Two-stage Classification Research Articles

Ultra-low complexity random forest for optical fiber communications.

In this paper, we present an efficient equalizer based on random forest for channel equalization in optical fiber communication systems. The results are experimentally demonstrated in a 120 Gb/s, 375 km, dual-polarization 64-quadrature magnitude modulation (QAM) optical fiber communication platform. Based on the optimal parameters, we choose a series of deep learning algorithms for comparison. We find that random forest has the same level of equalization performance as deep neural networks as well as lower computational complexity. Moreover, we propose a two-step classification mechanism. We first divide the constellation points into two regions and then use different random forest equalizers to compensate the points in different regions. Based on this strategy, the system complexity and performance can be further reduced and improved. Furthermore, due to the plurality voting mechanism and two-stage classification strategy, the random forest-based equalizer can be applied to actual optical fiber communication systems.

Postępowanie kliniczne w amelogenesis imperfecta w wieku rozwojowym – przypadki kliniczne

Amelogenesis imperfecta is a hereditary defect of enamel. It can affect both the primary and permanent dentition. It manifests as: change in enamel structure, shape of teeth, severe malocclusion and can also co-occur with changes in other tissues derived from the ectoderm. In the literature there are several classifications of this disease, the two main ones are the four-stage classification according to Witkop (1988) and the two-stage classification according to Sundell, distinguishing the form associated with enamel hypoplasia and enamel hypomineralization. The aim of this paper is to present the diagnostic and therapeutic management of patients with amelogenesis imperfecta at the developmental age, based on three clinical cases. Three cases were chosen to represent patients with AI: 1) hypoplastic type of AI in 14-year-old girl, 2) hypoplastic type of AI in 12-year-old girl with distoocclusion and retrusion of upper incisors, 3) hypoplastic type of AI in 13-year-old boy with deep overbite and functional retrognathia. Presented methods of treatment were selected depending on the form of lesions, patient’s age and coexisting malocclusion. Direct reconstruction with composite materials was used – free-hand, as well as the technique of indirect reconstruction with steel crowns combined with use of functional orthodontic appliances. Dental treatment in patients with AI should be interdisciplinary. The choice of treatment method depends on the coexisting disorders. It aims to eliminate caries associated with enamel development defects and to correct occlusal conditions, as well as to improve the psychosocial functioning of patients by improving the aesthetics of the teeth.

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.

A graph-based two-stage classification network for mobile screen defect inspection

A graph-based two-stage classification network for mobile screen defect inspection

Two-Stage Classifier for Resource Constrained On-Board Cardiac Arrhythmia Detection

Early automatic detection of cardiac arrhythmia in an ambulatory health monitoring setup can reduce the mortality rates. In the existing approaches on multiclass arrhythmia detection (MCAD) techniques, adopting either time-domain morphological features (TDMFs), fails to extract hidden pattern from the single-lead electrocardiogram (ECG) resulting in poor accuracy (Ac), or adopting deep-learning (DL) techniques becomes too complex or heavy in architecture, thus hindering their easy implementation in low resource architecture for wearable applications. This work presents a judicious hybridization of both approaches in an end-to-end MCAD using two-stage classifier to achieve higher Ac and low latency while detecting 15 classes of cardiac rhythms [14 types of arrhythmia beats or normal sinus rhythms (NSRs)]. The memory resource saving is achieved through the beat skeletonization process and two-stage feature minimization. The stage#1 classifier uses 14 TDMF features fed to a random forest (RF) regressor and provides probability values for NSR, atrial, or ventricular conduction problem. The stage#2 classifier, another RF, accepts stacked autoencoder (SAE) features, TDMF features, and output of stage#1 to achieve a blind test Ac of 99.21% using only 13 features while evaluated with full Massachusetts Institute of Technology-Beth Israel (MIT-BIH) arrhythmia database (mitdb) records. A stand-alone on-board application of the two-stage MCAD using ARMv6 single-core microcontroller yields a detection latency of 782 ms and consumes 390 kB of memory per cardiac cycle, thus proving a feasible solution for wearable cardiac monitors. It also provides the competitive performance with the State of the art (SOTA) methods and outperforms the limited available research on hardware implementation in terms of number of classes handled and Ac.

