Correct Class Research Articles

A Review of AI Based Classification of Medicinal Plant using VGG-19 Technique

Identifying the correct medicinal plants that goes into the preparation of a medicine is very important in the ayurvedic, folk, and herbal medicinal industry. Botanists invest a lot of time in identifying plant species by direct observation. Recognition of medicinal plants among various plant species is very difficult for ordinary people. So, in order to overcome these difficulties, we are developing an AI-based Automatic Classification system for classifying medicinal plants among the several plants. This is mostly useful for the society to identify the ayurvedic leaves, which can be used in traditional medicine. By using this system, normal people can easily recognize these medicinal plants. This study outlines a technique for classifying different medicinal plant species using color images of some medicinal plant species. With the aid of the pre-trained classifier VGG-19, the task is carried out utilizing transfer learning to increase accuracy. Image pre-processing, image augmentation, feature extraction, and recognition are the four main classification steps that are carried out as part of the overall model evaluation. By using pre-defined hidden layers like convolutional layers, max-pooling layers, and fully connected layers, the VGG-19 classifier is able to understand the features of leaves. After that, the soft-max layer is used to create a feature representation for all plant classes. In order to help estimate the correct class of an unidentified medicinal plant, the model gathers information about various medicinal plants, which contains around nineteen different classes. This system will classify the medicinal plant species with high accuracy. Identification and classification of medicinal plants are essential for better treatment.

Нейропсихологическое исследование когнитивных функций у подростков с сахарным диабетом 1 типа

Type 1 diabetes mellitus (DM1) is a complex disease, in the model of responsible attitude to the treatment of which it is important to take into account cognitive characteristics that determine the ability to take responsibility for treatment in order to achieve glycemic control. Cognitive impairments can affect the quality of patients' self-control: they can be the reason for skipping injections, incorrect calculation of bread units, the required dose of insulin etc. The purpose of this research was to establish the features of the cognitive status of adolescents with DM1 in comparison with healthy adolescents from the point of view of the neuropsychological approach being developed in Russian psychology. Methods used: a single-center cross-sectional study of 38 patients with DM1 (13 boys/25 girls) and 30 conditionally healthy children (20 boys/10 girls) aged 12 to 17 y/o was conducted. The study was conducted on the bases of the Endocrinology Department No. 1 of the Russian Children's Clinical Hospital with the Pirogov Russian National Research Medical University of the Ministry of Healthcare of Russia (Moscow, Russia) and General Education School No. 1321 “Kovcheg” (Moscow, Russia) in May-Nov. 2023. The method of medical documentation analysis coupled with the psychodiagnostics method (battery of neuropsychological tests by A.R. Luria with a quantitative assessment of the results according to J.M. Glozman (2012)) were used. The duration of the examination was 60 to 75 minutes. Results: in the group of adolescents with DM1 compared with adolescents in the control group there were deficits in the domains of thinking (p≤0.05), memory (p≤0.01), neurodynamic indicators of mental activity (p=0.001), regulation and control (p=0.026), manual praxis (p=0.030) and spatial representations (p=0.001). Conclusion: the study results showed the need for timely correctional psychological classes for children with DM1 aimed at improving their cognitive functions. In the future, such classes could lead to an improvement in the quality of implementing a responsible attitude to health, improving glycemic control and DM1 compensation in general as well as reducing the number of complications in future life.

A flavoromics approach to investigate the effect of Saskatoon berry powder on the sensory attributes, acceptability, volatile components, and electronic nose responses of a low-fat frozen yogurt.

