Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

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Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d2359.0410421 ◽

2021 ◽

Vol 10 (4) ◽

pp. 254-262

Author(s):

*Fadare Oluwaseun Gbenga ◽

Adetunmbi Adebayo Olusola ◽

(Mrs) Oyinloye Oghenerukevwe Eloho ◽

Mogaji Stephen Alaba

Keyword(s):

Feature Selection ◽

Malware Detection ◽

Feature Selection Method ◽

Ensemble Methods ◽

Nearest Neighbors ◽

Selection Method ◽

Gradient Boosting ◽

K Nearest Neighbors ◽

Chi Square ◽

Extreme Gradient Boosting

The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Decision Trees, and Logistic Regression. K-Nearest Neighbors returns the highest accuracy of 95.37%, 87.89% on chi-square, and without feature selection respectively. Extreme Gradient Boosting Classifier ensemble accuracy is the highest with 97.407%, 91.72% with Chi-square as feature selection, and ensemble methods without feature selection respectively. Extreme Gradient Boosting Classifier and Random Forest are leading in the seven evaluative measures of chi-square as a feature selection method and ensemble methods without feature selection respectively. The study results show that the tree-based ensemble model is compelling for malware classification.

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Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Frontiers in Public Health ◽

10.3389/fpubh.2021.762303 ◽

2021 ◽

Vol 9 ◽

Author(s):

Apeksha Shah ◽

Swati Ahirrao ◽

Sharnil Pandya ◽

Ketan Kotecha ◽

Suresh Rathod

Keyword(s):

Cardiac Arrest ◽

Real Time ◽

Cox Regression ◽

Risk Classification ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Time Data ◽

Extreme Gradient Boosting ◽

Real Time Data

Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Sensors ◽

10.3390/s20216336 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6336 ◽

Cited By ~ 1

Author(s):

Mnahi Alqahtani ◽

Hassan Mathkour ◽

Mohamed Maher Ben Ismail

Keyword(s):

Feature Selection ◽

Large Scale ◽

Feature Selection Method ◽

Selection Method ◽

Attack Detection ◽

Gradient Boosting ◽

Fisher Score ◽

Detection Approach ◽

Extreme Gradient Boosting ◽

Iot Devices

Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

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Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

Applied Sciences ◽

10.3390/app10196681 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6681 ◽

Cited By ~ 1

Author(s):

Zhizhen Liu ◽

Hong Chen ◽

Xiaoke Sun ◽

Hengrui Chen

Keyword(s):

Real Time ◽

Public Transportation ◽

Demand Forecasting ◽

Gradient Boosting ◽

Intelligent Transport System ◽

Transportation Services ◽

Intelligent Transport ◽

Time Prediction ◽

Extreme Gradient Boosting ◽

Demand Forecasting Model

The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

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An ARDS Severity Recognition Model based on XGBoost

Journal of Physics Conference Series ◽

10.1088/1742-6596/2138/1/012009 ◽

2021 ◽

Vol 2138 (1) ◽

pp. 012009

Author(s):

Huimin Zhang ◽

Renshuang Ding ◽

Qi Zhang ◽

Mingxing Fang ◽

Guanghua Zhang ◽

...

Keyword(s):

