Linear Support Vector Classifier Research Articles

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

The authors implemented an attack scenario that involved simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generates automated malicious commands which conform to device protocol standards and bypass compromise detection. The authors performed attack-detection testing with three different home setup simulations and referred to Accuracy of Detection, Ease of Precision, and Attack Recall, with the F1-Score as the parameter. The results obtained for anomaly detection of IoT logs and messages used K-Nearest Neighbor, Multilayer Perceptron, Logistic Regression, Random Forest, and linear Support Vector Classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated Precision, Accuracy, F1-Score, and Recall as attack-detection performance models. Finally, the authors evaluated the performance of the proposed IoT communication protocol attack framework by evaluating a range of anomalies and compared them with the maliciously generated log messages. IoT Home #1 results in which the model involving an IP Camera and NAS device traffic displayed 97.7% Accuracy, 96.54% Precision, 97.29% Recall, and 96.88% F1-Score. This demonstrated that the model classified the Home #1 dataset consistently.

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are used to perform binary and multiclassification on the data stream. When combined with the Max Abs Scaler technique, the multilayer perceptron performed satisfactorily in both binary (Accuracy 94.8%) and multiclassification (accuracy 88.2%). Compared to the other binary classification algorithms, the GBT delivered the right outcome (accuracy of 95.8%). Multilayer perceptrons, however, did well in multiple classifications. Techniques such as oversampling and undersampling have a negative impact on disease prediction. Machine learning methods like multilayer perceptrons and ensembles can be helpful for diagnosing cardiac conditions. For this kind of unbalanced data stream, sampling techniques like oversampling and undersampling are not practical.

Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment’s timeframe.

Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study.

ObjectiveTo compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC).MethodsNinety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models.ResultsAmong all 15 types of radiomics models, the model with the best performance was seen in the RHBP radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the RHBP radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75–0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63–0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the RHBP radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best RHBP radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low and high gray-level zone emphases based on gray-level size zone matrix with the same Shapley additive explanation values of 0.424.ConclusionThe RHBP radiomics model may be used as an effective model indicative of HCCs with β-catenin mutation preoperatively and thus could guide personalized medicine.

Essential Biodiversity Variables: extracting plant phenological data from specimen labels using machine learning

Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML). A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).

P68. Machine learning algorithms for predicting patient-controlled analgesia use after minimally invasive spine surgery

BACKGROUND CONTEXT Machine learning models have accurately predicted outcomes after various types of orthopedic surgery. These models have potential to enhance patient selection, risk stratification and preoperative planning. PURPOSE The aim of this study was to assess the efficacy of machine learning algorithms for prediction of patient-controlled analgesia use after minimally invasive spine surgery. STUDY DESIGN/SETTING Retrospective review. PATIENT SAMPLE Consecutive patients who underwent minimally invasive spine surgery between 2017 and 2021. OUTCOME MEASURES Whether or not patient-controlled analgesia (PCA) was used in the first 24 hours after surgery. METHODS From a single-institution, multi-surgeon, prospectively maintained database, patients undergoing minimally invasive spine surgery were included. Cases were excluded for revision, cancer, nonelective surgery, infection and missing categorical data. Cases were randomly partitioned into training (75%) and testing (25%) data sets. K-nearest neighbor imputation was used for missing continuous variables. A random forest classifier was used to select the top 10 most important features. Eighteen different models were trained to predict whether PCA was used in the immediate post-operative period. When appropriate, class weights were adjusted to address class imbalance and models were calibrated using Platt's (sigmoid) method. Using the testing set and the top 6 classifiers, model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC). RESULTS A total of 848 cases were included, where 636 (75%) were used for training and 212 (25%) for testing. Overall incidence of PCA use was 13.9%. The 10 most important predictors were BMI, age, number of operative table positions, fusion vs decompression, age-adjusted Charlson comorbidity index, number of operative levels, gender, primary vs revision, pre-operative narcotic use and type of insurance. The 6 best performing models were naive bayes classifier (AUC= 0.89; Accuracy= 86.8%), ridge classifier (AUC= 0.885; Accuracy= 89.2%), logistic regression (AUC= 0.884; Accuracy= 89.2%), linear support vector classifier (AUC= 0.882; Accuracy= 89.2%), perceptron (AUC= 0.873; Accuracy= 88.2%), and random forest classifier (AUC= 0.859; Accuracy= 87.7%). CONCLUSIONS Machine learning algorithms were found to reliably predict postoperative PCA use after minimally invasive spine surgery. These results can help guide important clinical decisions, reduce costs, and improve expectation management. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs. Machine learning models have accurately predicted outcomes after various types of orthopedic surgery. These models have potential to enhance patient selection, risk stratification and preoperative planning. The aim of this study was to assess the efficacy of machine learning algorithms for prediction of patient-controlled analgesia use after minimally invasive spine surgery. Retrospective review. Consecutive patients who underwent minimally invasive spine surgery between 2017 and 2021. Whether or not patient-controlled analgesia (PCA) was used in the first 24 hours after surgery. From a single-institution, multi-surgeon, prospectively maintained database, patients undergoing minimally invasive spine surgery were included. Cases were excluded for revision, cancer, nonelective surgery, infection and missing categorical data. Cases were randomly partitioned into training (75%) and testing (25%) data sets. K-nearest neighbor imputation was used for missing continuous variables. A random forest classifier was used to select the top 10 most important features. Eighteen different models were trained to predict whether PCA was used in the immediate post-operative period. When appropriate, class weights were adjusted to address class imbalance and models were calibrated using Platt's (sigmoid) method. Using the testing set and the top 6 classifiers, model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC). A total of 848 cases were included, where 636 (75%) were used for training and 212 (25%) for testing. Overall incidence of PCA use was 13.9%. The 10 most important predictors were BMI, age, number of operative table positions, fusion vs decompression, age-adjusted Charlson comorbidity index, number of operative levels, gender, primary vs revision, pre-operative narcotic use and type of insurance. The 6 best performing models were naive bayes classifier (AUC= 0.89; Accuracy= 86.8%), ridge classifier (AUC= 0.885; Accuracy= 89.2%), logistic regression (AUC= 0.884; Accuracy= 89.2%), linear support vector classifier (AUC= 0.882; Accuracy= 89.2%), perceptron (AUC= 0.873; Accuracy= 88.2%), and random forest classifier (AUC= 0.859; Accuracy= 87.7%). Machine learning algorithms were found to reliably predict postoperative PCA use after minimally invasive spine surgery. These results can help guide important clinical decisions, reduce costs, and improve expectation management.

Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning.

Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke. To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning. 136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models. Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606). The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.

To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms. In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition. The deep NLP bidirectional encoder representations from transformers (BERT) model and three feature-rich algorithms were trained on SOR to predict TRCs in FTOR. Models' F1 scores were compared against scores of radiologists, medical students, and radiology technologist students. Lexical and semantic analyses were conducted to investigate human and model performance on FTOR. Oncologic findings and TRCs were accurately mined from 9653 of 12 833 (75.2%) queried SOR, yielding oncology reports from 10 455 patients (mean age, 60 years ± 14 [SD]; 5303 women) who met inclusion criteria. On 802 FTOR in the test set, BERT achieved better TRC classification results (F1, 0.70; 95% CI: 0.68, 0.73) than the best-performing reference linear support vector classifier (F1, 0.63; 95% CI: 0.61, 0.66) and technologist students (F1, 0.65; 95% CI: 0.63, 0.67), had similar performance to medical students (F1, 0.73; 95% CI: 0.72, 0.75), but was inferior to radiologists (F1, 0.79; 95% CI: 0.78, 0.81). Lexical complexity and semantic ambiguities in FTOR influenced human and model performance, revealing maximum F1 score drops of -0.17 and -0.19, respectively. The developed deep NLP model reached the performance level of medical students but not radiologists in curating oncologic outcomes from radiology FTOR.Keywords: Neural Networks, Computer Applications-Detection/Diagnosis, Oncology, Research Design, Staging, Tumor Response, Comparative Studies, Decision Analysis, Experimental Investigations, Observer Performance, Outcomes Analysis Supplemental material is available for this article. © RSNA, 2022.

Classification of specialities in textual medical reports based on natural language processing and feature selection

Nowadays, a great deal of detailed information about patients, including disease status, medication history, and side effects, is collected in an electronic format; called an electronic medical record (EMR), and the data serves as a valuable resource for further analysis, diagnosis, and treatment. The huge q uantity of detailed patient information in these medical texts produces a huge challenge in terms of processing this data efficiently, however. Machine learning (ML) algorithms, artificial intelligence techniques, and natural language processing tools can have the potential effect of simplifying unstructured data, which could positively affect medical report analysis. Natural language processing (NLP) has recently made huge advances on a variety of tasks. In this paper, an automatic system was thus produced to classify specialist consultant interactions based on patients’ medical reports. NLP was used as a pre-processing step on a dataset formed of unstructured medical reports. Feature extraction and selection methods were used to convert the textual reports into sets of features and to extract the most effective features to increase classification accuracy and reduce execution time. Various classification methods were then applied (ML perceptron, logistic regression random forest (RF), and linear support vec tor classifier (LSVC)). The highest accuracy (99.39%) was achieved in ML-perceptron classification techniques .