Saskatoon berries are grown in Canada and some northwestern states in the United States, and are notable for containing abundant antioxidant polyphenols, vitamins, metal elements, and fiber. To increase consumer interest in and accessibility to Saskatoon berries, some producers have begun to develop processes for refining Saskatoon berries into a powder with an extended shelf life that can be incorporated into a variety of value-added food products. To assess the desirability of this approach, this study sought to determine how the sensory attributes, consumer acceptability, and volatile and non-volatile composition of a plain, Greek-style frozen yogurt (PY) changed when fortified with 16% Saskatoon berry powder (SBP). Greek-style frozen yogurt was chosen as the food to be fortified for this study due to its low fat and relatively high calcium and protein content as well as its popularity among consumers. Descriptive analysis of the two yogurt formulations by 11 participants determined that SBY was higher in berry aroma, berry flavor, and sweetness, and lower in cream aroma, dairy aroma, and sourness compared to PY. SBY was lower in iciness and degree of smoothness and higher in viscosity and mouth coating compared to PY. Untrained participants (n = 112), found no significant differences in color, flavor, and overall acceptability between SBY and PY. However, SBY was significantly less acceptable than PY for texture and aroma. Iciness was the most influential variable related to texture acceptability. For aroma acceptability, berry flavor (negatively related) and berry aroma (positively related) were the most influential attributes. The exposure of Saskatoon berry powder (SBP), PY, and SBY to e-nose sensors showed consistencies in replicate analysis (n = 25 measurements/sample), and cross validation of the PCA showed that the model could sort samples into the correct class with 98.7% accuracy. Key volatile organic compounds (VOCs) responsible for berry and fruity aroma in SBP were also found to be retained in the SBY. Several key phenolic compounds with therapeutic effects such as baicalein, chlorogenate, gallic acid, p-coumaric acid, and syringic acid were also identified in both SBP and SBY samples, potentially indicating that the SBY may retain some of the health benefits associated with the consumption of raw Saskatoon berries.

Non-invasive procedure implementation for enhanced smile facial muscle movement after orthognathic surgery: a case report.

This study examined the case of 27-year-old female who underwent orthognathic surgery to correct class 3 malocclusion, resulting in an unnatural smile. The research aimed to assess the efficacy of non-invasive treatment in enhancing facial muscle movement during smiling to achieve a natural smile. The patient received eight sessions of treatment using non-invasive devices, and facial assessment were conducted using three-dimensional scanner (Morpheus 3D Scanner) to evaluate facial features in both relaxed and maximum smile states pre- and post-treatment. The results demonstrated improvements in the symmetry of the lower lip height during smiling, along with increases in mouth width and volume in the upper central area. Subjectively, the patient also reported enhanced comfort while smiling. These findings suggest that non-invasive procedures can effectively improve unnatural smiles following orthognathic surgery, contributing to a more aesthetically pleasing smile presentation.

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients.

Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4% of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related to low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem. This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with Type 2 diabetes were included in the study. In the data preprocessing stage, firstly, outlier/excessive observation analyses were performed with LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first-level classifiers. Tree ensemble classifiers, namely ADA, XGBoost and SGB, were used as second-level classifiers. After the SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the SMOTE data. In terms of the testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when the SMOTE is used, can be preferred to correct class imbalance. The SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when the SMOTE was applied, the diagnostic accuracy decreased in some models but the sensitivity increased significantly. This shows the positive effects of the SMOTE in terms of better predicting the minority class.

SSAT: Active Authorization Control and User’s Fingerprint Tracking Framework for DNN IP Protection