ECG Measurement System for Vehicle Implementation and Heart Disease Classification Using Machine Learning

The 12-lead electrocardiogram (ECG) method can diagnose more cardiovascular disease than the single-lead method, but it is difficult to use in daily life because numerous electrodes must be attached to the body. As an aging society approaches, electrocardiography is expanding its use in daily life, even to the heart disease monitoring system for passengers in vehicles. This study proposes a single-lead ECG measurement system implemented in the steering wheel of the vehicle and a machine learning model to classify the driver’s heart health status. For the ECG measurement system, an algorithm to obtain stable ECG signals is proposed along with the measurement hardware. It uses the range and interval of the ECG signal to determine stability under noisy conditions caused by vehicle vibration and the driver movement. To classify four classes of heart diseases (normal, atrial fibrillation, other rhythms, noise), a two-stage machine learning structure is proposed. To train the machine learning models with an optimal feature subset, 188 features were extracted from the single-lead ECG dataset, and a sequential wrapper-type feature selection was conducted. As a result, when the naïve Bayes model using ten features was located in the first step and the support vector machine using 13 features in the second step, the proposed two-stage classification structure returned the best score <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>NAO</i></sub> = 0.7898 and real-time classification performances (0.86 seconds on average).

Crowdfunding in Europe. How to help participants of crowdfunding campaigns in making the right decisions?

The aim of the article is to present the state of crowdfunding in European countries and to enrich the available information for participants of crowdfunding campaigns to help them make the right decisions. They function in conditions of information asymmetry, i.e. lack of much information necessary for proper behavior in the crowd of campaign participants. The original methodology of the two-stage classification of project categories submitted for crowdfunding and the specification of analytical indicators presented in this work significantly complements - with the use of IT tools - the gaps in information available on crowdfunding platforms. Source data on crowdfunding in selected European countries were used for the research.

Toward Social Role-Based Interruptibility Management

Pervasive and ubiquitous computing facilitates immediate access to information in the sense of always-on. Information, such as news, messages, or reminders, can significantly enhance our daily routines but are rendered useless or disturbing when not being aligned with our intrinsic interruptibility preferences. Attention management systems use machine learning to identify short-term opportune moments, so that information delivery leads to fewer interruptions. Humans’ intrinsic interruptibility preferences—established for and across social roles and life domains—would complement short-term attention and interruption management approaches. In this article, we present our comprehensive results toward social role-based attention and interruptibility management. Our approach combines on-device sensing and machine learning with theories from social science to form a personalized two-stage classification model. Finally, we discuss the challenges of the current and future AI-driven attention management systems concerning privacy, ethical issues, and future directions.

Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Due to the lack of CAN frame encryption and authentication, CAN bus is vulnerable to various attacks, which can in general be divided into message injection, suspension, and falsification. Existing CAN bus anomaly detection mechanisms either can only detect one or two of these attacks, or require numerous CAN messages during predictions, which can hardly realize real-time performance. In this paper, we propose a CAN bus anomaly detection system that can detect all these attacks simultaneously in as short as 3 milliseconds (ms) based on Graph Neural Network (GNN). This work generates directed attributed graphs based on CAN message streams in given message intervals. Node attributes denote data contents in CAN messages while each edge attribute represents the frequency of a typical CAN ID pair in the given interval. Afterwards, a GNN is trained based on generated CAN message graphs. Considering highly imbalanced training data, a two-stage classifier cascade is developed in this paper, which is composed of a one-class classifier for anomaly detection and a multi-class classifier for attack classification. An openmax layer is further introduced to the multi-class classifier to tackle new anomalies from unknown classes. To take advantage of crowdsourcing while protecting user data privacy, we adopt federated learning to train a universal model that covers different driving scenarios and vehicle states. Extensive experiment results show the effectiveness and efficiency of our methodology.

Automatic Detection of Horner Syndrome by Using Facial Images.

Horner syndrome is a clinical constellation that presents with miosis, ptosis, and facial anhidrosis. It is important as a warning sign of the damaged oculosympathetic chain, potentially with serious causes. However, the diagnosis of Horner syndrome is operator dependent and subjective. This study aims to present an objective method that can recognize Horner sign from facial photos and verify its accuracy. A total of 173 images were collected, annotated, and divided into training and testing groups. Two types of classifiers were trained (two-stage classifier and one-stage classifier). The two-stage method utilized the MediaPipe face mesh to estimate the coordinates of landmarks and generate facial geometric features accordingly. Then, ten machine learning classifiers were trained based on this. The one-stage classifier was trained based on one of the latest algorithms, YOLO v5. The performance of the classifier was evaluated by the diagnosis accuracy, sensitivity, and specificity. For the two-stage model, the MediaPipe successfully detected 92.2% of images in the testing group, and the Decision Tree Classifier presented the highest accuracy (0.790). The sensitivity and specificity of this classifier were 0.432 and 0.970, respectively. As for the one-stage classifier, the accuracy, sensitivity, and specificity were 0.65, 0.51, and 0.84, respectively. The results of this study proved the possibility of automatic detection of Horner syndrome from images. This tool could work as a second advisor for neurologists by reducing subjectivity and increasing accuracy in diagnosing Horner syndrome.

