Intrusion Detection Model Based on Multi‐Kernel Approximation and Deep Neural Network

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Recently, machine learning methodologies are playing an important role in detecting network intrusions (or attacks), which further helps the network administrator to take precautionary measures for preventing intrusions. In this project report, the authors propose to use various techniques like support vector machine, logistic regression, random forest, decision tree, and a hybrid model using artificial neural networks and random forest. The new hybrid model for network intrusion detection aims to achieve better precision, recall, accuracy, and F1 score. To reify this, combined artificial neural network and random forest algorithms are used. In the hybrid model, ANN works as a feature extractor and random forest as a classifier. The data is provided to ANN and the ANN generates new features on the basis of given input. The new features are then provided to random forest as an input. The random forest then classifies the data into different classes on the basis of new data. Here, KddCup99 dataset has been used to evaluate the models and accuracy achieved in each model.

Introduction/Main Objectives: This research aims to conduct an aspect-based sentiment analysis of transportation electrification opinions on YouTube comment data. Background Problems: It is difficult to summarize the sentiment of many YouTube user comments related to electric vehicles (EVs) based on their aspects; therefore, aspect-based sentiment analysis is needed to conduct further analysis. Novelty: This study identifies five aspects of EV and their sentiments at the same time. The aspects are usefulness, ease of use, comfort, cost, and incentive policies. One of this study’s methods is the transfer learning model. This model can be a solution to overcome the shortcomings of deep learning in classifying aspect-based sentiment classification on small datasets. Research Methods: The sentiment classification model used is a machine learning model, namely support vector machine (SVM) and transfer learning models from pre-trained IndoBERT and mBERT. Finding/Results: Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 89.17% and 52.66%, respectively. Furthermore, the best IndoBERT model was developed with input in the form of a combination of aspects and comment sentences. Experimental results show that there is an improvement in performance with accuracy and F1-Score of 90% and 60.70%, respectively.

기업의 부도를 예측하는 것은 회계나 재무 분야에서 중요한 연구주제이다. 지금까지 기업 부도예측을 위해 여러 가지 데이터마이닝 기법들이 적용되었으나 주로 단일 모형을 사용함으로서 복잡한 분류 문제에의 적용에 한계를 갖고 있었다. 본 논문에서는 최근에 각광받고 있는 SVM (support vector machine) 모형들을 결합한 앙상블 SVM 모형 (ensemble SVM model)을 부도예측에 사용하고자 한다. 제안된 앙상블 모형은 v-조각 교차 타당성 (v-fold cross-validation)에 의해 얻어진 여러 가지 모형 중에서 성능이 좋은 상위 k개의 단일 모형으로 구성하고 과반수 투표 방식 (majority voting)을 사용하여 미지의 클래스를 분류한다. 본 논문에서 제안된 앙상블 SVM 모형의 성능을 평가하기 위해 실제 기업의 재무비율 자료와 모의실험자료를 가지고 실험하였고, 실험결과 제안된 앙상블 모형이 여러 가지 평가척도 하에서 단일 SVM 모형들보다 좋은 성능을 보임을 알 수 있었다. Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

PurposeMachine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.MethodsOne hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance.ResultsBoruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. ConclusionsThe proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Astrocytoma is the most common neuroepithelial neoplasm, and its grading greatly affects treatment and prognosis. According to relevant factors of astrocytoma, this study developed a support vector machine (SVM) model to predict the astrocytoma grades and compared the SVM prediction with the clinician's diagnostic performance. Patients were recruited from a cohort of astrocytoma patients in our hospital between January 2008 and April 2009. Among all astrocytoma patients, nine had grade I, 25 had grade II, 12 had grade III, and 60 had grade IV astrocytoma. An SVM model was constructed using radial basis kernel. The SVM model was trained with nine magnetic resonance (MR) features and one clinical parameter by fivefold cross-validation and differentiated astrocytomas of grades I-IV at two levels, respectively. The clinician also predicted the grade of astrocytoma. According to the two prediction methods above, the areas under receiving operating characteristics (ROC) curves to discriminate low- and high-grade groups, accuracies of high-grade grouping, overall accuracy, and overall kappa values were compared. For SVM, the overall accuracy was 0.821 and the overall kappa value was 0.679; for clinicians, the overall accuracy was 0.651 and the overall kappa value was 0.466. The diagnostic performance of SVM is significantly better than clinician performance, with the exception of the low-grade group. The SVM model can provide useful information to help clinicians improve diagnostic performance when predicting astrocytoma grade based on MR images.

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

This study compares single and hybrid soft computing models for estimating daily solar radiation flux for two scenarios. Scenario I developed single soft computing models, including multilayer perceptron (MLP), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multivariate adaptive regression splines (MARS), for estimating daily solar radiation flux at two stations from the USA and South Korea. The MLP model was used to evaluate the effect of factors controlling daily solar radiation flux. Using different combinations of controlling factors as input, the MLP and SVM models, based on evaluation measures, were found to be superior to the ANFIS and MARS models at Big Bend station, USA. In addition, the MLP, SVM, and MARS models performed better than did the ANFIS model at Incheon station, South Korea. Scenario II combined the discrete wavelet transform (DWT) and single soft computing models (e.g., MLP and SVM) for improved performance using 4-input combination. The wavelet-based MLP (WMLP) and SVM (WSVM) models were superior to other single soft computing models (MLP, SVM, ANFIS, and MARS) at two stations. Taylor diagrams, violin plots and point density plots were also utilized to examine the similarity between the observed and estimated solar radiation flux values. Results showed that scenarios I and II can be alternatives for estimating daily solar radiation flux based on different meteorological information, such as peninsular and continental conditions.