As training a high-performance deep neural network (DNN) model requires a large amount of data, powerful computing resources and expert knowledge, protecting well-trained DNN models from intellectual property (IP) infringement has raised serious concerns in recent years. Most existing methods using DNN watermarks to verify the ownership of the models after IP infringement occurs, which is reactive in the sense that they cannot prevent unauthorized users from using the model in the first place. Different from these methods, in this article, we propose an active authorization control and user’s fingerprint tracking method for the IP protection of DNN models by utilizing sample-specific backdoor attack. The proposed method inversely and multiplely exploits sample-specific trigger as the key to implement authorization control for DNN model, in which the generated triggers are imperceptible and sample-specific for clean images. Specifically, a U-Net model is used to generate backdoor instances. Then, the target model is trained on the clean images and backdoor instances, which are inversely labeled as wrong classes and correct classes, respectively. Only authorized users can use the target model normally by pre-processing the clean images through the U-Net model. Moreover, the images processed by the U-Net model will contain unique fingerprint that can be extracted to verify and track the corresponding user’s identity. This article is the first work that utilizes the sample-specific backdoor attack to implement active authorization control and user’s fingerprint management for DNN model under black-box scenarios. Extensive experimental results on ImageNet dataset and YouTube Aligned Face dataset demonstrate that the proposed method is effective in protecting the DNN model from unauthorized usage. Specifically, the protected model has a low inference accuracy (1.00%) for unauthorized users, while maintaining a normal inference accuracy (97.67%) for authorized users. Besides, the proposed method can achieve 100% fingerprint tracking success rates on both the ImageNet and YouTube Aligned Face datasets. Moreover, it is demonstrated that the proposed method is robust against fine-tuning attack, pruning attack, pruning attack with retraining, reverse-engineering attack, adaptive attack, and JPEG compression attack. The code is available at https://github.com/nuaaaisec/SSAT .

Social and pedagogical assistance to adolescents with intellectual disabilities

Importance. One of the pressing problems of correctional psychological and pedagogical assistance to children with intellectual developmental disabilities is the possibility of early initiation of correction and the choice of effective correctional and developmental methods for the purpose of their maximum socialization. Purpose of the study: orrectional and developmental activities introduction into adaptive physical education classes to increase the social adaptation level of adolescents 14–15 years old with mild mental disability.Materials and Methods. 20 teenagers aged 14–15 years with mild mental disability, studying in a correctional school of type VIII, took part in the pedagogical experiment. Two groups are formed: a control group, studying according to a standard program for people with disabilities, and an experimental group, in which, as part of pedagogical control, 30 correctional and developmental classes, 30 minutes each, are conducted 1–2 times a week, as part of adaptive physical education classes. The tools for assessing the effectiveness of the correctional classes are: determination of the group cohesion index using K.E. Sishor’s psychometric test, determining the aggressiveness level using Bass–Darki’s questionnaire, assessing communication skills using B.A. Fedorishin’s assessment.Results and Discussion. Corrective and developmental activities carried out during adaptive physical education classes had a positive impact on the communication abilities development in adolescents aged 14–15 years with mild mental disability. After conducting a pedagogical experiment, adolescents in the experimental group significantly improved the average diagnostic test scores (p ˂ 0.05) on the following tests: K.E. Sishor’s psychometric test, B.A. Fedorishin’s communicative inclinations assessment, Bass–Darki’s questionnaire.Conclusion. The introduction of developed correctional and educational activities into the adaptive physical education classes program made it possible to increase the social adaptation level of adolescents 14–15 years old with mild mental disability.

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Face Recognition Using The Subspace and Deep Learning Algorithms For Cases of Sufficient and Insufficient Data

In face recognition, the distance criterion significantly influences the recognition rate. Misclassified test signals can be accurately reassigned to the correct class using various distance measures and the nearest neighbor algorithm. This study uniquely explores the recognition performance of DCVA, Fisherface subspace classifiers, and Convolutional Neural Network (CNN) in face recognition, an aspect not thoroughly explored in the literature. Accordingly, this study introduces a Discriminative Common Vector-based (DCVA) algorithm utilizing various distance measures for face recognition for the first time. Additionally, the Fisherface-based algorithm uses different distance measures and nearest neighbors. Experiments were conducted on three different face databases. The images were downsampled to simulate both sufficient and insufficient data conditions. Experimental results indicate that the Correlation distance measure generally outperforms the Euclidean distance for the DCVA and Fisherface-KNN algorithms under both data conditions. The Fisherface-KNN algorithm surpasses the classical Fisherface in performance for various distance measures and nearest neighbor numbers and yields better recognition rates than the DCVA algorithm in sufficient data conditions. Moreover, while DCVA and Fisherface-KNN achieved superior results for two smaller face databases, CNN demonstrated better performance for larger databases.

