Binary Support Vector Machine Research Articles

NIMG-73. PREDICTING PERITUMORAL GLIOBLASTOMA INFILTRATION USING QUANTITATIVE MAGNETIC RESONANCE FINGERPRINTING RADIOMICS

Abstract PURPOSE Standard clinical magnetic resonance (MR) imaging of glioblastoma (GB) cannot identify malignant infiltration into the peritumoral non-enhancing region. This work implements quantitative MR fingerprinting (MRF) based radiomics to predict infiltrative regions of GB. METHODS Pre-operative MRF T1 and T2 maps along with multiparametric MR (mpMR) images (T1w, T2w, T1w-Gd, FLAIR, and ADC) from GB patients (n = 10) were analyzed. All subjects had histologically confirmed sites of non-enhancing peritumoral infiltration as identified by intra-operative 5-ALA fluorescence-guided tissue resection. These locations (n = 40) were manually annotated on pre-operative FLAIR by a board-certified neuroradiologist and labeled as “infiltration” (INF). For each patient, another FLAIR region ( > 3 cm from the enhancing tumor margin) was identified and labeled as “edema” (ED). Following image co-registration, 693 handcrafted radiomic voxel-based features were extracted from confirmed infiltration (INF) and edema (ED) voxels using a 3D 5x5x5 voxel sliding kernel. Feature selection was performed using the minimum redundancy maximal relevance (MRMR) algorithm, then INF and ED voxel features were used to train two cross-validated binary support vector machine (SVM) models for voxel-wise infiltration prediction: 1) MRF only model and 2) combined MRF and mpMRI model. Following MRMR selection, the combined model included features from T1, T1w, T2w, T1w-Gd, FLAIR, and ADC images. Model performance was evaluated using one withheld subject with multiple known infiltration sites. Balanced test accuracy and receiver operating characteristic (ROC) curve analysis was used to evaluate classification performance. RESULTS The MRF only and combined model achieved balanced test accuracies of 77.87% and 73.21% and AUCs of 0.91 and 0.97, respectively. CONCLUSIONS This study demonstrates that MRF-based radiomic features can predict GB tumor infiltration with high accuracy and provide complementary value to standard clinical MRI features. Our results indicate the high potential of employing MRF to guide personalized GB treatment strategies.

Support Vector Machine-Based Soft Decision for Consecutive-Symbol-Expanded 4-Dimensional Constellation in Underwater Visible Light Communication System

Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

Acoustic rainfall estimation with support vector machines and error correcting output codes

Ma and Nystuen (2005) successfully detected and estimated rainfall at sea from passive acoustics. They detected rain from three narrowband frequencies and then estimated log rainfall rate via a regression with energy in the 5 kHz band. Mallary et al. (2022) improved rainfall detection by exploiting broadband spectra while reducing the dimensionality through principal component analysis (PCA). This project builds upon Mallary’s work moving beyond detection to estimate the rainfall by quantization into discrete ranges based on PCA-reduced acoustic power spectra. The classification scheme combines multiple binary support-vector machine (SVM) classifiers (Boser et al. 1992) with Dietterich and Bakiri’s error-correcting output codes (1995) to classify acoustic PSDs into one of 6 rainfall rate classes. Evaluating the PCA/SVM classifier on 4 months of acoustic recordings and meteorological data collected from a shallow water pier in New Bedford, MA found the hourly accumulations from the rain gauge and acoustic estimates had a correlation of 0.97 ± 0.01. Emulating Ma & Nystuen’s estimator on the same data set yields a correlation of 0.76 ± 0.02. [Work supported by ONR.]

A Breast Cancer Contour Detection With Level Sets and SVM Model

Level sets have been widely used to isolate features of breast tumors in ultrasound images. However, region-based methods always produce multiple contours. Since tumors are regularly undefined from the shadows and muscular regions in breast ultrasound images, computerized tumors location and arrangement is significantly difficult. Therefore, the authors introduce a breast cancer contour detection model using support vector machine (SVM) as a binary classification. Features of the binary SVM model were extracted from level sets and FM method (the fusion of ultrasound, elasticity, and Doppler images). The model was accurately able to predict a correct breast tumor contour from false contours which were segmented by region-based level sets. The proposed method was evaluated on 60 datasets collected by professional radiologists at the Thammasat University Hospital of Thailand. From the experimental results, the breast cancer contours were detected correctly with high accuracy. The percentage of correct detection was 93%.

