Journal articles on the topic 'Bayes point machine'
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Qiang, Yang, Lei Zhang, Zhi Li Sun, Yi Liu, and Xue Bin Bai. "Reliability Analysis Based on Improved Bayes Method of AMSAA Model." Advanced Materials Research 482-484 (February 2012): 2336–40. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2336.
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Aiming at the defects that failure samples of five-axis NC machine tools is small, traditional reliability analysis is not accurate, this paper presents reliability analysis mode based on improved Bayesian method for AMSAA model. Firstly, we obtain the failure model of NC machine tools meets the AMSAA model according to goodness-of-fit test, and in order to meet the requirements of simplifying engineering calculations, this paper adpots a method of Coefficient equivalent which converts failure Data into index-life data; then using Bayesian methods to estimate reliability parameters for the Index-life data; for the last we proceed point estimation and interval estimation for the MTBF of the machine. Take High-speed five-axis NC machine tools t of VMC650m for example, the result proved that the method can take advantage of a small sample of the equipment to proceed point estimation and interval estimation for MTBF failure data, and provide a reference for the optimization of maintenance strategies and Diagnostic work of the NC machine tools.
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Bhalla, Rajni, and Amandeep Bagga. "Opinion mining framework using proposed RB-bayes model for text classication." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 477. http://dx.doi.org/10.11591/ijece.v9i1.pp477-484.
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<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>
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Bhalla, Rajni, and Amandeep Bagga. "Opinion mining framework using proposed RB-bayes model for text classication." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 477. http://dx.doi.org/10.11591/ijece.v9i1.pp477-485.
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<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>
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Guijo-Rubio, David, Javier Briceño, Pedro Antonio Gutiérrez, Maria Dolores Ayllón, Rubén Ciria, and César Hervás-Martínez. "Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation." PLOS ONE 16, no. 5 (May 21, 2021): e0252068. http://dx.doi.org/10.1371/journal.pone.0252068.
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Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.
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Siam, Ali I., Naglaa F. Soliman, Abeer D. Algarni, Fathi E. Abd El-Samie, and Ahmed Sedik. "Deploying Machine Learning Techniques for Human Emotion Detection." Computational Intelligence and Neuroscience 2022 (February 2, 2022): 1–16. http://dx.doi.org/10.1155/2022/8032673.
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Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.
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Aljwari, Fatima, Wahaj Alkaberi, Areej Alshutayri, Eman Aldhahri, Nahla Aljojo, and Omar Abouola. "Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News." Postmodern Openings 13, no. 1 Sup1 (March 14, 2022): 01–14. http://dx.doi.org/10.18662/po/13.1sup1/411.
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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.
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Polaka, Inese, Manohar Prasad Bhandari, Linda Mezmale, Linda Anarkulova, Viktors Veliks, Armands Sivins, Anna Marija Lescinska, et al. "Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection." Diagnostics 12, no. 2 (February 14, 2022): 491. http://dx.doi.org/10.3390/diagnostics12020491.
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Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.
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Chairani, Chairani, Widyawan Widyawan, and Sri Suning Kusumawardani. "Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM." JURNAL INFOTEL - Informatika Telekomunikasi Elektronika 7, no. 1 (May 10, 2015): 1. http://dx.doi.org/10.20895/infotel.v7i1.23.
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Penelitian ini membahas tentang estimasi posisi (localization) objek dalam gedung menggunakan jaringan wireless atau IEEE 802.11g dengan pendekatan Machine Learning. Metode pada pengukuran RSS menggunakan RSS-based fingerprint. Algoritma Machine Learning yang digunakan dalam memperkirakan lokasi dari pengukuran RSS-based menggunakan Naive Bayes. Localization dilakukan pada lantai 3 gedung Jurusan Teknik Elektro dan Teknologi Informasi (JTETI) dengan luas 1969,68 m2 dan memiliki 5 buah titik penempatan access point (AP). Untuk membentuk peta fingerprint digunakan dimensi 1 m x 1 m sehingga terbentuk grid sebanyak 1893 buah. Dengan menggunakan software Net Surveyor terkumpul data kekuatan sinyal yang diterima (RSS) dari jaringan wireless ke perangkat penerima (laptop) sebanyak 86.980 record. Hasil nilai rata-rata error jarak estimasi untuk localization seluruh ruangan di lantai 3 dengan menggunakan algoritma Naive Bayes pada fase offline tahap learning adalah 6,29 meter. Untuk fase online dan tahap post learning diperoleh rata-rata error jarak estimasi sebesar 7,82 meter.
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Zaboli, M., H. Rastiveis, A. Shams, B. Hosseiny, and W. A. Sarasua. "CLASSIFICATION OF MOBILE TERRESTRIAL LIDAR POINT CLOUD IN URBAN AREA USING LOCAL DESCRIPTORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1117–22. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1117-2019.
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Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.
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Susanto, Rian Dwi, and Dodik Arwin Dermawan. "Implementasi Finite State Machine dan Algoritma Naïve Bayes pada Game Lord Of Sewandono." Journal of Informatics and Computer Science (JINACS) 3, no. 01 (August 10, 2021): 71–78. http://dx.doi.org/10.26740/jinacs.v3n01.p71-78.
