Proposed Classification Framework Research Articles

A review of the role played by cilia in medusozoan feeding mechanics.

Cilia are widely present in metazoans and have various sensory and motor functions, including collection of particles through feeding currents in suspensivorous animals. Suspended particles occur at low densities and are too small to be captured individually, and therefore must be concentrated. Animals that feed on these particles have developed different mechanisms to encounter and capture their food. These mechanisms occur in three phases: (i) encounter; (ii) capture; and (iii) particle handling, which occurs by means of a cilia-generated current or the movement of capturing structures (e.g. tentacles) that transport the particle to the mouth. Cilia may be involved in any of these phases. Some cnidarians, as do other suspensivorous animals, utilise cilia in their feeding mechanisms. However, few studies have considered ciliary flow when examining the biomechanics of cnidarian feeding. Anthozoans (sessile cnidarians) are known to possess flow-promoting cilia, but these are absent in medusae. The traditional view is that jellyfish capture prey only by means of nematocysts (stinging structures) and mucus, and do not possess cilia that collect suspended particles. Herein, we first provide an overview of suspension feeding in invertebrates, and then critically analyse the presence, distribution, and function of cilia in the Cnidaria (mainly Medusozoa), with a focus on particle collection (suspension feeding). We analyse the different mechanisms of suspension feeding and sort them according to our proposed classification framework. We present a scheme for the phases of pelagic jellyfish suspension feeding based on this classification. There is evidence that cilia create currents but act only in phases 1 and 3 of suspension feeding in medusozoans. Research suggests that some scyphomedusae must exploit other nutritional sources besides prey captured by nematocysts and mucus, since the resources provided by this diet alone are insufficient to meet their energy requirements. Therefore, smaller particles and prey may be captured through other phase-2 mechanisms that could involve ciliary currents. We hypothesise that medusae, besides capturing prey by nematocysts (present in the tentacles and oral arms), also capture small particles with their cilia, therefore expanding their trophic niche and suggesting reinterpretation of the trophic role of medusoid cnidarians as exclusively plankton predators. We suggest further study of particle collection by ciliary action and its influence on the biomechanics of jellyfishes, to expand our understanding of the ecology of this group.

Deep-Learning-Based Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

A neighborhood’s walkability is associated with public health, economic and environmental benefits. The state of the walking surface on sidewalks is a key factor in assessing walkability, as it promotes pedestrian movement and exercise. Yet, conventional practices for assessing sidewalks are labor-intensive and rely on subject-matter experts, rendering them subjective, inefficient and ineffective. Wearable sensors can be utilized to address these limitations. This study proposes a novel classification method that employs a long short-term memory (LSTM) network to analyze gait data gathered from a single wearable accelerometer to automatically identify irregular walking surfaces. Three different input modalities—raw acceleration data, single-stride and multi-stride hand-crafted accelerometer-based gait features—were explored and their effects on the classification performance of the proposed method were compared and analyzed. To verify the effectiveness of the proposed approach, we compared the performance of the LSTM models to the traditional baseline support vector machine (SVM) machine learning method presented in our previous study. The results from the experiment demonstrated the effectiveness of the proposed framework, thereby validating its feasibility. Both LSTM networks trained with single-stride and multi-stride gait feature modalities outperformed the baseline SVM model. The LSTM network trained with multi-stride gait features achieved the highest average AUC of 83%. The classification performance of the LSTM model trained with single-stride gait features further improved to an AUC of 88% with post-processing, making it the most effective model. The proposed classification framework serves as an unbiased, user-oriented tool for conducting sidewalk surface condition assessments.

