Proposed Classifier Model Research Articles

Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging

Abstract In recent years, advanced deep learning techniques have emerged as pivotal tools in enabling the development of robust vision-based solutions for steel surface inspection. This resulted in enhanced inspection accuracy, all while significantly reducing costs in the manufacturing industry. However, the lack of actual steel surface defects datasets currently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, known for its prowess in image-related tasks, faces certain challenges, especially in classifying less common defects. This work proposes a novel hybrid CNN model with a Support Vector Machine (SVM) classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 and EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for classification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect dataset (X-SDD) are carried out and the accuracy and F1 scores are calculated for performance evaluation. The merged dataset contains eleven typical defect types with a total of 2660 defect images. Then, the adopted algorithm is compared with ten fine-tuned deep learning models to evaluate the performance of transfer learning for steel defect detection and identification. The evaluation results show that the deep feature extraction and SVM classification produced better results than the transfer learning. Finally, the proposed classifier model is validated on a newly collected dataset from a Computed Tomography scanner with an accuracy reaching over 96%.

Decoding the scientific creative-ability of subjects using dual attention induced graph convolutional-capsule network

There is an increasing demand of creative individuals in scientific research, innovation sectors of software industries and industrial research/development sectors. On the other hand, there are requirements of analytical minded people in investigation departments, academia and management sectors. Unfortunately, classification of individuals into creative and analytical categories based on their behavioral response is not easy. This paper makes an attempt to classify people into 4 categories: Analytical, High Creative, Medium Creative and Low Creative from their brain response during their participation in a creativity-test based on convergent problems. The proposed classification problem involves two main phases. In the first phase, a brain connectivity map is constructed from the electroencephalogram (EEG) response of the brain using Pearson’s correlation technique. In the second phase, a set of three centrality features, namely degree, closeness and betweenness are extracted from the connectivity map and fed to a classifier model for categorizing the afore-said class labels. The classifier model here synergistically combines one Graph Convolution Network (to abstract the brain connectivity-based centrality features) and one Capsule Network (to undertake the classification task) to develop the proposed Dual Attention Induced Graph Convolutional-Capsule network (DAIGC-CapsNet). The novelty of the proposed classifier model lies in the dual attention module and a new routing algorithm. The dual attention modules includes a) a Mish Induced Attention Module (MI-AM) to guide the graph convolution layers to focus on the most significant node attributes, and b) a Fused Attention Module (F-AM) to ensure the transmission of the most relevant predictions from the primary capsule to the class capsule layers. The latter attention module combines the effects of two sub-modules (channel and spatial) that concentrate on determining "what" and "where" to prioritize within the channel and spatial dimensions of the primary capsules. Lastly, the coupling between the primary and class capsule layers is strengthened by a Sparsemax based routing algorithm. Experiments conducted yield fruitful and definitive outcomes that substantiate the effectiveness of the proposed framework with respect to its conventional counterparts. Moreover, statistical validation of the proposed classifier using Friedman’s test also proves its efficacy compared to its competitors.

The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease

Objective. Diagnosing chronic obstructive pulmonary disease (COPD) using impulse oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors, which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results. Approach. Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier’s performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China–Japan Friendship Hospital for training and validating the model. Main results. The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy = 0.920, specificity = 0.941, precision = 0.875, recall = 0.875). Significance. The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS.

An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

Flames recognition methodology is most important for completely diminishing the flame losses in different fired environmental conditions. However, there is delayed detection and lower accuracy in the various common detection methods. Thus, optimum image/video fire detection technology is proposed in this paper based on a support vector machine (SVM) with the fuzzy c-mean, discrete wavelet transform (DWT), and gray level co-occurrence matrices (GLCM) feature extraction for the detection of fires. This algorithm has been tested on various fire and non-fire images for classification accuracy. A performance evaluation of the proposed classifier algorithm and existing algorithms is compared, showing that the accuracy and other metrics of the proposed classifier algorithm are higher than other algorithms. Furthermore, simulation results show that the proposed classifier model is improved the forecast detection accuracy of fires.

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats. In this paper, the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared; These two IDS dataset is preprocessed, then Auto Cryptographic Denoising (ACD) adopted to remove noise in the feature of the IDS dataset; the classifier algorithms, K-Means and Neural network classifies the dataset with adam optimizer. IDS classifier is evaluated by measuring performance measures like f-measure, recall, precision, detection rate and accuracy. The neural network obtained the highest classifying accuracy as 91.12% with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset. Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.