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Abstrak - Pemanfaatan teknologi untuk mengembangkan suatu kesenian budaya saat ini dirasa sangat tepat karena begitu pesatnya teknologi berkembang, salah satunya di bidang game. Di era modern seperti saat ini game bukan hanya digunakan sebagai media hiburan tetapi juga dapat dimanfaatkan sebagai media pengenalan suatu budaya. Reog Ponorogo merupakan kesenian budaya khas Jawa Timur yang berasal dari Ponorogo, kesenian ini biasa ditampilkan oleh sekelompok orang yang menari dengan memerankan beberapa tokoh. Klana Sewandono adalah salah satu tokoh yang terkenal dalam kesenian reog ponorogo karakternya digambarkan sebagai seorang raja memakai topeng bermahkota, berwajah merah dan membawa pecut yang dikenal dengan pecut Samandiman. Terinspirasi dari karakter tersebut terciptalah sebuah game survival berjudul “Lord of Sewandono”, game android dengan misi Prabu Sewandono mengalahkan Raja Singabarong untuk menyelamatkan Dwi Sanggalangit yang dibuat menggunakan gabungan antara metode finite state machine dan algoritma naïve bayes. Gabungan tersebut berfungsi untuk menentukan perilaku dari karakter NPC(Non Player Character) atau musuh utama yaitu Raja Singabarong yang terbagi menjadi 4 yaitu maju, mundur, serang, dan bertahan. Variable yang digunakan adalah AP(Attack Power), HP(Health Point), dan Jarak. Dari pengujian naïve bayes sebanyak 25 kali dengan confusion matrix, telah didapatkan hasil persentase tingkat akurasi pada confusion matrix sebesar 88% valid atau sesuai dengan harapan dan 12% invalid atau belum sesuai dengan harapan (error). Hasil pengujian beta rata-rata penilaian yang diperoleh sebesar 71,67% berdasarkan hasil dari responden terhadap tiap butir pertanyaan kuisioner.
Kata Kunci - reog ponorogo, klana sewandono, game, metode finite state machine, algoritma naïve bayes.
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Chaudhary, Kamika, and Neena Gupta. "E-Learning Recommender System for Learners: A Machine Learning based Approach." International Journal of Mathematical, Engineering and Management Sciences 4, no. 4 (August 1, 2019): 957–67. http://dx.doi.org/10.33889/ijmems.2019.4.4-076.
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Web mining procedure helps the surfers to get the required information but finding the exact information is as good as finding a needle in a haystack. In this work, an intelligent prediction model using Tensor Flow environment for Graphics Processing Unit (GPU) devices has been designed to meet the challenges of speed and accuracy. The proposed approach is isolated into two stages: pre-processing and prediction. In the first phase, the procedure starts via looking through the URLs of various e-learning sites particular to computer science subjects. At that point, the content of looked through URLs are perused and after that from their keywords are produced identified with a particular subject in the wake of playing out the pre-processing of the content. Second phase is prediction that predicts query specific links of e-learning website. The proposed Intelligent E-learning through Web (IEW) has content mining, lexical analysis, classification and machine learning based prediction as its key features. Algorithms like SVM, Naïve Bayes, K-Nearest Neighbor, and Random Forest were tested and it was found that Random Forest gave an accuracy of 98.98%, SVM 42%, KNN 63% and Naïve Bayes 66%. Based on the results IEW uses Random forest for prediction.
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Mohamed, M., S. Morsy, and A. El-Shazly. "MACHINE LEARNING FOR MOBILE LIDAR DATA CLASSIFICATION OF 3D ROAD ENVIRONMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 113–17. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-113-2021.
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Abstract. 3D road mapping is essential for intelligent transportation system in smart cities. Road features can be utilized for road maintenance, autonomous driving vehicles, and providing regulations to drivers. Currently, 3D road environment receives its data from Mobile Laser Scanning (MLS) systems. MLS systems are capable of rapidly acquiring dense and accurate 3D point clouds, which allow for effective surveying of long road corridors. They produce huge amount of point clouds, which requires automatic features classification algorithms with acceptable processing time. Road features have variant geometric regular or irregular shapes. Therefore, most researches focus on classification of one road feature such as road surface, curbs, building facades, etc. Machine learning (ML) algorithms are widely used for predicting the future or classifying information to help policymakers in making necessary decisions. This prediction comes from a pre-trained model on a given data consisting of inputs and their corresponding outputs of the same characteristics. This research uses ML algorithms for mobile LiDAR data classification. First, cylindrical neighbourhood selection method was used to define point’s surroundings. Second, geometric point features including geometric, moment and height features were derived. Finally, three ML algorithms, Random Forest (RF), Gaussian Naïve Bayes (GNB), and Quadratic Discriminant Analysis (QDA) were applied. The ML algorithms were used to classify a part of Paris-Lille-3D benchmark of about 1.5 km long road in Lille with more than 98 million points into nine classes. The results demonstrated an overall accuracy of 92.39%, 78.5%, and 78.1% for RF, GNB, and QDA, respectively.
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Sudarsa, Dorababu, Siva Kumar.P, and L. Jagajeevan Rao. "Sentiment Analysis for Social Networks Using Machine Learning Techniques." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 473. http://dx.doi.org/10.14419/ijet.v7i2.32.16271.
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The tremendous of the overall enormous net has conveyed a present day way of communicating the feelings of individuals. It's additionally a medium with a vast amount of data in which clients can see the assessment of different clients which can be ordered into exceptional entailment summons and are progressively more boom as a key component in decision making. This paper adds to the supposition assessment for customers assessment class that is utilized to analyze the records inside the type of the assortment of tweets wherein investigates are very unstructured and are both high fine or terrible, or somewhere in the middle of these . For this we first pre-prepared the dataset, after that extract the adjective from the dataset that has a couple of significance this is alluded to as capacity vector, at that point decided on the component vector posting and from that point accomplished device examining based write calculations particularly navie bayes, most entropy and svm along the edge of the semantic introduction based absolutely based on word net which extracts synonyms and similarity for the content characteristic. In the end, we measured the performance of the classifier in terms of considering, precision and accuracy.
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Dhamija, Samriti. "LEVERAGING THE MACHINE LEARNING ALGORITHMS TO EFFICACIOUSLY PREDICT THE RISK PARAMETERS OF STROKE." International Journal of Research in Medical Sciences and Technology 12, no. 01 (2022): 238–46. http://dx.doi.org/10.37648/ijrmst.v11i02.020.
