Linear Support Vector Classifier Research Articles

Detecting Fake News Using Machine Learning

Fake news has had a significant effect on society and politics. To aid in combating the spread of misinformation, we worked to develop a machine learning algorithm that could detect fake news based on textual data. We used a count vectorizer to vectorize our text which we then inputted into Logistic Regression, Support Vector Machine (SVM), and Linear Support Vector Classifier (SVC) models. The greatest accuracy score achieved was 99.97% with the Linear SVC. We discovered however that there was a significant difference in how the real and fake news datasets were constructed that would not translate into real life: the true news articles contained quotation marks, apostrophes, and dashes while these characters were not present in the fake news articles. Because of this, we also developed a more applicable Logistic Regression model removing these specific characters from the dataset all together with an accuracy score of 98.4%.

Data-Driven Prediction of Unsteady Vortex Phenomena in a Conical Diffuser

The application of machine learning to solve engineering problems is in extremely high demand. This article proposes a tool that employs machine learning algorithms for predicting the frequency response of an unsteady vortex phenomenon, the precessing vortex core (PVC), occurring in a conical diffuser behind a radial swirler. The model input parameters are the two components of the time-averaged velocity profile at the cone diffuser inlet. An empirical database was obtained using a fully automated experiment. The database associates multiple inlet velocity profiles with pressure pulsations measured in the cone diffuser, which are caused by the PVC in the swirling flow. In total, over 103 different flow regimes were measured by varying the swirl number and the cone angle of the diffuser. Pressure pulsations induced by the PVC were detected using two pressure fluctuations sensors residing on opposite sides of the conical diffuser. A classifier was constructed using the Linear Support Vector Classification (Linear SVC) model and the experimental data. The classifier based on the average velocity profiles at the cone diffuser inlet allows one to predict the emergence of the PVC with high accuracy (99%). By training a regression artificial neural network, the frequency response of the flow was predicted with an error of no more than 1.01 and 5.4% for the frequency and power of pressure pulsations, respectively.

Fraud Transaction Detection Approach Using Machine Learning Hybrid Techniques

Fraud detection is a crucial task in financial transactions to prevent monetary losses and maintain the integrity of the financial system. Some of the machine learning algorithms that we tested for their efficacy in identifying fraudulent activity include Linear Support Vector Classifier (Linear SVC), Support Vector Classifier with Radial Basis Function Kernel (SVC with RBF Kernel), Logistic Regression, Random Forest, Decision Tree, Naive Bayes, Stacking Classifier (Random Forest + SVM with Logistic Regression), and Voting Classifier (Random Forest + Perceptron Algorithm + Boosting. The performance of these algorithms is evaluated using a publicly available dataset consisting of mobile money transactions. We compare the accuracy, precision, recall, F1-score. Our suggested methods demonstrate the capability to accurately identify fraudulent transactions while keeping the number of false positives reasonably low.

Clinically adaptable machine learning model to identify early appreciable features of diabetes

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood sugar. This disease happens for a number of reasons including modern lifestyle, lethargic attitude, unhealthy food consumption, family history, age, overweight, etc. The aim of this study was to propose a machine learning based prediction model that detected diabetes at the beginning.Methods In this work, we collected 520 patients records from the University of California, Irvine (UCI) machine learning repository of Sylhet Diabetes Hospital, Sylhet. Then, a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this questionnaire. However, we accumulated patient records of these two datasets. In the next step, these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression (LR), K nearest neighbors (KNN), support vector classifier (SVC), Nave Byes (NB), decision tree (DT), random forest (RF), stochastic gradient descent (SGD), Perceptron, AdaBoost, XGBoost, passive aggressive classifier (PAC), ridge classifier (RC), Nu-support vector classifier (Nu-SVC), linear support vector classifier (LSVC), calibrated classifier CV (CCCV), nearest centroid (NC), Gaussian process classifier (GPC), multinomial NB (MNB), complement NB, Bernoulli NB (BNB), categorical NB, Bagging, extra tree(ET), gradiant boosting classifier (GBC), Hist gradiant boosting classifier (HGBC), one vs rest classifier (OVsRC), multi-layer perceptron (MLP), label propagation (LP), label spreading (LS), stacking, ridge classifier CV (RCCV), logistic regression CV (LRCV), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and light gradient boosting machine (LGBM) to explore best stable predictive model. The performance of the classifiers has been measured using five metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic. Finally, these outcomes were interpreted using Shapley additive explanations methods and identified relevant features for happening diabetes.Results In this work, different classifiers were shown their performance where ET outperformed any other classifiers with 97.11% accuracy for the Sylhet Diabetes Hospital dataset (SDHD) and MLP shows the best accuracy (96.42%) for the collected dataset. Subsequently, HGBC and LGBM provide the highest 94.90% accuracy for the combined datasets individually.Conclusion LGBM, stacking, HGBC, RF, ET, bagging, and GBC might represent more stable prediction results for each dataset.