Reinforcement-Learning-Based Localization of Hippocampus for Alzheimer's Disease Detection.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily impacting memory and cognitive functions. The hippocampus serves as a key biomarker associated with AD. In this study, we present an end-to-end automated approach for AD detection by introducing a reinforcement-learning-based technique to localize the hippocampus within structural MRI images. Subsequently, this localized hippocampus serves as input for a deep convolutional neural network for AD classification. We model the agent-environment interaction using a Deep Q-Network (DQN), encompassing both a convolutional Target Net and Policy Net. Furthermore, we introduce an integrated loss function that combines cross-entropy and contrastive loss to effectively train the classifier model. Our approach leverages a single optimal slice extracted from each subject's 3D sMRI, thereby reducing computational complexity while maintaining performance comparable to volumetric data analysis methods. To evaluate the effectiveness of our proposed localization and classification framework, we compare its performance to the results achieved by supervised models directly trained on ground truth hippocampal regions as input. The proposed approach demonstrates promising performance in terms of classification accuracy, F1-score, precision, and recall. It achieves an F1-score within an error margin of 3.7% and 1.1% and an accuracy within an error margin of 6.6% and 1.6% when compared to the supervised models trained directly on ground truth masks, all while achieving the highest recall score.

An efficient classification framework for Type 2 Diabetes incorporating feature interactions

Accurate and timely diagnosis of Type 2 Diabetes is a highly challenging task due to its initial asymptomatic nature and complex risk factor composition. Recently, Machine Learning (ML) has been actively used to build improved Type 2 Diabetes classification systems. One important aspect of these systems has been feature selection. Filter feature selection techniques especially based on mutual information have been popularly employed in recent works. However, most of them have focused on selecting relevant features and eliminating redundant ones. A third relationship called feature interaction may exist if input features are highly correlated, as in the case of Type 2 Diabetes. Feature interaction signifies the additional information about the target provided by the interaction between a subset of input features, that may not be relevant to the target individually. Second, many of the existing ML models are black-box, making the model interpretability very difficult. This paper proposes an efficient ML framework for the classification of Prediabetes and Type 2 Diabetes by incorporating feature interactions. It presents a hybrid filter-wrapper technique called Feature Interaction-based Greedy Sequential Feature selection. Agglomerative Feature Clustering and Dendrogram visualization for the analysis of interactive features is performed. A model-agnostic explainability technique of SHapley Additive explanations (SHAP) is augmented to provide local and global explanations of model predictions. The performance of the proposed classification framework was found to be interpretable as well as efficient with an accuracy of 98.8669%, precision of 98.8660%, recall of 98.8665%, and F-score of 0.9886 for Diabetes. For Prediabetes, an accuracy of 90.1187%, precision of 94.6958%, recall of 90.1187%, and F-score of 0.9403 was obtained.

A Hybrid 3D–2D Feature Hierarchy CNN with Focal Loss for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been extensively applied for analyzing remotely sensed images. HSI data consist of multiple bands that provide abundant spatial information. Convolutional neural networks (CNNs) have emerged as powerful deep learning methods for processing visual data. In recent work, CNNs have shown impressive results in HSI classification. In this paper, we propose a hierarchical neural network architecture called feature extraction with hybrid spectral CNN (FE-HybridSN) to extract superior spectral–spatial features. FE-HybridSN effectively captures more spectral–spatial information while reducing computational complexity. Considering the prevalent issue of class imbalance in experimental datasets (IP, UP, SV) and real-world hyperspectral datasets, we apply the focal loss to mitigate these problems. The focal loss reconstructs the loss function and facilitates effective achievement of the aforementioned goals. We propose a framework (FEHN-FL) that combines FE-HybridSN and the focal loss for HSI classification and then conduct extensive HSI classification experiments using three remote sensing datasets: Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SV). Using cross-entropy loss as a baseline, we assess the hyperspectral classification performance of various backbone networks and examine the influence of different spatial sizes on classification accuracy. After incorporating focal loss as our loss function, we not only compare the classification performance of the FE-HybridSN backbone network under different loss functions but also evaluate their convergence rates during training. The proposed classification framework demonstrates satisfactory performance compared to state-of-the-art end-to-end deep-learning-based methods, such as 2D-CNN, 3D-CNN, etc.

LDER: a classification framework based on ERP enhancement in RSVP task

Objective. Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications. Approach. To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework. Main results. Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain–computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes. Significance. As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.