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Unexpected hindrances of pathways bring strokes to the heart and cerebrum. Various classifiers have been developed to identify early stroke warning side effects, including Logistics Regression, Decision Tree, KNN, Random Forest, and Naïve Bayes. Besides, the proposed research has acquired a precision of around 95.4%, with the Random Forest beating different classifiers. This model has the most elevated stroke forecast accuracy. Accordingly, Random Forest is the ideal classifier for anticipating stroke, which specialists and patients can use to early endorse and recognize likely strokes. Here in our examination, we have made a site to which the model is unloaded/stacked to such an extent that the connection point will be cordial to the end clients.
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Grana, P. Ancy, and Vinod S. Agrawal. "Evaluation of Sentiment Analysis of Text Using Rule-Based and Automatic Approach." Research & Review: Machine Learning and Cloud Computing 1, no. 2 (May 26, 2022): 6–11. http://dx.doi.org/10.46610/rrmlcc.2022.v01i02.002.
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The technique of determining whether a text is good, negative or neutral is known as sentiment analysis (SA).Sentiment Analysis can be identified by many names like Textual Analysis, Opinion Mining. Sentiment Analysis is a branch of Natural Language Processing (NLP) that focuses on the expression of subjective views and feelings about a topic gathered from multiple sources. Sentiment Analysis is a collection of methods for detecting and extracting opinions and uses them for the benefit of business operation. It is a classification algorithm aimed at finding opinions and decision-making point of view. Sentiment Analysis is performed in many ways, Automatic classification approach involves Nave Bayes (NB), Support Vector Machine (SVM), and Linear Regression is examples of supervised machine learning methods (LR). The data is explored using unsupervised machine learning. Recurrent Neural Network (RNN) derivatives are also used for classification. Rule-based approach involves various NLP process for classification.
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A., Parkavi, Sangeetha V., Sini Anna Alex, and Syed Mustafa A. "A Case Study on Design of Covid-19 Detection and Alerting System Using Machine Learning Techniques." Webology 19, no. 1 (January 20, 2022): 1358–86. http://dx.doi.org/10.14704/web/v19i1/web19091.
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Coronavirus or 2019-nCoV is not, at this point, pandemic but instead endemic, with in excess of 14 million complete cases all throughout the planet getting the infection. At present, there is no particular treatment or solution for Coronavirus, and hence living with the sickness and its manifestations is unavoidable. The connection coefficient examination between different needy and free highlights was done to decide a strength connection between every reliant element and autonomous component of the dataset before building up the models. The database is divided into two parts, 80% of the database is used for model training and the remaining 20% is used for model testing and evaluation. In 2019, early Coronavirus predictions is useful to reduce colossal weight on medical service panels through the diagnosis of coronavirus patients. In the proposed work in this paper, Naive Bayes, Decision tree, Support Vector Machine (SVM) and Artificial neural network (ANN) models are used for forecasting COVID-19 prediction and occurrences.
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Zhang, MingFang, Rui Fu, YingShi Guo, and Li Wang. "Moving Object Classification Using 3D Point Cloud in Urban Traffic Environment." Journal of Advanced Transportation 2020 (March 17, 2020): 1–12. http://dx.doi.org/10.1155/2020/1583129.
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Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, we propose a novel approach for classifying moving objects into four classes of interest using 3D point cloud in urban traffic environment. Unlike most existing work on object recognition which involves dense point cloud, our approach combines extensive feature extraction with the multiframe classification optimization to solve the classification task when partial occlusion occurs. First, the point cloud of moving object is segmented by a data preprocessing procedure. Then, the efficient features are selected via Gini index criterion applied to the extended feature set. Next, Bayes Decision Theory (BDT) is employed to incorporate the preliminary results from posterior probability Support Vector Machine (SVM) classifier at consecutive frames. The point cloud data acquired from our own LIDAR as well as public KITTI dataset is used to validate the proposed moving object classification method in the experiments. The results show that the proposed SVM-BDT classifier based on 18 selected features can effectively recognize the moving objects.
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Jopri, Mohd Hatta, Mohd Ruddin Ab Ghani, Abdul Rahim Abdullah, Mustafa Manap, Tole Sutikno, and Jingwei Too. "K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification." Bulletin of Electrical Engineering and Informatics 9, no. 6 (December 1, 2021): 2650–57. http://dx.doi.org/10.11591/eei.v9i6.2685.
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This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.
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Chen, Hongli. "Identification of Grammatical Errors of English Language Based on Intelligent Translational Model." Mobile Information Systems 2022 (June 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/4472190.
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To address the problem of low kappa, precision and recall values, and high misjudgment rate in traditional methods, this study proposes an English grammatical error identification method based on a machine translation model. For this purpose, a bidirectional long short-term memory (Bi-LSTM) model is established to diagnose English grammatical errors. A machine learning (ML) model, i.e., Naive Bayes is used for the result classification of the English grammatical error diagnosis, and the N-gram model is utilized to effectively point out the location of the error. According to the preprocessing results, a grammatical error generation model is designed, a parallel corpus is built from which a training dataset for the model training is generated, and different types of grammatical errors are also checked. The overall architecture of the machine translation model is given, and the model parameters are trained on a large-scale modification of the wrong learner corpus, which greatly improves the accuracy of grammatical error identification. The experimental outcomes reveal that the model used in this study significantly improves the kappa value, the precision and recall values, and the misjudgment rate remains below 1.0, which clearly demonstrates that the detection effect is superior.
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Misra, Shashwat, Jasleen Kaur, and U. M. Prakash. "Sentimental Analysis using Machine Learning and Deep Learning: Performance Measurement, Challenges and Opportunities." International Journal of Current Engineering and Technology 11, no. 04 (August 4, 2021): 412–17. http://dx.doi.org/10.14741/ijcet/v.11.4.3.