AN EFFICIENT CNN ARCHITECTURE FOR OBJECT DETECTION AND CLASSIFICATION IN IMAGES USING DEEP LEARNING

Object Classification has received wide attention due to its real time applications using deep learning technique. It is a process of classifying objects into predefined and semantically meaningful categories. Due to less accuracy ,sometimes gives inaccurate result and wouldn’t be effective. Object Classification using CNN algorithm is a proposed system in which image is inserted and object is classified and displayed in UI based on trained model.Inthis,it extracts features from the image, undergoes three architectures and high accuracy output will be considered to overcome existing difficulties. Objects can be easily detected and identified by humans. The visual system of human is very fast and accurate and can perform complex tasks like object identification and detection very easily .Situation like where we have to find from so many things and trying to find that key will take long time and we have to face some difficulties. Object classification is very important for applications in automatic visual surveillance system. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. In this paper we propose a new model for detection and classification of objects by taking the features to classify the detected objects using Deep Neural Network (DNN). Deep Neural Networks are capable of handling large higher dimensional data with billions of parameters as like human brain. Simulation results obtained illustrate that the proposed classifier model produces more accurate results for feature extraction and DNN for classification.

Sentiment analysis of COVID-19 social media data through machine learning.

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques

Drug response classification constitutes a major challenge in personalized medicine. The suitable drug selection for cancer patients is substantial and the drug response prediction is generally based on the target information, genomic cohort and chemical structure. Hence, for classification process, feature selection approaches are highly essential, which is driven by prior knowledge of gene expression signatures, drug targets, and target pathways. Further, the classification is performed to assess the accurate drug response prediction. To the best of our knowledge, this is the first work to assess different optimization techniques for feature selection and performing drug response classification using different classifiers based on effective optimization algorithm using Cancer Cell Line Encyclopaedia- CCLE and Genomics of Drug Sensitivity in Cancer- GDSC dataset. For performing feature selection, Firefly, Whale and Grey wolf optimization algorithms are examined. Further, to perform classification, Adaboost, gradient boost and random forest classifiers are utilized. In addition to that, a newly developed classification approach, namely Discriminative Weight Updated Tuned Deep Multi-Layer Perceptron (DWUT-MLP) is used and compared with the other classifiers. The optimization algorithms with newly developed DWUT-MLP and other existing classification techniques are evaluated and results shows that the effective feature selection algorithm with suitable classification algorithm improves anticancer drug response prediction's accuracy. Thus, this research is substantial in general for choosing an appropriate feature selection approach, has the probability of improving the accuracy from the interpretable proposed classifier model for an indicative anticancer drug response prediction. From the comparative analysis, it shows that the proposed model performs nearly 10–15% better than existing frameworks in classifying the anticancer drug response.

Deep Learning-Based Electrocardiogram Signal Analysis for Abnormalities Detection Using Hybrid Cascade Feed Forward Backpropagation with Ant Colony Optimization Technique

In this paper, a time series data mining models is introduced for analysis of ECG data for prior identification of heart attacks. The ECG data sets extracted from Physionet are simulated in MATLAB. The Data used for model are preprocessed so that missing data are fulfilled. In this work cascade feedforward NN which is similar to Multilayer Perceptron (MLP) architecture is proposed along with Swarm Intelligence. A hybrid method combining cascade-Forward NN Classifier and Ant colony optimization is proposed in this paper. The swarm-based intelligence method optimizes the weight adjustment of neural network and enhances the convergence behavior. The novelty is on the optimization of the NN parameters for narrowing down the convergence with ACO implementation. Ant colony optimization is used here for choosing the optimized hidden node. The combined use of machine learning algorithm with neural network enhances the performance of the system. The performance is evaluated using parameters like True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) respectively. The Improved accuracy of proposed Classifier model raises the speed. In addition, the proposed method uses minimum memory. The implementation was done in MATLAB tool. Real time data was used.

Investigation of automated sleep staging from cardiorespiratory signals regarding clinical applicability and robustness

ObjectiveIn this contribution, we present an automated sleep stage classifier using modern machine learning methods with cardiorespiratory time series as input signals. We investigate (1) which classification quality we can yield, (2) how variations in the input data affect the results and (3) to which extend the proposed classifier model can serve as basis for medical diagnoses. MethodsWe extracted R-to-R intervals from electrocardiograms and breath-to-breath intervals from thoracic respiratory effort. Our analysis used 5036 patients from the Sleep Heart Health Study, 998 random patients served as independent hold-out test data. Our model architecture consists of convolutional and recurrent layers. From the predicted hypnograms, we calculated sleep metrics to estimate if the proposed model can serve as basis for medical diagnoses. We further modified the RR interval data and trained new models on that data to investigate its robustness to different data sources and resulting classification quality. ResultsThe proposed method yields a Cohen’s kappa score of 0.80 for distinguishing Wakefulness, NREM and REM, which is considered an almost perfect classification. Further, the proposed method is very robust concerning differing data sources. We found that some summarizing sleep metrics calculated from our predictions are reliable (e.g. sleep efficiency), others, however, are rather unreliable (e.g. percentage of deep sleep). ConclusionsOur results demonstrate the applicability and robustness of the proposed method for sleep staging. Additionally, they underline the requirement to consider not only scoring accuracy but also sleep metrics.