A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of poincaré plots and deep learning

BackgroundMobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives. ObjectiveThe purpose of this study was to design a two stage classifier using artificial intelligence (AI) to improve the PPV of MCOT detected atrial fibrillation episodes whilst maintaining high levels of detection sensitivity. MethodsA low complexity, RR-interval based, AF classifier was paired with a deep convolutional neural network (DCNN) to create a two-stage classifier. The DCNN was limited in size to allow it to be embedded on MCOT devices. The DCNN was trained on 491,727 ECGs from a proprietary database and contained 128,612 parameters requiring only 158 KB of storage. The performance of the two-stage classifier was then assessed using publicly available datasets. ResultsThe sensitivity of AF detected by the low complexity classifier was high across all datasets (>93%) however the PPV was poor (<76%). Subsequent analysis by the DCNN increased episode PPV across all datasets substantially (>11%), with only a minor loss in sensitivity (<5%). This increase in PPV was due to a decrease in the number of false positive detections. Further analysis showed that DCNN processing was only required on around half of analysis windows, offering a significant computational saving against using the DCNN as a one-stage classifier. ConclusionDCNNs can be combined with existing MCOT classifiers to increase the PPV of detected AF episodes. This reduces the review burden for physicians and can be achieved with only a modest decrease in sensitivity.

A deep learning and Grad-Cam-based approach for accurate identification of the fall armyworm (Spodoptera frugiperda) in maize fields

Population monitoring and early warning to the world's important invasive pest, the fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a basis for its management in the field. Deep learning is a useful intelligence approach for identifying Spodoptera frugiperda; however, it has a few limitations, such as too many model parameters, and the mismatch between training in simple backgrounds and complex real scenarios. Herein, we established a two-stage classification approach called MaizePestNet, which is based on EfficientNet, to address these concerns. It combines the Gradient-weighted Class Activation Mapping (Grad-CAM) technique for removing background interference with the knowledge distillation strategy for reducing model parameters and size. A dataset of adults and larvae of 36 common maize field pests, including Spodoptera frugiperda, was collected. The accuracy, precision, recall, and F1-score of the new model were acquired as 93.85%, 93.56%, 96.23%, and 93.85% through training and testing on the dataset. The new model’s accuracy was 5.65% and 9.04% higher than that of the ResNet101 and DenseNet161 models, respectively; the model size was 90% lower and 85% lower than the ResNet101 and DenseNet161 models, respectively. A WeChat applet and online real-time identification system (http://migrationinsect.cn/maizepestidentification) based on our classification model were developed for practical applications to accurately identify Spodoptera frugiperda adults and larvae. This study gave public an accurate, efficient, and straightforward method for detecting Spodoptera frugiperda in maize fields, and the findings can guide and assist plant protection agencies and farmers in monitoring, early warning, and control activities of the pest.

Study on the Effect of Electrical Conductivity on Road Surface Wetness Classification Using Capacitive Measurement Data

As the friction between a vehicle’s tires and the road surface correlates with the road-covering water film height, knowledge about the present wetness level is of relevance for drivers and autonomous systems. A promising approach for wetness quantification is based on a capacitive transducer array (TA) that is integrated at a front wheel arch liner and capable of detecting spray water ejected by the tires. While this approach has already shown that wetness classification using capacitive measurement data is feasible, potentially affecting factors such as the water’s electrical conductivity were assumed to be constant in the past. This article presents a study on the effect of the spray water’s conductivity on wetness classification for that approach and also demonstrates the feasibility of classifying conductivity using the same capacitive measurement data. For these purposes, we propose a test bench capable of simulating reproducible spray water scenarios. We show the effect of varying conductivity on the measured capacitance curves complicating the distinction of wetness classes. In partial investigations, we demonstrate the extent of the conductivity’s effect on classifier performance. While wetness classification with conductivities differing from training results in poor classifier performance, the mean accuracy (ACC) can be increased to approximately 0.98 considering all occurring conductivities. Furthermore, we propose a two-stage classification approach that initially determines the conductivity and subsequently considers it as an additional feature in wetness classification. The approach increases classifier performance to more than 0.99 indicating that knowledge of the spray’s present conductivity can optimize road surface wetness classification.