Evaluating multi-modal mobile behavioral biometrics using public datasets

Behavioral biometric-based continuous user authentication is promising for securing mobile phones while complementing traditional security mechanisms. However, the existing state of art perform continuous authentication to evaluate deep learning models, but lacks examining different feature sets over the data. Therefore, we evaluate the performance of user authentication based on acceleration, gyroscope (angular velocity), and swipe data from two public mobile datasets, HMOG (Hand-Movement, Orientation, and Grasp) (Sitová et al., (2015) dataset et al. (2015)) and BB-MAS (Behavioral Biometrics Multi-device and multi-Activity data from Same users) (Belman et al., (2019) dataset et al. (2019)) extracted with different feature sets to observe the variation in authentication performance. We evaluate the performances of both individual modalities and their fusion. Since the swipe data is intermittent but the motion event data continuous, we evaluate fusion of swipes with motion events that occur within the swipes versus fusion of motion events outside of swipes. Moreover, we extract Frank et al.’s (2012) Touchalytics features Frank et al. (2012) on the swipe data but three different feature sets (median, HMOG (Sitová et al. (2015)), and Shen’s (Shen et al. (2017))) on the motion event data, among which the Shen’s features were shown to perform the best. More specifically, we perform score-level fusion for a single modality utilizing binary SVMs (Support Vector Machine). Furthermore, we evaluate the fusion of multiple modalities using Nandakumar’s likelihood ratio-based score fusion (Nandakumar et al. (2007)) by utilizing both one-class and binary SVMs. The best EERs (Equal Error Rates) of fusing all three modalities when using the one-class SVMs are 8.8% and 0.9% for HMOG and BB-MAS respectively. On the other hand, the best EERs in the case of binary SVMs are 1.5% and 0.2% respectively. Observing the better performances of BB-MAS compared to HMOG in swipe-based experiments, we examine the difference of swipe trajectory between the two datasets and find that BB-MAS has longer swipes than HMOG which would explain the performance difference in the experiments.

A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Accurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.

Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.

BackgroundAlthough depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.MethodsBased on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.ResultsThe MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity.ConclusionOur findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.

Predictive utility of symptom measures in classifying anxiety and depression: A machine-learning approach

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent, co-occurring disorders with significant symptom overlap, posing challenges in accurately distinguishing and diagnosing these disorders. The tripartite model proposes that anxious arousal is specific to anxiety and anhedonia is specific to depression, though anxious apprehension may play a greater role in GAD than anxious arousal. The present study tested the efficacy of the Mood and Anxiety Symptom Questionnaire anhedonic depression (MASQ-AD) and anxious arousal (MASQ-AA) scales and the Penn State Worry Questionnaire (PSWQ) in identifying lifetime or current MDD, current major depressive episode (MDE), and GAD using binary support vector machine learning algorithms in an adult sample (n = 150). The PSWQ and MASQ-AD demonstrated predictive utility in screening for and identification of GAD and current MDE respectively, with the MASQ-AD eight-item subscale outperforming the MASQ-AD 14-item subscale. The MASQ-AA did not predict MDD, current MDE, or GAD, and the MASQ-AD did not predict current or lifetime MDD. The PSWQ and MASQ-AD are efficient and accurate screening tools for GAD and current MDE. Results support the tripartite model in that anhedonia is unique to depression, but inclusion of anxious apprehension as a separate dimension of anxiety is warranted.

Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential tremor

Essential tremor (ET) is the most common tremor disorder, and the intrinsic brain activity changes and diagnostic biomarkers of ET remain unclear. Combined multivariate pattern analysis (MVPA) with resting-state functional MRI (Rs-fMRI) data provides the most promising way to identify individual subjects, reveal brain activity changes, and further establish diagnostic biomarkers in neurological diseases. Using voxel-level amplitude of low-frequency fluctuations (ALFF) and local (regional homogeneity, ReHo) and global (degree centrality, DC) brain connectivity mappings based on three frequency bands (classical band: 0.01–0.10 Hz; slow-5: 0.01–0.023 Hz; slow-4: 0.023–0.073 Hz) of 162 ET patients and 153 well-matched healthy controls (HCs) as input features, MVPA (binary support vector machine, SVM) was performed to differentiate ET from HCs. Each modality achieved good classification performance, except for ReHo based on the slow-4 band with a sensitivity, specificity and total accuracy of 58.64%, 65.36%, 61.90%, respectively (P < 0.05). The classification performance with slow-4 bands was poorer than that with slow-5 and classical bands, but slow-4 bands could be used to reveal the spatial distribution changes in subcortical structures, especially the thalamus. The significant discriminative features were mostly located in the cerebello-thalamo-cortical pathway, and partial correlation analyses showed that significant discriminative features in the cerebello-thalamo-cortical pathway could be used to explain the clinical features of tremor in ET patients. Our findings revealed that voxel-level frequency-dependent ALFF, ReHo and DC could be used to discriminate ET from HCs and help to reveal intrinsic brain activity changes, further acting as potential diagnostic biomarkers.

Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable&#x2019;s sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6&#x0025; accuracy rate, 0.15sec lowest execution time, and 0.095&#x0025; maximum error rate for fault location at fractional factor (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>) equals to 0.8.

IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

Construction of multi-modal perception model of communicative robot in non-structural cyber physical system environment based on optimized BT-SVM model

With the rapid development of intelligent control technology, computer technology, bionics, artificial intelligence and other disciplines, more and more attention has been paid to the research of intelligent mobile robot technology, and autonomous positioning is the basis for mobile robots to conduct autonomous navigation and exploration research. Sensors assist each other to provide a wealth of perception information about the internal state of the robot and the external surrounding environment. This paper proposes a method for optimizing the Support Vector Machine (SVM) multi-classifier with a binary tree structure, which improves the accuracy of multi-modal tactile signal recognition. The improved particle swarm clustering algorithm is used to optimize the binary tree structure, reduce the error accumulation of the binary tree structure SVM multi-classifier, and further improve the accuracy of multi-modal tactile signal recognition. The effect of the method in this paper is verified by robot grasping experiments. The results show that the use of multi-modal information of two-dimensional images and three-dimensional point cloud images can effectively identify and locate target objects of different shapes. Compared with the processing method of two-dimensional or point cloud monomodal image information, the positioning error can be reduced by 54.8%, and the direction error can be reduced by 50.8%, which has better robustness and accuracy. The simulation results show that the improved PSOBT-SVM model has the best classification effect for artificial features, PCA features and spatio-temporal correlation features. The improved PSOBT-SVM optimizes the classification accuracy without changing the number of SVM classifiers, and proves its accuracy in classifying multimodal tactile signals.

Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET.Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET.Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

Multi-Channel Generative Framework and Supervised Learning for Anomaly Detection in Surveillance Videos.

Recently, most state-of-the-art anomaly detection methods are based on apparent motion and appearance reconstruction networks and use error estimation between generated and real information as detection features. These approaches achieve promising results by only using normal samples for training steps. In this paper, our contributions are two-fold. On the one hand, we propose a flexible multi-channel framework to generate multi-type frame-level features. On the other hand, we study how it is possible to improve the detection performance by supervised learning. The multi-channel framework is based on four Conditional GANs (CGANs) taking various type of appearance and motion information as input and producing prediction information as output. These CGANs provide a better feature space to represent the distinction between normal and abnormal events. Then, the difference between those generative and ground-truth information is encoded by Peak Signal-to-Noise Ratio (PSNR). We propose to classify those features in a classical supervised scenario by building a small training set with some abnormal samples of the original test set of the dataset. The binary Support Vector Machine (SVM) is applied for frame-level anomaly detection. Finally, we use Mask R-CNN as detector to perform object-centric anomaly localization. Our solution is largely evaluated on Avenue, Ped1, Ped2, and ShanghaiTech datasets. Our experiment results demonstrate that PSNR features combined with supervised SVM are better than error maps computed by previous methods. We achieve state-of-the-art performance for frame-level AUC on Ped1 and ShanghaiTech. Especially, for the most challenging Shanghaitech dataset, a supervised training model outperforms up to 9% the state-of-the-art an unsupervised strategy.

Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels.

Wireless capsule endoscopy is the commonly employed modality in the treatment of gastrointestinal tract pathologies. However, the time taken for interpretation of these images is very high due to the large volume of images generated. Automated detection of disorders with these images can facilitate faster clinical interventions. In this paper, we propose an automated system based on Gaussian mixture model superpixels for bleeding detection and segmentation of candidate regions. The proposed system is realized with a classic binary support vector machine classifier trained with seven features including color and texture attributes extracted from the Gaussian mixture model superpixels of the WCE images. On detection of bleeding images, bleeding regions are segmented from them, by incrementally grouping the superpixels based on deltaE color differences. Tested with standard datasets, this system exhibits best performance compared to the state-of-the-art approaches with respect to classification accuracy, feature selection, computational time, and segmentation accuracy. The proposed system achieves 99.88% accuracy, 99.83% sensitivity, and 100% specificity signifying the effectiveness of the proposed system in bleeding detection with very few classification errors.