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.

The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.

Classification of Cyber-Attack using Adaboost Regression Classifier and Securing the Network

In recent years, with adverse development of technology leads to several security breaches. To withstand those security threats and breaches especially for cyber attacks resources are optimized with improved network lifetime. Those security challenges are lead to confidentiality, privacy, integrity and availability. To prevent cyberattacks artificial intelligence-based technology is evolved. To adopt appropriate cybersecurity wireless communication systems are intended to withstand threats and challenges. This paper, presented a deep learning-based classification technique for cyber attack detection. Deep learning structure involved in attack detection with proposed AdaBoost Regression Classifier (ABRC). The proposed ABRC with deep learning involved in estimation of attacks in the network security with deep learning structure. The proposed classifier model is involved in estimation of threats. The developed algorithm integrates AdaBoost and Regression classifier for threat detection and classification. The performance analysis expressed that proposed ABRC exhibits significant performance for cyber-attack detection than the existing deep learning technique.

Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO

On March 2020 World Health Organization (WHO) has declared Covid-19 as global pandemic. As special agency of United Nation who responsible for international public healthy, WHO has done various actions to reduce this pandemic spreading rate. However, the handling of Covid-19 by WHO is not free from a number of controversies that gave rise to criticism and public opinion on the Twitter platform. In this research, a machine learning based classifier model has been made to determine the opinion or sentiment of the tweet. The dataset used is a set of tweets containing the phrase WHO and Covid-19 in period of March 1st until May 6th 2020 consisting of 4000 tweets with positive sentiments and 4000 tweets with negative sentiments. The proposed classifier model combined Support Vector Machine (SVM), N-Gram and Particle Swarm Optimization (PSO). The classifier model performance is evaluated using the value of Accuracy, Precision, Recall, and Area Under ROC Curve (AUC). Based on experiments conducted, the combination of SVM, N-gram (bigram), and PSO produced a pretty good performance in classifying tweet sentiment with values of Accuracy 0,755, Precision 0,719, Recall 0,837, and AUC 0,844.

Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine

This study proposes the use of ratio analysis-based features combined with the SVM classifier to identify fraudulent financial statements. The detection method used in this study applies a data mining classification approach. This method is expected to replace the expert in forensic accounting in identifying fraudulent financial statements that are usually done manually. The experimental results show that the proposed classifier model and ratio analysis-based features provide more than 90% accuracy results where the optimal number of features based on ratio analysis is 5 features, namely Capital Adequacy Ratio (CAR), (ANPB) to total earning assets and non-earning assets (ANP), Impairment provision on earning assets (CKPN) to earning assets, Return on Asset (ROA), and Return on Equity (ROE). The contribution of the study is to complement the research of fraudulent financial statements detection where the classifier method used here is different compare to other research. The selection of banking cases in Indonesia is also unique in this research which distinguishes it from other research because the financial reporting standards in each country can be different.

Prediction of the risk of cardiovascular complications with a segmented space of risk factors and synergy channels

To predict coronary heart disease, a basic structure of a multi-agent intelligent system is proposed, which contains “strong” and “weak” classifiers. “Weak” classifiers are divided into four groups, the first of which analyzes data obtained on the basis of traditional risk factors for coronary heart disease, the second - based on the analysis of electrocardiological studies, the third group of “weak” classifiers is designed to diagnose concomitant diseases and syndromes by predictors used the first two groups of agents, and the fourth analyzes environmental risk factors. The intelligent system allows you to manage the decision-making process through a combination of expert assessments, statistical data and current information. Experimental studies of various modifications of the proposed classifier model have been carried out. Analysis of the classification quality indicators in the experimental group of subjects with different ischemic risk indicators showed that taking into account the influence of the introduced risk factors in the control group increases the diagnostic efficiency by seven percent compared to the experimental group, which acts as a background.