Real Time ◽

Pearson Correlation ◽

Area Under The Curve ◽

Gradient Boosting ◽

Blood Oxygen Saturation ◽

Recognition Model ◽

Model Based ◽

Extreme Gradient Boosting ◽

Mimic Iii

Abstract Given the subjectivity and non-real-time of disease scoring system and invasive parameters in evaluating the development of acute respiratory distress syndrome (ARDS), combined with noninvasive parameters, this paper proposed an ARDS severity recognition model based on extreme gradient boosting (XGBoost). Firstly, the physiological parameters of patients were extracted based on the MIMIC-III database for statistical analysis, and the outliers and unbalanced samples were processed by the interquartile range and synthetic minority oversampling technique. Then, Pearson correlation coefficient and random forest were used as hybrid feature selection to score the noninvasive parameters comprehensively, and essential parameters for identifying diseases were obtained. Finally, XGBoost combined with grid search cross-validation to determine the best hyper-parameters of the model to realize the accurate classification of disease degree. The experimental results show that the model’s area under the curve (AUC) is as high as 0.98, and the accuracy is 0.90; the total score of blood oxygen saturation (SpO2) is 0.625, which could be used as an essential parameter to evaluate the severity of ARDS. Compared with traditional methods, this model has excellent advantages in real-time and accuracy and could provide more accurate diagnosis and treatment suggestions for medical staff.

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Redis-Based Messaging Queue and Cache-Enabled Parallel Processing Social Media Analytics Framework

The Computer Journal ◽

10.1093/comjnl/bxaa114 ◽

2020 ◽

Author(s):

Ravindra Kumar Singh ◽

Harsh Kumar Verma

Keyword(s):

Machine Learning ◽

Social Media ◽

Real Time ◽

Data Analytics ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Social Media Analytics ◽

Data Engineering ◽

Extreme Gradient Boosting

Abstract The extensive usage of social media polarity analysis claims the need for real-time analytics and runtime outcomes on dashboards. In data analytics, only 30% of the time is consumed in modeling and evaluation stages and 70% is consumed in data engineering tasks. There are lots of machine learning algorithms to achieve a desirable outcome in prediction points of view, but they lack in handling data and their transformation so-called data engineering tasks, and reducing its time remained still challenging. The contribution of this research paper is to encounter the mentioned challenges by presenting a parallelly, scalable, effective, responsive and fault-tolerant framework to perform end-to-end data analytics tasks in real-time and batch-processing manner. An experimental analysis on Twitter posts supported the claims and signifies the benefits of parallelism of data processing units. This research has highlighted the importance of processing mentioned URLs and embedded images along with post content to boost the prediction efficiency. Furthermore, this research additionally provided a comparison of naive Bayes, support vector machines, extreme gradient boosting and long short-term memory (LSTM) machine learning techniques for sentiment analysis on Twitter posts and concluded LSTM as the most effective technique in this regard.

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A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

Computational Intelligence and Neuroscience ◽

10.1155/2022/4694567 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Hamid Nasiri ◽

Seyed Ali Alavi

Keyword(s):

Feature Selection ◽

Deep Learning ◽

False Negative ◽

Feature Selection Method ◽

Multiclass Classification ◽

Selection Method ◽

Gradient Boosting ◽

X Ray ◽

Extreme Gradient Boosting ◽

Chest X Ray

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus’s transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method’s precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.

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Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV–Vis Spectrometry and SPSE-XGBoost Method

Sensors ◽

10.3390/s20174936 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4936 ◽

Cited By ~ 1

Author(s):

Fei Cheng ◽

Chunhua Yang ◽

Can Zhou ◽

Lijuan Lan ◽

Hongqiu Zhu ◽

...

Keyword(s):

Metal Ions ◽

Real Time ◽

Metal Ion ◽

Sulfate Solution ◽

Gradient Boosting ◽

Feature Subset ◽

Full Spectrum ◽

Ion Concentrations ◽

Extreme Gradient Boosting

Excessive discharge of heavy metal ions will aggravate environment pollution and threaten human health. Thus, it is of significance to real-time detect metal ions and control discharge in the metallurgical wastewater. We developed an accurate and rapid approach based on the singular perturbation spectrum estimator and extreme gradient boosting (SPSE-XGBoost) algorithms to simultaneously determine multi-metal ion concentrations by UV–vis spectrometry. In the approach, the spectral data is expanded by multi-order derivative preprocessing, and then, the sensitive feature bands in each spectrum are extracted by feature importance (VI score) ranking. Subsequently, the SPSE-XGBoost model are trained to combine multi-derivative features and to predict ion concentrations. The experimental results indicate that the developed “Expand-Extract-Combine” strategy can not only overcome problems of background noise and spectral overlapping but also mine the deeper spectrum information by integrating important features. Moreover, the SPSE-XGBoost strategy utilizes the selected feature subset instead of the full-spectrum for calculation, which effectively improves the computing speed. The comparisons of different data processing methods are conducted. It outcomes that the proposed strategy outperforms other routine methods and can profoundly determine the concentrations of zinc, copper, cobalt, and nickel with the lowest RMSEP. Therefore, our developed approach can be implemented as a promising mean for real-time and on-line determination of multi-metal ion concentrations in zinc hydrometallurgy.