Predicting Customer Satisfaction through Sentiment Analysis on Online Review

User-generated content, such as user reviews, posts, tags, ratings, and opinions on the internet, can be used as a business indicator if collected and appropriately analyzed. One of the examples is predicting customer satisfaction through implementing big data analytics on online reviews. In analyzing the user-generated content to predict customer satisfaction, the author implements machine learning approach using the Sentiment Analysis method. Five-fold cross-validation was performed to train the classification model. The training was performed with a combination of tokenization methods: term frequency-inverse document frequency (tf-idf) and bag-of-words; n-gram types: unigram, bigram, trigram, and combination of unigram, bigram, and trigram; and machine learning algorithms: linear support vector classification (LinearSVC) and multinomial naïve bayes (MultinomialNB). The result was then evaluated using classification performance metrics such as precision, recall, F1 measure, and AUC score. The result shows that the tf-idf vectorizer performs similarly to the bag-of-words method. A similar result was also observed for machine learning algorithm selection. Both MultinomialNB and LinearSVC produce the same performance. Low-level n-grams (such as unigrams and bigrams) tended to have higher precision, recall, F1 measure, and AUC score than high-order n-grams (such as trigrams). The best results were achieved by combining unigrams, bigrams, and trigrams, resulting in an average performance score of 0.94 for all measurements. From the result and analysis, the author finds that predicting customer satisfaction using text and sentiment analysis methods on user-generated content is possible. The model’s performance in this experiment is decent, with high precision, recall, F1, and AUC score.

Analysis of Electrochemically Active Substances in Malvaceae Leaves via Electroanalytical Sensing Technology for Species Identification

Electrochemical analysis has become a new method for plant analysis in recent years. It can not only collect signals of electrochemically active substances in plant tissues, but can also be used to identify plant species. At the same time, the signals of electrochemically active substances in plant tissues can also be used to investigate plant phylogeny. In this work, we collected electrochemical finger patterns in Malvaceae leaves based on the established methodological strategy. After the second derivative treatment, the collected electrochemical fingerprints can show more obvious differences. Three different recognition models were used to attempt electrochemical fingerprinting. The results show that linear support vector classification can be used to identify species with high accuracy by combining the electrochemical fingerprint signals collected in the phosphoric acid buffer solution and acetic acid buffer solution. In addition, the fingerprint information collected by the electrochemical sensor is further used for phylogenetic investigation. The 18 species were divided into three clusters. Species of the same genus have been clustered together. Dendrogram obtained by electrochemical fingerprinting was used to compare previously reported results deduced from morphological and complete chloroplast genomes.

An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma.

Programmed death ligand 1 (PD-L1) and tumor mutation burden (TMB) have been developed as biomarkers for the treatment of immune checkpoint inhibitors (ICIs). However, some patients who are TMB-high or PD-L1-high remained resistant to ICIs therapy. Therefore, a more clinically applicable and effective model for predicting the efficacy of ICIs is urgently needed. In this study, genomic data for 466 patients with melanoma treated with ICIs from seven independent cohorts were collected and used as training and validation cohorts (training cohort n = 300, validation cohort1 n = 61, validation cohort2 n = 105). Ten machine learning classifiers, including Random Forest classifier, Stochastic Gradient Descent (SGD) classifier and Linear Support Vector Classifier (SVC), were subsequently evaluated. The Linear SVC with a 186-gene mutation-based set was screened to construct the durable clinical benefit (DCB) model. Patients predicted to have DCB (pDCB) were associated with a better response to the treatment of ICIs in the validation cohort1 (AUC=0.838) and cohort2 (AUC=0.993). Compared with TMB and other reported genetic mutation-based signatures, the DCB model showed greater predictive power. Furthermore, we explored the genomic features in determining the benefits of ICIs treatment and found that patients with pDCB were associated with higher tumor immunogenicity. The DCB model constructed in this study can effectively predict the efficacy of ICIs treatment in patients with melanoma, which will be helpful for clinical decision-making.