Oil Palm Plantation Land Cover and Age Mapping Using Sentinel-2 Satellite Imagery and Machine Learning Algorithms

Nowadays, there are various techniques and methods used in land cover classification using remote sensing data especially in oil palm monitoring. This study discussed the oil palm mapping using satellite imagery (Sentinel-2) and classification of land cover features using machine learning algorithms such as linear support vector classifier (LSVC), random forests (RF) and deep neural network (DNN). A total 13218 sampling points (80% of the total sampling points used as training samples and 20% applied as testing samples) were randomly selected in the study area which were then classified into six land cover features; water, bare soil, forest, immature oil palm (the age of 2-8 year), mature oil palm (age >8 year) and built-up area. These data were validated by using spectral reflectance, Google Earth Pro and ground checking. The accuracy assessment was conducted by a confusion matrix method. The results showed that classification of land features using DNN with batch size 32 and epoch 100 has the highest accuracy which is 99.35% for overall accuracy and 98.49% kappa accuracy. This study demonstrated various machine learning algorithms that may be used to detect and classify the maturity of oil palm trees, which is vital to record in tree inventories for effective plantation management.

Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques

The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Nowadays, more and more people are sharing and expressing their feelings through social media platforms such as Twitter, Facebook and YouTube. Sentiment analysis is a process that explores, identifies and categorizes content. People that belong to multilingual communities tend to communicate through multiple regional languages. This type of text is represented using different languages and is known as code-mixed data. The proposed system utilizes a code-mixed data set of Tamil–English languages from FIRE 2021. To handle the class imbalance problem, re-sampling is performed and the impact is analyzed. Pre-processing of input text data can play a vital role in code-mixed data classification by removing unnecessary content. This research work aims to explore the impact of pre-processing on Tamil code-mixed data by employing various pre-processing steps such as emojis removal; repeated characters removal; and punctuations, symbols and number removal. The pre-processed text is applied to traditional machine learning, deep learning, transfer learning and hybrid deep learning models, and the accuracy of all these models before and after pre-processing is compared. Traditional machine learning models depend on various weighting schemes for the feature selection process. The main objective of this research work is to build hybrid deep learning models combining Convolutional Neural Network (CNN) with Long–Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with Bi-Long–Short Term Memory (LSTM) in order to capture the local and global features implicitly from the code-mixed data for conducting sentiment analysis, and then classify the Tamil code-mixed data into positive, negative, mixed_feelings and unknown_state. The performance of hybrid deep learning models were evaluated by comparing them with state-of-art methods that include various traditional machine learning techniques such as random forest, multinomial Naive Bayes, logistic regression and linear Support Vector Classification (SVC); deep learning techniques such as LSTM, BiLSTM, BiGRU (Bidirectional Gated Recurrent Unit) and CNN; and a transfer learning method, IndicBERT. This research work also summarizes the precision, recall, F1-score, accuracy, macro-average, weighted-average and confusion matrix for all mentioned models. The result indicates that among all the different models employed, the hybrid deep learning model, especially the CNN+BiLSTM model performs better, with an accuracy of 0.66 with preprocessed Tamil code-mixed data.

Dark Web Illegal Activities Crawling and Classifying Using Data Mining Techniques

Dark web is a canopy concept that denotes any kind of illicit activities carried out by anonymous persons or organizations, thereby making it difficult to trace. The illicit content on the dark web is constantly updated and changed. The collection and classification of such illegal activities are challenging tasks, as they are difficult and time-consuming. This problem has in recent times emerged as an issue that requires quick attention from both the industry and academia. To this end, efforts have been made in this article a crawler that is capable of collecting dark web pages, cleaning them, and saving them in a document database, is proposed. The crawler carries out an automatic classification of the gathered web pages into five classes. The classifiers used in classifying the pages include Linear Support Vector Classifier (SVC), Naïve Bayes (NB), and Document Frequency (TF-IDF). The experimental results revealed that an accuracy rate of 92% and 81% were achieved by SVC and NB, respectively.

MACHINE LEARNING FOR TEXT CLASSIFICATION IN BUILDING MANAGEMENT SYSTEMS

In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

A high-generalizability machine learning framework for predicting the progression of Alzheimer\u2019s disease using limited data

Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

An effective approach for Arabic document classification using machine learning

Arabic text classification is one application of Natural Language Processing (NLP). It has been used to analyze and categorize Arabic text. Analyzing text has become an essential part of our lives because of the increasing number of text data which makes text classification a big data problem. Arabic text classification systems become significant to maintain vital information in many domains such as education, and health sector, and public services. In the presented research work, the Arabic text classification model is developed using various algorithms namely Multinomial Naïve Bayesian (MNB), Bernoulli Naïve Bayesian (BNB), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Support vector classifier (SVC), Linear SVC, and convolutional neural networks (CNN). These algorithms have been implemented utilizing the Al-Khaleej dataset. The experiments are carried out with various representation models and it is observed that CNN with character level model outperforms others. The result of CNN exceeds the state-of-the-art machine learning method with an accuracy equal to 98. The presented methods will be useful in different domains, particularly on social media.

IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides

BackgroundInterleukin 13 (IL-13) is an immunoregulatory cytokine, primarily released by activated T-helper 2 cells. IL-13 induces the pathogenesis of many allergic diseases, such as airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition, IL-13 inhibits tumor immunosurveillance, leading to carcinogenesis. Since elevated IL-13 serum levels are severe in COVID-19 patients, predicting IL-13 inducing peptides or regions in a protein is vital to designing safe protein therapeutics particularly immunotherapeutic. ObjectiveThe present study describes a method to develop, predict, design, and scan IL-13 inducing peptides. MethodsThe dataset experimentally validated 313 IL-13 inducing peptides, and 2908 non-inducing homo-sapiens peptides extracted from the immune epitope database (IEDB). A total of 95 key features using the linear support vector classifier with the L1 penalty (SVC-L1) technique was extracted from the originally generated 9165 features using Pfeature. These key features were ranked based on their prediction ability, and the top 10 features were used to build machine learning prediction models. Various machine learning techniques were deployed to develop models for predicting IL-13 inducing peptides. These models were trained, tested, and evaluated using five-fold cross-validation techniques; the best model was evaluated on an independent dataset. ResultsOur best model based on XGBoost achieves a maximum AUC of 0.83 and 0.80 on the training and independent dataset, respectively. Our analysis indicates that certain SARS-COV2 variants are more prone to induce IL-13 in COVID-19 patients. ConclusionThe best performing model was incorporated in web-server and standalone package named ‘IL-13Pred’ for precise prediction of IL-13 inducing peptides. For large dataset analysis standalone package of IL-13Pred is available at (https://webs.iiitd.edu.in/raghava/il13pred/) webserver and over GitHub link: https://github.com/raghavagps/il13pred.

An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning

In Benin, after the GCSE (General Certificate of Secondary Education), learners can either enroll in a Technical and Vocational Education and Training (TVET), or further their studies in the general education. Majority of those who take the latter path enroll in Senior High School by choosing the Biology stream or field of study. However, most of them do not have the abilities required to succeed in this field. For instance, for the last edition of the Senior Secondary Education Certificate (French baccalaureate) held in June 2022 in Benin, the Biology field of study had a low success rate of 42%. Therefore, one may consider that there is a problem in the orientation of the students. In recent years, Machine Learning has been used in almost every field to optimize processes or to assist in decision-making. Improving academic performance has always been of general interest. And, good academic performance implies good academic orientation. The goal of this study is to optimally help learners who have just obtained their GCSE to select their field of study. For this purpose, two major elements are predicted: i) Scientific or Literary ability of students, ii) Literature or Mathematics and Physical Sciences (MPS) or Biology stream of learners. More precisely, the average marks in Mathematics, Physics and Chemistry Technology (PCT) and Biology from 6th to 9th grade for 325 students are used. Machine Learning algorithms such as Decision Tree, Random Forest, Linear Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Logistic Regression are used to predict learners’ ability and the stream. As a result, for learners’ ability prediction, we obtained the best accuracy of 99% with the random forest algorithm for a split that reserved around 21% of the dataset for testing. As for the learners’ stream prediction, we obtained the best accuracy of 95% with the Linear SVC algorithm for a split that reserved around 20%of the dataset for testing. This study contributes to Educational Data Mining (EDM) by performing academic data exploration using numerous methods. Furthermore, it provides a tool to ease students academic path selection, which may be used by educational institutes to ensure student performance. This paper presents the steps and the outputs of the study, we performed with some recommendations for future research.

Real-Time Monitoring of Cables Based on Network Interface Controllers for Predictive Maintenance

Conventional cable diagnostic methods require an interrupt of the communication link and are therefore unsuitable for real-time quality monitoring and predictive maintenance. In this paper, we propose a novel diagnosis method, which can be implemented on regular low-cost network interface controllers by just implementing dedicated signal acquisition and processing. The novel method passively monitors the state transitions between voltage levels, during the running communication, by undersampling and sorting the collected data into a histogram. By fitting the collected histogram to a sum of three Gaussian distribution functions, robust features can be extracted, having more independence on noise and the transmitted signals. Based on the extracted features, an early faults detection can be realized using a Linear Support Vector classifier. The novel method has been validated for fast Ethernet cables using STM32-based microcontroller measurements. Open circuit and short circuit faults could be accurately identified. Classification accuracy of 100% could be realized even by k-fold cross-validation and testing. Furthermore, the proposed method can easily be applied to other cable types after adaptation to the corresponding communication signals.