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning.

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%.

The biofilm characteristics and management of skin flap infection following cochlear implantation.

SUMMARYObjectiveThis study aims to assess the frequency, bacteriology, biofilm characteristics and management of skin flap infection (SFI) following cochlear implantation (CI).MethodsThe study enrolled 1,251 patients receiving CI in the First Affiliated Hospital of Fujian Medical University between August 2001 and March 2021. Scanning electron microscopy (SEM) was utilised to characterise the aetiology of infection. A proposed classification system was applied to optimise treatments for post-operative skin flap infection.ResultsAfter CI, SFI was reported in 16 patients (1.28%) and occurred more frequently in patients under 6 years of age. Of all SFI cases Staphylococcus aureus was the most common pathogen for flap infection, with 8 cases (50%) and bacterial biofilm was evident within the jelly-like substance on the surface of implanted devices in SFI patients. A two-stage classification was proposed to optimise the treatment schemes. Conservative therapy was recommended for stage I cases and surgical treatment for stage II patients.ConclusionsPaediatric patients are more susceptible to SFI after CI, which may be attributed to the formation of bacterial biofilm. The proposed classification can facilitate the management of SFI.

Applying the FN-corrector to improve the quality of audio event classification

The paper deals with the problem of acoustic events classification which is actively applied to the problems of a safe city, smart home, IoT devices, and for the detection of industrial accidence. A solution to improve the accuracy of classifiers without changing their structure and collecting additional data is proposed. The main data source for the experiments was the TUT Urban Acoustic Scenes 2018, Development Dataset. The paper presents the way to increase the accuracy of audio event classification by using the FN-corrector. The FN-corrector is a linear two-stage classifier performing the transformation of the feature space into a linearly separable space and the linear separation of one class from another. If a corrector is applied, the responses of the original classifier generate four classes: positive (P), negative (N), false positive (FP), and false negative (FN). As a result, it becomes possible to train two types of correctors: the FP-corrector separating positive and false positive classifier responses, and the FN-corrector separating negative and false negative classifier responses. In the experiments, the VGGish convolutional neural network was used as the initial classifier. The audio signal is converted into a spectrogram and is fed to the input of the neural network which forms the spectrogram feature description and performs a classification. As an example, two ”confused“ classes are selected to demonstrate the increase in classification accuracy. Using the feature description of audio recordings of these classes, an FN-corrector was built, trained and connected to the original classifier. The response from the classifier, as well as the feature description, has been passed to the corrector input. Next, the corrector translated the feature space into a new basis (into a linearly separable space) and classified the classifier answer responding to the question whether the original classifier makes a mistake on such a feature vector or not. If the original classifier made a mistake, then his answer is changed by the corrector to the opposite, otherwise the answer remains the same. The results of the experiments demonstrated a decrease in the level of class confusion and, accordingly, an increase in the accuracy of the original classifier without changing its structure and without collecting an additional data set. The results obtained can be used on IoT devices that have significant limitations on the size of the models used, as well as in solving the problems of domain adaptation which is relevant in audio analytics

CAN Bus Intrusion Detection Based on Auxiliary Classifier GAN and Out-of-distribution Detection

The Controller Area Network (CAN) is a ubiquitous bus protocol present in the Electrical/Electronic (E/E) systems of almost all vehicles. It is vulnerable to a range of attacks once the attacker gains access to the bus through the vehicle’s attack surface. We address the problem of Intrusion Detection on the CAN bus and present a series of methods based on two classifiers trained with Auxiliary Classifier Generative Adversarial Network (ACGAN) to detect and assign fine-grained labels to Known Attacks and also detect the Unknown Attack class in a dataset containing a mixture of (Normal + Known Attacks + Unknown Attack) messages. The most effective method is a cascaded two-stage classification architecture, with the multi-class Auxiliary Classifier in the first stage for classification of Normal and Known Attacks, passing Out-of-Distribution (OOD) samples to the binary Real-Fake Classifier in the second stage for detection of the Unknown Attack class. Performance evaluation demonstrates that our method achieves both high classification accuracy and low runtime overhead, making it suitable for deployment in the resource-constrained in-vehicle environment.

