Automatic Classification Techniques Research Articles

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.

Classification of Subgroups of Solar and Heliospheric Observatory (SOHO) Sungrazing Kreutz Comet Group by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Algorithm

Sungrazing comets, known for their proximity to the Sun, are traditionally classified into broad groups like Kreutz, Marsden, Kracht, Meyer, and non-group comets. While existing methods successfully categorize these groups, finer distinctions within the Kreutz subgroup remain a challenge. In this study, we introduce an automated classification technique using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to categorize sungrazing comets. Our method extends traditional classifications by finely categorizing the Kreutz subgroup into four distinct subgroups based on a comprehensive range of orbital parameters, providing critical insights into the origins and dynamics of these comets. Corroborative analyses validate the accuracy and effectiveness of our method, offering a more efficient framework for understanding the categorization of sungrazing comets.

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning.

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier's performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks' end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images

Brain tumors represent a significant healthcare challenge, affecting both children and adults with potentially aggressive consequences. Accounting for a substantial percentage of all primary Central Nervous System (CNS) tumors, brain tumors pose a substantial burden, with approximately 11,700 new diagnoses annually. The classification of brain tumors into benign, malignant, pituitary, and other types necessitates precise diagnostic techniques and treatment planning to enhance patient life expectancy. Traditionally, the detection of brain tumors relied on the expertise of specialists analyzing Magnetic Resonance Images (MRI) without the aid of advanced technology. MRI remains the gold standard for brain tumor detection, generating vast amounts of image data for radiologists to interpret. Manual examinations, however, carry a risk of errors due to the intricacies and diverse properties of brain tumors, potentially leading to delayed treatment and, tragically, loss of lives. In this context, the application of automated classification techniques using Machine Learning (ML) and Deep Learning (DL) has emerged as a promising solution. These techniques, primarily employing Deep Learning Algorithms such as Convolutional Neural Networks (CNN) like 2D-convolutions and Deep Learning Models like ResNet50 and EfficientNetB0, in addition to traditional Machine Learning algorithms like Support Vector Machines (SVM), have consistently demonstrated superior accuracy in brain tumor detection compared to manual prediction. These automated methods have consistently exhibited superior accuracy in the detection and classification of brain tumors compared to manual approaches. This research proposes a robust system for the early detection and accurate classification of brain tumors, leveraging the power of Deep Learning and Machine Learning. By incorporating state-of-the-art techniques, this system aims to empower medical professionals worldwide in identifying brain tumors at their earliest stages, ultimately leading to more timely and effective treatments. Such advancements hold great promise in reducing the human suffering associated with brain tumors and improving patient outcomes.

A blood cell classification method based on MAE and active learning

Cell morphology analysis is a crucial diagnostic tool for identifying blood diseases, including acute leukemia. However, the traditional analysis process is time-consuming and requires significant investment in labor and expertise from laboratory doctors. In recent years, deep learning-based automatic blood cell classification techniques have gained popularity. But acquiring image data and annotations in the medical field is often challenging and costly. With the increasing use of deep learning techniques in clinical practice, it has become vital to ensure both accuracy and high-quality annotations. To address these challenges, this paper proposes a blood cell classification method based on Masked Autoencoder (MAE) and active learning (AL), namely MAE4AL. This method utilizes the self-supervised loss of MAE and sample uncertainty to select the most valuable samples for labeling. A comprehensive comparison is conducted between our method and the state-of-the-art blood cell classification technique, which employed ResNeXt. Remarkably, our proposed approach achieves comparable classification performance to ResNeXt when utilizing only 20% of the labeled data. When employing half of the labeled data, our method achieves a classification accuracy of 96.36%, surpassing the ResNeXt model trained with 100% labeled data by 0.79%.