COMPARISON OF THE PERFORMANCE OF GRADIENT BOOSTING, LOGISTIC REGRESSION, AND LINEAR SUPPORT VECTOR CLASSIFIER ALGORITHMS IN CLASSIFYING TRAVEL MODES BASED ON GNSS DATA

Abstract. Public transportation system capacity must be compatible with the frequency of daily trips. Smart mobile phones can collect positioning data at different times, which can detect transportation modes people use for their daily commutes. This information helps the government predict how many vehicles are needed to satisfy public transportation system demands. This article investigates the performance of three different machine learning models, including Gradient Boosting (GB), Logistic Regression (LR), and linear Support Vector Classifier (SVC) in classifying the trip types. Thirty-nine features, including statistical parameters of velocity, acceleration, and jerk, and also parameters representing the time of each trip, are given to the models as input. To increase the performance of the models, with the help of thresholding, points corresponding to noise are detected and removed from the dataset. Moreover, to fill the possible gaps and smooth the trajectories, spline interpolation and Savitzky-Golay filter are also investigated in feature calculation. The results show that the linear models are incapable of distinguishing between different classes well and they are over-fitted to classed with more samples. Hence, the GB by 0.93 recall, precision, and F-score was the best model in determining the vehicle used compared to LR and linear SVC.

A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer.

Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series. Two independent cohorts' series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC. The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830. Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.

Machine‐learning based MRI neuro‐anatomical signatures associated with cardiovascular and metabolic risk factors

AbstractBackgroundLower gray matter (GM) and higher white matter (WM) volumes have been observed in cognitively unimpaired people with cardiovascular and metabolic risk factors (CVMs) such as hypertension, hyperlipidemia, diabetes, obesity, and smoking [Erus 2015, Habes 2016]. However, these group‐level observations do not explain the heterogeneous accumulated effects of CVMs on individual neuroanatomy. Multivariate CVM‐related changes characterized at the individual level using machine learning (ML)‐derived summary indices may better capture their differential roles in brain aging and dementia. In this work, we derive ML‐based indices that summarize brain changes from MRI related to CVMs.MethodCognitively normal volunteers that were pooled and harmonized for the iSTAGING cohort were included in this study (N=24,902 from 8 independent studies, 54.5% female, average age=62.4, age range 45‐75 years). Linear support vector classifiers were trained to discriminate between people with a specific CVM from those without the CVM using nested cross‐validation for hyperparameter tuning and model selection. Input features (n=150) consisted of age, sex, intracranial volume, harmonized regional GM and WM volumes, and lobar volumes of WM hyperintensities. The resulting distance of the projection onto the vector perpendicular to the decision plane yields a continuous scalar representing ‘spatial pattern of abnormality for recognition’ (SPARE) of each CVM. SPARE‐CVMs were computed for each participant and correlated with previously established imaging markers for brain age [Habes 2021] and Alzheimer’s disease (SPARE‐AD)[Davatzikos 2009]. Effect sizes (Cohen’s d) were calculated to assess the utility of SPAREs with higher values indicating greater separability between the CVM+ and CVM‐ groups.ResultFigure 1 shows the distribution, effect sizes and associated brain regions for each SPARE‐CVM. SPARE‐diabetes and SPARE‐hypertension yielded the largest effect sizes. SPAREs for cooccurring CVMs showed higher correlation (Figure 2) and greater corresponding effect sizes (Figure 3). For example, hyperlipidemia was separable by SPARE‐hypertension, but not other SPAREs. By contrast, brain age gap (SPARE‐BA) and SPARE‐AD were unrelated to CVMs.ConclusionWe derived sensitive and partially non‐overlapping quantitative indices that characterize the degree of CVM‐related neuroanatomical differences in cognitively unimpaired participants. SPARE‐CVMs were correlated for cooccurring CVMs but dissociated from MR imaging biomarkers for typical aging and Alzheimer’s disease.