Comparative analysis of parental attitude and the adaptive potential of adolescents with overweight and obesity

Introduction. Childhood obesity is a pressing public health problem worldwide. According to WHO, in 2022, there were 390 million overweight children and adolescents aged 5 to 19 years in the world. Experts consider this problem not only from a somatic point of view, but also from a psychological component.The objective was to identify the features of adaptive resource potential, personal anxiety and the specifics of parental attitudes of overweight and obese adolescents.Methods and materials. A total of 120 respondents took part in the study, including 30 overweight and obese adolescents and 30 their parents. The comparison group consisted of adolescents with a normal body mass index and their parents. Psychodiagnostic survey techniques were used aimed at studying adaptive resource potential, the level of personal anxiety and the specifics of parental attitudes.Results. It was revealed that overweight and obese adolescents highly value their external adaptive potential more than their internal individual psychological adaptive potential. The level of psychological stability is most based on the external system: support from the family, friendly relationships with peers. In assessing the level of manifestation of personal anxiety in adolescents, a low level of anxiety is observed, possibly as an external manifestation of a defense mechanism. In the system of child-parent relations among overweight and obese adolescents, there is a tendency to display negative feelings, low control, and perhaps a more permissive attitude towards the process of raising and educating the child.Conclusions. For overweight and obese adolescents, correctional and developmental classes should be conducted on self-regulation of their emotional state, increasing self-esteem, accepting their individual psychological qualities, as well as psychological work with the parents of adolescents, in general, with the family system.

Resummation of combined QCD-electroweak effects in Drell Yan lepton-pair production

We consider neutral- and charged-current Drell Yan lepton-pair production at hadron colliders, and include dominant classes of electroweak and mixed QCD-electroweak corrections to all orders in perturbation theory. The accurate description of these physical effects is vital for a precise determination of fundamental Standard Model parameters, such as the W-boson mass and the electroweak mixing angle, as well as for a solid assessment of the associated theoretical uncertainties. Our state-of-the-art resummation reaches next-to-leading-logarithmic accuracy in both the electroweak and the mixed QCD-electroweak perturbative expansions, including constant terms at first order beyond Born level in both couplings, i.e. at order α and αsα. These effects are incorporated on top of QCD predictions at next-to-next-to-next-to-leading-logarithmic accuracy, which include constant terms at third order in the strong coupling. Our results retain, for the first time at this accuracy, full dependence on the kinematics of the final-state leptons, thereby enabling a realistic comparison with experimental analyses at the differential level in presence of fiducial cuts. We present a phenomenological analysis of the impact of electroweak corrections in relevant observables at the LHC. We find visible shape distortions in resummation-dominated kinematical regions with respect to pure-QCD predictions, highlighting the importance of a complete description, not limited to QCD, for precision Drell Yan physics.

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines

Alzheimer's is a very challenging brain disease to recognize, diagnose, and treat correctly when it appears in its earliest forms. The primary contribution of this research study is about machine learning models, techniques, and approaches. In contrast, Random Forest and Support Vector Machine (SVM) are particularly suitable for identifying and staging Alzheimer's disease stages using multimodal data sources. In this paper, the aim was to develop well-performing predictive models to help diagnose Alzheimer's disease at an early stage by combining neuroimaging data (MRI/PET images), imaging-based biomarkers, both structural and functional measures from MRI(P) /PET image analysis along with subject-specific demographics like age using clinical features in a probabilistic fashion obtained from the Alzheimer's Disease Neuro-Imaging Initiative (ADNI) database. The methodology focuses on data pre-processing, feature selection, and model building using supervised learning algorithms. The accuracy of the Random Forest model is 78%, having a high performance in classifying some classes while showing different marks of performances across other courses. SVM reached an accuracy of 61%, or the model's performance is good in some classes and not reliable to identify samples from the others. The findings of this study underscore the capabilities and limits of these machine learning models in identifying Alzheimer’s disease and highlight the importance of feature engineering, data pre-processing, and model tuning to increase performance and correct class unevenness and misclassification.

