning algorithm, which is an ensemble learning method that operates by constructing a multitude of decision trees at training time. The experiments conducted using the most common and standard data sets, NSL-KDD, and CICIDS 2017, achieved a detection accuracy of up to 99.09% for the first dataset and 99.97% for the second dataset respectively. The proposed system performs well when compared to other methods in terms of accuracy, detection rate, and low false-positive rate.

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis.

Kucing merupakan hewan piaraan yang sangat digemari saat ini, tingkah lakunya yang lucu dan bentuk tubuhnya yang imut membuat orang-orang dari semua lapisan masyarakat menyukainya. Masyarakat, apalagi yang menyukai dan mengurus kucing harus mengecampkan keadaan kesehatan kucing tersebut, karena tidak menutup peluang penyakit infeksi virus yang diderita oleh kucing tersebut dapat menular. Pada saat sekarang ini masyarakat tidak cukup pengetahuan mengenai edukasi penyakit kucing akibat infeksi virus sehingga mengakibatkan kucing sering terlambat mendapatkan penanganan. Penelitian ini bertujuan untuk menganalisis penyakit kucing akibat infeksi virus menggunakan metode Forward Chaining berdasarkan gejala-gejala dan merancang Sistem Pakar dalam mengukur akurasi menganalisis penyakit kucing akibat infeksi virus. Data yang diperlukan saat penelitian ini adalah data kucing, data gejala dan data solusi atau penanganan yang butuhkan untuk mengambil keputusan yang bersumber dari dokter hewan dari Paw’s Vet Padang. Berdasarkan analisis data yang diberikan oleh pakar, pakar memiliki mode pengambilan keputusan, yaitu mengumpulkan fakta-fakta terlebih dahulu untuk mencapai suatu kesimpulan atau keputusan, sehingga metode Forward Chaining dapat digunakan untuk melakukan penelitian ini. Tahapan pengolahan datanya seperti mempersiapkan input data, tabel keputusan pakar, menentukan rule, melakukan proses pelacakan, membuat pohon keputusan dan hasil pelacakan. Hasil yag didapat berhasil menganalisa gejala-gejala dan dapat menentukan penyakit yang berhubungan dengan penyakit infeksi virus pada kucing sehingga dapat ditentukan solusi serta langkah awal untuk penanganannya. Hasil uji coba yang dilakukan dengan membanding data dengan sistem yang telah dirancang memiliki tingkat akurasi yang sangat baik.

In Kuwait, the transport sector is facing a daily traffic congestion pandemic. The traffic congestion is significantly influencing the economy and obstructing the development and production in the country. Extreme weather conditions also affect the roads’ traffic, causing considerable hazards to the transportation system. Precipitation, temperature, wind speed, and visibility are the principal weather variables influencing Kuwait’s traffic. Two selected roads were studied and analyzed to conduct this research. It includes comprehensive deep learning to investigate and analyze the correlation between the weather variables and their impact on Kuwait’s roads and traffic congestion. Heat maps were used as a qualitative measure of the output variables for a better understanding of the data. Three machine learning approaches were selected: fuzzy logic, long short-term memory (LSTM), and decision trees. They were implemented on a dataset from Kuwait Control and Meteorological Center for the year of 2018. Moreover, a validation test of unseen data was implemented to verify the outputs of machine learning. The results indicated that traffic congestion is evidently associated with the weather variables and that the temperature is the essential variable. Machine learning was developed to predict traffic congestion using weather data. The three presented models have demonstrated an overall good performance accuracy in classifying the data based on input features. However, the LSTM has proven to have the best results of the three models.

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning techniques of Gaussian process regression (GPR), support vector regression, decision trees, long-short term memory, deep neural networks, and K-nearest neighbors were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS were employed in the models. The K-fold (K = 5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772% was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.