Machine‐learning based MRI neuro‐anatomical signatures associated with cardiovascular and metabolic risk factors

AbstractBackgroundLower gray matter (GM) and higher white matter (WM) volumes have been observed in cognitively unimpaired people with cardiovascular and metabolic risk factors (CVMs) such as hypertension, hyperlipidemia, diabetes, obesity, and smoking [Erus 2015, Habes 2016]. However, these group‐level observations do not explain the heterogeneous accumulated effects of CVMs on individual neuroanatomy. Multivariate CVM‐related changes characterized at the individual level using machine learning (ML)‐derived summary indices may better capture their differential roles in brain aging and dementia. In this work, we derive ML‐based indices that summarize brain changes from MRI related to CVMs.MethodCognitively normal volunteers that were pooled and harmonized for the iSTAGING cohort were included in this study (N=24,902 from 8 independent studies, 54.5% female, average age=62.4, age range 45‐75 years). Linear support vector classifiers were trained to discriminate between people with a specific CVM from those without the CVM using nested cross‐validation for hyperparameter tuning and model selection. Input features (n=150) consisted of age, sex, intracranial volume, harmonized regional GM and WM volumes, and lobar volumes of WM hyperintensities. The resulting distance of the projection onto the vector perpendicular to the decision plane yields a continuous scalar representing ‘spatial pattern of abnormality for recognition’ (SPARE) of each CVM. SPARE‐CVMs were computed for each participant and correlated with previously established imaging markers for brain age [Habes 2021] and Alzheimer’s disease (SPARE‐AD)[Davatzikos 2009]. Effect sizes (Cohen’s d) were calculated to assess the utility of SPAREs with higher values indicating greater separability between the CVM+ and CVM‐ groups.ResultFigure 1 shows the distribution, effect sizes and associated brain regions for each SPARE‐CVM. SPARE‐diabetes and SPARE‐hypertension yielded the largest effect sizes. SPAREs for cooccurring CVMs showed higher correlation (Figure 2) and greater corresponding effect sizes (Figure 3). For example, hyperlipidemia was separable by SPARE‐hypertension, but not other SPAREs. By contrast, brain age gap (SPARE‐BA) and SPARE‐AD were unrelated to CVMs.ConclusionWe derived sensitive and partially non‐overlapping quantitative indices that characterize the degree of CVM‐related neuroanatomical differences in cognitively unimpaired participants. SPARE‐CVMs were correlated for cooccurring CVMs but dissociated from MR imaging biomarkers for typical aging and Alzheimer’s disease.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs’ manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia

It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients’ frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients’ frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.

Origin identification of Gardeniae Fructus based on hyperspectral imaging technology

In order to realize rapid and non-destructive identification of the origin of Gardeniae Fructus, a technical method based on hyperspectral imaging technology was established in this study. Spectral information of Gardeniae Fructus samples from eight production origins was acquired from visible NIR(410-990 nm, VNIR) and short wavelength NIR(950-2 500 nm, SWIR) bands based on hyperspectral imaging techniques. The average spectral reflectance within the region of interest was extracted and calculated using the ENVI 5.3 software, resulting in 1 600 sample data. The visible short wavelength infrared band(fused bands) spectral data covering the range 410-2 500 nm were obtained after combining the spectral data of VNIR and SWIR. Data were de-noised by five common preprocessing methods, including multivariate scatter correction, Savitzky-Golay smoothing, standard normal variate, first derivative(FD), and second derivative from VNIR, SWIR, and fused bands(VNIR+SWIR). Partial least squares discriminant analysis, linear support vector classification(LinearSVC), and random forest were used to establish the model for origin identification of Gardeniae Fructus. The results indicated that the identification model of Gardeniae Fructus origin established after FD pretreatment of the spectral data in the fused bands could yield good results. According to the confusion matrix evaluation results, the model prediction set using LinearSVC reached 100% accuracy, so the optimum identification model of Gardeniae Fructus origin was determined as fusion bands-FD-LinearSVC. Therefore, the hyperspectral imaging technology can achieve rapid, nondestructive, and accurate identification of Gardeniae Fructus samples of different origins, which provides a technical reference for the differential detection of Gardeniae Fructus and other Chinese medicines.