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

Breast cancer is one of the most common types of cancer and is the second main cause of cancer death in females. Early detection of breast cancer is crucial for the survival of a patient as well as for the quality of life throughout cancer treatment. The aim of this study is to develop improved machine learning models for early diagnosis of breast cancer with high accuracy. In this context, a performance comparison of machine learning algorithms including Support Vector Machines, Decision Trees, Naive Bayes, K-Nearest Neighbor, and Ensemble Classifiers was performed on a dataset consisting of routine blood analysis combined with anthropometric measurements to diagnose breast cancer. Neighborhood component analysis was applied as a feature selection method to reveal relevant biomarkers that can be used in breast cancer prediction. In order to assess the performance of each proposed classifier model, two different data division procedures such as hold-out and 10-fold cross-validation were employed. Bayesian Optimization algorithm was applied to all classifiers for the maximizing the prediction accuracy. Different performance criteria such as accuracy, precision, sensitivity, specificity, and F-measure were used to measure the success of each classifier. Experimental results show that the Bayesian optimization-based K-Nearest Neighbor performs better than other machine learning algorithms under the hold-out data division protocol with an accuracy of 95.833%. The results obtained in this study may provide a new perspective on the application of improved machine learning techniques for the early detection of breast cancer.

A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

Many factors can affect the users’ quality of experience (QoE) in speech communication services. The impairment factors appear due to physical phenomena that occur in the transmission channel of wireless and wired networks. The monitoring of users’ QoE is important for service providers. In this context, a non-intrusive speech quality classifier based on the Tree Convolutional Neural Network (Tree-CNN) is proposed. The Tree-CNN is an adaptive network structure composed of hierarchical CNNs models, and its main advantage is to decrease the training time that is very relevant on speech quality assessment methods. In the training phase of the proposed classifier model, impaired speech signals caused by wired and wireless network degradation are used as input. Also, in the network scenario, different modulation schemes and channel degradation intensities, such as packet loss rate, signal-to-noise ratio, and maximum Doppler shift frequencies are implemented. Experimental results demonstrated that the proposed model achieves significant reduction of training time, reaching 25% of reduction in relation to another implementation based on DRBM. The accuracy reached by the Tree-CNN model is almost 95% for each quality class. Performance assessment results show that the proposed classifier based on the Tree-CNN overcomes both the current standardized algorithm described in ITU-T Rec. P.563 and the speech quality assessment method called ViSQOL.

An Implementation of Support Vector Machine on the Multi-Label Classification of English-Translated Quranic Verses

One of the attempts to understand the meaning and content of the Quran, the central religious text of Islam, is the topic classification of Quranic verses. Verse topic classification aims to help the reader, so he can easily and quickly find information or knowledge contained in the Quran. In this paper, we build a classification model for the topics of English- translated Quranic verses using Support Vector Machine (SVM). The problem of classification of topics of Quranic verses is categorized as a multi-label classification problem. Hence, we design an SVM-based classifier to solve the multi-label classification of topics of Quranic verses. We also implement several techniques such as preprocessing, feature extraction, and dimensionality reduction to solve this problem. Then, we use Hamming Loss as a performance measure to evaluate our proposed classifier model. We find that our proposed model yields outstanding results.

Proposed Self – Regulated Gray Wolf Optimizer based Extreme Learning Machine Neural Network Classifier for Lung Cancer Classification

The major issue in the development of pattern recognition towards lung cancer classification is the formation of feature extraction process and the proposed classifier model. In the proposed approach, a self-regulated gray wolf optimizer based extreme learning machine classifier is proposed to carry out lung cancer classification along with the statistical feature extraction methods. Simulation shows that the proposed approach works well and produces higher classification accuracy than the conventional classifier methods. The modeled SelfRegulated Gray Wolf Optimizer (SRGWO) and Extreme Learning Machine (ELM) along with feature and segmentation process shows highest improvement in comparison with the other existing literature studies in neural networks. In particular, the significant finding of this work employing ELM, SRGWO and feature analysis validates the correlation of Computed Tomography (CT) measures as well as classification pathological parameters. Thus, the proposed SRGWO and ELM classifier is developed in the present approach for lung cancer classification of CT images reducing the computational cost and time of all the earlier classifiers and as well increasing the classification accuracy. On performing trial runs for the proposed SRGWO – ELM to compute the classification results for the considered real time and Lung Image Database Consortium (LIDC) lung images, it has been noted that at certain trials, the extreme learning machine neuronal classifier is noted to get stuck up with the local minima problem and it is necessary to restart the generation process to achieve classification solutions.

Gestational Diabetes Diagnosis with MSVM, MJ48 Classifier Models

This paper focuses on designing an automated system for diagnosing gestational diabetes. Classification is one of the common predictive data mining tasks. It arranges the information and assembles a model to deliver the new grouped information. ‘Gestational diabetes mellitus’ (GDM) is a form of diabetes that occurs during pregnancy due to hormonal changes. Pregnant Women with GDM are at highest threat of future diabetes, especially type-2 diabetes. To diagnose the GDM, the two classifier models are proposed such as .Modified Support Vector Machine (MSVM) and Modified J48 (MJ48). Based on the performance analysis, the classifier model MJ48 provides more accuracy and less error rate than MSVM proposed classifier model