Early Classification Research Articles

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.

Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine.

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection. Most currently used iris segmentation techniques are based on edge data and noncellular images. The size of the pigment patches on the surface of the iris increases with eye syndrome. In addition, iris images taken in uncooperative settings frequently have negative noise, making it difficult to segment them precisely. The traditional diagnosis processes are costly and time consuming since they require highly qualified personnel and have strict environments. This paper presents an explainable deep learning model integrated with a multiclass support vector machine to analyze iris cellular images for early pigment spot segmentation and classification. Three benchmark datasets MILE, UPOL, and Eyes SUB were used in the experiments to test the proposed methodology. The experimental results are compared on standard metrics, demonstrating that the proposed model outperformed the methods reported in the literature regarding classification errors. Additionally, it is observed that the proposed parameters are highly effective in locating the micro pigment spots on the iris surfaces.

Coconut Plant Disease Identified and Management for Agriculture Crops using Machine Learning

This research paper introduces an innovative approach to improve the quality and sustainability of coconut farming and exports in Sri Lanka. It employs advanced image processing techniques to detect, classify, and grade pests and diseases early in coconut palms. This allows for swift interventions and reduces the need for harsh chemical treatments, promoting eco-friendly farming practices. Furthermore, the study goes beyond pest control to evaluate optimal conditions for coconut growth, considering factors like soil quality, water availability, and climate. It empowers farmers with insights to maximize coconut palm yield. Additionally, the system incorporates a growth prediction component using historical data and machine learning, enabling farmers to plan and allocate resources effectively. By combining early pest detection, pest management, growth classification, and predictive analysis, this research offers a comprehensive strategy to enhance Sri Lanka's coconut quality for export. This approach not only improves product quality but also safeguards the industry's sustainability by reducing economic losses and ecological impact. Leveraging cutting-edge tools like image processing and machine learning, this research aims to boost efficiency, economic viability, and international competitiveness in Sri Lanka's coconut farming sector.

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review.

The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy.

Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays

The human respiratory system might be seriously affected by COVID-19 infection. Therefore, early classification of it is a crucial task. Quantum machine learning and quantum neural network models can play an effective role in multiclass classification problems. Compared to standard deep and machine learning classifiers, the quantum variational classifier may lead to less memory usage, accuracy, and portability for respiratory disease detection. This article proposes a hybrid respiratory lung disease detection framework based on classical CNN and Quantum classifiers. It combines a classical deep feature extraction model with quantum classifiers. A new custom convolutional neural network (CCNN) deep learning model is proposed to perform feature extraction, and the Multi-Multi-Single (MMS) & Multi-Single-Multi-Single (MSMS) are proposed as quantum machine learning algorithms. These two quantum classifiers are built via a quantum variational circuit with encoding, entanglement, and measurement properties. The tests were carried out on the COVID-19 Radiography Dataset (CRD), which contains 15,153 chest X-ray images of COVID-19, Viral, and Normal. The experimental results revealed that the proposed model had the highest training and testing accuracy of 98.9% and 98.1%, on the CRD dataset, with a computation cost of 0.07 and 0.08 respectively. This hybrid model performs better than the other standard deep learning models. Additionally, we validated our MMS and MSMS quantum classifiers by deploying them on the IBM Q-QASM real-time quantum computer.