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Our regular existence has consistently been impacted with the aid of what individuals think. Thoughts and tests of others have consistently inspired our personal sentiments. Web 2.0 has caused extended action in Podcasting, Tagging, Blogging, and Social Networking. As an end result, social media web sites have emerged as one of the structures to raise consumer’s opinions and influence the way any commercial enterprise is commercialized. Sentiment analysis is the prediction of feelings in a word, sentence, or corpus of files. It is deliberate to fill in as a software to recognize the mentalities, conclusions, and feelings communicated interior a web point out. This paper reviews at the design of sentiment evaluation, mining the sizeable resources of information for evaluations. The number one goal is to provide a way for studying sentiment rating in social media platforms. Here we discuss diverse methods to perform a computational remedy of sentiments and reviews, diverse supervised or facts-driven techniques to research sentiments like Naïve Bayes, Support Vector Machine, and SentiWordNet technique to Sentiment Analysis. Results classify consumer’s belief through social media posts into positive, negative, and neutral.
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de-Marcos, Luis, José-Javier Martínez-Herráiz, Javier Junquera-Sánchez, Carlos Cilleruelo, and Carmen Pages-Arévalo. "Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics." Electronics 10, no. 14 (July 7, 2021): 1622. http://dx.doi.org/10.3390/electronics10141622.
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Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.
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Dai, Qian. "Construction of English and American Literature Corpus Based on Machine Learning Algorithm." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/9773452.
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In China, the application of corpus in language teaching, especially in English and American literature teaching, is still in the preliminary research stage, and there are various shortcomings, which have not been paid due attention by front-line educators. Constructing English and American literature corpus according to certain principles can effectively promote English and American literature teaching. The research of this paper is devoted to how to automatically build a corpus of English and American literature. In the process of keyword extraction, key phrases and keywords are effectively combined. The similarity between atomic events is calculated by the TextRank algorithm, and then the first N sentences with high similarity are selected and sorted. Based on ML (machine learning) text classification method, a combined classifier based on SVM (support vector machine) and NB (Naive Bayes) is proposed. The experimental results show that, from the point of view of accuracy and recall, the classification effect of the combined algorithm proposed in this paper is the best among the three methods. The best classification results of accuracy, recall, and F value are 0.87, 0.9, and 0.89, respectively. Experimental results show that this method can quickly, accurately, and persistently obtain high-quality bilingual mixed web pages.
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Patidar, Pawan Kumar, and Rekha Jain. "A Review on Prediction of Diabetes Using Various Machine Learning Algorithms." ECS Transactions 107, no. 1 (April 24, 2022): 5785–95. http://dx.doi.org/10.1149/10701.5785ecst.
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In a world of fast paces and a general adoption of poor health care habits, with the same speed that the 21st century demands of us, it is common to feel that the fields of illnesses and their spread have grown dramatically. It is a group of business intelligence (BI) appliances that employ advanced machine learning approaches to find connections and patterns in huge quantities of facts. These facts-driven relationships and patterns help us anticipate behavior and occurrences. By utilizing previous occurrences, predictive analytics gives you a glimpse into the future. Although the predictive model is not based on the creation of a mathematical model or approaches for the development of the prognosis, it is essential to point out that it was constructed using past predictive methods, but on the use of approaches available in the identified appliance. Through the use of the model, it is proposed that the entities that provide public and private health services implement it in a commercial context, using the predictive abilities of the model for the benefit of the client's diagnosis and the optimization of consultation processes. This work proposed a review of 10 papers on diabetes prognosis using various machine learning approaches namely Logistic Regression, Random Forest, KNN, Naïve Bayes, ANN, Gradient Boosting, CNN, Support Vector Machine, and Ada Boost Approach.
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Setiawan Sumadi, Fauzi Dwi, Alrizal Rakhmat Widagdo, Abyan Faishal Reza, and Syaifuddin. "SD-Honeypot Integration for Mitigating DDoS Attack Using Machine Learning Approaches." JOIV : International Journal on Informatics Visualization 6, no. 1 (March 26, 2022): 39. http://dx.doi.org/10.30630/joiv.6.1.853.
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Distributed Denial of Services (DDoS) is still considered the main availability problem in computer networks. Developing a programmable Intrusion Prevention System (IPS) application in a Software Defined Network (SDN) may solve the specified problem. However, the deployment of centralized logic control can create a single point of failure on the network. This paper proposed the integration of Honeypot Sensor (Suricata) on the SDN environment, namely the SD-Honeypot network, to resolve the DDoS attack using a machine learning approach. The application employed several algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), and Random Forest (RF)) and comparatively analyzed. The dataset used during the emulation utilized the extracted Internet Control Message Protocol (ICMP) flood data from the Suricata sensor. In order to measure the effectiveness of detection and mitigation modules, several variables were examined, namely, accuracy, precision, recall, and the promptness of the flow mitigation installation process. The Honeypot server transmitted the flow rule modification message for blocking the attack using the Representational State Transfer Application Programming Interface (REST API). The experiment results showed the effectiveness of CART algorithm for detecting and resolving the intrusion. Despite the accuracy score pointed at 69-70%, the algorithm could promptly deploy the mitigation flow within 31-49ms compared to the SVM, which produced 93-94% accuracy, but the flow installation required 112-305ms. The developed CART module can be considered a solution to prevent the attack effectively based on the analyzed variable.
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Fang, Kwo-Ting, and Ching-Hsiang Ping. "Using Machine Learning to Explore the Crucial Factors of Assistive Technology Assessments: Cases of Wheelchairs." Healthcare 10, no. 11 (November 9, 2022): 2238. http://dx.doi.org/10.3390/healthcare10112238.
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The global population is gradually entering an aging society; chronic diseases and functional disabilities have increased, thereby increasing the number of people with limitations. Therefore, the demand for assistive devices has increased substantially. Due to numerous and complex types of assistive devices, an assessment by a professional therapist is required to help the individual find a suitable assistive device. According to actual site data, the assessment needs of “wheelchairs” accounted for most of the cases. Therefore, this study identified five key evaluation characteristics (head condition, age, pelvic condition, cognitive ability, and judgment) for “transit wheelchairs” and “reclining and tilting wheelchairs” from the diagnostic records of “wheelchairs” using the classification and regression trees (CART) decision tree algorithm. Furthermore, the study established an evaluation model through the Naïve Bayes classification method and obtained an accuracy rate of 72.0% after a 10-fold cross-validation. Finally, the study considered users’ convenience and combined it with a LINE BOT to allow the user or the user’s family to engage in self-evaluation. Preliminary suggestions for wheelchair types were given through the assessment model so that evaluators could not only determine a case’s situation in advance and reduce the time required for fixed-point or home assessments, but also help cases find the appropriate wheelchair type more easily and quickly.