Research on Data News Propagation Path Based on the Big Data Algorithm

News propagation originates from a person/location, dwelling with an event that grabs significance. News data propagation relies on telecommunication and big data for precise content distribution and mitigation of false news. Considering these factors, the event-dependent data propagation technique (EDPT) was introduced to improve the data precision. These data refer to the news information originating and propagating from digital media. The data analysis considers the external factors for fake information and precise projection medium for preventing multiviewed false circulations. In this technique, the liability of the information is analyzed using a linear pattern support vector classifier. The data modification and propagation changes are classified based on liability information across the circulation time. The SVM classifier identifies these two factors with close liability validation, preventing false data. The data accumulation and analysis rates for the abovementioned classifications are performed in the propagation process using the classifier hyperplane. This plane is updated from the previous propagation point from which the events are identified. The proposed technique’s performance is analyzed using propagation accuracy, precision, false rate, time, and rate.

Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance

Considering the large size of the agricultural sector in Romania, increasing the crop insurance adoption rate and identifying the factors that drive adoption can present a real interest in the Romanian market. The main objective of this research was to identify the performance of machine learning (ML) models in predicting Romanian farmers’ purchase of crop insurance based on crop-level and farmer-level characteristics. The data set used contains 721 responses to a survey administered to Romanian farmers in September 2021, and includes both characteristics related to the crop as well as farmer-level socio-demographic attributes, perception about risk, perception about insurers and knowledge about agricultural insurance. Various ML algorithms have been implemented, and among the approaches developed, the Multi-Layer Perceptron Classifier (MLP) and the Linear Support Vector Classifier (SVC) outperform the other algorithms in terms of overall accuracy. Tree-based ensembles were used to identify the most prominent features, which included the farmer’s general perception of risk, their likelihood of engaging in risky behaviour, as well as their level of knowledge about crop insurance. The models implemented in this study could be a useful tool for insurers and policymakers for predicting potential crop insurance ownership.

Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector classifier approach.

Avatar Therapy (AT) is a modern therapeutic alternative for patients with schizophrenia suffering from persistent auditory verbal hallucinations. Its intrinsic therapeutical process is currently qualitatively analyzed via human coders that annotate session transcripts. This process is time and resource demanding. This creates a need to find potential algorithms that can operate on small datasets and perform such annotations. The first objective of this study is to conduct the automated text classification of interactions in AT and the second objective is to assess if this classification is comparable to the classification done by human coders. A Linear Support Vector Classifier was implemented to perform automated theme classifications on Avatar Therapy session transcripts with the use of a limited dataset with an accuracy of 66.02% and substantial classification agreement of 0.647. These results open the door to additional research such as predicting the outcome of a therapy.

Classifying Illegal Activities on Tor Network using Hybrid Technique

With the freedom offered by the Deep Web, people have the opportunity to express themselves freely and discretely, and sadly, this is one of the reasons why people carry out illicit activities there. In this work, a novel dataset for Dark Web active domains known as crawler-DB is presented. To build the crawler-DB, the Onion Routing Network (Tor) was sampled, and then a web crawler capable of crawling into links was built. The link addresses that are gathered by the crawler are then classified automatically into five classes. The algorithm built in this study demonstrated good performance as it achieved an accuracy of 85%. A popular text representation method was used with the proposed crawler-DB crossed by two different supervised classifiers to facilitate the categorization of the Tor concealed services. The results of the experiments conducted in this study show that using the Term Frequency-Inverse Document Frequency (TF-IDF) word representation with a linear support vector classifier achieves 91% of 5 folds cross-validation accuracy when classifying a subset of illegal activities from crawler-DB, while the accuracy of Naïve Bayes was 80.6%. The good performance of the linear SVC might support potential tools to help the authorities in the detection of these activities. Moreover, outcomes are expected to be significant in both practical and theoretical aspects, and they may pave the way for further research.

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.