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.

Leveraging bone age assessment via a novel joint decomposition teacher–student learning paradigm from X-ray images

ABSTRACT Advancements in technology has accelerated the evolution of bone age assessment (BAA) methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each region of interest (ROI). In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model. Empirical results on digital hand atlas report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age.

Orthognathic surgery improves compromised natural head position and pharyngeal airway in patients with Skeletal Class II or III malocclusion.

Natural head position (NHP), pharyngeal airway and maxillofacial growth pattern are correlated. The author's previous studies proved that following surgical correction of Skeletal Class II malocclusion, the over-extended NHP returned upright, and the pharyngeal airway space (PAS) dimension expanded. The present study compares the post-operative change in NHP and PAS after orthognathic surgery in Skeletal Class II and III malocclusion patients. Patients receiving orthognathic procedures to correct Skeletal Class II or III malocclusions were reviewed in this retrospective study. Pre-operative and 6-week post-operative cone-beam computed tomography datasets were collected. Variables representing the craniofacial pattern, the NHP and the PAS were measured three-dimensionally. Post-operative variables were compared with their pre-operative counterparts using either repeat-measure 2-way analysis of variance or Wilcoxon matched-pairs signed rank test. Thirty cases of Skeletal Class II malocclusion and 13 cases of Skeletal Class III malocclusion were collected. Preoperatively, the inter-group differences were significant in craniofacial pattern (68.14 ± 3.552 degree vs. 79.63 ± 2.497 degree, p < .0001) and the NHP (68.77 ± 11.02 degree vs. 82.83 ± 7.738 degree, p = .0002) while not significant in PAS; after surgery, the intergroup differences in craniofacial pattern and the NHP between groups decreased, and the PAS increased in both groups. Orthognathic surgery may improve compromised NHP and increase PAS in Skeletal Class II and III malocclusion patients.

Three-Dimensional Changes of Condylar Position After Bimaxillary Surgery to Correct Skeletal III Malocclusion: Cone Beam Computed Tomography Voxel-Based Superimposition Analysis

BackgroundThe stability of the condylar position within the first three months after orthognathic surgery in patients with skeletal Class III malocclusion is crucial for subsequent orthodontic treatment. PurposeThe purpose was to compare condylar positional changes three months after bimaxillary surgery to correct skeletal Class III deformities, utilizing the conventional two-dimensional (2D) combined with the cone beam computed tomography (CBCT) voxel-based superimposition analysis. Study design, setting, sampleThis retrospective cohort study included skeletal Class III patients undergoing bimaxillary orthognathic surgery at Khon Kaen University, Thailand in 2020. Exclusion criteria were patients with temporomandibular disorder (TMD) symptoms, facial asymmetry prior to the surgery, or surgical complications. Exposure variableThe exposure variable was the timing, both preoperatively and 3 months postoperatively. This 3-month postoperative time point was chosen because complete healing of the bimaxillary surgery is expected by then, without any potential influence of condylar changes resulting from subsequent orthodontic treatment. Main outcome variablesThe outcome variable is condylar position measurement, joint space (mm) and axial condylar angle (degrees). CovariatesDemographics (age, sex), and surgical details (direction and amount of movement) were collected as covariates. AnalysesStatistical analysis of condylar positional changes and correlations was performed using paired t-test and linear correlation (P-value < 0.05), respectively. ResultsThe sample included 11 subjects (22 condyles), with a mean age of 24±5.24 years. Both 2D measurements and voxel-based 3D superimposition showed significant changes in condylar position three months after bimaxillary surgery: inferior (0.45±0.26 mm, P < 0.001), posterior (0.46±0.39 mm, P = 0.003), lateral (0.38±0.42 mm, P = 0.01) displacement, and inward rotation (5.21±2.54°, P < 0.001). No significant correlation was found between jaw movement distance and condylar changes. Conclusions and relevanceTo our knowledge, this is the first study to report measures of condylar changes at 3 months, when complete healing of the osteotomies would be expected, using Le Fort I osteotomy for maxillary advancement combined with bilateral sagittal split ramus osteotomy (BSSRO) for mandibular setback. These changes are small in magnitude and may be of little relevance to patient care.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Профессиональная позиция педагога-психолога в проектировании инклюзивной образовательной среды