Wavelet Time Scattering Based Classification of Interictal and Preictal EEG Signals

If it were possible to reliably identify the preictal brain state from dynamical changes in EEG data of epilepsy patients, then the age long problem of actualizing a fully automated closed-loop seizure – warning or seizure-prevention system that is clinically deployable would have been resolved. Accordingly, through feature engineering, a great deal of effort has been invested over the discovery of EEG features or measures that are always indicative of the preictal brain state. However, this has proven to be difficult, time consuming and apparently unsuccessful. Therefore, lately, attention has shifted to feature learning-methods that automatically learn and extracts useful discriminatory features from raw data. This paper studies the efficacy of wavelet time scattering learned EEG features for interictal and preictal EEG classification. Wavelet time scattering network developed in Matlab and two different EEG datasets: CHB-MIT scalp EEG and AES intracranial EEG datasets were used for the study. The learned interictal and preictal EEG features were used to train and evaluate a simple binary support vector machine classifier. Three different classification accuracy results namely ordinary cross validation, true cross validation and test classification accuracy results were reported for the analysis. Mean classification accuracy values of 93.15%, 97.57% and 91.33% were obtained respectively for the scalp EEG while mean classification accuracy values of 98.33%, 100% and 96.73% were obtained respectively for the intracranial EEG. A general comparison showed that the combination of wavelet time scattering learned EEG features and a simple binary support vector machine classifier performed equally or even better than deep convolutional neural networks in EEG classification tasks. Finally, wavelet time scattering has proven to be a very good EEG feature learner and may greatly improve the sensitivity and specificity of seizure prediction algorithms.

Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale

Successful hydrocarbon production in the Eagle Ford relies on technological advances such as directional geosteering, horizontal drilling, and hydraulic fracturing, as well as the identification and delineation of organic-rich intervals and fracture zones. Total organic carbon (TOC) is an excellent indicator for the organic richness and the hydrocarbon potential of shale in unconventional reservoirs; however, common methods for TOC estimation have many underlying assumptions and rely on empirical formulas. It is useful to develop a robust data-driven approach that could be used to reliably identify TOC-rich zones in unconventional plays such as the Eagle Ford. Using gamma-ray, deep resistivity, and sonic wireline well log data from La Salle County, South Texas, we generate a layer unit database and label the layer units using core measured TOC values. We apply a data-driven binary support vector machine (SVM) machine learning approach to identify TOC-rich zones within the Eagle Ford. We evaluate the performance of the SVM classifier and obtain F1 scores ranging from 97.4% to 99.4%. The methodology successfully identifies TOC-rich zones that match with geological observations, the ΔlogR method, and independently obtained core TOC measurements. We demonstrate the successful cross-well application of the methodology within the Eagle Ford Play Area and also demonstrate the successful cross-basin application of the methodology using data from the Barnett Shale Formation, Fort Worth Basin, North Texas, and the Duvernay Shale Formation of the West Canadian Sedimentary Basin, Canada.

Unobtrusive patient identification using smart pill-bottle systems

Accurately accounting for medication use is important for the efficacy and safety of patients and family members. Monitoring is also important for medication adherence. This work investigates passive identification of persons taking medication using a sensor-equipped pill-bottle. The bottle is equipped with inertial and switch sensors in both the cap and body, making the added hardware unobtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using two datasets that we collected from 36 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one individual among 3 people with > 90% accuracy and recall, and has > 80% accuracy and recall, using a single sensor. Using the same technique, our system has 85% accuracy and 72% recall for discriminating one subject in a population of 16. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%. We also show that we can infer the correct class for a previously unseen subject in a group of 3 subjects by using one-class SVM, with 75% average overall accuracy and 83% average recall.

Secure decision tree twin support vector machine training and classification process for encrypted IoT data via blockchain platform

SummaryA secure decision tree twin support vector machine (DT‐TSVM) multi‐classification algorithm has been proposed in this paper for improving the reliability and security of the collected IoT data from multiple data providers. The multiclass secure DT‐TSVM algorithm has been employed to train a machine learning model using the encrypted training dataset. The training dataset is collected via a blockchain platform. A blockchain method has been adopted to construct a secure and reliable distributed platform among dataset providers. The Paillier homomorphic cryptosystem has been applied for encrypting the IoT dataset. Then, the dataset has been recorded on the distributed ledger. The secure DT‐TSVM algorithm's‐based train model effectiveness has been compared with the other two available algorithms, namely the multiclass binary support vector machine (MBSVM) and one‐to‐one SVM algorithms. The experiment results showed that the privacy‐preserving multiclass secure DT‐TSVM‐based model did not reduce the accuracy, but it increased the average precision and recall by 0.53% and 0.44% than MBSVM and 0.82% and 0.71% than one‐to‐one SVM, respectively. Further, the time consumption of data providers and data analysts did not change significantly with the increase of number of data provider.

Optical character recognition system for Baybayin scripts using support vector machine.

In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.