Classification of Pneumonia and Covid-19 using Convolutional Neural Network

Purpose: The early and exact classification and identification is necessary for proper treatment which needs excessive time and effort of professionals. This examination is meant to foster a task to recognize Pneumonia and Coronavirus utilizing the idea of the Convolutional Neural Network (CNN) for picture grouping and is centered on building the profound learning model that aids in the characterization utilizing chest X-beam pictures in one of the quick and financially savvy ways. Design/Methodology/Approach: This study uses a wide dataset comprising of chest X-beam pictures accumulated from the Mendeley dataset. Include extraction strategies like picture pre-handling and data augmentation are applied to improve the arrangement execution. The framework utilizes the ResNet-18, which is a sort of CNN model for order. The examination includes assessing the exactness, accuracy, review, F1 score, and area under the receiver working trademark bend (AUC-ROC) for every classification model. Findings/Result: The dataset is separated into preparing and testing subsets to ensure unbiased performance evaluation. For the development and deployment of an accurate and reliable system, factors like data quality, model interpretability, and ethical considerations are considered. We successfully used the pre-trained ResNet-18 CNN model with chest X-ray image data that helped to build a robust classification system with a learning rate of 0.0001 and epoch size 10 having approx. 98.12% train accuracy and 97.70% test accuracy. Since the start of the project, we researched several methodologies to build the system. The other models (e.g., ResNet-50) were too big algorithms for our problem which created a problem of overfitting. Hence performance was not very accurate. So, we planned to go with the ResNet-18 model. As per our plan, we developed a system that operates as expected. Originality/Value: It helps medical professionals in diagnosing and managing these diseases. Paper Type: Research paper

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50.

The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques. The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients. The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization. The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Toward transparent taxonomy: an interactive web-tool for evaluating competing taxonomic arrangements.

Informative and consistent taxonomy above the species level is essential to communication about evolution, biodiversity and conservation, and yet the practice of taxonomy is considered opaque and subjective by non-taxonomist scientists and the public alike. While various proposals have tried to make the basis for the ranking and inclusiveness of taxa more transparent and objective, widespread adoption of these ideas has lagged. Here, we present TaxonomR, an interactive online decision-support tool to evaluate alternative taxonomic classifications. This tool implements an approach that quantifies the criteria commonly used in taxonomic treatments and allows the user to interactively manipulate weightings for different criteria to compare scores for taxonomic groupings under those weights. We use the butterfly taxon Argynnis to demonstrate how different weightings applied to common taxonomic criteria result in fundamentally different genus-level classifications that are predominantly used in different continents and geographic regions. These differences are objectively compared and quantified using TaxonomR to evaluate the kinds of criteria that have been emphasized in earlier classifications, and the nature of the support for current alternative taxonomic arrangements. The main role of TaxonomR is to make taxonomic decisions transparent via an explicit prioritization scheme. TaxonomR is not a prescriptive application. Rather, it aims to be a tool for facilitating our understanding of alternative taxonomic classifications that can, in turn, potentially support global harmony in biodiversity assessments through evidence-based discussion and community-wide resolution of historically entrenched taxonomic tensions.

Recurrence and Complication Rates of Surgical Treatment for Blount's Disease in Children: A Systematic Review and Meta-Analysis.

Blount's disease is a growth disorder of the proximal tibia that causes progressive genu varum in children. Surgical treatment is recommended if the deformity worsens, but which intervention is best remains controversial. This study aims to identify factors influencing outcomes and determine the most effective surgical approach. A systematic review was conducted of studies published before January 2022. In total, 63 retrospective studies with CEBM IIIb/IV levels were included (1672 knees in 1234 patients). The most commonly reported treatment was acute correction via osteotomy (47%), followed by hemiepiphysiodesis (22%) and gradual correction (18%). Combined procedures were reported in 13% of cases. The overall recurrence rate was 18%, with a significant difference when comparing the recurrence rates after gradual correction with those after hemiepiphysiodesis (7% and 29%, respectively). Major complications beyond recurrence were observed in 5% of cases. A meta-analysis of the available raw data showed a significantly increased recurrence rate (39%) among treated children who were between 4.5 and 11.25 years of age and were followed for a minimum follow-up of 2.5 years. Overall, poor evidence with which to establish an optimal treatment for Blount's disease was found. This study remarked on the need for early diagnosis, classification, and treatment of infantile tibia vara, since a significant rate of recurrence was found in neglected cases.