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Aljojo, Nahla. "Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 11 (November 15, 2021): 47. http://dx.doi.org/10.3991/ijoe.v17i11.25025.
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<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. </p>
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Furuya, Danielle Elis Garcia, João Alex Floriano Aguiar, Nayara V. Estrabis, Mayara Maezano Faita Pinheiro, Michelle Taís Garcia Furuya, Danillo Roberto Pereira, Wesley Nunes Gonçalves, et al. "A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery." Remote Sensing 12, no. 24 (December 14, 2020): 4086. http://dx.doi.org/10.3390/rs12244086.
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Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context.
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Eketnova, Yu M. "Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients." Finance: Theory and Practice 25, no. 5 (October 28, 2021): 186–99. http://dx.doi.org/10.26794/2587-5671-2020-25-5-186-199.
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In the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institutions
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Genc, Onur, and Ali Dag. "A Bayesian network-based data analytical approach to predict velocity distribution in small streams." Journal of Hydroinformatics 18, no. 3 (October 23, 2015): 466–80. http://dx.doi.org/10.2166/hydro.2015.110.
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Developing a reliable data analytical method for predicting the velocity profile in small streams is important in that it substantially decreases the amount of money and effort spent on measurement procedures. In recent studies it has been shown that machine learning models can be used to achieve such an important goal. In the proposed framework, a tree-augmented Naïve Bayes approach, a member of the Bayesian network family, is employed to address the aforementioned two issues. Therefore, the proposed study presents novelty in that it explores the relations among the predictor attributes and derives a probabilistic risk score associated with the predictions. The data set of four key stations, in two different basins, are employed and the eight observational variables and calculated non-dimensional parameters were utilized as inputs to the models for estimating the response values, u (point velocities in measured verticals). The results showed that the proposed data-analytical approach yields comparable results when compared to the widely used, powerful machine learning algorithms. More importantly, novel information is gained through exploring the interrelations among the predictors as well as deriving a case-specific probabilistic risk score for the prediction accuracy. These findings can be utilized to help field engineers to improve their decision-making mechanism in small streams.
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Sutoyo, Edi, and Ahmad Almaarif. "Twitter sentiment analysis of the relocation of Indonesia's capital city." Bulletin of Electrical Engineering and Informatics 9, no. 4 (August 1, 2020): 1620–30. http://dx.doi.org/10.11591/eei.v9i4.2352.
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Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.
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Balasubramanian, Dr K., and K. Shobiya. "Water Level Prediction In Water Shed Management Utilizing Machine Learning." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 11 (September 1, 2021): 10–27. http://dx.doi.org/10.55529/11.10.27.
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Due to uneven rainfall, nowadays the amount of rain to be showered in a month is getting showered in few days. The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. To avoid this, the proposal suggests an idea to develop a watershed and to predict the water level measurement by Bayesian classification, clustering, and optimization techniques. Artificial Neural Network is one of the previous techniques used to predict water level which gives approximate result only. To overcome the disadvantage, this proposal suggests an idea to develop the watershed by using different machine learning techniques. The level of water that can be stored is calculated using Bayes Network which will classify the data into labels according to the condition of the capacity of the minimum and maximum storage level of the watershed. The standardized data considered for the classification are normalized using the z-score normalization. Classification will represent the result by means of the instances that are correctly classified. The output of the classified data is fed into clustering algorithm where the labels are grouped into different clusters. The K-Mean algorithm is utilized for clustering which iteratively assign data point to one of the k group according to the given attribute. The clustered output gives the result of how many instances are correctly clustered. The clustered output will be refined for further process such that the data will be extracted as ordered dataset of year wise and month wise data. For the extracted data gradient descent algorithm is applied for reducing the error and predicting the amount of water stored in watershed for upcoming years by means of calculating the actual and prediction value. Later the result will be visualized in the form of graph. The obtained output is considered as an input for posterior probability that uses J48 algorithm which gives the result of probability of event happened after all the evidence is taken for consideration and gives the accurate result. The above methodology provides high performance and efficient result.
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Balasubramanian, Dr K., and K. Shobiya. "Water Level Prediction In Water Shed Management Utilizing Machine Learning." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 11 (September 1, 2021): 10–27. http://dx.doi.org/10.55529/jaimlnn.11.10.27.
Full textAbstract:
Due to uneven rainfall, nowadays the amount of rain to be showered in a month is getting showered in few days. The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. To avoid this, the proposal suggests an idea to develop a watershed and to predict the water level measurement by Bayesian classification, clustering, and optimization techniques. Artificial Neural Network is one of the previous techniques used to predict water level which gives approximate result only. To overcome the disadvantage, this proposal suggests an idea to develop the watershed by using different machine learning techniques. The level of water that can be stored is calculated using Bayes Network which will classify the data into labels according to the condition of the capacity of the minimum and maximum storage level of the watershed. The standardized data considered for the classification are normalized using the z-score normalization. Classification will represent the result by means of the instances that are correctly classified. The output of the classified data is fed into clustering algorithm where the labels are grouped into different clusters. The K-Mean algorithm is utilized for clustering which iteratively assign data point to one of the k group according to the given attribute. The clustered output gives the result of how many instances are correctly clustered. The clustered output will be refined for further process such that the data will be extracted as ordered dataset of year wise and month wise data. For the extracted data gradient descent algorithm is applied for reducing the error and predicting the amount of water stored in watershed for upcoming years by means of calculating the actual and prediction value. Later the result will be visualized in the form of graph. The obtained output is considered as an input for posterior probability that uses J48 algorithm which gives the result of probability of event happened after all the evidence is taken for consideration and gives the accurate result. The above methodology provides high performance and efficient result.