A Two-Stage Model for Breast Cancer Detection and Classification from Mammograms

Breast cancer is a type of disease which is common to women. It is an abnormal behaviour which hinders the functionality of the normal breast cells which leads to forming a tumor in the form of lump. Early detection of breast cancers tends to reduce death risk and provides a chance of a suitable and better treatment. A two-stage classification model was proposed to detect and classify breast cancer using two publicly available datasets namely MIAS and the DDSM dataset. The first classification stage uses Convolution Neural Network (CNN) to classify the mammogram image as either normal or abnormal while K-Nearest Neighbour (KNN) was used for the second classification stage to classify the abnormal image into benign or malignant. The features used for the second stage classification were extracted using Gray Level Co-occurrence Matrix (GLCM), which was later used for classification. The performance was evaluated based on accuracy, sensitivity, specificity, precision, False Positive Rate (FPR), F1 score and Matthews Correlation Coefficient (MCC). The results show that CNN obtained the highest accuracy, sensitivity, specificity, precision, FPR, F1 score and MCC of 99.03%, 0.9831, 1.0000, 1.0000, 0.0000, 0.9915 and 0.9804 respectively, while KNN has an accuracy, sensitivity, specificity, precision, FPR, F1 score and MCC of 76.27%, 0.7667, 0.7586, 0.7667, 0.2414, 0.7667 and 0.5253 respectively.

“Rough Day … Need a Hug”: Learning Challenges and Experiences of the Alzheimer’s Disease and Related Dementia Caregivers on Reddit

Alzheimer’s disease and related dementia (ADRD) is a collection of disorders involving mental deterioration, which is often quite distressing for the individuals and those caring for them. Online social media platforms have become popular environments for people to share their ADRD caring challenges and experiences. Despite encouraging findings in the literature regarding online support for ADRD caregivers, studies to date have focused only on a single online community about ADRD, which leads to an incomplete picture of the needs of ADRD caregivers. Additionally, the large volume of data from online communities makes it challenging for both researchers and caregivers to efficiently discover discussions about ADRD care. In this paper, we focus on Reddit, an online rating and discussion platform that consists of many communities, or subreddits, and aim to analyze the topic difference regarding ADRD care between ADRD and non-ADRD subreddits. To do so, we first develop a two-stage classification framework to extract posts about ADRD care from the entire Reddit. Then, we apply structured topic modeling to investigate what has been discussed on ADRD care and how such discussions are prevalent in different types of subreddits. Our results show that non-ADRD subreddits contribute 68.5% of submissions of ADRD care, more than twice as many as ADRD subreddits. Moreover, non-ADRD subreddits are more likely to disclose legal and financial issues, negative relationships and mental health, while ADRD subreddits are more likely to talk about memory loss, sleeping and diet issues, the disease and clinical visits. Our findings suggest that research in this area should look into discussions beyond ADRD communities to gain a comprehensive understanding of ADRD caring experiences and challenges.

Is noma a neglected/overlooked tropical disease?

Noma is a debilitating orofacial necrotizing bacterial disease that disproportionately affects impoverished malnourished persons, particularly young children, the vast majority of whom live in tropical and subtropical areas in sub-Saharan Africa. It has a very high mortality rate; causes significant physical and psychological morbidity, stigmatization and social discrimination; could be prevented, controlled and indeed eliminated by common public health interventions; and is overlooked with regard to public health awareness, in-depth scientific research activities and allocation of funding for prevention, treatment and research.According to the WHO, noma comprises five sequential ‘stages’: (1) necrotizing gingivitis, (2) edema, (3) gangrene, (4) scarring and (5) sequelae. This WHO staging of noma is contentious, leading to diagnostic confusion with misestimation of the number of noma cases reported in epidemiological studies. We therefore suggest a simpler, more practical and scientifically valid two-stage classification comprising only (1) acute noma and (2) arrested noma.Noma meets all the WHO criteria for classification as a neglected tropical disease (NTD). Most survivors of noma live with gross physical disfigurement and disability, and with impaired psychosocial functioning, so they are very often stigmatized and unjustifiably discriminated against. Owing to the paucity of evidence-based epidemiological data on noma, the relatively low number of people affected worldwide, and its apparently limited geographic distribution, noma does not yet feature on the WHO's list of NTDs, or on any global health agenda, and thus has not become a health priority for global action. We strongly support the inclusion of noma within the WHO list of NTDs. Without doubt this will increase the awareness of noma among healthcare providers and promote the systematic international accumulation and recording of data about noma.