Validity and reliability of the italian-Neonatal skin risk assessment scale (i-NSRAS)

BackgroundThe skin of infants has important anatomical and physiological differences from that of children and adults. Because of skin immaturity, reduced mobility, neurological changes, and the need for medical devices, infants are at high risk for pressure injuries. Specific, validated tools are needed to assess the risk of pressure injuries in this population. ObjectivesTo assess the psychometric properties of the Italian version of Neonatal Skin Risk Assessment Scale (construct validity, internal consistency and reliability). MethodA cross-sectional descriptive study was conducted in the neonatal units of the two Italian hospitals. 200 infants were examined 3 times by 54 nurses to assess the risk of pressure injuries. Exploratory and confirmatory factor analysis were performed jointly to assess construct validity. Internal consistency was analyzed using McDonalds omega coefficient and Cronbach's alpha, while intra and interobserver agreement using Intraclass Correlation Coefficients (ICC). ResultsExploratory factor analysis confirmed a 2-factor model; the factor “duration and intensity of pressure” explained four subscales (mental state, mobility, activity and nutrition), while “skin immaturity” explained the subscales (general physical condition and skin moisture). Confirmatory factor analysis results indicated good model fits (X2/df = 0.84, p = 0.002, RMSEA = 0, NNFI = 1.01, NFI = 0.98 y CFI = 1 (M1 with MI). The italian-Neonatal Skin Risk Assessment Scale showed a good internal consistency, McDonalds omega coefficient and Cronbach's alpha both 0.86. An excellent intra and inter observer reliability was also observed, ICC 0.99 and 0.98 respectively. ConclusionThe psychometric characteristics indicate that the italian-Neonatal Skin Risk Assessment Scale is useful, valid and reliable for measuring risk of pressure injuries in the neonatal population.Implications for Clinical Practice.This is the first valid and reliable newborn-specific scale that assesses the presence of medical devices. Its use could enable efficient management of preventive resources, early classification of newborns at risk and assignment of preventive interventions, facilitate the development of risk assessment protocols.

Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

Respiratory sound-base disease classification and characterization with deep/machine learning techniques

Respiratory diseases (RDs) are a leading cause of death globally, with over 490,000 deaths in the EU and the US alone in 2017. Early detection of these diseases is crucial for improving treatment options and prolonging survival. However, diagnosing respiratory diseases can be challenging due to various factors such as physician shortages, financial constraints, and inexperienced doctors. To address these challenges, this paper presents a novel two-stage approach for the early detection and accurate classification of respiratory diseases using sound processing, machine learning, and deep learning techniques. The proposed approach utilizes a voice recorder and the internet to create a numerical feature vector using statistical properties of sound files from various datasets. The feature vector goes through a 2-stage feature elimination process for deep learning models and a 3-stage feature elimination process for machine learning models. The resulting feature vector is then fed into a supervised model to classify patients as healthy or suffering from a respiratory disease. A second classifier is then used to characterize the specific respiratory disease. Experiments with various machine/deep learning algorithms demonstrate that the proposed model outperforms existing methods in the literature in terms of accuracy and F1-score. The model achieves an accuracy score of 0.998 and an F1-score of 0.998 for distinguishing between healthy and respiratory disease-affected patients and an accuracy of 0.947 and an F1-score of 0.94 for categorizing ‘unhealthy’ records into COPD, asthma, and other respiratory diseases. The proposed approach has significant potential low-cost and efficient tool aiding early detection and classification. Simple SummaryEvery year, millions of people die as a result of respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. Early diagnosis is critical for reducing morbidity and mortality from respiratory diseases because the best treatment options are frequently discovered in the early stages. Even though there are numerous studies and screening programs that can aid in the early detection of these diseases, there are still significant gaps in screening tools. In this study, we investigated the prospects of a two-stage pipeline for an automated system for identifying respiratory diseases, as well as the potential applications of sound processing, machine learning, and deep learning techniques in respiratory diseases within the context of respiratory sounds. In the first stage, a feature vector was extracted directly from the respiratory sound, and it was determined whether the feature vector was related to a patient suffering from a respiratory disease using supervised machine learning techniques. In the second stage, the method can also distinguish between asthma, chronic obstructive pulmonary disease, and other respiratory diseases (Pneumonia, URTI, Bronchiectasis, Bronchiolitis, LRTI). Preliminary findings show that deep/machine learning-based methods have the potential to detect and classify respiratory illnesses in real time. The proposed model has great potential as a low-cost, easily accessible tool for screening and detecting respiratory diseases, thereby closing the gap in early diagnosis.