Interpretable Emotion Classification Using Multidomain Feature of EEG Signals

Research on affective computing by physiological signals has become a new and important research branch of artificial intelligence. However, most of the research focuses only on the improvement of emotion classification models while neglecting the correlation between subjective emotional labels and implicit EEG feature information. In this study, we proposed an interpretable emotion classification framework based on food images as visual stimuli and a proposed EEG dataset acquired by a portable single-electrode EEG device. Then, we construct a multi-domain feature extraction method based on sliding time windows, which can effectively extract single-channel EEG features. Finally, we chose the extreme gradient boosting (XGBoost) model as our classifier in the proposed classification framework. The optimal accuracy of our single-channel EEG classification model was 94.76%, which reached the classification performance of multi-channel EEG classification. Based on XGBoost, we interpret the global and the local emotion feature selection method by calculating Shapley additive explanation (SHAP) values. Through the analysis of interpretable methods, we can not only obtain the contribution degree of features from three types of emotional labels (positive, negative, and neutral), but also know whether features have a positive or negative effect on classification results. The source codes of this work are publicly available at:https://github.com/VCMHE/MDF-EEG.

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.

Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.

Classifying the unknown: Insect identification with deep hierarchical Bayesian learning

Abstract Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.

Classification of Migraine Using Static Functional Connectivity Strength and Dynamic Functional Connectome Patterns: A Resting-State fMRI Study

Migraine is a common, chronic dysfunctional disease with recurrent headaches. Its etiology and pathogenesis have not been fully understood and there is a lack of objective diagnostic criteria and biomarkers. Meanwhile, resting-state functional magnetic resonance imaging (RS-fMRI) is increasingly being used in migraine research to classify and diagnose brain disorders. However, the RS-fMRI data is characterized by a large amount of data information and the difficulty of extracting high-dimensional features, which brings great challenges to relevant studies. In this paper, we proposed an automatic recognition framework based on static functional connectivity (sFC) strength features and dynamic functional connectome pattern (DFCP) features of migraine sufferers and normal control subjects, in which we firstly extracted sFC strength and DFCP features and then selected the optimal features using the recursive feature elimination based on the support vector machine (SVM-RFE) algorithm and, finally, trained and tested a classifier with the support vector machine (SVM) algorithm. In addition, we compared the classification performance of only using sFC strength features and DFCP features, respectively. The results showed that the DFCP features significantly outperformed sFC strength features in performance, which indicated that DFCP features had a significant advantage over sFC strength features in classification. In addition, the combination of sFC strength and DFCP features had the optimal performance, which demonstrated that the combination of both features could make full use of their advantage. The experimental results suggested the method had good performance in differentiating migraineurs and our proposed classification framework might be applicable for other mental disorders.

Skin cancer image recognition based on similarity clustering and attention transfer.

Melanoma is a tumor caused by melanocytes with a high degree of malignancy, easy local recurrence, distant metastasis, and poor prognosis. It is also difficult to be detected by inexperienced dermatologist due to their similar appearances, such as color, shape, and contour. To develop and test a new computer-aided diagnosis scheme to detect melanoma skin cancer. In this new scheme, the unsupervised clustering based on deep metric learning is first conducted to make images with high similarity together and the corresponding model weights are utilized as teacher-model for the next stage. Second, benefit from the knowledge distillation, the attention transfer is adopted to make the classification model enable to learn the similarity features and information of categories simultaneously which improve the diagnosis accuracy than the common classification method. In validation sets, 8 categories were included, and 2443 samples were calculated. The highest accuracy of the new scheme is 0.7253, which is 5% points higher than the baseline (0.6794). Specifically, the F1-Score of three malignant lesions BCC (Basal cell carcinoma), SCC (Squamous cell carcinomas), and MEL (Melanoma) increase from 0.65 to 0.73, 0.28 to 0.37, and 0.54 to 0.58, respectively. In two test sets of HAN including 3844 samples and BCN including 6375 samples, the highest accuracies are 0.68 and 0.53 for HAM and BCN datasets, respectively, which are higher than the baseline (0.649 and 0.516). Additionally, F1 scores of BCC, SCC, MEL are 0.49, 0.2, 0.45 in HAM dataset and 0.6, 0.14, 0.55 in BCN dataset, respectively, which are also higher than F1 scores the results of baseline. This study demonstrates that the similarity clustering method enables to extract the related feature information to gather similar images together. Moreover, based on the attention transfer, the proposed classification framework can improve total accuracy and F1-score of skin lesion diagnosis.