Investigating and Classifying the Level of Thermal Runaway-Induced Failure Energy Storage Systems in Photovoltaics

In a photovoltaic system, the electricity generated by a solar panel can depend on the amount of sunlight available at any given time. Due to the nonlinear behavior of this energy, the storage component of the system is critical. As a result, many solar panel systems are equipped with batteries. However, changes in environmental and other conditions can cause damage to these batteries. This study explores novel technologies for detecting thermal runaway failures in lithium-ion batteries. Specifically, the study employs image processing techniques to detect structural failures and applies deep learning techniques for automatic classification. Thermal damage to a battery can result in irreparable harm, making design and construction considerations crucial. Through image analysis, any internal changes in the battery can be transformed into a measurable variable, providing a reliable indication of potential failure. The study compares the current series with previous ones to highlight the structural differences. Results confirm that the proposed approach has significant potential for detecting and estimating internal variations during production. Overall, the proposed method can serve as a valuable tool for drafting and implementing a comprehensive plan to address early problems in the battery.

Performance analysis of deep transfer learning approaches in detecting and classifying brain tumor from magnetic resonance images

The Central Nervous System (CNS) is one of the most crucial parts of the human body. Brain tumor is one of the deadliest diseases that affect CNS and they should be detected earlier to avoid serious health implications. As it is one of the most dangerous types of cancer, its diagnosis is a crucial part of the healthcare sector. A brain tumor can be malignant or benign and its grade recognition is a tedious task for the radiologist. In the recent past, researchers have proposed various automatic detection and classification techniques that use different imaging modalities focusing on increased accuracy. In this paper, we have done an in-depth study of 19 different trained deep learning models like Alexnet, VGGnet, DarkNet, DenseNet, ResNet, InceptionNet, ShuffleNet, NasNet and their variants for the detection of brain tumors using deep transfer learning. The performance parameters show that NASNet-Large is outperforming others with an accuracy of 98.03% for detection and 97.87% for classification. The thresholding algorithm is used for segmenting out the tumor region if the detected output is other than normal.

Text Study of Reader Magazine in the Context of Big Data

Abstract Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text analysis methods, latent semantic analysis and probabilistic latent semantic analysis are described, starting from the analysis model of text research, and the automatic text classification technique is illustrated. The principles of support vector machines are explained, the optimal decision function selection solution is performed using kernel functions, and a PLSA-SVM text analysis model is also constructed using the PLSA method jointly with SVM. The structural features of Reader magazine were analyzed and explained, and the PLSA-SVM text analysis model was used to analyze the data on the narrative features of Reader magazine. In terms of narrative themes, the main narrative themes of Reader magazine were distributed as affection and love, which increased by 16.84% and 17.42% from 2012 to 2021, respectively. In terms of narrative perspectives, the proportion of first-person and third-person narrative perspectives is comparable, increasing by 9.03% and 12.3% from 2012 to 2021, respectively. Thus, in the context of big data, the PLSA-SVM text analysis model can be used to analyze the text narrative features of Reader magazine effectively, and Reader magazine can use the analysis to optimize further the text content and issue texts that are attuned to readers’ emotions.

Hybrid feature extraction technique for automatic classification of COVID-19 chest CT images

Hybrid feature extraction technique for automatic classification of COVID-19 chest CT images

Automated classification of common maternal fetal ultrasound planes using multi-layer perceptron with deep feature integration

Ultrasound is a standard diagnostic tool used during prenatal care to monitor the growth and development of the fetus. During routine clinical obstetric examinations, fetal ultrasound standard planes play a significant role in evaluating fetal growth parameters and assessing abnormalities. However, acquiring common fetal ultrasound planes with accurate fetal anatomical structures is tedious and time-consuming, even for skilled sonographers. Therefore, in this article an automated classification technique is presented for common maternal fetal ultrasound planes using deep learning models to improve detection efficiency and diagnostic accuracy. Feature integration and classification modules are the main components of the proposed approach. Initially, the deep features are extracted using AlexNet and VGG-19 with the global average pooling layer as the last pooling layer and are integrated. Fusing the deep features extracted from different convolutional neural networks strengthens the overall feature representation. After that, the integrated deep features are applied to a multi-layer perceptron to classify fetal ultrasound images into six categories. The proposed model is evaluated on a common maternal fetal ultrasound dataset, and its efficiency is computed in terms of accuracy, recall, precision, and F1-score. The proposed method achieved higher classification efficiency compared to other existing state-of-the-art models.

Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

AbstractMicroseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and Fβ score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.

Improving in-text citation reason extraction and classification using supervised machine learning techniques

In the last decade, automatic extraction and classification of in-text citations have received immense popularity and have become one of the most frequently used techniques to evaluate research. Due to the large volume of in-text citations in various digital libraries such as Web of Science, Scopus, Google Scholar, Microsoft Academic, etc., machine learning models and natural language processing techniques are being used to extract, classify, and analyze them. Typical automatic in-text classification techniques use sentiment-based classes (Positive, Negative, and Neutral). However, there are cognitive-based schemes as well that classify in-text citations based on the author’s perspective. In such schemes, extracting citation reasons with high recall is challenging. To address this challenge, we have used eight citations’ context and reason classes defined by CCRO (Citation’s Context and Reasons Ontology) to develop a machine learning model to achieve high recall without compromising on precision. We have worked on Association for Computational Linguistics Corpus with over 7000 in-text citations, randomly annotated by experts in CCRO classes. Afterwards, an array of machine-learning models is implemented on the annotated dataset: Support Vector Machine (SVM), Naïve Bayesian (NB), and Random Forest (RF). We have used various part-of-speech (Nouns, Verbs, Adverbs, and Adjectives) as novel features. Our results show that we have outperformed the three comparative models by achieving 91% accuracy.

Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept Communications

The detection of deceptive jamming in low probability of intercept/low probability of detection (LPI/LPD) tactical communications has been receiving increasing attention, especially in the context of radar communications. In this article, we propose a novel deep-learning-based algorithm, named two-stage deceptive jammer detection (TDJD), for pseudonoise (PN) code-integrated linear frequency modulation chirp jamming signals in radar communications. Specifically, the proposed algorithm uses the time–frequency distribution (TFD) and in-phase and quadrature (I/Q) representations of the received signals to obtain more discriminating features for the detection of unknown deceptive jammers under the realistic assumption that information about the deceptive jammer, such as the PN-code signal, power, and range, is not available. In particular, the received radar signals are converted into two kinds of TFD representations using the Choi–William distribution (CWD) and I/Q representations. Next, a two-stage approach based on deep learning-enabled outlier detection and automatic modulation classification techniques is applied to the CWD and I/Q representations to extract high-level features for jammer detection. Simulation results show that the TDJD algorithm outperforms other relevant deep learning algorithms in terms of the probability of detection and the probability of false alarm. In addition, the TDJD algorithm provides accurate detection at a low signal-to-jammer-plus-noise ratio.

Leaf Disease Detection

Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that display four disease symptoms. We created a model of CNN for feature extraction and categorization automatically. Color Research on plant leaf diseases actively uses information. In our model, three channels based on RGB are subjected to filters.

Data mining approach for dry bean seeds classification

Product quality certification is an important process in agricultural production and productivity. Traditional methods for seed quality classification have shown limitations such as complex steps, low precision, and slow inspection for large production volumes. Automatic classification techniques based on machine learning and computer vision offer fast and high throughput solutions. Despite the major advances in state-of-the-art automatic classification models, there is still a need to improve these models by incorporating other techniques. In this article, we developed a computer vision system for the automatic classification of different seed varieties based on machine learning models, combined with data mining techniques using a set of features related to the geometry of bean seeds, extracted from binary images. Three machine learning techniques were compared, namely: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), including Principal Component Analysis (PCA), Hyperparameter tuning in machine learning algorithms, and dataset balancing based on Synthetic Minority Oversampling Technique (SMOTE). The results showed that data mining processes, such as Principal Component Analysis, Hyperparameter tuning, and application of the SMOTE technique, help to improve the quality of classification results. The KNN classifier showed better performance, with around 95% accuracy and 96% precision and recall. The best results were obtained applying hyperparameter tuning and the SMOTE technique, in the preprocessing step, obtaining an increase around 2.6%. The results proved that the combined use of data mining in the preprocessing step and machine learning classification methods can effectively and efficiently increase the classification accuracy and help automatic bean seed selection based on digital images. This can help small farmers and/or agricultural managers make decisions regarding seed selection to increase production.