Impact of secondary infections on dengue presentation: A cross-sectional study in a tertiary care hospital in Uttar Pradesh, India

BackgroundRecently, a wide range variety of manifestations, including a self‑limiting to severe illness, has been increasingly reported in dengue. Few studies attract attention to severe dengue, mainly observed in secondary infection. With this background, this study aims to provide a comprehensive overview to differentiate primary from secondary dengue using serology (IgG) and the possible association of severity of illness in secondary dengue. MethodsPresent retrospective cross-sectional study was conducted at a North Indian tertiary care center from September 2021 to January 2022. Clinical data of confirmed dengue patients from the medicine department were collected and assigned as primary and secondary dengue. ResultsOf the 220 dengue patients, 22 (10 %) had secondary dengue infection. Hemorrhagic manifestations were reported in 58/220 (26.4 %) cases while 7/22 (31.8 %) in secondary dengue. Prevalent hemorrhagic manifestations in secondary dengue include purpura (27.3 %), vaginal bleeding (4.5 %), melaena (9.1 %), and epistaxis (4.5 %). In addition, 42 (19.1 %) patients had pancytopenia, and 8 (36.6 %) cases were of secondary dengue. Hepatic dysfunction was noted in 164 (74.5 %) cases. Notably, all secondary dengue cases (22;100 %) had hepatic dysfunction and severe in 9 (40.9 %) cases. In addition, in secondary dengue patients, evidence of plasma leakages such as hypoproteinemia 7 (31.8 %) and ascites (35 %) were statistically more frequent. Overall, two deaths (0.9 %) were reported, and were one in each group. ConclusionsMany parameters, including hemorrhagic manifestation (melaena), hematological characteristic (pancytopenia), evidence of plasma leakage (hypoproteinemia and ascites), gastrointestinal (GB wall thickening and hepatic dysfunction) and reduction in mean hemoglobin and platelet count were found to be statistically significant in secondary dengue infection. Additionally, early classification of secondary dengue may help to anticipate its severity and allow for early strategic intervention/management to lower morbidity and mortality.

Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool

Cardiac Arrhythmia (CA) is a disorder of heartbeat or rhythm, that happens when the electrical signals that synchronize the heartbeats do not function properly. The Electro Cardio Gram (ECG) is the electrical realization of the expanding and contracting action of the heart and can be registered easily with the electrodes placed near the chest. Hence, due to the complexity of analyzing the huge number of signals in ECG records, it has become one of the major challenges to cardiologists to make early and accurate diagnoses and prognoses. Therefore, there is an essential need for accurate automatic arrhythmia classification. According to the records of the World Health Organization (WHO), 4.5 million CA patients are reporting alone in the United States. Therefore, it is stated as one of the most common reasons for death worldwide and it is very essential to the early diagnosis and prevention of CA. Hence, this research article mainly focuses to analyse the various methods used for the classification, early diagnosis, and prevention of CA. This research presents the overview of a few research articles suggesting different methods based on various fields like IOT, Machine Learning (ML) approaches, Deep Learning (DL) approaches, and so on for the automatic detection of Cardiac Arrhythmia. The literature work mainly focuses on various early Detection, prediction, and classification techniques for CA. The research gaps were also analyzed from these papers and elaborated for further research work which can be helpful for society.

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

Prevalence and risk factors associated with dysglycemia among overweight and obese Palestinian adolescents in the Hebron governorate

Background: The prevalence of dysglycemia among adolescents and younger children has been rising, yet health professionals are still unaware of the significance of this problem. According to the Palestinian Ministry of Health (MOH) records, most diabetic children under the age of 20 in Palestine are classified as type 1; nonetheless, very limited data are available for policymakers to frame cost-effective screening programs. This study aims to determine the prevalence of dysglycemia in a sample of obese and overweight Palestinian adolescents, identify risk factors associated with dysglycemia, and examine risk factors variance by gender. Methods: A cross-sectional sample of observed obese and overweight adolescents was selected from public schools in the Hebron governorate. Informed consent, physical examination, anthropometric, and laboratory tests (Random Blood Glucose Level (BGL) and fasting BGL ) were performed on a sample of 511 students (44.6% boys and 55.4% girls) aged 13–18-years (13-15 years =46.2% and 16-18 years =53.8%). Results: The prevalence of confirmed overweight and obese cases was 73.2%, and dysglycemia prevalence among the confirmed cases was 3.7% (5.3% boys and 2.5% girls). The BMI classifications of the dysglycemic children indicated that 42.1% were overweight and 31.1% were obese. Furthermore, 64.6% reported high systolic and/or diastolic blood pressure readings. Conclusions: The results of this study provide valuable information about the rising problem of dysglycemia among Palestinian adolescents and underlines the need for rapid screening programs and protocols for early detection and classification of the disease, leading to initiation of early prevention and treatment plans.

