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We focus on an estimation method based on deep learning in terms of fault correction time for the operation reliability assessment of open-source software (OSS) under the environment of an edge computing service. Then, we discuss fault severity levels in order to consider the difficulty of fault correction. We use a deep feedforward neural network in order to estimate fault correction times. In particular, we consider the characteristics of fault trends by using three-dimensional graphs. Therefore, we can increase the recognizability of the proposed method based on deep learning for large-scale fault data from the standpoint of fault severity levels under edge OSS operation.

Retinal fundus image analysis (RFIA) for diabetic retinopathy (DR) screening can be used to reduce the risk of blindness among diabetic patients. The RFIA screening programs help the ophthalmologists to cope with this paramount visual impairment problem. In this article, an automatic recognition of the DR stage is proposed based on a new multi-layer architecture of active deep learning (ADL). To develop the ADL system, we used the convolutional neural networks (CNN) model to automatically extract features compare to handcrafted-based features. However, the training of CNN procedure requires an immense size of labeled data that makes it almost difficult in the classification phase. As a result, a label-efficient CNN architecture is presented known as ADL-CNN by using one of the active learning methods known as an expected gradient length (EGL). This ADL-CNN model can be seen as a two-stage process. At first, the proposed ADL-CNN system selects both the most informative patches and images by using some ground truth labels of training samples to learn the simple to complex retinal features. Next, it provides useful masks for prognostication to assist clinical specialists for the important eye sample annotation and segment regions-of-interest within the retinograph image to grade five severity-levels of diabetic retinopathy. To test and evaluate the performance of ADL-CNN model, the EyePACS benchmark is utilized and compared with state-of-the-art methods. The statistical metrics are used such as sensitivity (SE), specificity (SP), F-measure and classification accuracy (ACC) to measure the effectiveness of ADL-CNN system. On 54,000 retinograph images, the ADL-CNN model achieved an average SE of 92.20%, SP of 95.10%, F-measure of 93% and ACC of 98%. Hence, the new ADL-CNN architecture is outperformed for detecting DR-related lesions and recognizing the five levels of severity of DR on a wide range of fundus images.

Background Although the use of patient-reported outcome measures (PROs) has increased markedly, clinical interpretation of scores remains lacking. We developed a method to identify clinical severity thresholds for pain, fatigue, depression, and anxiety in people with cancer. Methods Using available Patient-Reported Outcomes Measurement Information System (PROMIS) item bank response data collected on 840 cancer patients, symptom vignettes across a range of symptom severity were developed and placed on index cards. Cards represented symptom severity at five-point intervals differences on the T score metric [mean = 50; standard deviation (SD) = 10]. Symptom vignettes for each symptom were anchored on these standardized scores at 0.5 SD increments across the full range of severity. Clinical experts, blind to the PROMIS score associated with each vignette, rank-ordered the vignettes by severity, then arrived at consensus regarding which two vignettes were at the upper and lower boundaries of normal and mildly symptomatic for each symptom. The procedure was repeated to identify cut scores separating mildly from moderately symptomatic, and moderately from severely symptomatic scores. Clinician severity rankings were then compared to the T scores upon which the vignettes were based. Results For each of the targeted PROs, the severity rankings reached by clinician consensus perfectly matched the numerical rankings of their associated T scores. Across all symptoms, the thresholds (cut scores) identified to differentiate normal from mildly symptomatic were near a T score of 50. Cut scores differentiating mildly from moderately symptomatic were at or near 60, and those separating moderately from severely symptomatic were at or near 70. Conclusions The study results provide empirically generated PROMIS T score thresholds that differentiate levels of symptom severity for pain interference, fatigue, anxiety, and depression. The convergence of clinical judgment with self-reported patient severity scores supports the validity of this methodology to derive clinically relevant symptom severity levels for PROMIS symptom measures in other settings.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restricted, repetitive patterns of behavior and interests (RRBIs). With the latest update to the Diagnostic and Statistical Manual of Mental Disorders, a severity level rating is assigned to the two core features of ASD (American Psychiatric Association in Diagnostic and statistical manual of mental disorders 5 American Psychiatric Association Washington, D.C., 2013). Previous studies have identified factors associated with RRBI severity; however, the relationship among RRBIs, adaptive functioning, and diagnostic severity level remains unclear. The present study investigated whether adaptive functioning and parent-reported ASD symptoms predict RRBI severity in young children with ASD. Additionally, a fine-grained analysis was conducted to examine the factors associated with diagnostic severity level ratings. Several significant associations were found. Study findings and implications for assessment and treatment of RRBIs are discussed.

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

Flooding has become a persistent and intensifying threat in Bangladesh, causing widespread damage to infrastructure and affecting large portions of the population each year. The increasing frequency and intensity of these events underscore the need for advanced methods to assess and predict flood severity effectively. This study aims to develop a robust machine learning model for accurately classifying flood severity in both multi-class and binary formats, specifically addressing imbalanced data challenges by utilizing historical weather and water level data. A systematic approach was employed, beginning with extensive data preprocessing to ensure quality and consistency. The dataset was then prepared in multiple formats (multi-class and binary) to capture different aspects of flood severity classification. To tackle class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to each format, enhancing model reliability. Multiple classification models were evaluated, including individual classifiers and ensemble techniques, with the stacking ensemble emerging as the top performer. This model achieved accuracies of 98.62% for multi-class and 98.87% for binary classification before SMOTE, improving to 99.89% and 99.14%, respectively, after applying SMOTE. These findings demonstrate the model's potential as an effective tool for flood severity prediction, with significant implications for enhanced risk management and disaster response strategies. Future research will focus on deploying this model for real-time flood alerts, aiming to bolster resilience and preparedness in flood-prone communities.

Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.

Aims Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care. Methods We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model’s ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels. Results The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model’s color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels. Conclusions The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.