Classification Framework Research Articles

An intelligent deep residual learning framework for tomato plant leaf disease classification

Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.

Deep learning ensemble framework for multiclass diabetic retinopathy classification

Deep learning ensemble framework for multiclass diabetic retinopathy classification

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches

LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches

Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring.

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office. A team of informatics safety analysts retrospectively classified 1year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward. Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify. The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.

Sustainable Affordable Housing: State-of-the-Art and Future Perspectives

Nowadays, there are many studies with a significant focus on affordable housing. The relevance of this theme, which is usually the central object of public housing policies, requires an updated review of the problems and challenges to be overcome, especially in terms of sustainability, in favor of an improvement in the quality of products delivered to beneficiaries. This research concentrates on applying the sustainability concept to affordable housing, emphasizing technical, social, and governance aspects. A novel classification framework is introduced, encompassing these aspects in the context of sustainability integrated with affordable housing. A systematic literature review is conducted and more than 100 articles are examined based on bibliometric and bibliographic analyses to highlight the main dimensions and topics involved in the housing public policy sphere. The study has been elaborated based on collecting relevant materials, building a descriptive analysis of the literature examined, highlighting the classification structure that categorizes the studies examined, and evaluating the material identified based on the classification structure. The outcomes aim to spotlight the diverse dimensions of sustainable affordable housing and associated research themes. Furthermore, the research outlines deficiencies in current approaches and outlines a future research agenda for implementing sustainability in affordable housing. It establishes a strong connection between technical, social, and governance aspects of affordable housing and sustainability, considering anticipated approaches, techniques, and challenges.

Integrating Convolutional Neural Network and Weighted Moving Average for Enhanced Human Fall Detection Performance

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

Syndromic MEN1 Parathyroid Adenomas Consist of Both Subclonal Nodules and Clonally Independent Tumors

Abstract Background Primary hyperparathyroidism with parathyroid tumors is a characteristic presentation in Multiple Endocrine Neoplasia Type 1 (MEN1), historically referred to as "primary hyperplasia." The question of whether these tumors signify a multi-glandular clonal disorder or hyperplasia remains inconclusive. Loss of Menin protein expression serves as a reliable surrogate marker for biallelic inactivation and indicates a mutation in the MEN1 gene. The cyclin-dependent kinase inhibitor 1B (CDKN1B) gene, associated with MEN4, encodes the p27 protein, whose expression is inadequately explored in the context of syndromic MEN1. Aims The aim was to explore the molecular basis of syndromic MEN1 parathyroid adenomas, identifying hyperplasia, multiple independent clones, and the extent of Menin loss. Additionally, p27 expression was assessed to evaluate its potential in indicating a germline mutation, especially in distinguishing MEN4 as a clinical alternative to MEN1. Methods In this investigation, we examined histomorphology and protein expression of Menin and p27 in parathyroid adenomas from 25 patients in two independent, well-characterized MEN1 cohorts. Loss of heterozygosity (LOH) patterns were evaluated using fluorescence in situ hybridization (FISH) in one MEN1-associated parathyroid adenoma. Furthermore, next-generation sequencing (NGS) was conducted on eleven nodules from four MEN1 patients. Results Morphologically, the majority of MEN1 adenomas displayed multiple distinct nodules, characterized by predominantly lost Menin expression and reduced p27 protein expression. FISH analysis indicated that most nodules exhibited MEN1 loss, with or without centromere 11 loss. NGS demonstrated both subclonal evolution and the existence of clonally unrelated tumors. Conclusion Syndromic MEN1 parathyroid adenomas, therefore, consist of multiple clones with subclones, aligning with the current framework of the novel WHO classification of parathyroid tumors (2022). The observed loss of p27 expression in a significant fraction of MEN1 parathyroids emphasizes the need for caution when inferring MEN4 solely based on p27 expression.

Value Sets of Business-owning Families: An Indigenous African Perspective

ABSTRACT While the literature highlights the important role played by family values in family businesses, knowledge on the nature of these values is limited or close to non-existent from certain perspectives, notably an indigenous African family business perspective. Therefore, utilizing seven indigenous Black South African family businesses as case studies, our study seeks to identify the family value sets in the business-owning families using a qualitative design. The findings show that these families uphold and teach the next-generation (NextGen) four family value sets, namely moral and ethical, economic, spiritual and religious, and cultural values. The NextGen is expected to use these value sets as they navigate their lives in and outside the family. Our study contributes to theory by identifying, developing and providing detailed insights into the nature of family values from an indigenous Black South African business-owning family perspective, and our value classification framework potentially provides other researchers with a theoretical foundation to conduct further research. Business-owning families should uphold value sets that accommodate the needs of individual family members, entire business-owning families, and their respective cultures. We also believe that non-indigenous businesses need to understand the family value sets upheld by indigenous Black African businesses to engage with them appropriately and improve business relationships.