&lt;p style="text-align: justify;"&gt;&lt;span lang="EN-US"&gt;The article discusses the problem of professional involvement of a teacher-psychologist in the inclusive educational process of a school and his role in creating an inclusive educational environment (IEE). The results of federal monitoring of the state of the inclusive educational environment in schools of the Russian Federation and indicators related to psychological and pedagogical support for students with special educational needs in educational organizations (EO) are considered, namely: 85% of OOs provide socio-psychological support to children who find themselves in difficult life situations situations, consultations with a teacher-psychologist account for 84%, and 78% of educational institutions conduct individual correctional classes, which indicates a high level of support. 76% of public organizations have Regulations on psychological services, 85.2% of public organizations have Regulations on psychological and pedagogical council; Offices of educational psychologists are provided in 71% of schools. Based on a discussion of the data, conclusions are drawn about the need for professional development of educational psychologists and the formation of their inclusive competencies and role in building an inclusive educational environment.&lt;/span&gt;&lt;/p&gt;

Comparison of discriminant analysis coupled with principal component analysis (PCA-DA) and back propagation neural network (BPNN) as classification model for decision support system in porous ceramic matrix (PCM) based burner

Two classification models discriminant analysis coupled with principal component analysis (PCA-DA) and back propagation neural network (BPNN), are for the first time applied for decision support system in porous ceramic matrix (PCM) based burner. The PCM based burner is simulated numerically, and 121 pairs of gas and solid temperature profiles are generated as input data. The operation of PCM based burner is classified into four, on the basis of important properties of PCM like extinction coefficient and convective coupling. With the help of the data, the classification models are developed. The classification models are monitored and analyzed through different plots and classification parameters like specificity, sensitivity and precision. Further, new samples are correctly allocated to their corresponding class by the classification models. The classification models are also explored and compared under noisy data (2% and 5%). The performances of both the classification models are found to be good for no noise case with all the parameters like sensitivity, specificity, and precision values greater than 0.69, for both the models. However, with 2% noise case, BPNN performs better than PCA-DA. The minimum value of parameters (sn, sp, &amp; pr) is 0.67 with BPNN and 0.50 with PCA-DA, respectively. Under 5% noise, the minimum values of the parameters dropped to 0.47 for PCA-DA and 0.50 for BPNN, respectively. With the help of plots though, the new samples are easily identified to their correct class 3.

Mechanical intelligence via fully reconfigurable elastic neuromorphic metasurfaces

The ability of mechanical systems to perform basic computations has gained traction over recent years, providing an unconventional alternative to digital computing in off grid, low power, and severe environments, which render the majority of electronic components inoperable. However, much of the work in mechanical computing has focused on logic operations via quasi-static prescribed displacements in origami, bistable, and soft deformable matter. Here, we present a first attempt to describe the fundamental framework of an elastic neuromorphic metasurface that performs distinct classification tasks, providing a new set of challenges, given the complex nature of elastic waves with respect to scattering and manipulation. Multiple layers of reconfigurable waveguides are phase-trained via constant weights and trainable activation functions in a manner that enables the resultant wave scattering at the readout location to focus on the correct class within the detection plane. We further demonstrate the neuromorphic system’s reconfigurability in performing two distinct tasks, eliminating the need for costly remanufacturing.