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P283 Patient-reported symptoms over a period of 14 days reliably predict endoscopic and histological disease activity in ulcerative colitis (UC)

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjz203.412 ◽

2020 ◽

Vol 14 (Supplement_1) ◽

pp. S293-S294 ◽

Cited By ~ 1

Author(s):

A Kormilitzin ◽

A J Walsh ◽

L Matini ◽

R Kantschuster ◽

M Lepetyukh ◽

...

Keyword(s):

Ulcerative Colitis ◽

Longitudinal Data ◽

Disease Activity ◽

Data Representation ◽

Classification Algorithm ◽

Gradient Boosting ◽

Machine Learning Classification ◽

Extreme Gradient Boosting ◽

Patient Reported ◽

Active Disease

Abstract Background Disease activity assessment in UC is most accurately evaluated by endoscopy and biopsy, which often correlates poorly with current symptoms. We investigated whether a machine learning classification algorithm using patient-reported Simple Clinical Colitis Activity Index (SCCAI) over 14 days and signature features could reliably distinguish endoscopic and histopathologic activity from remission. Methods The TrueColours ulcerative colitis (UC) monitoring platform was used to collect symptoms (SCCAI, daily) and endoscopic/histopathological activity, as per the UCEIS and Nancy indices (twice in 6 months) in 233 patients. The longitudinal data may be seen as a trajectory. The signature is a collection of statistics which efficiently summarises the trajectory and serves as a non-parametric hierarchical method for longitudinal data representation. Signature features were used as input to the extreme gradient boosting classification algorithm to categorise subjects into remission or active disease groups. Remission: UCEIS ≤1 AND Nancy ≤1; Active disease: UCEIS ≥4 AND Nancy ≥2. The advanced signature-based approach was compared with a baseline model with manually constructed features (mean SCCAI score over 14 days preceding endoscopy). Cross-validation was used to report metrics, Table 1. Results Disease activity classification with signature features significantly outperformed mean-SCCAI based approach (AUC 0.87 vs. AUC 0.79 correspondingly, p = 0.011, Figure 1). All other statistical metrics (including sensitivity (0.89), specificity (0.78), PPV (0.82) and NPV) demonstrated the same trend, favouring the signature method. The decision-tree-based ensemble gradient boosting classification algorithm (XGBoost) has the intrinsic ability to rank feature importance. The signature feature of the fourth item within the SCCAI (blood in stool) dominated, followed by cross-correlation between the first (daily bowel frequency) and fourth item. Conclusion Gradient boosting classification algorithm with signature features extracted from the longitudinal six-item symptoms of the SCCAI, reliably identified subjects with endoscopic and histopathological disease activity. The developed algorithm may be used with the TrueColours platform as a decision support tool.

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Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors

Electronics ◽

10.3390/electronics10050613 ◽

2021 ◽

Vol 10 (5) ◽

pp. 613

Author(s):

Jaakko Tervonen ◽

Kati Pettersson ◽

Jani Mäntyjärvi

Keyword(s):

Heart Rate ◽

Cognitive Load ◽

Real Time ◽

Wearable Sensors ◽

Classification Performance ◽

Gradient Boosting ◽

Window Length ◽

Load Detection ◽

Continuous State ◽

Extreme Gradient Boosting

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors.

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