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer.

The early detection and classification of lung cancer is crucial for improving a patient's outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.

Resolving Misconception Challenges in the Teaching and Learning of Computer Science Amongst First-Year Undergraduate Students

Misconceptions in the field of computer science can lead to confusion and loss of confidence for students. These misconceptions are based on students' concepts that are not scientifically accurate interpretations. One of the major reasons why students find certain computing and scientific concepts so tough to acquire is because they suffer an inequality or gap between their early classification of a concept and the new learning framework. Resolving misconception challenges in the teaching and learning of sciences is not new because according to the past studies, it is believed to manifest throughout the life of individuals where students are greatly involved when it comes to teaching and learning sciences. The study aims to resolve and correct students’ misconceptions using a practical approach and its impact on students’ gender differences. To address these misconceptions, an experimental design study was conducted in order to examine the effects of practical approach correcting misconceptions among students in computing. The study compared students' scores on Pre-Test and Post-Test, comparing their perceptions of computing and the effectiveness of practical methods to address them. The population of the study consisted of all undergraduate students in higher education institutions in Lagos State. Through the use of random sampling method, 75 undergraduate students from Lagos State University of Education were selected. The instrument was subjected to reliability index after it was validated by experts in computing. An index of .887 results showed significant highly reliable instrument. The results of findings established significant differences between the pre-and post-test scores, but only a slight difference between male and female scores observed. The study recommends that students should be given the opportunity to investigate and test their own findings of computer science components and software functions in order to build a deeper understanding without preconceptions from their prior experiences.

In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees

The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis–NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1–5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800–1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.

“Characteristic features of shale barrier materials”

Deep wells in soft formations are completed with a steel tube (“casing”) that isolates the well from the surrounding rock. The annulus between the casing and the rock is usually left open in long sections of the well, thus creating a potential pathway for leakage which must somehow be sealed by a barrier. In many wells, it has been observed that shale formations creep in and close the annulus all by themselves, thus creating shale barriers. This may represent huge cost savings for the operator. However, this does not happen in all shale formations. It is therefore of great interest for the operator to identify at an early stage whether a given shale is a potential shale barrier material.The objective of this study was to identify some characteristic features of shale barrier materials, that may allow for early identification and classification of shales in terms of their potential as shale barrier material. In this study, the performance of shale barriers created in laboratory tests have been compiled with several characteristic properties for a set of shale materials. As a first step in this process, a quantitative classification of the shales' willingness to form barriers was established, based on their resistance against annulus closure, and their resistance against forming a sealing barrier. The definitions of these parameters reflect that formation of shale barriers depends on several factors in addition to rock properties.Porosity emerges as a key parameter from this study. This result is supported by theoretical considerations. High porosity implies low shear stiffness and strength which is a requirement for a shale barrier material. Low porosity on the other hand is often associated with over-consolidation and brittle behaviour which is unfavourable for the process of forming a sealing barrier by deformation of the rock. An interesting implication of this result is that several other parameters that are often available from well logs, such as density, and P- and S-wave velocities, may be used as indicators for shale barrier potential, since they are all closely related to porosity.Contrary to expectations, the test results revealed only weak connection between shale barrier quality and mineralogy related parameters such as total clay content and quartz content. A possible explanation may be the limited selection of materials in our study, which contained only shales with clay content above 45% and quartz content below 30%. This result may indicate that clay content is less important if it is higher than a given threshold, and that quartz content is less important if it is lower than a given threshold.