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Balasubramanian, Dr K., and K. Shobiya. "Water Level Prediction In Water Shed Management Utilizing Machine Learning." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 11 (September 1, 2021): 10–27. http://dx.doi.org/10.55529/jaimlnn11.10.27.
Full textAbstract:
Due to uneven rainfall, nowadays the amount of rain to be showered in a month is getting showered in few days. The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. To avoid this, the proposal suggests an idea to develop a watershed and to predict the water level measurement by Bayesian classification, clustering, and optimization techniques. Artificial Neural Network is one of the previous techniques used to predict water level which gives approximate result only. To overcome the disadvantage, this proposal suggests an idea to develop the watershed by using different machine learning techniques. The level of water that can be stored is calculated using Bayes Network which will classify the data into labels according to the condition of the capacity of the minimum and maximum storage level of the watershed. The standardized data considered for the classification are normalized using the z-score normalization. Classification will represent the result by means of the instances that are correctly classified. The output of the classified data is fed into clustering algorithm where the labels are grouped into different clusters. The K-Mean algorithm is utilized for clustering which iteratively assign data point to one of the k group according to the given attribute. The clustered output gives the result of how many instances are correctly clustered. The clustered output will be refined for further process such that the data will be extracted as ordered dataset of year wise and month wise data. For the extracted data gradient descent algorithm is applied for reducing the error and predicting the amount of water stored in watershed for upcoming years by means of calculating the actual and prediction value. Later the result will be visualized in the form of graph. The obtained output is considered as an input for posterior probability that uses J48 algorithm which gives the result of probability of event happened after all the evidence is taken for consideration and gives the accurate result. The above methodology provides high performance and efficient result.
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Arora, Amita, Ashlesha Gupta, Manvi Siwach, Pankaj Dadheech, Krishnaveni Kommuri, Majid Altuwairiqi, and Basant Tiwari. "Web-Based News Straining and Summarization Using Machine Learning Enabled Communication Techniques for Large-Scale 5G Networks." Wireless Communications and Mobile Computing 2022 (June 23, 2022): 1–15. http://dx.doi.org/10.1155/2022/3792816.
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In recent times, text summarization has gained enormous attention from the research community. Among the many uses of natural language processing, text summarization has emerged as a critical component in information retrieval. In particular, within the past two decades, many attempts have been undertaken by researchers to provide robust, useful summaries of their findings. Text summarizing may be described as automatically constructing a summary version of a given document while keeping the most important information included within the content itself. This method also aids users in quickly grasping the fundamental notions of information sources. The current trend in text summarizing, on the other hand, is increasingly focused on the area of news summaries. The first work in summarizing was done using a single-document summary as a starting point. The summarizing of a single document generates a summary of a single paper. As research advanced, mainly due to the vast quantity of information available on the internet, the concept of multidocument summarization evolved. Multidocument summarization generates summaries from a large number of source papers that are all about the same subject or are about the same event. Because of the content duplication, the news summarization system, on the other hand, is unable to cope with multidocument news summarizations well. Using the Naive Bayes classifier for classification, news websites were distinguished from nonnews web pages by extracting content, structure, and URL characteristics. The classifier was then used to differentiate between the two groups. A comparison is also made between the Naive Bayes classifier and the SMO and J48 classifiers for the same dataset. The findings demonstrate that it performs much better than the other two. After those important contents have been extracted from the correctly classified newscast web pages. Then, extracted relevant content is used for the keyphrase extraction from the news articles. Keyphrases can be a single word or a combination of more than one word representing the news article’s significant concept. Our proposed approach of crucial phrase extraction is based on identifying candidate phrases from the news articles and choosing the highest weight candidate phrase using the weight formula. Weight formula includes features such as TFIDF, phrase position, and construction of lexical chain to represent the semantic relations between words using WordNet. The proposed approach shows promising results compared to the other existing techniques.
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Atacak, İsmail. "An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection." Applied Sciences 13, no. 3 (January 23, 2023): 1484. http://dx.doi.org/10.3390/app13031484.
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In this study, a fuzzy logic-based dynamic ensemble (FL-BDE) model was proposed to detect malware exposed to the Android operating system. The FL-BDE model contains a structure that combines both the processing power of machine learning (ML)-based methods and the decision-making power of the Mamdani-type fuzzy inference system (FIS). In this structure, six different methods, namely, logistic regression (LR), Bayes point machine (BPM), boosted decision tree (BDT), neural network (NN), decision forest (DF) and support vector machine (SVM) were used as ML-based methods to benefit from their scores. However, through an approach involving the process of voting and routing, the scores of only three ML-based methods which were more successful in classifying either the negative instances or positive instances were sent to the FIS to be combined. During the combining process, the FIS processed the incoming inputs and determined the malicious application score. Experimental studies were performed by applying the FL-BDE model and ML-based methods to the balanced dataset obtained from the APK files downloaded in the Drebin database and Google Play Store. The obtained results showed us that the FL-BDE model had a much better performance than the ML-based models did, with an accuracy of 0.9933, a recall of 1.00, a specificity of 0.9867, a precision of 0.9868, and an F-measure of 0.9934. These results also proved that the proposed model can be used as a more competitive and powerful malware detection model compared to those of similar studies in the literature.
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Mrabet, Hichem, Adeeb Alhomoud, Abderrazek Jemai, and Damien Trentesaux. "A Secured Industrial Internet-of-Things Architecture Based on Blockchain Technology and Machine Learning for Sensor Access Control Systems in Smart Manufacturing." Applied Sciences 12, no. 9 (May 5, 2022): 4641. http://dx.doi.org/10.3390/app12094641.