Autism spectrum disorder identification using multi‐model deep ensemble classifier with transfer learning

AbstractIdentifying autism spectrum disorder (ASD) symptoms accurately is a challenging task. The traditional subjective diagnostic process of ASD relies on time‐consuming behavioural and psychological observations. In this study, we introduce an ensemble learning‐based classification model using an open‐access database focusing on functional magnetic resonance imaging (fMRI). We propose a novel multi‐model ensemble classifier (MMEC) and multisite ensemble classifier (MSEC) with transfer learning (TL) for ASD classification to improve the prediction accuracy. The MMEC utilizes four base classifiers, Inception V3, ResNet50, MobileNet, and DenseNet to boost the performance of the individual convolutional neural network (CNN) models. The MSEC combined the base classifiers trained from different data sites. We evaluate the two models with ensemble averaging, weighted averaging, and stacking methods. The proposed MMEC with stacking shows the state of art performance compared to MSEC, improving the prediction accuracy by 3.25%. The obtained results have shown an accuracy of 97.82%, 97.82%, and 97.78% for ensemble averaging, weighted averaging, and stacking methods, respectively, on multi‐site datasets. The ensemble classifier MMEC performed better than a single classifier on the multi‐site dataset. The proposed MMEC opens a new paradigm to design a universal ASD classification framework.

Narcissistic personality disorder in the ICD-11: Severity and trait profiles of grandiosity and vulnerability.

Modern diagnostic and classification frameworks such as the ICD-11 and DSM-5-AMPD have adopted a dimensional approach to diagnosing personality disorder using a dual "severity" and "trait" model. As narcissistic personality has historically struggled to be adequately captured in dominant diagnostic systems, this study investigated the utility of the new ICD-11 framework in capturing diverse narcissistic expressions. Participants were mental health clinicians (N = 180, 67% female, age = 38.9), who completed ratings of ICD-11 personality severity, trait domains and a clinical reflection for two hypothetical case vignettes reflecting either prototypical "grandiose" or "vulnerable" narcissism. The majority of clinicians (82%) endorsed a diagnosis of personality disorder for both grandiose and vulnerable vignettes. Discriminant elements of personality impairment included rigid, unrealistically positive self-view, low empathy and high conflict with others for grandiosity, and incoherent identity, low self-esteem and hypervigilant, avoidant relations with others for vulnerability. Regarding trait profile, grandiose narcissism was predominately dissocial whereas vulnerable narcissism was primarily associated with negative affectivity and detachment. Qualitative responses highlight distinct clinical themes for each presentation. These findings suggest that clinicians using the ICD-11 framework are able to identify common core elements of personality dysfunction in grandiose and vulnerable narcissism while also recognizing their distinctive differences.

NMF-guided feature selection and genetic algorithm-driven framework for tumor mutational burden classification in bladder cancer using multi-omics data

NMF-guided feature selection and genetic algorithm-driven framework for tumor mutational burden classification in bladder cancer using multi-omics data

A new ViT-Based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains

Wafer map defect classification plays a crucial role in sustaining the semiconductor supply chain during industrial disruptions by ensuring continuity, resilience, and efficiency while aligning with sustainability principles. Up to 30 percent of production costs is lost to chip testing and yield losses for semiconductor manufacturing supply chain. In the semiconductor practice, wafer map defects recognition plays a linchpin role in the front-end-of-line stage. Defect pattern recognition can directly pinpoint the assignable causes and provide the domain experts with actionable insights. However, various wafer defect types occur differently from each other, which makes the collected wafer map dataset to be highly imbalanced in defect classes. In the face of data imbalance, the classification model usually cannot provide satisfactory classification performance. In this aspect, this paper intends to investigate the wafer defect map classification problem by using Vision Transformer (ViT) as an alternative data augmentation approach. The primary purpose is to alleviate the class-imbalance issue and then the performance of a deep learning-based convolutional neural network for wafer defect classification can be effectively improved. The experimental results demonstrate that the proposed data augmentation method by using ViT proves to be a potential generative model for improving wafer map defect classification, particularly robust on the individual minority class. In a word, the proposed augmentation framework for wafer map defect classification facilitates a more targeted and efficient approach to quality control, resource utilization, and production optimization in maintaining a sustainable semiconductor supply chain, particularly in times of industrial disruption.

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning.

Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

TFSemantic: A Time–Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

HGR Correlation Pooling Fusion Framework for Recognition and Classification in Multimodal Remote Sensing Data

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.

Interpretable Machine Learning for Geochemical Anomaly Delineation in the Yuanbo Nang District, Gansu Province, China

Machine learning (ML) has shown its effectiveness in handling multi-geoinformation. Yet, the black-box nature of ML algorithms has restricted their widespread adoption in the domain of mineral prospectivity mapping (MPM). In this paper, methods for interpreting ML model predictions are introduced to aid ML-based MPM, with the goal of extracting richer insights from the ML modeling of an exploration geochemical dataset. The partial dependence plot (PDP) and accumulated local effect (ALE) plot, along with the SHAP value analysis, were utilized to demonstrate the application of random forest (RF) modeling within both regression and classification frameworks. Initially, the random forest regression (RFR) model established the relationship between the concentrations of Au and those of elements such as As, Sb, and Hg in the study area, and from this model, the most important geochemical elements and their quantitative relationships with Au were revealed by their contributions in the modeling through PDP and ALE analyses. Secondly, the RF classification modeling established the relationships of mineralization occurrences (i.e., known mineral deposits) with geochemical elements (i.e., Au, As, Sb, Hg, Cu, Pb, Zn, and Ag), as did RFR modeling. The most important geochemical elements for indicating regional Au mineralization and the trajectories of PDP and ALE reached a consensus that As and Sb contributed the most, both in the regression and classification modeling, with regard to Au mineralization. Finally, the SHAP values illustrated the behavior of the training samples (i.e., known mineral deposits) in RF modeling, and the resulting prospectivity map was evaluated using receiver operating characteristics.