Animal-origin fibers exhibit characteristics that determine their quality, price, and comfort, with diameter and medullation type being particularly important. Medullated fibers are considered problematic for the textile industry as they increase the sensation of itchiness on the skin and the heterogeneity of fabrics, leading to a decrease in fiber quality. Thus, having practical instruments to identify them is very essential. On the other hand, some fibers, depending on the species they come from, are considerably cheaper than others. Hence the existence of counterfeit products, which are detrimental to legitimate producers and consumers alike. Fourier-Transform Infrared (FTIR) spectroscopy can extract a characteristic waveform from fibers than can be later used for classification. However, visually inspecting such waveforms is imprecise. In parallel, Artificial Intelligence is an emerging field that could be useful in this area, offering new possibilities and significant advances. Therefore, the first aim of this thesis was to develop and validate a novel automatic system (referred to as S-Fiber Med) for medullation and diameter assessment of animal fibers based on artificial intelligence. The second objective was to develop and validate two machine learning models, namely, Deep Neural Networks (DNN) and Support Vector Machine (SVM) to classify spectra of fibers by species. For the first objective, the medullation of 88 samples of alpaca, llama and mohair fibers (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fiber diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). For the second objective, fiber spectra were acquired using an FTIR spectrometer in Attenuated Total Reflectance (ATR) mode (FTIR-ATR). Camelid (alpaca: n = 51, llama: n=50, vicuña: n = 50) and goat (mohair: n = 35 and cashmere: n = 20) samples were evaluated, from which 1,236 FTIR-ATR spectra were obtained. Results of our first objective indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (p-value < 0.001). The AFDs of alpaca and llama fiber samples obtained with the two methods are very similar, because no significant difference was found at the t-test (p-value>0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with p-value <0.001), high Concordance coefficient and bias correction factor. Similar results were found when PFT and OFDA100 were compared with S-Fiber Med. While in the results of our second objective, some visual differences were observed between the spectra of the different species. Accuracies up to 96.75% and 95.12% were obtained when evaluating the DNN and SVM models. Furthermore, an accuracy of 97.8% was obtained when evaluating the FTIR-ATR spectra of South American Camelids (SAC) fibers with DNN, and 97.2% when evaluating them with SVM. A 100% accuracy was obtained when evaluating the FTIR-ATR spectra of vicuña fibers with both models. No significant differences were found (p-value=0.368) by comparing the number of hits against the total number of alpaca, llama, vicuña, mohair and cashmere fibers using DNN. As per the results, it seems that DNN is more accurate than SVM. In conclusion, a novel system was developed that provides rapid (40 seconds/sample), precise, accurate, and automatic measurements of medullation and AFD. Additionally, FTIR-ATR spectrometry techniques combined with machine learning models are a reliable alternative for the identification of SAC and goats through the spectrum of their fibers.

Existing studies have attempted to determine the tool chipping condition using the indirect method of data capture and intelligent analysis techniques considering machine parameters, and tool conditions using signal processing techniques. Due to the obstructive nature of the machining operation, however, it is daunting to use signal capturing to intelligently capture the condition of the tool as well as that of the workpiece. This study aimed to apply some advanced signal processing techniques to the vibration signals captured experimentally during machining operation for the decision making and analysis of tool and workpiece conditions. Vibration signals were captured during turning operations while using four (4) classes of tools, based on their flank wear. The signals were first pre-processed and decomposed using the Empirical Mode Decomposition (EMD) method. The Hilbert–Huang transform (HHT) was applied to the resulting IMFs obtained to compute the feature vectors used to classify the condition of the tool and workpiece. A total of 12 features, consisting of instantaneous properties such as instantaneous energy, instantaneous frequencies, and amplitudes, were obtained for data training and classification of tool conditions. To optimize the classification process, feature selection was performed using a genetic algorithm (GA) to reduce the number of features from 12 to 4 for data training and classification. The feature vectors were first trained for tool classification with a neural network scaled conjugate gradient (SCG) algorithm. The result showed that the model classification error was 0.102. Two other machine learning models, support vector machine (SVM) and K-Nearest Neighbors (KNN), were also implemented for classifying the tool conditions, from the feature vector, to determine the model that most accurately predicted the condition of the tool. To avoid bias and reduce misclassification errors, the k-fold cross-validation technique was applied with ‘k’ taken as 5 and 10. The computed feature vectors were used as inputs to train the machine learning model using both SVM and KNN models to classify the tool and workpiece condition during machining. The error loss of each model was evaluated and plotted to review the performance. The average overall error loss of 0.5031 was observed for the SVM model with 5-fold cross-validation, whereas the error loss of 0.0318 was observed for the KNN model with 5-fold cross-validation. The average overall error loss of 0.5009 was observed for the SVM model with 10-fold cross-validation when trained using the features selected by a genetic algorithm (GA), while the average overall error loss of 0.0343 was observed for the KNN model. The optimal performance of the SVM model was obtained when all features were used for the training, whereas the KNN model performed better when feature selection was implemented. The error losses of the models were evaluated to be less in KNN models, compared to SVM and SCG. The obtained results also showed that the developed KNN models performed 10 times better than the SVM model in predicting the tool condition from the captured vibration signal during the machining process.

Purpose: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients.Methods and Materials: A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility.Results: The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P<0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P=0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram.Conclusion: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.