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In this paper, a layered architecture incorporating Blockchain technology (BCT) and Machine Learning (ML) is proposed in the context of the Industrial Internet-of-Things (IIoT) for smart manufacturing applications. The proposed architecture is composed of five layers covering sensing, network/protocol, transport enforced with BCT components, application and advanced services (i.e., BCT data, ML and cloud) layers. BCT enables gathering sensor access control information, while ML brings its effectivity in attack detection such as DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, man in the middle (MitM), brute force, cross-site scripting (XSS) and scanning attacks by employing classifiers differentiating between normal and malicious activity. The design of our architecture is compared to similar ones in the literature to point out potential benefits. Experiments, based on the IIoT dataset, have been conducted to evaluate our contribution, using four metrics: Accuracy, Precision, Sensitivity and Matthews Correlation Coefficient (MCC). Artificial Neural Network (ANN), Decision Tree (DT), Random Forest, Naive Bayes, AdaBoost and Support Vector Machine (SVM) classifiers are evaluated regarding these four metrics. Even if more experiments are required, it is illustrated that the proposed architecture can reduce significantly the number of DDoS, injection, brute force and XSS attacks and threats within an advanced framework for sensor access control in IIoT networks based on a smart contract along with ML classifiers.
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Mir, Mahmood Hussain, Sanjay Jamwal, Abolfazl Mehbodniya, Tanya Garg, Ummer Iqbal, and Issah Abubakari Samori. "IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud." Journal of Healthcare Engineering 2022 (April 12, 2022): 1–16. http://dx.doi.org/10.1155/2022/7713939.
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COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
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Liu, Guoman, Yufeng Luo, and Jing Sheng. "Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma." Mathematical Problems in Engineering 2022 (August 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/4163419.
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At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.
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Farahani, Gholamreza. "Feature Selection Based on Cross-Correlation for the Intrusion Detection System." Security and Communication Networks 2020 (September 22, 2020): 1–17. http://dx.doi.org/10.1155/2020/8875404.
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One of the important issues in the computer networks is security. Therefore, trusted communication of information in computer networks is a critical point. To have a safe communication, it is necessary that, in addition to the prevention mechanisms, intrusion detection systems (IDSs) are used. There are various approaches to utilize intrusion detection, but any of these systems is not complete. In this paper, a new cross-correlation-based feature selection (CCFS) method is proposed and compared with the cuttlefish algorithm (CFA) and mutual information-based feature selection (MIFS) features with use of four different classifiers: support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (KNN). The experimental results on the KDD Cup 99, NSL-KDD, AWID, and CIC-IDS2017 datasets show that the proposed method has a better performance in accuracy, precision, recall, and F1-score criteria in comparison with the other two methods in different classifiers. Also, the results on different classifiers show that the usage of the DT classifier for the proposed method is the best.
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Muminov, Azamjon, Mukhriddin Mukhiddinov, and Jinsoo Cho. "Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors." Sensors 22, no. 23 (December 4, 2022): 9471. http://dx.doi.org/10.3390/s22239471.
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The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets’ behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data. Data were received from the accelerometer and gyroscope sensors that are designed to be attached to the dog’s smart costume. Once data are received, the module computes a quaternion value for each data point that provides handful features for classification. Next, to perform the classification, we used several supervised machine learning algorithms, such as the Gaussian naïve Bayes (GNB), Decision Tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM). In order to evaluate the performance, we finally compared the proposed approach’s F-score accuracies with the accuracy of classic approach performance, where sensors’ data are collected without computing the quaternion value and directly utilized by the model. Overall, 18 dogs equipped with harnesses participated in the experiment. The results of the experiment show a significantly enhanced classification with the proposed approach. Among all the classifiers, the GNB classification model achieved the highest accuracy for dog behavior. The behaviors are classified with F-score accuracies of 0.94, 0.86, 0.94, 0.89, 0.95, and 1, respectively. Moreover, it has been observed that the GNB classifier achieved 93% accuracy on average with the dataset consisting of quaternion values. In contrast, it was only 88% when the model used the dataset from sensors’ data.
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Khan, Bilal, Rashid Naseem, Muhammad Arif Shah, Karzan Wakil, Atif Khan, M. Irfan Uddin, and Marwan Mahmoud. "Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques." Journal of Healthcare Engineering 2021 (March 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/8899263.
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Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.
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Karajeh, Ola, Dirar Darweesh, Omar Darwish, Noor Abu-El-Rub, Belal Alsinglawi, and Nasser Alsaedi. "A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets." Future Internet 13, no. 1 (January 16, 2021): 19. http://dx.doi.org/10.3390/fi13010019.
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Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively.
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Gadallah, Waheed G., Nagwa M. Omar, and Hosny M. Ibrahim. "Machine Learning-based Distributed Denial of Service Attacks Detection Technique using New Features in Software-defined Networks." International Journal of Computer Network and Information Security 13, no. 3 (June 8, 2021): 15–27. http://dx.doi.org/10.5815/ijcnis.2021.03.02.
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Software-Defined Networking is a new network architecture that separates control and data planes. It has central network control and programmability facilities, so it improves manageability, scaling, and performance. However, it may suffer from creating a single point of failure against the controller, which represents the network control plane. So, defending the controller against attacks such as a distributed denial of service attack is a valuable and urgent issue. The advances of this paper are to implement an accurate and significant method to detect this attack with high accuracy using machine learning-based algorithms exploiting new advanced features obtained from traffic flow information and statistics. The developed model is trained with kernel radial basis function. The technique uses advanced features such as unknown destination addresses, packets inter-arrival time, transport layer protocol header, and type of service header. To the best knowledge of the authors, the proposed approach of the paper had not been used before. The proposed work begins with generating both normal and attack traffic flow packets through the network. When packets reach the controller, it extracts their headers and performs necessary flow calculations to get the needed features. The features are used to create a dataset that is used as an input to linear support ector machine classifier. The classifier is used to train the model with kernel radial basis function. Methods such as Naive Bayes, K-Nearest Neighbor, Decision Tree, and Random Forest are also utilized and compared with the SVM model to improve the detection operation. Hence, suspicious senders are blocked and their information is stored. The experimental results prove that the proposed technique detects the attack with high accuracy and low false alarm, compared to other related techniques.