Towards human-centric reconfigurable manufacturing systems: Literature review of reconfigurability enablers for reduced reconfiguration effort and classification frameworks

The unpredictable market scenario in the manufacturing industry demands the adoption of reconfigurability enablers. These enablers reduce the reconfiguration effort throughout the system life cycle and allow frequent reconfigurations of the manufacturing system. Despite the relevance of the subject, examples and concepts of reconfigurability enablers are fragmented in literature. Therefore, this study systematically reviews literature in order to: (i) outline the state of the art on reconfigurability enablers in automated, mixed and manual systems; and, (ii) provides classification frameworks for reconfigurability enablers for manufacturing systems, machines, robots, material handling systems, and operators. Additionally, new reconfigurability enablers related to Industry 4.0 are outlined, which connect systems and human resources with different roles and facilitate responsive adaptation of humans to changes. Directions for future research include extending the theory on reconfigurable manufacturing with fundamentals of human-centric automation and operationalising the proposed classification framework.

Unified Classification Framework for Multipolarization and Dual-Frequency SAR

For synthetic aperture radar (SAR), multi-polarization and multi-frequency modes greatly enrich the acquired earth resource information and have been widely applied in remote sensing fields. In this paper, we compare the classification capabilities of multi-polarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For PolSAR system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multi-polarization and dual-frequency SAR payloads and the design of future SAR systems.

Supervised learning software model for the diagnosis of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.

Value Creation and Value Appropriation

This article discusses the nine research studies compiled for the Journal of Creating Value (JCV) special issue, which has embarked on securing a deeper understanding of the divergent aspects of defining, measuring and maximizing value. Altogether, the nine studies collectively offer insights in this regard that are applicable to a wide range of organizations. This special issue runs the gamut of management actions in terms of value management and prepares companies for the future. The proposed classification framework in this article categorizes the nine studies based on their focus on value creation potential and value appropriation potential. Further, this article identifies and delineates future research directions that extend beyond the path forward identified by each of the studies themselves. Overall, this article is aimed to serve as a guide to understand and appreciate this special issue on a topic of vital interest.

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework.

How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model

Context:Cross-project defect prediction (CPDP) models are being developed to optimise the testing resources. Objectives:Proposing an ensemble classification framework for CPDP as many existing models are lacking with better performances and analysing the main objectives of CPDP from the outcomes of the proposed classification framework. Method:For the classification task, we propose a bootstrap aggregation based hybrid-inducer ensemble learning (HIEL) technique that uses probabilistic weighted majority voting (PWMV) strategy. To know the impact of HIEL on the software project, we propose three project-specific performance measures such as percent of perfect cleans (PPC), percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the predictions to calculate the amount of saved cost, remaining service time, and percent of the failures in the target project. Results:On many target projects from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure. In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the HIEL model. Conclusion:For better predictions, we recommend ensemble learning approaches for the CPDP models. And, to estimate the benefits from the SDP models, we recommend the above project-specific performance measures.