A Novel Method Using Deep Learning Technique for Automatic Grading and Classification of Interferometry Video Frames for Dry Eye Analysis

A Novel Method Using Deep Learning Technique for Automatic Grading and Classification of Interferometry Video Frames for Dry Eye Analysis

Towards automatic Privacy-Preserving Record Linkage: A Transfer Learning based classification step

Privacy-Preserving Record Linkage (PPRL) intends to identify records that match the same real-world entities across disparate data sources while preserving the privacy of the individual entities. To identify matching records across different data sources and still preserve the privacy of the information, PPRL needs to consider several restrictions due to privacy limitations. For instance, PPRL is executed over anonymized (or encrypted) data to avoid re-identification. Moreover, the classification step of PPRL does not have access to labeled information (indicating if a pair of records is a match) and an oracle (specialist) to label a few instances. These limitations make it hard to employ automatic classification techniques. Most PPRL techniques use a simple threshold (defined by a specialist) to define whether a pair of records represent the same real-world entity or not. To overcome these problems, we present a Transfer Learning-based unsupervised classification step to PPRL, which leverages the information available in public (or synthetic) datasets to train accurate classifiers in a privacy-preserving context. We evaluate our approach using real-world and synthetic data, and the results demonstrate that our unsupervised classification step is able to overcome the most used classification strategies in PPRL.

Efficient key frame extraction and hybrid wavelet convolutional manta ray foraging for sports video classification

ABSTRACT Sports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an efficient key frame extraction method and hybrid Wavelet Convolution Neural Network (WCNN) framework with optimization scheme to classify sports videos. Initially, input videos are converted into number of frames, and keyframes are extracted using Enhanced threshold with Discrete Wavelet Transform (ETDWT) method. Then, Cross Guided Bilateral Filter (CGBF) method eliminates the noise from the keyframe. After that, segmentation process is performed by the Fuzzy Equilibrium Optimizer (FEO) algorithm, and then motions are detected using the Farneback optical flow (OF) method. Finally, classification process is performed using Hybrid Wavelet Convolutional Manta Ray Foraging Optimization (HWCMRFO) algorithm to categorize different sports videos. The overall work is implemented using Python language. Simulation results proved that the proposed work achieved the highest accuracy (93.17%) compared to existing approaches.

Multi-Class Heart Abnormalities Detection Based on ECG Graph Using Transfer Learning Method

The heart is one of the vital organs in the circulatory system. Regular checkups are very important to prevent heart disease. The most basic examination is blood pressure then further examination is related to the evaluation of the electrical activity of the heart using an electrocardiogram (ECG). The ECG carries important information regarding various abnormalities of heart function. Several automated classification techniques have been proposed to facilitate diagnosis. However, not all digital ECG devices provide raw data for analysis. ECG classification method based on images can be an alternative in classification. Therefore, in this study, it is proposed to classify ECG based on signal images. The proposed classification method uses transfer learning with VGG, AlexNet, and DenseNet architectures. The method used for the classification of multi-class ECG consists of normal, PVC, Atrial Fibrilation, AFL, Bigeminy, LBBB, and APB. The simulation results generate the best accuracy of 92% and F1-score of 92%. Best performance is achieved using DenseNet architecture at 60 epochs. This study is expected to be a new reference technique in the classification of ECG signals.

Vision-based techniques for automatic marine plankton classification

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.