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Al-Ozeer, Ali ZA, Alaa M. Al-Abadi, Tariq Abed Hussain, Alan E. Fryar, Biswajeet Pradhan, Abdullah Alamri, and Khairul Nizam Abdul Maulud. "Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq." Water 13, no. 23 (November 24, 2021): 3330. http://dx.doi.org/10.3390/w13233330.
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Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.
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Cantoni, Valeria, Roberta Green, Carlo Ricciardi, Roberta Assante, Leandro Donisi, Emilia Zampella, Giuseppe Cesarelli, et al. "Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach." Computational and Mathematical Methods in Medicine 2021 (October 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/5288844.
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We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k -nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy ( p value = 0.02 and p value = 0.01) and recall ( p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall.
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Angarita-Zapata, Juan S., Gina Maestre-Gongora, and Jenny Fajardo Calderín. "A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities." Sensors 21, no. 24 (December 16, 2021): 8401. http://dx.doi.org/10.3390/s21248401.
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Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.
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Maheswari, S., S. Kalaiselvi, D. Thamarai Selvi, and M. Manochitra. "Applying Government Schemes in Rural Sectors Prediction System for Evaluation of Data Science Algorithm." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 70–79. http://dx.doi.org/10.35940/ijrte.e5140.019521.
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The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy's information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors
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Gupta, Kanika, and Vaishnavi Mall. "COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION." International Research Journal of Computer Science 9, no. 2 (March 4, 2022): 9–15. http://dx.doi.org/10.26562/irjcs.2022.v0902.003.
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Nowadays, in the global computing environment, online payments are a necessary evil as it makes payment conveniently easier and can be done via an ample of available options like a Credit card, Debit Card, Net Banking, PayPal, Paytm available to make payments easier. The most common mode of payment used in online shopping is Credit Card as it is easier for the customers to directly transfer money from one account to another; without the withdrawal of cash at any point. However, this easy payment mode has opened up paths for multiple frauds which involve theft or illegal tampering of data of the credit card owner. Thus, with the increasing number of fraud cases and losses, it is important to find the best solution to detect credit card fraud as well as minimize the number of frauds in online systems. With the analysis of different sets of research performed on the given problem statement, we have concluded that the issue requires a substantial amount of predictions and application of machine learning to find the accuracy score of those commonly used algorithms to predict which of these three state-of-art-algorithms - Naive Bayes, Logistic Regression and K Neighbours, is best suitable to carry out the research in this area. In order to support our findings, we apply two different approaches i.e. with sampling and without sampling on these algorithms against the same dataset. We claim on the basis of our results that K Neighbours outperformed all in both the approaches and is more suitable to carry forward the fraud detection research using machine learning. The analysis will be useful for those working to derive anti-fraud strategies to predict the fraud patterns and reduce the risk during hefty transactions.
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Shakhovska, Natalya, Vitaliy Yakovyna, and Valentyna Chopyak. "A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system." Mathematical Biosciences and Engineering 19, no. 6 (2022): 6102–23. http://dx.doi.org/10.3934/mbe.2022285.
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<abstract> <p>Starting from December 2019, the COVID-19 pandemic has globally strained medical resources and caused significant mortality. It is commonly recognized that the severity of SARS-CoV-2 disease depends on both the comorbidity and the state of the patient's immune system, which is reflected in several biomarkers. The development of early diagnosis and disease severity prediction methods can reduce the burden on the health care system and increase the effectiveness of treatment and rehabilitation of patients with severe cases. This study aims to develop and validate an ensemble machine-learning model based on clinical and immunological features for severity risk assessment and post-COVID rehabilitation duration for SARS-CoV-2 patients. The dataset consisting of 35 features and 122 instances was collected from Lviv regional rehabilitation center. The dataset contains age, gender, weight, height, BMI, CAT, 6-minute walking test, pulse, external respiration function, oxygen saturation, and 15 immunological markers used to predict the relationship between disease duration and biomarkers using the machine learning approach. The predictions are assessed through an area under the receiver-operating curve, classification accuracy, precision, recall, and F1 score performance metrics. A new hybrid ensemble feature selection model for a post-COVID prediction system is proposed as an automatic feature cut-off rank identifier. A three-layer high accuracy stacking ensemble classification model for intelligent analysis of short medical datasets is presented. Together with weak predictors, the associative rules allowed improving the classification quality. The proposed ensemble allows using a random forest model as an aggregator for weak repressors' results generalization. The performance of the three-layer stacking ensemble classification model (AUC 0.978; CA 0.920; F1 score 0.921; precision 0.924; recall 0.920) was higher than five machine learning models, viz. tree algorithm with forward pruning; Naïve Bayes classifier; support vector machine with RBF kernel; logistic regression, and a calibrated learner with sigmoid function and decision threshold optimization. Aging-related biomarkers, viz. CD3+, CD4+, CD8+, CD22+ were examined to predict post-COVID rehabilitation duration. The best accuracy was reached in the case of the support vector machine with the linear kernel (MAPE = 0.0787) and random forest classifier (RMSE = 1.822). The proposed three-layer stacking ensemble classification model predicted SARS-CoV-2 disease severity based on the cytokines and physiological biomarkers. The results point out that changes in studied biomarkers associated with the severity of the disease can be used to monitor the severity and forecast the rehabilitation duration.</p> </abstract>
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Klyus, Oleh. "The Method for Quantitative Determination of Wear and Tear of Machines’ Elements in Diagnostic Tests." Solid State Phenomena 252 (July 2016): 71–80. http://dx.doi.org/10.4028/www.scientific.net/ssp.252.71.
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A method for quantitative determination of wear and tear of machines’ elements in the scope of endoscopic tests using artificial and reference bases as point of machine parts are presented in this paper. The proposed method allows also for determining the shape and form of the operated parts, since locating artificial bases in a few points on the circumference, in one plane, allows the form of the tested part to be determined. To use artificial base in which relevant reference bases, demonstrating that a certain dimension has been reached, are included.