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation

Background and objectiveTiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal disease. Pathological technology is the gold standard in the diagnosis of spike-like and other MNs, and automatic classification of spike-like projection is a crucial step in assisting pathologists in their diagnosis. However, owing to hard-to-label spile-like projections and the scarcity of patient data, classification of glomeruli with spike-like projections based on supervised learning methods is a challenging task. MethodTo overcome the aforementioned problems, the idea of integrating weakly-supervised learning and data augmentation methods is utilized in designing the classification framework. Specifically, a multiple instance learning with instance-level data augmentation (IDA-MIL) method for the classification of glomeruli with spike-like projections is established in this paper. The proposed classification framework first trains the MIL model on a dataset with image-level labels, and the well-trained MIL model is used to extract instances that include spike-like projections in the whole glomerular image. Then, rather than using an image-level generative adversarial network (ImgGAN), an instance-level generative adversarial network (InsGAN) based on the StyleGAN2-ADA model is trained on the spike-like instances obtained by the MIL model and synthesizes new spike-like projection instances. Finally, the synthesized spike-like instances are extended to the training dataset to further improve the classification performance of MIL. ResultsThe designed IDA-MIL model is verified and evaluated from two aspects based on the in-house dataset. On the one hand, the performance comparisons between InsGAN and ImgGAN on five metrics, which involve FID, KID, Precision, Recall and IS, show that InsGAN obtains a better score and can synthesize effective spike-like projections. However, the proposed IDA-MIL model achieves the best classification performance with an accuracy of 0.9405. Then, to make nephrologists believe the inference result of the proposed model, we use heatmap technology to visualize the basis of the model inferences and show the top 4 probability spike-like instances per glomerulus. Furthermore, we analyze the correlation between the disease and the proportion of spike-like instances in bags from historical cases. ConclusionCompared with the ImgGAN, the InsGAN can synthesize natural and varied spike-like projections, which results in the classification performance of the MIL model achieving great improvement by adding synthesized instance samples into the training dataset. The heatmap of spike-like inferences and the proportion of spike-like instances can help nephrologists to make a preliminary reliable diagnosis in clinical practice. This work provides a valuable reference for medical image classification with limited data and small-scale lesions based on deep learning.

Involuntary resettlement outcomes following the regeneration of informal communities: The case of Baishizhou urbanizing village in Shenzhen, China

AbstractIn recent decades developing nations have witnessed rapid urbanization with a massive inflow of rural migrants into their cities, creating large areas of informal settlements. However, city administrators have begun the regeneration of informal settlements resulting in gentrification and the involuntary resettlement of the informal settlement residents. Although a great deal of research has been conducted on regeneration projects, little is known about the resettlement outcomes of those removed. Therefore, it is essential to explore the spatial effect of urban regeneration and the relocation pathways of the forcibly removed tenants. This study examined the heterogeneity of residential relocation brought about by urban regeneration. With a support vector machine model applied under the proposed classification framework, six relocation patterns were identified in Baishizhou urban regeneration area, in Shenzhen, China. By analysing the variations in changes in housing security, neighbourhood and accessibility, the heterogeneity of relocation pathways revealed the relocation preference of different groups and the vulnerability of the residents. While the urban landscape's aesthetic quality may benefit from regeneration, the quality of life of many of those removed was shown to decrease. The results call for more social inclusion and sustainable urban regeneration, with efficient and targeted policy needing to be designed to protect the housing wellbeing of this vulnerable group.

Human state anxiety classification framework using EEG signals in response to exposure therapy.

Human anxiety is a grave mental health concern that needs to be addressed in the appropriate manner in order to develop a healthy society. In this study, an objective human anxiety assessment framework is developed by using physiological signals of electroencephalography (EEG) and recorded in response to exposure therapy. The EEG signals of twenty-three subjects from an existing database called “A Database for Anxious States which is based on a Psychological Stimulation (DASPS)” are used for anxiety quantification into two and four levels. The EEG signals are pre-processed using appropriate noise filtering techniques to remove unwanted ocular and muscular artifacts. Channel selection is performed to select the significantly different electrodes using statistical analysis techniques for binary and four-level classification of human anxiety, respectively. Features are extracted from the data of selected EEG channels in the frequency domain. Frequency band selection is applied to select the appropriate combination of EEG frequency bands, which in this study are theta and beta bands. Feature selection is applied to the features of the selected EEG frequency bands. Finally, the selected subset of features from the appropriate frequency bands of the statistically significant EEG channels were classified using multiple machine learning algorithms. An accuracy of 94.90% and 92.74% is attained for two and four-level anxiety classification using a random forest classifier with 9 and 10 features, respectively. The proposed state anxiety classification framework outperforms the existing anxiety detection framework in terms of accuracy with a smaller number of features which reduces the computational complexity of the algorithm.